{ "schema": "quivent.readme-registry/v1", "generated_at": "2026-08-30T23:46:26.265165+00:00", "project_count": 460, "readme_count": 357, "organization_count": 20, "similarity_groups": { "Agents & Orchestration": 57, "Creative & Media": 11, "Data & Storage": 13, "Developer Tools": 81, "Infrastructure & Operations": 34, "Models & Machine Learning": 81, "Other Experiments": 95, "Research & Knowledge": 23, "Security & Identity": 6, "Web & Applications": 59 }, "projects": [ { "organization": "AGI-Film", "name": "Architecture", "source": "R2 Git bundle", "published_at": "2025-11-30T09:48:22-05:00", "readme": "# Analytical Engine\n\nA self-improving multi-agent system for screenplay analysis.\n\n## Project Structure\n\n```\nArchitecture/\n├── cli/ # Command-line interface\n├── docs/ # Documentation\n│ ├── architecture.md # Full technical reference\n│ └── architecture-agent-context.md # Agent-optimized context\n└── tui/ # Terminal UI (future)\n```\n\n## Quick Start\n\n```bash\n# Analyze a screenplay\ncoverage analyze screenplay.pdf --protocol v7\n\n# Run multiple protocol variants\ncoverage analyze screenplay.pdf --protocol v6,v7,v8 --compare\n\n# Queue batch analysis\ncoverage batch screenplays/ --protocol v7 --armies 10\n```\n\n## Core Concept\n\nArchitecture beats model capability. Orchestrated agents with adversarial structure outperform single-pass frontier models.\n\nSee [docs/architecture.md](docs/architecture.md) for full details.", "has_readme": true, "url": "https://github.com/AGI-Film/Architecture", "category": "unclassified", "value_tier": "standard", "value_rank": 2, "editorial_note": null, "similarity_group": "Research & Knowledge", "group_score": 3, "similar": [ { "id": "quivent/coverage-go", "score": 0.1782, "signals": [ "analysis", "screenplay", "screenplays" ] }, { "id": "quivent/Screenplays", "score": 0.143, "signals": [ "screenplays" ] }, { "id": "AGI-Film/Screenplays", "score": 0.143, "signals": [ "screenplays" ] }, { "id": "quivent/coverage-architecture-analysis", "score": 0.1102, "signals": [ "analysis", "documentation", "analytical" ] }, { "id": "quivent/Coverage", "score": 0.1101, "signals": [ "analysis", "documentation", "screenplay" ] } ] }, { "organization": "AGI-Film", "name": "Autonomous", "source": "R2 Git bundle", "published_at": "2025-11-10T21:19:05+00:00", "readme": "# Autonomous\n\n**Autonomous Protocol Development System**\n\nAutonomous is a self-evolving multi-agent system that autonomously develops protocols through iterative research, experimentation, and coordinated learning. The system orchestrates specialized agents working in sequential parallelism to discover, define, and refine processes with measurable growth and complete visibility.\n\n## Overview\n\nThis project represents a novel approach to protocol development where the system itself learns through coordinated agent collaboration, research synthesis, and experimental iteration. Rather than following predefined pipelines, Autonomous discovers and evolves its own protocols through autonomous exploration and measured improvement.\n\n## Key Features\n\n- **Multi-Agent Orchestration** - Specialized agents (workers, architects, data specialists, learners, researchers) working in coordinated parallel workflows\n- **Self-Learning System** - Agents evolve their capabilities through experimental trials and recorded learning\n- **Sequential Parallelism** - Structured concurrent execution with sequential control and synchronization\n- **Research-Driven Development** - Descriptive element research, data aggregation, and synthesis for protocol discovery\n- **Observable Progress** - Complete visibility into agent activities, experimental results, and system growth\n- **Background Execution** - Autonomous operation with monitoring dashboards and progress tracking\n- **Iterative Evolution** - Both the protocol output and the agents themselves improve through measured cycles\n- **Scaffold CLI** - Built-in tools to clone, configure, and deploy new systems from this template\n\n## Using as a Template\n\nAutonomous includes a powerful scaffold CLI that lets you create new projects from this template:\n\n```bash\n# Clone this template to create a new project\nautonomous scaffold clone my-new-project\n\n# The CLI will:\n# 1. Copy the entire template structure\n# 2. Rename everything to your project name\n# 3. Configure database, ports, and secrets\n# 4. Set up for immediate use\n\ncd ../my-new-project\nmake run-all\n```\n\nSee [SCAFFOLD_CLI.md](SCAFFOLD_CLI.md) for complete documentation on:\n- Creating new projects from this template\n- Configuring existing projects\n- Deploying to Docker, local, or cloud\n- Managing multiple projects\n\n## Quick Start\n\n### Prerequisites\n\n- Python 3.11+\n- Node.js 18+ (for web dashboard)\n- Database (choose one):\n - **Turso** (recommended) - Cloud SQLite with edge replication\n - **Supabase** - Cloud PostgreSQL with real-time features\n - **Local PostgreSQL** - Via Docker or native install\n - **SQLite** - Fully standalone, no external services\n\n### Installation\n\n#### Option 1: Turso (Recommended for Cloud)\n\n```bash\n# Clone the repository\ngit clone https://github.com/yourusername/Autonomous.git\ncd Autonomous\n\n# Install dependencies\npip install -e .\n\n# Configure Turso\ncp .env.example .env\n# Edit .env with your Turso credentials\n\n# Initialize database\npython -c \"from src.models import init_db; import asyncio; asyncio.run(init_db())\"\n\n# Start the system\nautonomous start --monitor\n```\n\n#### Option 2: Local Development\n\n```bash\n# Clone and install\ngit clone https://github.com/yourusername/Autonomous.git\ncd Autonomous\n\n# Start local PostgreSQL + Redis\nmake db-up\n\n# Install dependencies and initialize\nmake dev-install\n\n# Start the system\nmake run-all\n```\n\n#### Option 3: Standalone (SQLite)\n\n```bash\n# Clone and install\ngit clone https://github.com/yourusername/Autonomous.git\ncd Autonomous\n\n# Install dependencies\npip install -e .\n\n# Configure for SQLite\ncp .env.example .env\n# Set DATABASE_URL=sqlite:///autonomous.db\n\n# Start the system\nmake run\n```\n\n### Basic Usage\n\n```bash\n# Start autonomous protocol development\nautonomous develop --prompt \"Create efficient data processing protocol\"\n\n# Monitor system in real-time\nautonomous monitor --dashboard\n\n# View agent status\nautonomous agents list\n\n# Check experiments\nautonomous experiments list --status running\n\n# View learning records\nautonomous learning --recent 10\n```\n\n## System Architecture\n\n```\n┌─────────────────────────────────────────────────────────────┐\n│ Orchestration Layer │\n│ - AgentOrchestrator (agent_orchestrator.py) │\n│ - Task Queue Management │\n│ - Agent Pool Coordination │\n└──────────────────────┬──────────────────────────────────────┘\n │\n ┌─────────────┴─────────────┐\n │ │\n ┌────▼────┐ ┌──────▼──────┐\n │ Agents │ │ Storage │\n │ Workers │ │ - SQLite │\n │ │ │ - Postgres │\n └────┬────┘ │ - Turso │\n │ └─────────────┘\n │\n ┌────▼──────────────┐\n │ API Layer │\n │ - FastAPI REST │\n │ - WebSocket │\n │ - Integration │\n └───────────────────┘\n```\n\n## Agent Types\n\n1. **Worker Agents** - Execute and refine protocol techniques\n2. **Architect Agents** - Design system structures\n3. **Data Specialist Agents** - Aggregate and organize findings\n4. **Learner Agents** - Analyze results and propose adaptations\n5. **Researcher Agents** - Discover concepts through research\n\n## Web Dashboard\n\nThe system includes a real-time monitoring dashboard built with React and Vite:\n\n```bash\n# Start web dashboard only\nmake run-web\n\n# Start both API and web dashboard\nmake run-all\n```\n\nAccess the dashboard at: `http://localhost:5948`\n\nThe dashboard provides:\n- Real-time agent status monitoring\n- Task queue visualization\n- Experiment tracking\n- Learning records display\n- System metrics and statistics\n\n## API Endpoints\n\nThe system exposes a RESTful API:\n\n- `GET /api/v1/agents/` - List all agents\n- `POST /api/v1/agents/` - Create new agent\n- `GET /api/v1/tasks/` - List all tasks\n- `POST /api/v1/tasks/` - Create new task\n- `GET /api/v1/experiments/` - List experiments\n- `POST /api/v1/experiments/` - Create experiment\n- `GET /api/v1/protocols/` - List discovered protocols\n- `WS /api/v1/ws` - WebSocket for real-time updates\n\n## Development\n\n### Run Tests\n\n```bash\nmake test\n```\n\n### Code Quality\n\n```bash\n# Format code\nmake format\n\n# Run linter\nmake lint\n\n# Type checking\nmake type-check\n\n# All quality checks\nmake quality\n```\n\n### Database Management\n\n```bash\n# Start database\nmake db-up\n\n# Stop database\nmake db-down\n\n# Reset database (WARNING: destroys data)\nmake db-reset\n```\n\n## Configuration\n\nKey configuration options in `.env`:\n\n```bash\n# Database\nDATABASE_URL=sqlite:///autonomous.db\nTURSO_AUTH_TOKEN=your_token_here\n\n# API Server\nAPI_HOST=0.0.0.0\nAPI_PORT=9284\nAPI_WORKERS=4\n\n# Agent Configuration\nMAX_CONCURRENT_AGENTS=50\nAGENT_TASK_TIMEOUT=300\n\n# Experimental Framework\nMAX_CONCURRENT_TRIALS=10\nTRIAL_TIMEOUT=600\nMIN_QUALITY_THRESHOLD=0.7\n```\n\n## Integration with Tauri\n\nThis system is designed to integrate with Tauri desktop applications:\n\n```rust\n// Example Tauri integration\n#[tauri::command]\nasync fn start_protocol_development(prompt: String) -> Result {\n let client = reqwest::Client::new();\n let res = client\n .post(\"http://localhost:9284/api/v1/develop\")\n .json(&serde_json::json!({ \"prompt\": prompt }))\n .send()\n .await\n .map_err(|e| e.to_string())?;\n\n Ok(res.text().await.unwrap())\n}\n```\n\nSee `KAMAJI_INTEGRATION_SETUP.md` for complete integration guide.\n\n## CLI Commands\n\n### System Commands\n\n```bash\nautonomous init # Initialize system\nautonomous start # Start protocol development\nautonomous status # Show system status\nautonomous develop PROMPT # Develop from prompt\nautonomous monitor # Real-time monitoring\n```\n\n### Scaffold Commands\n\n```bash\nautonomous scaffold clone NAME # Clone template for new project\nautonomous scaffold configure PATH # Configure project settings\nautonomous scaffold deploy PATH # Deploy project\nautonomous scaffold list-projects # List all projects\nautonomous scaffold info PATH # Show project info\n```\n\nSee [SCAFFOLD_CLI.md](SCAFFOLD_CLI.md) for detailed scaffold documentation.\n\n## Documentation\n\n- [SCAFFOLD_CLI.md](SCAFFOLD_CLI.md) - Scaffold and deployment CLI\n- [QUICKSTART.md](QUICKSTART.md) - 5-minute setup guide\n- [PURPOSE.md](PURPOSE.md) - Mission statement and objectives\n- [INTENT.md](INTENT.md) - User goals and use cases\n- [CONCEPTS.md](CONCEPTS.md) - Key concepts and terminology\n- [METHODS.md](METHODS.md) - Implementation approaches\n- [SPECIFICATION.md](SPECIFICATION.md) - Technical architecture\n- [CLAUDE.md](CLAUDE.md) - AI collaboration guidelines\n\n## License\n\nMIT License - see [LICENSE](LICENSE) file for details\n\n## Contributing\n\nContributions are welcome! Please read our contributing guidelines before submitting PRs.\n\n## Support\n\nFor issues, questions, or contributions, please open an issue on GitHub.", "has_readme": true, "url": "https://github.com/AGI-Film/Autonomous", "category": "unclassified", "value_tier": "standard", "value_rank": 2, "editorial_note": null, "similarity_group": "Agents & Orchestration", "group_score": 17, "similar": [ { "id": "quivent/Animate", "score": 0.6009, "signals": [ "multi-agent", "autonomous", "collaboration" ] }, { "id": "Geijutsu/Duchess", "score": 0.2106, "signals": [ "multi-agent", "autonomous", "collaboration" ] }, { "id": "Oceantics/Instruments", "score": 0.2057, "signals": [ "collaboration", "agents", "agent" ] }, { "id": "Oceantica/Instruments", "score": 0.2057, "signals": [ "collaboration", "agents", "agent" ] }, { "id": "Moestradamus-Productions/digidali-mcp-gpu", "score": 0.1912, "signals": [ "collaboration", "orchestration", "prompt" ] } ] }, { "organization": "AGI-Film", "name": "cinema-desktop", "source": "R2 Git bundle", "published_at": "2025-12-30T20:30:05-07:00", "readme": "# CINEMA Desktop\n\n_Cross-platform desktop app_\n\n\n# Installation\n\n1. Clone repo\n\n2. Run `npm install` in the projects folder\n\n3. Duplicate `.env.example` to `.env.local`:\n\n```bash\ncp .env.example .env.local\n```\n\n### Development\n\nRun desktop app dev environment\n\n```bash\nnpm run dev\n```\n\n\n##### Codebase Consistency\nBefore a commit can be made, codebase enforces linting checks.\n\nRun the linting tools to ensure code consistency:\n\n```bash\nnpm run lint\n```\n##### Codebase Stability\n\nBefore a commit can be pushed, all tests must pass.\n\nRun the tests to ensure codebase stability:\n\n```bash\nnpm run test\n```\n\n\n\n##### Building App\nBuild the debug application (contains dev tools):\n\n```bash\nnpm run build:debug\n```\n\nBuild the release application:\n\n```bash\nnpm run build:release\n```\n\nFind the built distributables in the following locations:\n`/src-tauri/target/release/bundle/nsis/`\n`/src-tauri/target/debug/bundle/nsis/`", "has_readme": true, "url": "https://github.com/AGI-Film/cinema-desktop", "category": "unclassified", "value_tier": "standard", "value_rank": 2, "editorial_note": null, "similarity_group": "Web & Applications", "group_score": 9, "similar": [ { "id": "AGI-Film/Storyboarding", "score": 0.1326, "signals": [ "app", "cinema", "bundle" ] }, { "id": "quivent/kamaji", "score": 0.1209, "signals": [ "desktop", "app", "application" ] }, { "id": "quivent/matrix-terminal-origins", "score": 0.1169, "signals": [ "desktop", "application", "linting" ] }, { "id": "Oceantics/Savant", "score": 0.1148, "signals": [ "desktop", "application", "lint" ] }, { "id": "quivent/Cinema", "score": 0.1123, "signals": [ "desktop", "app", "application" ] } ] }, { "organization": "AGI-Film", "name": "documentation", "source": "R2 Git bundle", "published_at": "2025-12-12T22:46:03-05:00", "readme": "# Cinema Documentation Wiki\n\nComprehensive Wikipedia-style documentation for the Cinema AI-powered screenplay to film platform.\n\n## Quick Start\n\n```bash\n# Serve the wiki locally\npython3 -m http.server 8080\n\n# Open in browser\nopen http://localhost:8080/cinema-wiki.html\n```\n\n## Files\n\n| File | Description |\n|------|-------------|\n| `cinema-wiki.html` | Main combined wiki (all documentation) |\n| `index.html` | Template page |\n| `wiki-style.css` | Wikipedia-style black & white CSS |\n| `wiki.js` | Interactive JavaScript (search, navigation) |\n| `serve.py` | Python server script |\n\n## Content Sources\n\n| Source | Description |\n|--------|-------------|\n| `CINEMA_DOCUMENTATION_CATALOG.json` | Extracted from `/docs/` |\n| `cinema-app-documentation.json` | Technical docs from `/app/` |\n| `wiki-articles/` | Wikipedia-style markdown articles |\n\n## Wiki Sections\n\n- **Cinema Platform** - Overview, features, architecture\n- **CAIP Protocol** - Character Animation Iterative Protocol\n- **Development Philosophy** - Five guiding personas\n- **Technology Stack** - Rust, Tauri, React, Neon PostgreSQL\n- **API Reference** - 50+ Tauri commands\n- **Glossary** - Key terms and definitions\n\n## Statistics\n\n- 3,398+ documentation files aggregated\n- 769 animated characters documented\n- 50+ API commands referenced\n- 5 development personas detailed\n\n---\n\nGenerated December 12, 2025\n\n**We're going to Hollywood!**", "has_readme": true, "url": "https://github.com/AGI-Film/documentation", "category": "unclassified", "value_tier": "standard", "value_rank": 2, "editorial_note": null, "similarity_group": "Creative & Media", "group_score": 6, "similar": [ { "id": "quivent/Cinema", "score": 0.2457, "signals": [ "film", "cinema", "screenplay" ] }, { "id": "AGI-Film/Storyboarding", "score": 0.1893, "signals": [ "film", "cinema", "screenplay" ] }, { "id": "quivent/CV", "score": 0.1379, "signals": [ "sections", "javascript", "markdown" ] }, { "id": "quivent/Topology", "score": 0.1064, "signals": [ "aggregated", "extracted", "tauri" ] }, { "id": "quivent/CinemaMarketing", "score": 0.104, "signals": [ "cinema", "animation", "css" ] } ] }, { "organization": "AGI-Film", "name": "Eyecon", "source": "R2 Git bundle", "published_at": "2025-11-09T11:57:44-05:00", "readme": "# 🎨 Eyecon - AI Icon Maker\n\n**Create stunning icons with AI-powered generation and professional editing tools.**\n\nEyecon brings together AI magic and powerful editing in one beautiful app. Generate icons from text, refine them with professional tools, and export in any format you need.\n\n## Table of Contents\n\n- [Features](#features)\n- [Technology Stack](#technology-stack)\n- [Getting Started](#getting-started)\n - [Prerequisites](#prerequisites)\n - [Installation](#installation)\n - [Development](#development)\n- [Project Structure](#project-structure)\n- [Documentation](#documentation)\n- [Development Workflow](#development-workflow)\n- [Building for Production](#building-for-production)\n- [Contributing](#contributing)\n- [License](#license)\n- [Support](#support)\n\n## ✨ Features\n\n### 🤖 AI-Powered Generation\n- **Local AI Generation** with ComfyUI - Free, unlimited, private\n- **Cloud AI** with OpenAI DALL-E - High quality, no setup\n- Generate icons from text prompts in seconds\n- 14 style presets (flat, 3D, gradient, neon, vintage, and more)\n- 40+ example prompts to get you started\n- Fine-tune quality, creativity, and detail\n- Custom workflow support for advanced users\n\n### 🎨 Professional Editing\n- 11 drawing tools (brush, shapes, pen tool, and more)\n- Unlimited layers with full control\n- Advanced color picker with gradients\n- Effects and filters with real-time preview\n- Transform tools (move, scale, rotate)\n\n### 💾 Export & Storage\n- 7 export formats (SVG, PNG, JPG, WebP, ICO, ICNS, PDF)\n- Platform presets (iOS, Android, Windows, macOS, Web)\n- Auto-save - never lose your work\n- Export all sizes at once\n\n### ⌨️ Power User Features\n- 60+ keyboard shortcuts\n- Customizable shortcuts\n- Dark and light themes\n- Grid, guides, and snapping\n\n## 🚀 Quick Start\n\n```bash\n# Clone and install\ngit clone https://github.com/AGI-Film/Eyecon.git\ncd Eyecon\nnpm install\n\n# Start creating icons!\nnpm run dev\n```\n\nOpen **http://localhost:3000** and start making icons! 🎉\n\n## 🎯 How It Works\n\n1. **Generate** - Type what you want (e.g., \"rocket ship\") and pick a style\n2. **Edit** - Use drawing tools, layers, and effects to refine your icon\n3. **Export** - Choose your format or use a platform preset (iOS, Android, Web)\n\n## ⌨️ Keyboard Shortcuts\n\n| Shortcut | Action | Shortcut | Action |\n|----------|--------|----------|--------|\n| `Ctrl+N` | New project | `Ctrl+Z` | Undo |\n| `Ctrl+S` | Save | `Ctrl+Y` | Redo |\n| `Ctrl+E` | Export | `Delete` | Delete selected |\n| `V` | Select tool | `B` | Brush tool |\n| `L` | Line tool | `R` | Rectangle tool |\n| `C` | Circle tool | `T` | Text tool |\n\nPress **`?`** in the app to see all shortcuts!\n\n## 🎨 Drawing Tools\n\n- **Select (V)** - Move and resize objects\n- **Brush (B)** - Free-hand drawing\n- **Pencil (P)** - Simple drawing\n- **Line (L)** - Straight lines\n- **Rectangle (R)** - Rectangles with rounded corners\n- **Circle (C)** - Perfect circles\n- **Ellipse (E)** - Ellipses\n- **Polygon (G)** - Regular polygons\n- **Star (S)** - Star shapes\n- **Arrow (A)** - Arrows\n- **Pen Tool (N)** - Bezier curves\n\n## 🛠️ Built With\n\n- **React** + **TypeScript** - UI and type safety\n- **Vite** - Lightning-fast build tool\n- **Tailwind CSS** - Beautiful styling\n- **Fabric.js** - Canvas manipulation\n- **Zustand** - Simple state management\n- **Radix UI** - Accessible components\n\n## 📦 Commands\n\n```bash\nnpm run dev # Start dev server\nnpm run build # Build for production\nnpm run preview # Preview production build\nnpm run lint # Check code quality\nnpm run format # Format code\nnpm run build:analyze # Analyze bundle size\n```\n\n## 📁 Project Structure\n\n```\nEyecon/\n├── src/\n│ ├── components/ # UI components\n│ ├── services/ # AI generation, export, storage\n│ ├── stores/ # State management\n│ ├── hooks/ # Custom React hooks\n│ ├── utils/ # Helper functions\n│ └── workers/ # Background processing\n├── docs/ # Comprehensive documentation\n└── public/ # Static files\n```\n\n## 📚 Documentation\n\n📖 **[Complete Documentation →](docs/README.md)**\n\nOur comprehensive documentation is organized by role and topic:\n\n### Quick Links\n- **[Quick Start Guide](docs/guides/quick-start.md)** - Get started in 5 minutes\n- **[User Guide](docs/USER_GUIDE.md)** - Complete feature guide\n- **[ComfyUI Integration](docs/integrations/comfyui-overview.md)** - Local AI generation setup\n- **[API Documentation](docs/API_DOCUMENTATION.md)** - Developer API reference\n- **[Architecture](docs/ARCHITECTURE.md)** - System architecture overview\n\n### Documentation Categories\n- **[AI & Intelligence](docs/ai/)** - AI architecture and intelligent systems\n- **[Architecture & Design](docs/architecture/)** - System architecture, components, state management\n- **[UX/UI Design](docs/design/)** - User experience flows and interaction patterns\n- **[Implementation Guides](docs/implementation/)** - Step-by-step implementation and project status\n- **[Integrations](docs/integrations/)** - ComfyUI and third-party integrations\n- **[User Guides](docs/guides/)** - Quick starts, performance, production builds\n- **[Reference](docs/reference/)** - API docs, keyboard shortcuts, technical reference\n\n**Browse all:** See [docs/README.md](docs/README.md) for the complete documentation index.\n\n## 🖥️ Local AI Generation with ComfyUI\n\nEyecon supports **local AI generation** using ComfyUI - generate icons on your own computer for free!\n\n### Why Use Local Generation?\n\n| Benefit | Description |\n|---------|-------------|\n| 💰 **Zero Cost** | No API fees - generate unlimited icons |\n| 🔒 **Complete Privacy** | All processing happens on your machine |\n| ⚡ **Fast** | GPU-accelerated generation in seconds |\n| 🎨 **Customizable** | Use any Stable Diffusion model or LoRA |\n| 🔧 **Advanced Control** | Custom workflows for specialized styles |\n\n### Quick Setup\n\n1. **Install ComfyUI** (one-time setup)\n ```bash\n # Download from https://github.com/comfyanonymous/ComfyUI\n # Or use portable Windows version\n ```\n\n2. **Download a Model** (e.g., SDXL)\n ```bash\n # Place in ComfyUI/models/checkpoints/\n ```\n\n3. **Start ComfyUI**\n ```bash\n cd ComfyUI\n python main.py\n ```\n\n4. **Use in Eyecon** - It just works!\n - Eyecon auto-detects ComfyUI on `localhost:8188`\n - No configuration needed\n - Start generating icons!\n\n### System Requirements\n\n**Minimum:**\n- 8GB RAM\n- NVIDIA GPU with 4GB VRAM (or AMD/Apple Silicon)\n- 20GB free storage\n\n**Recommended:**\n- 16GB RAM\n- NVIDIA RTX 3060 or better (8GB+ VRAM)\n- SSD with 50GB free storage\n\nFor detailed setup instructions, see **[ComfyUI Integration Guide](docs/COMFYUI_INTEGRATION.md)**.\n\n## ☁️ Cloud AI Generation\n\n### OpenAI DALL-E (Easy Setup)\n\n1. **Get API Key** from [platform.openai.com](https://platform.openai.com)\n\n2. **Configure Eyecon**\n ```env\n # Create .env file\n VITE_OPENAI_API_KEY=sk-your-key-here\n VITE_OPENAI_ENABLED=true\n ```\n\n3. **Start Generating** - Eyecon will use DALL-E 3\n\n**Pricing:** ~$0.04 per icon (1024x1024)\n\n### Custom API\n\nUse your own hosted AI service:\n\n```env\nVITE_CUSTOM_API_ENDPOINT=https://your-api.com/generate\nVITE_CUSTOM_API_KEY=your-secret-key\nVITE_CUSTOM_API_ENABLED=true\n```\n\nSee **[Backend Configuration Guide](docs/BACKEND_CONFIGURATION.md)** for all options.\n\n## 🔄 Multi-Backend System\n\nEyecon automatically selects the best available backend:\n\n```\nPriority 1: ComfyUI (local) → Free, unlimited\nPriority 2: Custom API → Your hosted service\nPriority 3: OpenAI DALL-E → High quality, paid\nPriority 4: Mock → Testing fallback\n```\n\n**Configure priority:**\n```env\nVITE_BACKEND_PRIORITY=comfyui,openai,mock\nVITE_BACKEND_AUTO_FALLBACK=true\n```\n\n### Backend Status\n\nView real-time backend status in **Settings → AI Generation → Backends**:\n- 🟢 **Online** - Ready to use\n- 🟡 **Connecting** - Checking availability\n- 🔴 **Offline** - Not available\n- ⚪ **Disabled** - Turned off in settings\n\n## 🤝 Contributing\n\nWe'd love your help making Eyecon better!\n\n1. Fork the repo\n2. Create a branch (`git checkout -b cool-feature`)\n3. Make your changes\n4. Push and open a Pull Request\n\nSee [CONTRIBUTING.md](CONTRIBUTING.md) for more details.\n\n## 🗺️ Roadmap\n\n- [x] ComfyUI integration for local generation\n- [x] Multi-backend system with automatic fallback\n- [x] Custom workflow support\n- [ ] More AI models and LoRAs\n- [ ] Workflow marketplace\n- [ ] Template library\n- [ ] Collaboration features\n- [ ] Animation support\n- [ ] Mobile app\n- [ ] Plugin system\n\n## 📄 License\n\nMIT License - see [LICENSE](LICENSE)\n\n## 💬 Support\n\n- 🐛 [Report bugs](https://github.com/AGI-Film/Eyecon/issues)\n- 💡 [Request features](https://github.com/AGI-Film/Eyecon/issues)\n- 📖 [Read docs](docs/)\n\n## 🌟 Show Your Support\n\nGive us a ⭐️ if Eyecon helped you create awesome icons!\n\n---\n\n**Made with ❤️ by the AGI-Film team**", "has_readme": true, "url": "https://github.com/AGI-Film/Eyecon", "category": "unclassified", "value_tier": "standard", "value_rank": 2, "editorial_note": null, "similarity_group": "Web & Applications", "group_score": 9, "similar": [ { "id": "Moestradamus-Productions/digidali-v1", "score": 0.1957, "signals": [ "mobile", "react", "web" ] }, { "id": "Moestradamus-Productions/digidali-mcp-gpu", "score": 0.1946, "signals": [ "mobile", "react", "web" ] }, { "id": "quivent/Cinema", "score": 0.1726, "signals": [ "react", "app", "backend" ] }, { "id": "TransformerOS/atlas", "score": 0.1718, "signals": [ "react", "press", "stores" ] }, { "id": "TSMCP/tool-of-tools", "score": 0.1709, "signals": [ "drawing", "brings", "curves" ] } ] }, { "organization": "AGI-Film", "name": "Gate", "source": "R2 Git bundle", "published_at": "2025-12-05T16:10:01+00:00", "readme": "# CinemaAGI Authentication Gate\n\nA production-ready, reusable authentication overlay system with a cinematic movie theater theme. Deploy secure Google OAuth authentication over any domain or application with beautiful, cinema-inspired UI.\n\n![Status](https://img.shields.io/badge/status-ready%20for%20implementation-green)\n![License](https://img.shields.io/badge/license-MIT-blue)\n![Node](https://img.shields.io/badge/node-20%20LTS-brightgreen)\n![React](https://img.shields.io/badge/react-18%2B-61dafb)\n\n---\n\n## Features\n\n### Universal Authentication Gateway\n- Deploy as an authentication layer over any web application\n- Seamless integration without modifying underlying application code\n- Multi-domain support with configurable whitelisting\n- Preserve original URLs after authentication\n\n### Google OAuth Integration\n- Secure OAuth 2.0 flow with PKCE enhancement\n- Automatic token refresh\n- Graceful session management\n- 24-hour sessions (configurable)\n\n### CinemaAGI Cinematic Theme\n- Movie theater inspired design language\n- Classic cinema red and gold color palette\n- Curtain reveal animations\n- Film reel loading effects\n- Ticket booth login interface\n- Professional, polished presentation\n\n### Enterprise-Grade Security\n- httpOnly, secure, sameSite cookies\n- CSRF token protection\n- Rate limiting (100 requests per 15 minutes)\n- XSS prevention with Content Security Policy\n- Domain whitelist validation\n- Security headers enforcement\n\n### Production Ready\n- Docker containerization\n- Kubernetes manifests included\n- Horizontal scaling capability\n- Redis-based distributed sessions\n- Health check endpoints\n- Comprehensive monitoring hooks\n\n---\n\n## Quick Start\n\n### Prerequisites\n- Node.js 20 LTS or higher\n- Docker and Docker Compose\n- Google Cloud Console account (for OAuth credentials)\n- Redis 7+ (or use Docker)\n\n### 1. Clone and Setup\n\n```bash\n# Navigate to project directory\ncd /home/alice/CinemaAGI/Gate\n\n# Install backend dependencies\ncd backend\nnpm install\n\n# Install frontend dependencies\ncd ../frontend\nnpm install\n\n# Return to root\ncd ..\n```\n\n### 2. Configure Google OAuth\n\n1. Go to [Google Cloud Console](https://console.cloud.google.com)\n2. Create a new project or select existing\n3. Enable Google+ API\n4. Configure OAuth consent screen\n5. Create OAuth 2.0 credentials (Web application)\n6. Add authorized redirect URI: `https://your-domain.com/auth/google/callback`\n7. Save Client ID and Client Secret\n\n### 3. Environment Configuration\n\n```bash\n# Copy example environment file\ncp .env.example .env\n\n# Edit .env with your configuration\nnano .env\n```\n\nRequired variables:\n```bash\nGOOGLE_CLIENT_ID=your-client-id-here\nGOOGLE_CLIENT_SECRET=your-client-secret-here\nGOOGLE_CALLBACK_URL=https://auth.cinemaagi.com/auth/google/callback\n\nSESSION_SECRET=generate-random-256-bit-secret\nCSRF_SECRET=generate-random-256-bit-secret\n\nREDIS_URL=redis://localhost:6379\nALLOWED_DOMAINS=app1.example.com,app2.example.com\n```\n\n### 4. Start Development Environment\n\n```bash\n# Start Redis\ndocker run -d -p 6379:6379 --name cinema-redis redis:7-alpine\n\n# Terminal 1 - Backend (Port 3000)\ncd backend\nnpm run dev\n\n# Terminal 2 - Frontend (Port 5173)\ncd frontend\nnpm run dev\n```\n\nAccess the development site:\n- Frontend: http://localhost:5173\n- Backend API: http://localhost:3000\n\n---\n\n## Production Deployment\n\n### Docker Compose (Recommended for Small-Medium Scale)\n\n```bash\n# Build images\ndocker-compose -f deployment/docker-compose.prod.yml build\n\n# Start services\ndocker-compose -f deployment/docker-compose.prod.yml up -d\n\n# Check status\ndocker-compose -f deployment/docker-compose.prod.yml ps\n```\n\n### Kubernetes (Recommended for Large Scale)\n\n```bash\n# Apply configurations\nkubectl apply -f deployment/k8s/\n\n# Check deployment status\nkubectl get pods\nkubectl get services\n\n# Scale backend\nkubectl scale deployment auth-gate-backend --replicas=5\n```\n\n### Manual Deployment\n\n```bash\n# Build frontend\ncd frontend\nnpm run build\n# Output: frontend/dist\n\n# Build backend\ncd backend\nnpm run build\n# Output: backend/dist\n\n# Deploy built artifacts to your hosting platform\n```\n\n---\n\n## Architecture\n\n```\n┌─────────────────────────────────────────┐\n│ Client Browser │\n└────────────────┬────────────────────────┘\n │\n ▼\n┌─────────────────────────────────────────┐\n│ Caddy Proxy (SSL Termination) │\n│ + Auth Middleware │\n└────────────────┬────────────────────────┘\n │\n ┌───────┴───────┐\n │ │\n ▼ ▼\n┌──────────────┐ ┌──────────────┐\n│ Auth Service │ │ Protected │\n│ (Express) │ │ Application │\n└──────┬───────┘ └──────────────┘\n │\n ▼\n┌──────────────┐\n│ Redis │\n│ Session Store│\n└──────────────┘\n```\n\n**Flow**:\n1. User requests protected resource\n2. Proxy checks for valid session cookie\n3. If no session: redirect to CinemaAGI auth gate\n4. User authenticates via Google OAuth\n5. Auth service creates session in Redis\n6. Proxy forwards authenticated requests to protected app\n\n---\n\n## Project Structure\n\n```\nGate/\n├── frontend/ # React + TypeScript frontend\n│ ├── src/\n│ │ ├── components/ # React components\n│ │ │ ├── auth/ # Authentication components\n│ │ │ ├── cinema/ # CinemaAGI themed UI\n│ │ │ └── common/ # Reusable components\n│ │ ├── hooks/ # Custom React hooks\n│ │ ├── services/ # API service layer\n│ │ └── styles/ # Tailwind + custom styles\n│ └── package.json\n│\n├── backend/ # Express.js + TypeScript backend\n│ ├── src/\n│ │ ├── controllers/ # Route handlers\n│ │ ├── middleware/ # Express middleware\n│ │ ├── services/ # Business logic\n│ │ ├── routes/ # API routes\n│ │ └── config/ # Configuration\n│ └── package.json\n│\n├── proxy/ # Reverse proxy configs\n│ ├── Caddyfile # Caddy configuration\n│ └── nginx.conf # Nginx alternative\n│\n├── deployment/ # Deployment configurations\n│ ├── docker/ # Dockerfiles\n│ ├── k8s/ # Kubernetes manifests\n│ └── docker-compose.yml # Docker Compose config\n│\n├── docs/ # Documentation\n│ ├── API.md\n│ ├── DEPLOYMENT.md\n│ ├── SECURITY.md\n│ └── ARCHITECTURE.md\n│\n├── scripts/ # Automation scripts\n│ ├── setup.sh\n│ ├── build.sh\n│ └── deploy.sh\n│\n└── README.md # This file\n```\n\n---\n\n## Configuration\n\n### Multi-Domain Setup\n\nThe auth gate can protect multiple domains simultaneously:\n\n```bash\n# In .env\nALLOWED_DOMAINS=app1.example.com,app2.example.com,app3.example.com\nAUTH_COOKIE_DOMAIN=.example.com\n```\n\n### Session Configuration\n\n```bash\n# Session duration\nSESSION_EXPIRY_HOURS=24\n\n# Token refresh threshold\nTOKEN_REFRESH_THRESHOLD_MINUTES=60\n\n# Cookie settings\nAUTH_COOKIE_SECURE=true\nAUTH_COOKIE_SAME_SITE=lax\n```\n\n### Security Configuration\n\n```bash\n# Rate limiting\nRATE_LIMIT_MAX=100\nRATE_LIMIT_WINDOW_MS=900000 # 15 minutes\n\n# CSRF protection\nCSRF_SECRET=your-csrf-secret\n```\n\n---\n\n## API Endpoints\n\n### Authentication Endpoints\n\n```\nGET /auth/google # Initiate Google OAuth flow\nGET /auth/google/callback # OAuth callback handler\nPOST /auth/logout # Logout user\nGET /auth/session # Check session status\nPOST /auth/refresh # Refresh access token\n```\n\n### Health Check Endpoints\n\n```\nGET /health # Basic health check\nGET /ready # Readiness probe (checks dependencies)\nGET /metrics # Prometheus metrics (if enabled)\n```\n\nSee `docs/API.md` for complete API documentation.\n\n---\n\n## Development\n\n### Running Tests\n\n```bash\n# Backend tests\ncd backend\nnpm test # Run all tests\nnpm test -- --coverage # With coverage report\nnpm test -- --watch # Watch mode\n\n# Frontend tests\ncd frontend\nnpm test # Run all tests\nnpm test -- --coverage # With coverage report\nnpm test -- --ui # Vitest UI mode\n```\n\n### Code Quality\n\n```bash\n# Linting\nnpm run lint # Check for issues\nnpm run lint:fix # Auto-fix issues\n\n# Formatting\nnpm run format # Format code with Prettier\nnpm run format:check # Check formatting\n\n# Type checking\nnpm run type-check # TypeScript type checking\n```\n\n### Pre-commit Hooks\n\nHusky is configured to run automatic checks:\n- ESLint on staged files\n- Prettier formatting\n- TypeScript type checking\n- Unit tests for changed files\n\n---\n\n## Security Best Practices\n\n### Session Management\n- Sessions stored in Redis with encryption\n- 24-hour expiry (configurable)\n- Automatic cleanup of expired sessions\n- Secure session cookies (httpOnly, secure, sameSite)\n\n### Token Handling\n- Access tokens encrypted at rest\n- Refresh tokens rotated on use\n- Token blacklist for revoked sessions\n- Automatic token refresh before expiry\n\n### Input Validation\n- All user inputs validated with Joi/Zod\n- SQL injection prevention (parameterized queries)\n- XSS prevention via Content Security Policy\n- CSRF token validation on state-changing operations\n\n### Network Security\n- HTTPS enforced (automatic with Caddy)\n- Security headers (Helmet.js)\n- Rate limiting per IP and per user\n- DDoS protection via Cloudflare (optional)\n\nSee `docs/SECURITY.md` for comprehensive security documentation.\n\n---\n\n## Monitoring and Observability\n\n### Health Checks\n\n```bash\n# Basic health\ncurl https://auth.cinemaagi.com/health\n\n# Readiness check (includes dependencies)\ncurl https://auth.cinemaagi.com/ready\n```\n\n### Metrics\n\nThe system exposes Prometheus-compatible metrics:\n- Request rates\n- Response times\n- Error rates\n- Session counts\n- OAuth flow metrics\n\n### Logging\n\nStructured logging with Winston:\n- Request/response logging\n- Error tracking\n- Audit trail for security events\n- Configurable log levels\n\n---\n\n## Troubleshooting\n\n### Common Issues\n\n**OAuth redirect fails**\n- Verify `GOOGLE_CALLBACK_URL` matches Google Console configuration\n- Check that domain is in authorized redirect URIs\n- Ensure HTTPS is enabled\n\n**Session not persisting**\n- Check Redis connectivity\n- Verify `SESSION_SECRET` is set\n- Confirm cookie domain configuration\n\n**Cross-domain authentication fails**\n- Verify `AUTH_COOKIE_DOMAIN` starts with `.` (e.g., `.example.com`)\n- Check that all domains share the same parent domain\n- Confirm `sameSite` cookie attribute is set correctly\n\nSee `docs/TROUBLESHOOTING.md` for comprehensive troubleshooting guide.\n\n---\n\n## Performance\n\n### Benchmarks\n\n- **Authentication flow**: < 3 seconds (90th percentile)\n- **Session validation**: < 200ms\n- **Token refresh**: < 500ms\n- **Concurrent users**: 10,000+ (with proper scaling)\n\n### Optimization Tips\n\n1. **Enable CDN** for static assets\n2. **Use Redis clustering** for high availability\n3. **Implement connection pooling** for Redis\n4. **Enable Gzip compression** in proxy\n5. **Use horizontal scaling** for backend services\n\n---\n\n## Contributing\n\nWe welcome contributions! Please see our contributing guidelines:\n\n1. Fork the repository\n2. Create a feature branch (`git checkout -b feature/amazing-feature`)\n3. Commit your changes (`git commit -m 'Add amazing feature'`)\n4. Push to the branch (`git push origin feature/amazing-feature`)\n5. Open a Pull Request\n\n### Development Workflow\n\n- Follow TypeScript best practices\n- Write tests for new features\n- Update documentation\n- Ensure CI passes before requesting review\n\n---\n\n## Documentation\n\n### Available Documentation\n\n- **[IMPLEMENTATION_ROADMAP.md](IMPLEMENTATION_ROADMAP.md)** - Comprehensive implementation plan\n- **[QUICK_REFERENCE.md](QUICK_REFERENCE.md)** - Quick reference guide\n- **docs/API.md** - API endpoint documentation\n- **docs/ARCHITECTURE.md** - Architecture deep dive\n- **docs/SECURITY.md** - Security best practices\n- **docs/DEPLOYMENT.md** - Deployment guide\n- **docs/CONFIGURATION.md** - Configuration options\n- **docs/TROUBLESHOOTING.md** - Common issues and solutions\n\n---\n\n## Technology Stack\n\n### Frontend\n- React 18 with TypeScript\n- Vite (build tool)\n- Tailwind CSS (styling)\n- Framer Motion (animations)\n- Zustand (state management)\n- Axios (HTTP client)\n\n### Backend\n- Node.js 20 LTS\n- Express.js with TypeScript\n- Passport.js (OAuth)\n- Redis (sessions)\n- Helmet (security)\n- Winston (logging)\n\n### Infrastructure\n- Docker & Docker Compose\n- Kubernetes\n- Caddy 2 (reverse proxy)\n- Redis 7+\n- Prometheus & Grafana (monitoring)\n\n---\n\n## Roadmap\n\n### Phase 1: Core Authentication ✅\n- Google OAuth integration\n- Session management\n- Basic middleware\n\n### Phase 2: CinemaAGI UI ⏳\n- Themed components\n- Cinematic animations\n- Responsive design\n\n### Phase 3: Security Hardening ⏳\n- CSRF protection\n- Rate limiting\n- Security headers\n\n### Phase 4: Testing & Documentation ⏳\n- Comprehensive test suite\n- API documentation\n- User guides\n\n### Phase 5: Production Deployment ⏳\n- Docker containers\n- CI/CD pipeline\n- Monitoring setup\n\n---\n\n## License\n\nThis project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.\n\n---\n\n## Support\n\n- **Documentation**: See `docs/` folder\n- **Issues**: [GitHub Issues](https://github.com/cinemaagi/gate/issues)\n- **Discussions**: [GitHub Discussions](https://github.com/cinemaagi/gate/discussions)\n\n---\n\n## Acknowledgments\n\n- Google OAuth 2.0 for authentication infrastructure\n- Passport.js for OAuth integration\n- The React and Node.js communities\n- All contributors to this project\n\n---\n\n**Project Status**: 🚀 Ready for Implementation\n\nStart building with the [Quick Reference Guide](QUICK_REFERENCE.md) or dive into the [Full Implementation Roadmap](IMPLEMENTATION_ROADMAP.md).", "has_readme": true, "url": "https://github.com/AGI-Film/Gate", "category": "unclassified", "value_tier": "standard", "value_rank": 2, "editorial_note": null, "similarity_group": "Infrastructure & Operations", "group_score": 20, "similar": [ { "id": "MorchestraWorld/Zappiest", "score": 0.2581, "signals": [ "kubernetes", "docker", "cloud" ] }, { "id": "Moestradamus-Productions/lightbrush-website-overhaul", "score": 0.2415, "signals": [ "docker", "monitoring", "deploy" ] }, { "id": "Moestradamus-Productions/digidali-v1", "score": 0.2323, "signals": [ "kubernetes", "docker", "cloud" ] }, { "id": "Moestradamus-Productions/digidali-mcp-gpu", "score": 0.2285, "signals": [ "kubernetes", "docker", "cloud" ] }, { "id": "MorchestraWorld/Agentsy", "score": 0.2275, "signals": [ "proxy", "docker", "service" ] } ] }, { "organization": "AGI-Film", "name": "model-comparison", "source": "R2 Git bundle", "published_at": "2025-12-16T13:42:30-07:00", "readme": "# Leonardo AI Model Comparison - Performance Optimized\n\nA high-performance website for comparing 29 video generation models (T2V/I2V) and 10 image generation collections, optimized for sub-3 second load times and 60fps scrolling.\n\n## Performance Achievements\n\n- **Initial Load**: < 2 seconds (from 5-8s)\n- **Data Transfer**: 150MB (from 370MB, 60% reduction)\n- **Memory Usage**: < 400MB (from 800MB+)\n- **Scroll Performance**: 60fps consistently\n- **Mobile Experience**: Smooth and responsive\n- **Core Web Vitals**: All green scores\n\n## Asset Inventory\n\n### Videos (29 files)\n- **T2V (Text-to-Video)**: 13 models\n - Kling 2.5, 2.6, Video-O-1\n - Sora 2, Sora 2 Pro\n - Veo 3.1 variants\n - Hailuo 2.3\n - Seedance 1.0 variants\n - LTXV 2.0 variants\n\n- **I2V (Image-to-Video)**: 16 models\n - Same models as T2V with image input\n\n### Images (10 collections)\n- Flux Dev, Flux.2 Pro, Flux Schnell, Flux.1 Kontext\n- Lucid Origin, Lucid Realism\n- Ideogram 3\n- Nano Banana, Nano Banana Pro\n- Seedream 4\n\n## Quick Start\n\n### 1. Optimize Assets (First Time Setup)\n\n```bash\n# Optimize videos (creates 3 quality levels per video)\nchmod +x scripts/video-optimization.sh\nbash scripts/video-optimization.sh\n\n# Optimize images (extracts ZIPs and creates WebP/AVIF)\nchmod +x scripts/image-optimization.sh\nbash scripts/image-optimization.sh\n```\n\nThis will create:\n- `optimized-videos/` - 87 video files (29 × 3 qualities)\n- `optimized-images/` - Modern format images (WebP, AVIF)\n\n### 2. Preview Locally\n\n```bash\n# Simple HTTP server\npython -m http.server 8000\n# Or\nnpx http-server -p 8000\n\n# Open browser\n# http://localhost:8000/example.html\n```\n\n### 3. Deploy to CDN\n\n**Recommended: Cloudflare (Free Tier)**\n- Sign up at https://cloudflare.com\n- Upload optimized assets to R2 or Pages\n- Configure caching rules (see QUICK_START_GUIDE.md)\n\n## Project Structure\n\n```\nmodel-comparison/\n├── README.md # This file\n├── QUICK_START_GUIDE.md # Step-by-step setup\n├── PERFORMANCE_OPTIMIZATION_STRATEGY.md # Comprehensive guide\n├── example.html # Example implementation\n│\n├── scripts/\n│ ├── video-optimization.sh # Video compression pipeline\n│ └── image-optimization.sh # Image optimization pipeline\n│\n├── src/\n│ ├── VideoManager.js # Intelligent video loading\n│ ├── PerformanceMonitor.js # Core Web Vitals tracking\n│ └── styles/\n│ └── performance.css # Optimized CSS\n│\n├── optimized-videos/ # Generated by scripts\n│ ├── *_preview.mp4 # Mobile/slow connections (1-2MB)\n│ ├── *_medium.mp4 # Desktop (4-5MB)\n│ └── *_high.mp4 # High quality (8-10MB)\n│\n└── optimized-images/ # Generated by scripts\n ├── *.webp # WebP format (30% smaller)\n └── *.avif # AVIF format (50% smaller)\n```\n\n## Key Features\n\n### Intelligent Video Loading\n- **Lazy Loading**: Videos load only when near viewport\n- **Quality Adaptation**: Selects video quality based on network speed\n- **Memory Management**: Unloads off-screen videos automatically\n- **Preloading**: Predicts next videos based on scroll direction\n- **Concurrent Limiting**: Max 2 videos loading simultaneously\n\n### Image Optimization\n- **Modern Formats**: AVIF (best) → WebP (good) → JPEG (fallback)\n- **Responsive Sizing**: 3 sizes per image (640px, 1024px, 1920px)\n- **Progressive Loading**: Blur-up placeholder effect\n- **Lazy Loading**: Native browser lazy loading with Intersection Observer fallback\n\n### Performance Monitoring\n- **Core Web Vitals**: LCP, FID, CLS tracking\n- **Custom Metrics**: Video load time, scroll FPS, interaction response\n- **Memory Monitoring**: Automatic cleanup at 80% threshold\n- **Analytics Integration**: Send metrics to your analytics service\n\n## Browser Support\n\n- **Modern Browsers**: Chrome 90+, Firefox 88+, Safari 14+, Edge 90+\n- **Mobile**: iOS 14+, Android Chrome 90+\n- **Progressive Enhancement**: Graceful fallback for older browsers\n\n## Performance Targets\n\n| Metric | Target | Status |\n|--------|--------|--------|\n| First Contentful Paint (FCP) | < 1.8s | ✓ |\n| Largest Contentful Paint (LCP) | < 2.5s | ✓ |\n| First Input Delay (FID) | < 100ms | ✓ |\n| Cumulative Layout Shift (CLS) | < 0.1 | ✓ |\n| Time to Interactive (TTI) | < 3.0s | ✓ |\n| Scroll FPS | 60fps | ✓ |\n| Memory Usage | < 500MB | ✓ |\n\n## Testing\n\n### Lighthouse Audit\n```bash\n# Chrome DevTools → Lighthouse tab\n# Target: 90+ performance score\n```\n\n### WebPageTest\n```bash\n# https://webpagetest.org\n# Test on 3G connection\n# Target: < 5s load time\n```\n\n### Performance API\n```javascript\n// Browser console\nwindow.performanceMonitor.getSummary()\nwindow.videoManager.getStats()\n```\n\n## Optimization Techniques\n\n1. **Video Compression**: FFmpeg with H.264, CRF 23-32, faststart\n2. **Image Compression**: WebP/AVIF with quality 85, responsive sizes\n3. **Lazy Loading**: Intersection Observer with 400px margin\n4. **Code Splitting**: Dynamic imports for features\n5. **Caching**: Service Worker + HTTP cache headers\n6. **CDN**: Cloudflare edge network\n7. **GPU Acceleration**: CSS transforms and will-change\n8. **Memory Management**: LRU cache with automatic eviction\n\n## Development\n\n### Debug Mode\n```javascript\n// Enable verbose logging\nlocalStorage.setItem('debug', 'true')\n\n// Check stats periodically\nsetInterval(() => {\n console.table(window.performanceMonitor.getSummary())\n}, 30000)\n```\n\n### Custom Configuration\n```javascript\n// Customize VideoManager\nconst videoManager = new VideoManager({\n maxConcurrent: 3, // Load 3 videos simultaneously\n memoryLimit: 700 * 1024 * 1024, // 700MB limit\n preloadDistance: 600 // Preload 600px before viewport\n})\n```\n\n## Documentation\n\n- **QUICK_START_GUIDE.md**: Step-by-step implementation guide\n- **PERFORMANCE_OPTIMIZATION_STRATEGY.md**: Complete technical reference\n - Video loading strategies\n - Image optimization\n - CDN recommendations\n - Memory management\n - Mobile optimization\n - Three.js performance\n - And more...\n\n## Troubleshooting\n\n### Videos Not Loading\n1. Check console for errors\n2. Verify optimized videos exist in `optimized-videos/`\n3. Ensure `data-lazy` attribute is present\n4. Check VideoManager initialization\n\n### Slow Performance\n1. Monitor memory: `performance.memory`\n2. Check loaded videos: `videoManager.getStats()`\n3. Reduce `maxConcurrent` to 1-2\n4. Verify GPU acceleration in DevTools Performance tab\n\n### High Memory Usage\n1. Enable emergency cleanup: Triggered automatically at 80%\n2. Reduce viewport preload margin\n3. Check for memory leaks in console\n4. Unload videos manually: `videoManager.unloadVideo(video)`\n\n## Contributing\n\nContributions welcome! Please:\n1. Test performance impact with Lighthouse\n2. Maintain 60fps scrolling\n3. Keep bundle size minimal\n4. Document performance considerations\n\n## License\n\nMIT License - Feel free to use for your own projects\n\n## Credits\n\n- **Video Compression**: FFmpeg\n- **Image Optimization**: cwebp, avifenc\n- **Performance Monitoring**: Web Vitals API\n- **Lazy Loading**: Intersection Observer API\n\n---\n\n## Need Help?\n\n1. Check **QUICK_START_GUIDE.md** for common issues\n2. Review **PERFORMANCE_OPTIMIZATION_STRATEGY.md** for detailed explanations\n3. Open browser DevTools Console for error messages\n4. Test with Lighthouse for performance insights\n\n## Performance Checklist\n\n- [x] Video compression (3 quality levels)\n- [x] Image optimization (WebP/AVIF)\n- [x] Lazy loading implementation\n- [x] Memory management\n- [x] Network quality detection\n- [x] Performance monitoring\n- [x] Mobile optimization\n- [x] GPU acceleration\n- [x] Code splitting ready\n- [x] CDN deployment guide\n\n**Status**: Production Ready ✓\n\n---\n\nBuilt with performance in mind. Sub-3s load, 60fps scrolling, instant interactions.", "has_readme": true, "url": "https://github.com/AGI-Film/model-comparison", "category": "unclassified", "value_tier": "standard", "value_rank": 2, "editorial_note": null, "similarity_group": "Models & Machine Learning", "group_score": 9, "similar": [ { "id": "Moestradamus-Productions/lightbrush-website-overhaul", "score": 0.2407, "signals": [ "generation", "scrolling", "webp" ] }, { "id": "quivent/underscore.film", "score": 0.1838, "signals": [ "generation", "fid", "lcp" ] }, { "id": "TSMCP/Orchestra", "score": 0.1363, "signals": [ "generation", "optimize", "formats" ] }, { "id": "MorchestraWorld/Orchestra", "score": 0.1363, "signals": [ "generation", "optimize", "formats" ] }, { "id": "Geijutsu/orchestra", "score": 0.1363, "signals": [ "generation", "optimize", "formats" ] } ] }, { "organization": "AGI-Film", "name": "Models", "source": "R2 Git bundle", "published_at": "2025-11-19T05:45:03+00:00", "readme": "", "has_readme": false, "url": "https://github.com/AGI-Film/Models", "category": "unclassified", "value_tier": "standard", "value_rank": 2, "editorial_note": null, "similarity_group": "Models & Machine Learning", "group_score": 4, "similar": [ { "id": "quivent/Models", "score": 1.0, "signals": [ "models" ] }, { "id": "quivent/gemmachain", "score": 0.1488, "signals": [ "models" ] }, { "id": "quivent/docs", "score": 0.0998, "signals": [ "models" ] }, { "id": "quivent/MoneroInfo", "score": 0.0892, "signals": [ "models" ] }, { "id": "AGI-Film/Architecture", "score": 0.0792, "signals": [ "models" ] } ] }, { "organization": "AGI-Film", "name": "Morchestrator", "source": "R2 Git bundle", "published_at": "2025-11-09T15:05:37-05:00", "readme": "# Morchestrator\n\n[![Go Version](https://img.shields.io/badge/Go-1.21+-blue.svg)](https://golang.org)\n[![Build Status](https://img.shields.io/badge/build-passing-green.svg)]()\n[![License](https://img.shields.io/badge/license-MIT-blue.svg)]()\n\n> A revolutionary self-healing protocol-driven development orchestrator that implements autonomous gap detection and resolution\n\n## Overview\n\n**Morchestrator** is a sophisticated development orchestration system that implements the \"MORCHESTRATED_COMMUNICATION_PROTOCOL\" - an 8-phase development methodology with built-in gap detection and autonomous resolution capabilities. It represents a new paradigm in software development where systems can identify, analyze, and resolve their own implementation gaps through coordinated AI agent interactions.\n\n### Key Features\n\n- **🔄 Self-Healing Development**: Automatically detects and resolves implementation gaps in codebases\n- **📋 Protocol-Driven Orchestration**: Implements systematic 8-phase development methodology\n- **🤖 Multi-Agent Coordination**: Orchestrates specialized AI agents for comprehensive problem-solving\n- **🎯 Gap Detection System**: Advanced pattern recognition for identifying incomplete implementations\n- **📊 Quality Assurance**: Built-in validation ensuring 90% accuracy and 95% rigor standards\n- **🚀 Autonomous Evolution**: Continuously improves its own protocols and detection capabilities\n\n## The 8-Phase Development Protocol\n\nMorchestrator implements a comprehensive development methodology:\n\n1. **Requirements Analysis & Decomposition** - Extract and analyze explicit/implicit requirements\n2. **Architecture Design & Component Structure** - Design patterns and component relationships\n3. **Technology Stack Selection** - Match technologies to requirements and context\n4. **Development Environment Setup** - Project scaffolding and tooling configuration\n5. **Core Implementation** - Incremental feature development with quality gates\n6. **Testing & Quality Assurance** - Comprehensive testing strategy and validation\n7. **Documentation & User Guides** - Complete user and developer documentation\n8. **Build, Package, and Deploy** - Multi-platform distribution and deployment\n\n## Gap Detection and Resolution\n\n### Detection Capabilities\n\nThe system identifies various types of implementation gaps:\n\n- **TODO Comments**: `TODO(human)`, `TODO(auto-detect)`, implementation markers\n- **Missing Logic**: Unimplemented functions, incomplete error handling\n- **Testing Gaps**: Missing test coverage, validation needs\n- **Documentation Issues**: Accuracy validation, completeness checks\n\n### Agent Coordination\n\nFour specialized agents work together to resolve detected gaps:\n\n- **Research Agent**: Analyzes requirements and documents solution patterns\n- **Learner Agent**: Processes examples and optimizes approaches\n- **Solver Agent**: Generates implementation code and integrates solutions\n- **Protocol Designer**: Evolves protocol definitions and improves detection rules\n\n## Installation\n\n### Prerequisites\n\n- Go 1.21 or higher\n- Unix-like system (Linux/macOS) or Windows with Go support\n\n### Build from Source\n\n```bash\n# Clone the repository\ngit clone \ncd morchestrator\n\n# Build the binary\ngo build -o morchestrator main.go\n\n# Install to local bin (optional)\ngo build -o ~/.local/bin/morchestrator main.go\n```\n\n### Verify Installation\n\n```bash\nmorchestrator --help\n```\n\n## Quick Start\n\n### Basic Gap Detection\n\nScan a project for implementation gaps:\n\n```bash\nmorchestrator orchestrate /path/to/project\n```\n\n### Automatic Gap Resolution\n\nEnable autonomous gap resolution:\n\n```bash\nmorchestrator orchestrate /path/to/project --auto-resolve\n```\n\n### Configuration Options\n\n```bash\n# Limit self-healing iterations\nmorchestrator orchestrate /path/to/project --max-iterations 5\n\n# Verbose output for detailed progress\nmorchestrator orchestrate /path/to/project --verbose\n\n# Different output formats\nmorchestrator orchestrate /path/to/project --output json\n```\n\n## Architecture\n\n### Project Structure\n\n```\nmorchestrator/\n├── main.go # Application entry point\n├── cmd/ # CLI command definitions\n│ ├── root.go # Root command and configuration\n│ └── orchestrate.go # Main orchestration command\n├── internal/ # Private application logic\n│ ├── protocol/ # Protocol implementation\n│ │ ├── types.go # Core data structures\n│ │ └── gap_detector.go # Gap detection algorithms\n│ └── agents/ # Multi-agent system\n│ ├── system.go # Agent coordination\n│ └── placeholder_agents.go # Agent implementations\n├── docs/ # Protocol documentation\n├── sessions/ # Development session records\n└── synthesis/ # Research artifacts\n```\n\n### Core Components\n\n- **Protocol System**: Manages the 8-phase development methodology\n- **Gap Detector**: Scans codebases using advanced pattern recognition\n- **Agent System**: Coordinates specialized AI agents for resolution\n- **CLI Interface**: Professional command-line interface with Cobra framework\n\n## Development\n\n### Running Tests\n\n```bash\ngo test ./...\n```\n\n### Building for Different Platforms\n\n```bash\n# Linux\nGOOS=linux GOARCH=amd64 go build -o morchestrator-linux main.go\n\n# macOS\nGOOS=darwin GOARCH=amd64 go build -o morchestrator-macos main.go\n\n# Windows\nGOOS=windows GOARCH=amd64 go build -o morchestrator-windows.exe main.go\n```\n\n### Contributing\n\n1. Fork the repository\n2. Create a feature branch\n3. Make your changes following the 8-phase protocol\n4. Run tests and ensure quality standards\n5. Submit a pull request\n\n## Configuration\n\nMorchestrator supports configuration through:\n\n- Command-line flags\n- YAML configuration files\n- Environment variables\n\n### Example Configuration\n\n```yaml\n# morchestrator.yaml\nmax_iterations: 10\nauto_resolve: true\noutput_format: table\nscan_paths:\n - ./src\n - ./internal\nquality_standards:\n accuracy_threshold: 0.90\n rigor_threshold: 0.95\n```\n\n## Use Cases\n\n### Development Assessment\n\nAnalyze existing projects to identify implementation gaps and technical debt:\n\n```bash\nmorchestrator orchestrate /path/to/legacy-project --output json > gaps-report.json\n```\n\n### Protocol Execution\n\nFollow the systematic 8-phase methodology for new projects:\n\n```bash\nmorchestrator orchestrate /path/to/new-project --auto-resolve --max-iterations 15\n```\n\n### Quality Validation\n\nEnsure adherence to quality standards across development phases:\n\n```bash\nmorchestrator orchestrate /path/to/project --verbose\n```\n\n## Technical Specifications\n\n- **Language**: Go 1.21+\n- **CLI Framework**: Cobra\n- **Configuration**: Viper\n- **Concurrency**: Go routines for agent coordination\n- **Pattern Matching**: Regex-based gap detection\n- **Output Formats**: Table, JSON, YAML\n\n## Roadmap\n\n- [ ] Integration with popular IDEs and editors\n- [ ] Support for additional programming languages\n- [ ] Enhanced AI agent capabilities\n- [ ] Real-time collaboration features\n- [ ] Cloud-based orchestration platform\n- [ ] Plugin architecture for custom agents\n\n## Support\n\nFor questions, issues, or contributions:\n\n- Check existing issues in the repository\n- Review the comprehensive [USAGE.md](USAGE.md) documentation\n- Examine session logs in the `sessions/` directory\n- Consult protocol documentation in `docs/`\n\n## License\n\nThis project is licensed under the MIT License - see the LICENSE file for details.\n\n## Acknowledgments\n\nMorchestrator represents a novel approach to autonomous software development, combining protocol-driven methodology with self-healing capabilities. It demonstrates the potential for AI-assisted development orchestration and continuous system improvement.\n\n---\n\n*Built with the power of self-healing protocol-driven development* 🚀", "has_readme": true, "url": "https://github.com/AGI-Film/Morchestrator", "category": "unclassified", "value_tier": "standard", "value_rank": 2, "editorial_note": null, "similarity_group": "Agents & Orchestration", "group_score": 12, "similar": [ { "id": "Moestradamus-Productions/morchestrator", "score": 1.0, "signals": [ "multi-agent", "orchestrator", "autonomous" ] }, { "id": "CherryMesh/gatherer", "score": 0.2208, "signals": [ "agents", "agent", "morchestrator" ] }, { "id": "AmadeusInnovations/gatherer", "score": 0.2208, "signals": [ "agents", "agent", "morchestrator" ] }, { "id": "quivent/AutonomousProtocol", "score": 0.2042, "signals": [ "orchestrator", "collaboration", "orchestration" ] }, { "id": "quivent/CollaborativeIntelligence", "score": 0.1989, "signals": [ "multi-agent", "collaboration", "agents" ] } ] }, { "organization": "AGI-Film", "name": "omnigen", "source": "R2 Git bundle", "published_at": "2026-01-03T10:51:56-07:00", "readme": "
\n\n\n\n
\n\n# OMNIGEN\n\n
\n\n[![AI Generation](https://img.shields.io/badge/AI_GENERATION-MASTER-7b2cbf?style=for-the-badge&labelColor=0d1117)](https://github.com/AGI-Film/omnigen)\n  \n[![Self-Updating](https://img.shields.io/badge/SELF-UPDATING-e0aaff?style=for-the-badge&labelColor=0d1117)](https://github.com/AGI-Film/omnigen)\n  \n[![ComfyUI](https://img.shields.io/badge/COMFYUI-EXPERT-ff6b6b?style=for-the-badge&labelColor=0d1117)](https://github.com/AGI-Film/omnigen)\n\n
\n\n---\n\n
\n\n*A self-updating Claude Code skill with comprehensive knowledge of all frontier*\n*AI generation models — image, video, 3D, and audio — designed to stay current*\n*with the rapidly evolving landscape of generative AI.*\n\n
\n\n---\n\n
\n\n## THE KNOWLEDGE\n\n
\n\n| | | | |\n|:---:|:---:|:---:|:---:|\n| ![](https://img.shields.io/badge/◆-7b2cbf?style=for-the-badge) | ![](https://img.shields.io/badge/◆-9d4edd?style=for-the-badge) | ![](https://img.shields.io/badge/◆-c77dff?style=for-the-badge) | ![](https://img.shields.io/badge/◆-e0aaff?style=for-the-badge) |\n| **IMAGE** | **VIDEO** | **3D** | **AUDIO** |\n| **GENERATION** | **GENERATION** | **GENERATION** | **GENERATION** |\n| FLUX.2, Qwen-Image | Runway, Kling O1 | TRELLIS.2, Hunyuan3D | Suno, Stable Audio |\n| Nano Banana, Reve | Wan, Hailuo, Sora | TripoSR, 3DGS | ElevenLabs, Udio |\n\n
\n\n---\n\n
\n\n## FRONTIER MODELS (January 2026)\n\n
\n\n### Image Generation\n\n| Tier | Model | Parameters | Key Innovation |\n|:---:|:---|:---:|:---|\n| ![](https://img.shields.io/badge/S+-7b2cbf?style=flat-square) | **FLUX.2 Pro** | 32B | Multi-reference, 4MP, superior typography |\n| ![](https://img.shields.io/badge/S+-7b2cbf?style=flat-square) | **Nano Banana Pro** | — | 94% text accuracy, 14 refs, thinking mode |\n| ![](https://img.shields.io/badge/S+-7b2cbf?style=flat-square) | **Reve Image 1.0** | 12B | 98% text accuracy, #1 leaderboard |\n| ![](https://img.shields.io/badge/S-9d4edd?style=flat-square) | **Qwen-Image-2512** | 20B | Best realism, layer editing |\n| ![](https://img.shields.io/badge/S-9d4edd?style=flat-square) | **HiDream-I1** | 17B | MIT license, highest GenEval |\n\n
\n\n### Video Generation\n\n| Tier | Model | Access | Key Innovation |\n|:---:|:---|:---:|:---|\n| ![](https://img.shields.io/badge/S+-7b2cbf?style=flat-square) | **Runway Gen-4.5** | API | #1 benchmark, professional grade |\n| ![](https://img.shields.io/badge/S+-7b2cbf?style=flat-square) | **Sora 2** | API | Physics understanding, complex scenes |\n| ![](https://img.shields.io/badge/S-9d4edd?style=flat-square) | **Kling O1** | API | CoT reasoning, editing, motion transfer |\n| ![](https://img.shields.io/badge/S-9d4edd?style=flat-square) | **Hailuo 02** | API | #2 global, best human motion |\n| ![](https://img.shields.io/badge/A-c77dff?style=flat-square) | **Wan 2.2 14B** | Local | Best open-source, SVI infinite length |\n\n
\n\n### 3D Generation\n\n| Tier | Model | Output | Key Innovation |\n|:---:|:---|:---:|:---|\n| ![](https://img.shields.io/badge/S+-7b2cbf?style=flat-square) | **TRELLIS.2** | PBR Mesh | SOTA quality, native PBR materials |\n| ![](https://img.shields.io/badge/S-9d4edd?style=flat-square) | **Hunyuan3D V2** | Mesh | Best open-source, complex geometry |\n| ![](https://img.shields.io/badge/S-9d4edd?style=flat-square) | **Rodin Gen-2** | Rigged | Avatar generation with animation |\n| ![](https://img.shields.io/badge/A-c77dff?style=flat-square) | **TripoSR** | Mesh | Sub-second, rapid prototyping |\n\n
\n\n### Audio Generation\n\n| Tier | Model | Type | Key Innovation |\n|:---:|:---|:---:|:---|\n| ![](https://img.shields.io/badge/S+-7b2cbf?style=flat-square) | **Suno v4.5** | Full Song | Industry-leading vocals, Studio tier |\n| ![](https://img.shields.io/badge/S+-7b2cbf?style=flat-square) | **Udio v2** | Full Song | EDM/rock specialist, inpainting |\n| ![](https://img.shields.io/badge/S-9d4edd?style=flat-square) | **ElevenLabs Music** | Custom Voice | Voice cloning + music |\n| ![](https://img.shields.io/badge/S-9d4edd?style=flat-square) | **Stable Audio 2.0** | Music | Best open-source, 5 minutes |\n\n
\n\n---\n\n
\n\n## KNOWLEDGE DOMAINS\n\n
\n\n| Reference File | Coverage |\n|:---|:---|\n| `image-models-reference.md` | FLUX.2, Qwen-Image, Nano Banana, SD3, SDXL, all image models |\n| `video-models-reference.md` | Runway, Kling, Wan, Hailuo, Sora, HunyuanVideo, AnimateDiff |\n| `3d-generation-reference.md` | TRELLIS.2, Hunyuan3D, TripoSR, Zero123, Gaussian Splatting |\n| `audio-generation-reference.md` | Suno, Udio, Stable Audio, ElevenLabs, MusicGen, AudioLDM |\n| `training-finetuning-reference.md` | LoRA, DoRA, Dreambooth, full fine-tuning pipelines |\n| `controlnet-conditioning-reference.md` | All ControlNets, IPAdapter, face/style transfer |\n| `upscaling-restoration-reference.md` | ESRGAN, Real-ESRGAN, CodeFormer, diffusion upscale |\n| `prompt-engineering-reference.md` | Model-specific prompting techniques |\n| `custom-nodes-reference.md` | Essential ComfyUI node packs |\n| `optimization-reference.md` | VRAM, speed, troubleshooting |\n\n
\n\n---\n\n
\n\n## SELF-UPDATING SYSTEM\n\n
\n\nOmnigen includes an automatic knowledge update protocol:\n\n```\n┌─────────────────────────────────────────────────────────┐\n│ INITIALIZATION │\n├─────────────────────────────────────────────────────────┤\n│ 1. Check knowledge-state.md for last update date │\n│ 2. If >7 days old → trigger research protocol │\n│ 3. WebSearch for new models and techniques │\n│ 4. Update reference files with discoveries │\n│ 5. Log updates in knowledge-state.md │\n└─────────────────────────────────────────────────────────┘\n```\n\n**Update Triggers:**\n- User mentions unknown model\n- User asks about \"latest\" or \"newest\"\n- Knowledge older than 7 days\n- Start of each month (deep scan)\n\n
\n\n---\n\n
\n\n## USAGE\n\n
\n\n### As Claude Code Skill (Local)\n\nThis is a **Claude Code skill**. To use it:\n\n1. Clone this repo to your Claude skills directory:\n ```bash\n git clone https://github.com/AGI-Film/omnigen.git ~/.claude/skills/omnigen\n ```\n\n2. The skill activates automatically when you ask about:\n - AI image/video/3D/audio generation\n - ComfyUI workflows and custom nodes\n - Model comparisons and recommendations\n - LoRA training and fine-tuning\n - Optimization and troubleshooting\n\n
\n\n### As Production API (Agent Service)\n\nFor integrating with web apps, Cinema App, or production systems:\n\n```bash\ncd agent-service\nnpm install\nnpm run dev\n```\n\n**Quick Start:**\n```bash\n# Query the agent\ncurl -X POST http://localhost:3000/query \\\n -H \"Content-Type: application/json\" \\\n -d '{\"message\": \"Best video model for human motion?\"}'\n\n# Response:\n# {\"response\": \"For realistic human motion, I recommend Hailuo 02...\"}\n```\n\n**Deploy to Production:**\n- **Vercel**: `vercel deploy` (serverless)\n- **Docker**: `docker-compose up` (containerized)\n- **Railway**: `railway up` (managed hosting)\n\nSee [`agent-service/README.md`](./agent-service/README.md) for full documentation.\n\n
\n\n---\n\n
\n\n## QUICK REFERENCE\n\n
\n\n### VRAM Requirements\n\n| VRAM | Image | Video | 3D |\n|:---:|:---|:---|:---|\n| **4-6GB** | SD1.5 GGUF | AnimateDiff | — |\n| **8-12GB** | SDXL, Flux Schnell | AnimateDiff XL | TripoSR |\n| **16GB** | Flux.1 Dev FP8 | CogVideoX, LTX | Hunyuan3D V2 |\n| **24GB+** | FLUX.2 Dev, Qwen-Image | Wan 2.2, HunyuanVideo | 3DGS Training |\n\n
\n\n### Optimal Settings\n\n| Model | Resolution | Sampler | Steps | CFG |\n|:---|:---:|:---:|:---:|:---:|\n| FLUX.2 | 1024-2048 | Euler | 8-28 | 1.0-3.5 |\n| Qwen-Image | 1024+ | Euler | 20-30 | 3.5-7 |\n| SDXL | 1024x1024 | DPM++ 2M | 25-35 | 7-8 |\n| Wan 2.2 | 480p-720p | — | 30-50 | 5-7 |\n\n
\n\n---\n\n
\n\n## PART OF AGI-FILM\n\n
\n\n[![AGI-Film](https://img.shields.io/badge/AGI--FILM-Organization-DC143C?style=for-the-badge&logo=github&logoColor=white&labelColor=0d1117)](https://github.com/AGI-Film)\n\n
\n\n*Building the future of cinema with artificial general intelligence.*\n\n
\n\n---\n\n
\n\n\n\n**★ · ☆ · ★    O M N I G E N    ★ · ☆ · ★**\n\n*M A S T E R    O F    A L L    G E N E R A T I O N*\n\nLast Updated: January 2026\n\n\n\n
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", "has_readme": true, "url": "https://github.com/AGI-Film/omnigen", "category": "unclassified", "value_tier": "standard", "value_rank": 2, "editorial_note": null, "similarity_group": "Models & Machine Learning", "group_score": 11, "similar": [ { "id": "Moestradamus-Productions/digidali-v1", "score": 0.1127, "signals": [ "diffusion", "audio", "training" ] }, { "id": "Moestradamus-Productions/digidali-mcp-gpu", "score": 0.1123, "signals": [ "diffusion", "audio", "training" ] }, { "id": "AGI-Film/model-comparison", "score": 0.1117, "signals": [ "models", "generation", "model" ] }, { "id": "AGI-Film/Eyecon", "score": 0.1052, "signals": [ "diffusion", "models", "generation" ] }, { "id": "quivent/Cinema", "score": 0.105, "signals": [ "generation", "sub", "height" ] } ] }, { "organization": "AGI-Film", "name": "Onboarding", "source": "R2 Git bundle", "published_at": "2025-12-02T10:39:43-05:00", "readme": "", "has_readme": false, "url": "https://github.com/AGI-Film/Onboarding", "category": "unclassified", "value_tier": "standard", "value_rank": 2, "editorial_note": null, "similarity_group": "Other Experiments", "group_score": 0, "similar": [ { "id": "quivent/neurohealth", "score": 0.0763, "signals": [ "onboarding" ] }, { "id": "TSMCP/waynes-world", "score": 0.0661, "signals": [ "onboarding" ] }, { "id": "quivent/discourse", "score": 0.0594, "signals": [ "onboarding" ] }, { "id": "quivent/NovaBauer", "score": 0.0546, "signals": [ "onboarding" ] }, { "id": "Moestradamus-Productions/Training", "score": 0.0546, "signals": [ "onboarding" ] } ] }, { "organization": "AGI-Film", "name": "Screenplays", "source": "R2 Git bundle", "published_at": "2025-12-01T18:12:15+00:00", "readme": "", "has_readme": false, "url": "https://github.com/AGI-Film/Screenplays", "category": "unclassified", "value_tier": "standard", "value_rank": 2, "editorial_note": null, "similarity_group": "Other Experiments", "group_score": 0, "similar": [ { "id": "quivent/Screenplays", "score": 1.0, "signals": [ "screenplays" ] }, { "id": "AGI-Film/Architecture", "score": 0.143, "signals": [ "screenplays" ] }, { "id": "quivent/coverage-go", "score": 0.1101, "signals": [ "screenplays" ] }, { "id": "quivent/Coverage", "score": 0.0965, "signals": [ "screenplays" ] }, { "id": "quivent/Cinema", "score": 0.0835, "signals": [ "screenplays" ] } ] }, { "organization": "AGI-Film", "name": "Storyboarding", "source": "R2 Git bundle", "published_at": "2025-11-22T16:47:35-07:00", "readme": "# 🎬 Cinema\n\n> AI-Powered Screenplay to Film Platform\n\n**Status**: Active Development | **Version**: 0.1.27\n\n## Quick Start\n\n```bash\n# Install Cinema CLI\nmake install\n\n# Start development\nmake dev\n```\n\n## What is Cinema?\n\nCinema transforms screenplays into films using AI-powered animation systems. Built with:\n\n- **Tauri** (Rust backend)\n- **React + TypeScript** (Frontend)\n- **GCP Cloud SQL** (PostgreSQL database)\n- **Cloud Storage** (Media & renders)\n- **Firestore** (Real-time collaboration)\n\n## Installation\n\n### 1. Install Cinema CLI\n\n```bash\nmake install\n```\n\n### 2. Verify\n\n```bash\ncinema --version\n# Output: Cinema CLI v0.1.27\n\ncinema help\n# Shows all available commands\n```\n\n## Development\n\n### Start Development Server\n\n```bash\nmake dev\n# or: cinema dev\n```\n\n### Build for Production\n\n```bash\nmake build\n# or: cinema build\n```\n\n### Run Tests\n\n```bash\nmake test\n# or: cinema test\n```\n\n## Database Setup\n\n### 1. Start Cloud SQL Proxy\n\n```bash\nmake proxy-start\n# or: cinema proxy start\n```\n\n### 2. Check Database Status\n\n```bash\nmake db-status\n# or: cinema db status\n```\n\n### 3. Run Migrations\n\n```bash\ncinema db migrate\n```\n\n## CLI Commands\n\nCinema includes a comprehensive CLI with 38+ commands:\n\n```bash\ncinema help # Full command list\ncinema doctor # Diagnose issues\ncinema status # System status\ncinema config # Show configuration\n```\n\nSee [CLI Documentation](./README_CLI.md) for complete reference.\n\n## Project Structure\n\n```\nCinema/\n├── cli/ # Cinema CLI tool\n├── migrations/ # Database migrations\n├── src/ # React frontend\n│ ├── components/ # React components\n│ ├── tabs/ # Tab components\n│ ├── lib/ # Libraries & utilities\n│ └── styles/ # CSS styles\n├── src-tauri/ # Rust backend\n│ └── src/\n│ ├── main.rs # Tauri app entry\n│ ├── postgres.rs # Database logic\n│ └── db.rs # SQLite (local)\n├── research/ # CAIP research data\n├── scripts/ # Utility scripts\n└── Makefile # Build & install\n```\n\n## Features\n\n### ✅ Implemented\n\n- **Character Database**: 120+ Miyazaki/Ghibli characters\n- **CAIP System**: Character Animation Iterative Protocol\n- **3D Animation**: Three.js integration\n- **2D Animation**: PixiJS canvas\n- **Database**: Cloud SQL with migrations\n- **Real-time**: Firestore collaboration\n- **CLI Tool**: 38+ commands for management\n\n### 🚧 In Progress\n\n- **Phase 3 Reconstruction**: Multi-library character generation\n- **Automated Workflows**: CAIP automation\n- **Rendering Pipeline**: Film output system\n\n## Database\n\n**Type**: PostgreSQL 15 (GCP Cloud SQL)\n**Instance**: cinema-db\n**Connection**: Via Cloud SQL Proxy (port 3307)\n\n### Tables\n\n- `users` - User authentication\n- `scripts` - Screenplay storage\n- `animated_characters` - Character database (120+)\n- `animation_software` - Software reference\n- `reconstruction_sessions` - Phase 3 tracking\n- `reconstruction_iterations` - Multi-library results\n\n## GCP Infrastructure\n\n```bash\n# Authenticate\ncinema gcp auth\n\n# Check status\ncinema gcp status\n\n# List buckets\ncinema gcp buckets\n```\n\n**Resources**:\n- Cloud SQL (PostgreSQL 15)\n- Cloud Storage (3 buckets)\n- Firestore\n- Service Accounts\n\n## Troubleshooting\n\n### Quick Diagnostics\n\n```bash\ncinema doctor\n```\n\nThis checks:\n- Node.js, npm, Rust, Cargo\n- gcloud CLI\n- Cloud SQL Proxy\n- PostgreSQL client\n- Dependencies\n- Running services\n\n### Common Issues\n\n**Database connection failed?**\n```bash\ncinema proxy restart\ncinema db status\n```\n\n**Dependencies missing?**\n```bash\ncinema deps\n```\n\n**Build errors?**\n```bash\ncinema clean\ncinema build\n```\n\n## Documentation\n\n- **[CLI Reference](./README_CLI.md)** - Complete CLI documentation\n- **[CAIP Specification](./research/)** - Character animation protocol\n- **[Database Migration](./GCP_MIGRATION_COMPLETE.md)** - GCP setup guide\n- **[Development Principles](./DEVELOPMENT_PRINCIPLES.md)** - Core principles\n\n## Makefile Commands\n\n```bash\nmake help # Show all commands\nmake install # Install Cinema CLI\nmake uninstall # Remove Cinema CLI\nmake dev # Start development\nmake build # Production build\nmake clean # Clean artifacts\nmake deps # Install dependencies\nmake test # Run tests\nmake lint # Run linter\nmake format # Format code\nmake proxy-start # Start Cloud SQL Proxy\nmake proxy-stop # Stop proxy\nmake proxy-restart # Restart proxy\nmake db-status # Database status\nmake doctor # System diagnostics\nmake gcp-auth # GCP authentication\n```\n\n## Development Workflow\n\n### Daily Routine\n\n```bash\n# 1. Check system health\ncinema doctor\n\n# 2. Start proxy\nmake proxy-start\n\n# 3. Start Cinema\nmake dev\n```\n\n### Before Committing\n\n```bash\n# Format & lint\nmake format\nmake lint\n\n# Run tests\nmake test\n```\n\n### Production Build\n\n```bash\n# Clean & build\nmake clean\nmake build\n\n# Output: src-tauri/target/release/bundle/\n```\n\n## Contributing\n\n1. Fork the repository\n2. Create feature branch\n3. Make changes\n4. Run `cinema doctor` to verify\n5. Submit pull request\n\n## Requirements\n\n- **Node.js** >= 18.0.0\n- **Rust** >= 1.70.0\n- **gcloud CLI**\n- **Cloud SQL Proxy**\n- **PostgreSQL client** (psql)\n\nCheck with: `cinema doctor`\n\n## License\n\nMIT\n\n## Version\n\n**0.1.27** - Current development version\n\nCheck with: `cinema version`\n\n---\n\n**Built with** ❤️ **by the Cinema Team**\n\nFor issues or questions, see: `cinema help`", "has_readme": true, "url": "https://github.com/AGI-Film/Storyboarding", "category": "unclassified", "value_tier": "standard", "value_rank": 2, "editorial_note": null, "similarity_group": "Creative & Media", "group_score": 10, "similar": [ { "id": "quivent/Cinema", "score": 0.4587, "signals": [ "film", "cinema", "screenplay" ] }, { "id": "AGI-Film/documentation", "score": 0.1893, "signals": [ "film", "cinema", "screenplay" ] }, { "id": "quivent/kamaji", "score": 0.1725, "signals": [ "tab", "tauri", "collaboration" ] }, { "id": "InfotonDB/Luminary", "score": 0.1663, "signals": [ "authenticate", "accounts", "gcp" ] }, { "id": "Oceantica/Savant", "score": 0.165, "signals": [ "tables", "tauri", "lint" ] } ] }, { "organization": "AGI-Tooling", "name": "PortAuthority", "source": "R2 Git bundle", "published_at": "2026-02-11T21:27:17-05:00", "readme": "# Port Authority - Centralized Port Management System\n\n**Zero port conflicts. Zero port stealing. Total control.**\n\nPort Authority is a standalone service that manages port allocation across all your projects, ensuring services never conflict or steal ports from each other.\n\n## Features\n\n- ✅ **Centralized Port Management** - Single source of truth for all port allocations\n- ✅ **Automatic Conflict Detection** - Prevents port conflicts before they happen\n- ✅ **Multi-Language Support** - Client libraries for Node.js, Python, Rust, Go\n- ✅ **CLI Tool** - Simple command-line interface for port management\n- ✅ **Enforcement Layer** - Automatically kills unauthorized port usage\n- ✅ **φ-Based Optimization** - Golden ratio port allocation for optimal distribution\n- ✅ **Health Monitoring** - Track service health and port usage\n- ✅ **REST API** - HTTP API for programmatic access\n\n## Quick Start\n\n### 1. Start Port Authority Service\n\n```bash\ncd /home/alice/PortAuthority\nnpm install\nnpm start\n```\n\n### 2. Request a Port\n\n```bash\n# Using CLI\nportauth allocate my-service\n\n# Using Node.js\nconst { PortAuthorityClient } = require('portauth-client');\nconst client = new PortAuthorityClient();\nconst port = await client.allocate('my-service');\n\n# Using Python\nfrom portauth import PortAuthorityClient\nclient = PortAuthorityClient()\nport = client.allocate('my-service')\n```\n\n### 3. Start Your Service on Assigned Port\n\nYour service receives an authorized port and starts without conflicts.\n\n## Architecture\n\n```\n┌─────────────────────────────────────────┐\n│ Port Authority Service │\n│ - Port Registry (SQLite) │\n│ - Allocation Engine │\n│ - Conflict Detector │\n│ - REST API (Port 9999) │\n└─────────────────┬───────────────────────┘\n │\n ┌─────────┴─────────┐\n │ │\n ┌────▼────┐ ┌────▼────┐\n │ CLI │ │ Clients │\n │ portauth│ │ (libs) │\n └─────────┘ └─────────┘\n │ │\n └─────────┬─────────┘\n │\n ┌─────────▼─────────┐\n │ Your Services │\n │ (authorized) │\n └───────────────────┘\n```\n\n## Installation\n\nSee [INSTALLATION.md](docs/INSTALLATION.md)\n\n## Usage\n\nSee [USAGE.md](docs/USAGE.md)\n\n## API Documentation\n\nSee [API.md](docs/API.md)\n\n## License\n\nMIT", "has_readme": true, "url": "https://github.com/AGI-Tooling/PortAuthority", "category": "unclassified", "value_tier": "standard", "value_rank": 2, "editorial_note": null, "similarity_group": "Developer Tools", "group_score": 4, "similar": [ { "id": "quivent/PortAuthority", "score": 0.115, "signals": [ "cli", "api", "portauthority" ] }, { "id": "Oceantica/Map", "score": 0.1113, "signals": [ "cli", "conflicts", "allocations" ] }, { "id": "TransformerOS/atlas", "score": 0.1103, "signals": [ "api", "conflicts", "ports" ] }, { "id": "MorchestraWorld/PortAuthority", "score": 0.1025, "signals": [ "cli", "api", "portauthority" ] }, { "id": "Moestradamus-Productions/TaoBot-Trader-Dynamic-Tunneler", "score": 0.1001, "signals": [ "api", "golden", "ports" ] } ] }, { "organization": "AGI-Tooling", "name": "train", "source": "R2 Git bundle", "published_at": "2025-12-07T13:41:26+00:00", "readme": "# Train-SAM - Trump Small Audio Model Training CLI\n\n**Version:** 1.0.0\n**Status:** Production Ready\n**Hardware:** 8x H100 / 8x B200 / 1x GH200\n\nAutomated training pipeline for Trump SAM (Small Audio Model) - a transformer-based speech synthesis model trained on Trump's voice.\n\n## Quick Start\n\n### Installation\n\n```bash\n# Build and install globally\ngo build -o ~/.local/bin/train-sam cmd/train-sam/main.go\n\n# Verify installation\ntrain-sam --version\n```\n\n### Usage\n\n**For first-time users:**\n```bash\ntrain-sam wizard\n```\n\n**For experienced users:**\n```bash\ntrain-sam train --num-gpus 8 --epochs 50\n```\n\n**Read the guide:**\n```bash\ntrain-sam guide\n```\n\n## Features\n\n- ✅ **Automated Training Pipeline** - Complete 4-phase training workflow\n- ✅ **Interactive Wizard** - Step-by-step configuration\n- ✅ **Embedded Guide** - Quick start guide built into binary\n- ✅ **Real-Time Monitoring** - GPU utilization, memory, temperature\n- ✅ **Distributed Training** - PyTorch DDP across 8 GPUs\n- ✅ **Mixed Precision** - FP16 training with gradient scaling\n- ✅ **Progress Tracking** - Checkpoint detection and status monitoring\n\n## Commands\n\n| Command | Description |\n|---------|-------------|\n| `train` | Run complete automated training pipeline |\n| `wizard` | Interactive step-by-step configuration |\n| `guide` | Show quick start guide (embedded) |\n| `monitor` | Real-time GPU and training monitoring |\n| `status` | Check training progress |\n| `resume` | Resume training from checkpoint |\n\n## Hardware Support\n\n| Hardware | Timeline | Cost | Recommendation |\n|----------|----------|------|----------------|\n| 8x H100 | 2.7 hours | $65 | ✅ Recommended |\n| 1x GH200 | 4.75 hours | $24 | Budget option |\n| 8x B200 | 2.65 hours | $128 | Not recommended |\n\n## Architecture\n\n**Model:** Transformer-based sequence prediction\n- Prototype: 15M parameters (4 layers, 4 heads, 512 hidden)\n- Production: 55M parameters (6 layers, 8 heads, 1024 hidden)\n\n**Training:**\n- Context size: 5 previous phonemes → predict next\n- Vocabulary: 9,455 unique phonemes\n- Batch size: 256 per GPU (2,048 global on 8 GPUs)\n- Optimizer: AdamW with cosine decay\n- Mixed precision: FP16 with gradient scaling\n\n## Expected Results\n\nAfter 50 epochs on 8x H100:\n- Top-1 Accuracy: 50-60%\n- Top-5 Accuracy: 80-85%\n- Validation Loss: <1.5\n- Discontinuities: 150-180/sec (vs 264/sec baseline)\n- SAM Activation: 40-60% (vs 0% baseline)\n\n## Training Pipeline\n\n### Phase 1: Data Preparation (60 min, CPU)\n- Load 9,455 phonemes from JSON\n- Create training sequences\n- Train/val split (80/20)\n\n### Phase 2: Feature Extraction (15 min, 8x H100)\n- Distributed across 8 GPUs\n- Mel spectrograms → CNN encoder → 256-dim embeddings\n- Mixed precision (FP16)\n\n### Phase 3: Distributed Training (32 min, 8x H100)\n- Distributed Data Parallel (DDP)\n- Global batch size: 2,048\n- NVLink gradient synchronization\n- Checkpoints: epochs 10, 20, 30, 40, 50\n\n### Phase 4: Validation (56 min, CPU + GPU)\n- Generate test synthesis\n- Measure quality metrics\n- Save best model\n\n## Requirements\n\n**Hardware:**\n- 8x NVIDIA H100 (80GB) - recommended\n- OR 1x NVIDIA GH200 - budget option\n- OR 8x NVIDIA B200 (192GB) - expensive\n\n**Software:**\n- Go 1.21+\n- Python 3.9+\n- PyTorch 2.0+\n- CUDA 12.0+\n\n**Python Dependencies:**\n```bash\npip install torch>=2.0.0 torchaudio>=2.0.0 numpy scipy soundfile tqdm\n```\n\n## Building from Source\n\n```bash\n# Install dependencies\ngo mod tidy\n\n# Build binary\ngo build -o train-sam cmd/train-sam/main.go\n\n# Install globally\nmv train-sam ~/.local/bin/\n\n# Verify\ntrain-sam --version\n```\n\n## Example Usage\n\n### Complete Training Run\n\n```bash\n# Terminal 1: Start training\ntrain-sam train --num-gpus 8 --epochs 50\n\n# Terminal 2: Monitor progress\ntrain-sam monitor --refresh 2\n\n# Terminal 3: Check status\ntrain-sam status\n```\n\n### Custom Configuration\n\n```bash\ntrain-sam train \\\n --num-gpus 8 \\\n --epochs 100 \\\n --batch-size 256 \\\n --d-model 1024 \\\n --num-layers 6 \\\n --num-heads 8 \\\n --checkpoint-dir my_checkpoints\n```\n\n## Documentation\n\nComplete documentation is available in the `docs/` directory:\n\n### Implementation Guides\n- [`docs/IMPLEMENTATION_PLAN_8xH100.md`](docs/IMPLEMENTATION_PLAN_8xH100.md) - Complete 4-phase pipeline with all Python code (803 lines)\n- [`docs/HARDWARE_CONFIGURATIONS.md`](docs/HARDWARE_CONFIGURATIONS.md) - Phase-by-phase workflows for each GPU type (527 lines)\n- [`docs/MODEL_ARCHITECTURE_SPEC.md`](docs/MODEL_ARCHITECTURE_SPEC.md) - Transformer architecture details (577 lines)\n\n### User Guides\n- [`QUICK_START_GUIDE.md`](QUICK_START_GUIDE.md) - Step-by-step guide (also embedded in CLI binary)\n- [`docs/INSTALLATION.md`](docs/INSTALLATION.md) - Installation and verification instructions\n\n### Technical References\n- [`docs/GPU_TIMING_COMPARISON.md`](docs/GPU_TIMING_COMPARISON.md) - B200 vs H100 vs GH200 specifications and timing\n- [`docs/REALISTIC_GPU_CONFIG.md`](docs/REALISTIC_GPU_CONFIG.md) - Hardware configuration details\n- [`docs/CPU_GPU_WORKLOAD_SPLIT.md`](docs/CPU_GPU_WORKLOAD_SPLIT.md) - CPU vs GPU breakdown per phase\n\n### Summary Documents\n- [`docs/DELIVERY_COMPLETE.md`](docs/DELIVERY_COMPLETE.md) - Complete delivery status\n- [`docs/FINAL_DELIVERY_SUMMARY.md`](docs/FINAL_DELIVERY_SUMMARY.md) - Feature list and usage examples\n- [`docs/TRAINING_COMPLETE_SUMMARY.md`](docs/TRAINING_COMPLETE_SUMMARY.md) - Timeline and costs\n- [`DELIVERABLES_CHECKLIST.md`](DELIVERABLES_CHECKLIST.md) - Complete checklist\n\n## Project Structure\n\n```\ntrump-sam-trainer/\n├── cmd/\n│ └── train-sam/\n│ └── main.go # CLI entry point (680 lines)\n├── docs/ # Complete documentation\n│ ├── IMPLEMENTATION_PLAN_8xH100.md # Full training pipeline with code\n│ ├── HARDWARE_CONFIGURATIONS.md # Phase-by-phase workflows\n│ ├── MODEL_ARCHITECTURE_SPEC.md # Architecture details\n│ ├── GPU_TIMING_COMPARISON.md # Hardware comparison\n│ ├── REALISTIC_GPU_CONFIG.md # GPU configurations\n│ ├── CPU_GPU_WORKLOAD_SPLIT.md # CPU vs GPU breakdown\n│ ├── INSTALLATION.md # Installation guide\n│ ├── DELIVERY_COMPLETE.md # Delivery status\n│ ├── FINAL_DELIVERY_SUMMARY.md # Feature summary\n│ └── TRAINING_COMPLETE_SUMMARY.md # Timeline and costs\n├── QUICK_START_GUIDE.md # Quick start (embedded in binary)\n├── DELIVERABLES_CHECKLIST.md # Complete checklist\n├── README.md # This file\n├── go.mod # Go module definition\n├── go.sum # Dependency checksums\n└── .gitignore # Git ignore patterns\n```\n\n## License\n\nPrivate repository - AGI-Tooling\n\n## Support\n\n```bash\n# General help\ntrain-sam --help\n\n# Command-specific help\ntrain-sam train --help\ntrain-sam wizard --help\ntrain-sam monitor --help\n```\n\n## Version History\n\n### v1.0.0 (2025-12-07)\n- Initial release\n- 6 commands: train, wizard, guide, monitor, status, resume\n- Embedded quick start guide\n- Interactive wizard\n- Real-time monitoring\n- Distributed training support (8 GPUs)\n- Single GPU support (GH200)\n- Mixed precision (FP16)\n- Automatic checkpointing\n\n---\n\n**Repository:** https://github.com/AGI-Tooling/train\n**Status:** ✅ Production Ready", "has_readme": true, "url": "https://github.com/AGI-Tooling/train", "category": "unclassified", "value_tier": "standard", "value_rank": 2, "editorial_note": null, "similarity_group": "Models & Machine Learning", "group_score": 9, "similar": [ { "id": "quivent/conduct", "score": 0.1774, "signals": [ "training", "model", "epochs" ] }, { "id": "quivent/producer", "score": 0.1574, "signals": [ "checkpoint", "training", "model" ] }, { "id": "quivent/coverage-architecture-analysis", "score": 0.1367, "signals": [ "model", "nvlink", "breakdown" ] }, { "id": "TSMCP/Orchestra", "score": 0.133, "signals": [ "synchronization", "option", "timing" ] }, { "id": "MorchestraWorld/Orchestra", "score": 0.133, "signals": [ "synchronization", "option", "timing" ] } ] }, { "organization": "AmadeusInnovations", "name": ".maude", "source": "R2 Git bundle", "published_at": "2025-09-05T20:00:44+02:00", "readme": "", "has_readme": false, "url": "https://github.com/AmadeusInnovations/.maude", "category": "unclassified", "value_tier": "standard", "value_rank": 2, "editorial_note": null, "similarity_group": "Other Experiments", "group_score": 0, "similar": [ { "id": "Moestradamus-Productions/.maude", "score": 1.0, "signals": [ "maude" ] }, { "id": "AmadeusInnovations/AmadeusInnovations", "score": 0.2821, "signals": [ "maude" ] }, { "id": "Moestradamus-Productions/Moestradamus-Productions", "score": 0.1277, "signals": [ "maude" ] } ] }, { "organization": "AmadeusInnovations", "name": "AmadeusInnovations", "source": "R2 Git bundle", "published_at": "2025-09-06T01:02:27+02:00", "readme": "
\n \n# AMADEUS INNOVATIONS\n*Private Collection*\n\n▪︎ ▫︎ ▪︎ ▫︎ ▪︎\n\n
\n\n**entropy** ⚛️ [`/entropy`](https://github.com/AmadeusInnovations/entropy) \n*Modular architecture CLI → Shannon's influence*\n\n**Training** 🎯 [`/Training`](https://github.com/AmadeusInnovations/Training) \n*AI agent optimization systems*\n\n**silence** 〰 [`/silence`](https://github.com/AmadeusInnovations/silence) \n*Ephemeral cryptographic communication*\n\n────\n\n**TravelAgent** [`/TravelAgent`](https://github.com/AmadeusInnovations/TravelAgent) ◦ **pointsio** [`/pointsio`](https://github.com/AmadeusInnovations/pointsio)\n\n[Research](https://github.com/AmadeusInnovations/Research) ◦ [cherry](https://github.com/AmadeusInnovations/cherry) ◦ [.maude](https://github.com/AmadeusInnovations/.maude)", "has_readme": true, "url": "https://github.com/AmadeusInnovations/AmadeusInnovations", "category": "unclassified", "value_tier": "standard", "value_rank": 2, "editorial_note": null, "similarity_group": "Models & Machine Learning", "group_score": 2, "similar": [ { "id": "AmadeusInnovations/TravelAgent", "score": 0.3497, "signals": [ "travelagent" ] }, { "id": "Moestradamus-Productions/.maude", "score": 0.2821, "signals": [ "maude" ] }, { "id": "AmadeusInnovations/.maude", "score": 0.2821, "signals": [ "maude" ] }, { "id": "quivent/training-data", "score": 0.1773, "signals": [ "training" ] }, { "id": "Moestradamus-Productions/Moestradamus-Productions", "score": 0.1603, "signals": [ "training", "pointsio", "maude" ] } ] }, { "organization": "AmadeusInnovations", "name": "chasm", "source": "R2 Git bundle", "published_at": "2025-10-10T12:33:20+02:00", "readme": "# 🏛️ Chasm UI Framework\n\n[![Production Ready](https://img.shields.io/badge/Status-Production%20Ready-brightgreen)](https://github.com/chasm-ui/framework)\n[![Completion](https://img.shields.io/badge/Completion-99%25-brightgreen)](docs/PROJECT_DASHBOARD.md)\n[![Performance](https://img.shields.io/badge/Performance-Exceeds%20SwiftUI-blue)](docs/PERFORMANCE_OPTIMIZATION_GUIDE.md)\n[![Cross Platform](https://img.shields.io/badge/Platforms-iOS%20%7C%20Android%20%7C%20Web%20%7C%20Desktop-orange)](docs/PLATFORM_INTEGRATION_GUIDES.md)\n\n> **Revolutionary pure C application development framework delivering SwiftUI-level functionality with superior performance and true cross-platform compatibility.**\n\n## ⚡ **Performance That Exceeds SwiftUI**\n\n- **🚀 73fps Rendering** (22% faster than SwiftUI's 60fps)\n- **💾 347MB Memory** (40% lower than typical SwiftUI apps)\n- **⚡ 12ms Touch Latency** (25% faster than SwiftUI)\n- **🎯 89% Frame Consistency** (industry-leading smoothness)\n\n## 🎨 **Complete Theme System**\n\nExperience luxury design with our comprehensive theme collection:\n\n| Theme | Description | Perfect For |\n|-------|-------------|-------------|\n| **Elite** 🏆 | Luxury design with premium effects | High-end applications |\n| **Modern** 🔧 | Clean contemporary aesthetic | Business applications |\n| **Zen** 🧘 | Peaceful minimalist design | Wellness & productivity |\n| **Focus** 🎯 | Distraction-free productivity | Work & concentration |\n| **Power** ⚡ | High-energy dynamic styling | Gaming & sports apps |\n\n*Plus 4 additional themes: Retro, Playful, Classic, Pure*\n\n### **Dynamic Theme Switching**\n- **5 Transition Modes**: Instant, Fade, Slide, Morph, Ripple\n- **<1ms Application Time**: Industry-leading performance\n- **State Persistence**: Themes survive app restarts\n- **Smooth Animations**: Cinema-quality transitions\n\n## 🏗️ **Architecture Excellence**\n\n```\n📁 Chasm Framework\n├── 🛠️ src/ # Modular source code\n│ ├── core/ # Graphics, animation, layout\n│ ├── components/ # 25+ UI components\n│ ├── themes/ # 9 complete themes\n│ └── platform/ # Cross-platform support\n├── 📚 docs/ # Comprehensive documentation\n├── 🎮 demos/ # Interactive demonstrations\n├── 📖 examples/ # Code examples & tutorials\n└── 🧪 tests/ # Extensive test suite\n```\n\n**📁 Repository Organization**: Professionally organized with enforced directory structure for maintainable development. See [Repository Organization Guide](docs/REPOSITORY_ORGANIZATION.md) for complete details.\n\n## 🚀 **Quick Start**\n\n### **1. Clone & Build**\n```bash\ngit clone https://github.com/chasm-ui/framework.git\ncd Chasm\nmake all\n```\n\n### **2. Run Theme Showcase**\n```bash\n./demos/comprehensive_theme_showcase_demo\n```\n\n### **3. Your First App**\n```c\n#include \"chasm.h\"\n\nint main() {\n chasm_init();\n \n // Create window\n chasm_window_t* window = chasm_window_create(\"My App\", 800, 600);\n \n // Create UI\n chasm_view_t* root = chasm_vstack_create(20.0f);\n chasm_text_t* title = chasm_text_create(\"Hello, Chasm!\");\n chasm_button_t* button = chasm_button_create(\"Press Me\");\n \n // Build layout\n chasm_vstack_add_child(root, (chasm_view_t*)title);\n chasm_vstack_add_child(root, (chasm_view_t*)button);\n chasm_window_set_root_view(window, root);\n \n // Apply theme\n chasm_dynamic_theme_switch_to(CHASM_THEME_MODERN);\n \n // Run app\n chasm_window_show(window);\n chasm_main_loop();\n \n return 0;\n}\n```\n\n## 🌟 **Key Features**\n\n### **💎 SwiftUI-Level Components**\n- **Layout**: VStack, HStack, ZStack, ScrollView\n- **Controls**: Button, Toggle, Slider, Picker, TextField\n- **Navigation**: NavigationView, TabView, Sheet, Alert\n- **Data**: List, ForEach with dynamic content\n- **Graphics**: Shape, Path, Gradient, Shadow effects\n\n### **🎭 Advanced Theming**\n- **Luxury Themes**: Elite theme with gold effects, shimmer, glow\n- **Modern Themes**: Clean design with Material Design integration \n- **Mindful Themes**: Zen theme with breathing animations\n- **Productivity**: Focus theme with distraction blocking\n\n### **⚡ Performance Optimized**\n- **SIMD Vectorization**: Math operations accelerated\n- **Memory Pooling**: 40% reduction in allocations\n- **GPU Acceleration**: Hardware compositing enabled\n- **Dirty Regions**: Minimal redraw for 60fps\n\n### **🌐 True Cross-Platform**\n- **iOS/macOS**: Native Core Graphics integration\n- **Android**: NDK with Skia + Vulkan acceleration \n- **Web**: WebAssembly with WebGL/WebGPU\n- **Desktop**: OpenGL/DirectX for Windows/Linux\n\n## 📱 **Platform Support**\n\n| Platform | Status | Performance | Notes |\n|----------|--------|-------------|-------|\n| **iOS** | ✅ Production | 73fps | Core Graphics optimized |\n| **macOS** | ✅ Production | 73fps | Native app support |\n| **Android** | ✅ Production | 68fps | NDK + Vulkan |\n| **Web** | ✅ Production | 60fps | WASM + WebGL |\n| **Windows** | ✅ Production | 65fps | DirectX acceleration |\n| **Linux** | ✅ Production | 67fps | OpenGL rendering |\n\n## 🎮 **Interactive Demos**\n\nExplore our comprehensive demo collection:\n\n```bash\n# Launch demo browser\n./launch_demos.sh\n\n# Specific demos\n./demos/comprehensive_theme_showcase_demo # All 9 themes\n./demos/real_time_sync_demo # Data synchronization \n./demos/advanced_lighting_demo # Visual effects\n./demos/cinematic_theme_demo # Theme transitions\n```\n\n### **Web Demos**\nVisit our [online demos](https://chasm-ui.github.io/demos) to experience Chasm in your browser.\n\n## 📖 **Documentation**\n\n| Document | Description |\n|----------|-------------|\n| [📋 API Documentation](docs/API_DOCUMENTATION.md) | Complete API reference |\n| [🚀 Getting Started](docs/GETTING_STARTED_TUTORIAL.md) | Step-by-step tutorial |\n| [🔄 SwiftUI Migration](docs/SWIFTUI_MIGRATION_GUIDE.md) | Migrate from SwiftUI |\n| [⚡ Performance Guide](docs/PERFORMANCE_OPTIMIZATION_GUIDE.md) | Optimization techniques |\n| [🌐 Web Platform](docs/WEB_PLATFORM_GUIDE.md) | Web deployment |\n| [🏗️ Project Structure](docs/PROJECT_STRUCTURE.md) | Codebase organization |\n\n## 🧪 **Quality Assurance**\n\n### **Test Coverage**\n- **✅ Visual Regression**: Pixel-perfect validation\n- **✅ Performance Tests**: 15 benchmark scenarios \n- **✅ Memory Tests**: Zero leaks detected\n- **✅ Cross-Platform**: Identical behavior\n- **✅ Stress Tests**: 20 scenarios passing\n\n### **Production Benchmarks**\n```\nRendering Performance: 73fps ✅ (Target: 60fps)\nMemory Efficiency: 347MB ✅ (Target: <500MB) \nTouch Responsiveness: 12ms ✅ (Target: <16ms)\nFrame Consistency: 89% ✅ (Target: 85%)\nTheme Switch Speed: <1ms ✅ (Production ready)\n```\n\n## 🤝 **Contributing**\n\nWe welcome contributions! See our [Development Guide](docs/DEVELOPMENT_ASSESSMENT.md) for:\n\n- **Development Lanes**: 16 parallel development tracks\n- **Component Guidelines**: Creating new UI components\n- **Performance Standards**: Maintaining 60fps+ performance\n- **Testing Requirements**: Quality assurance standards\n\n### **Current Priorities**\n1. **Documentation Enhancement**: API examples and tutorials\n2. **Advanced Visual Effects**: 3D transformations, particles\n3. **Enterprise Features**: Analytics, security, accessibility\n4. **Community Tools**: Plugin system, marketplace\n\n## 📊 **Project Status**\n\n### **Completion: 99%** 🎉\n\n| Milestone | Progress | Status |\n|-----------|----------|--------|\n| **Core Infrastructure** | 100% | ✅ Complete |\n| **Component Library** | 100% | ✅ Complete |\n| **Advanced Features** | 90% | ⚡ Near Complete |\n\n### **Recent Achievements**\n- ✅ **Complete Theme System**: 9 themes with dynamic switching\n- ✅ **Cross-Platform Support**: iOS, Android, Web, Desktop\n- ✅ **Performance Excellence**: Exceeds all SwiftUI benchmarks\n- ✅ **Production Ready**: Enterprise-grade stability\n\n## 🏆 **Why Choose Chasm?**\n\n### **vs SwiftUI**\n- **🚀 30-80% Better Performance**: Native C implementation\n- **🌐 True Cross-Platform**: One codebase, all platforms\n- **🎨 Superior Theming**: 9 professional themes vs basic SwiftUI\n- **💾 Lower Memory Usage**: 40-60% reduction\n- **⚡ Instant Startup**: No Swift runtime overhead\n\n### **vs Flutter**\n- **📱 Native Performance**: No widget overhead\n- **🎯 Smaller Binary Size**: Pure C implementation \n- **🔧 Direct Platform Access**: No abstraction penalties\n- **💡 Professional Themes**: Luxury design built-in\n\n### **vs React Native**\n- **⚡ 3x Faster Rendering**: No JavaScript bridge\n- **🏠 Native UI Components**: Platform-specific optimization\n- **🔒 Type Safety**: C compilation catches errors early\n- **📦 Self-Contained**: No external dependencies\n\n## 📞 **Support & Community**\n\n- **📧 Email**: support@chasm-ui.com\n- **💬 Discord**: [Chasm UI Community](https://discord.gg/chasm-ui)\n- **🐛 Issues**: [GitHub Issues](https://github.com/chasm-ui/framework/issues)\n- **📚 Wiki**: [Community Wiki](https://github.com/chasm-ui/framework/wiki)\n\n## 📄 **License**\n\nMIT License - see [LICENSE](LICENSE) for details.\n\n---\n\n
\n\n**🏛️ Built with Chasm UI Framework**\n\n*The next generation of cross-platform application development*\n\n[**🚀 Get Started**](docs/GETTING_STARTED_TUTORIAL.md) • [**📖 Documentation**](docs/) • [**🎮 Try Demos**](demos/) • [**⭐ Star on GitHub**](https://github.com/chasm-ui/framework)\n\n
", "has_readme": true, "url": "https://github.com/AmadeusInnovations/chasm", "category": "unclassified", "value_tier": "standard", "value_rank": 2, "editorial_note": null, "similarity_group": "Web & Applications", "group_score": 10, "similar": [ { "id": "quivent/Chasm", "score": 1.0, "signals": [ "desktop", "react", "next" ] }, { "id": "MozArchAngelos/chasm", "score": 1.0, "signals": [ "desktop", "react", "next" ] }, { "id": "Moestradamus-Productions/chasm", "score": 1.0, "signals": [ "desktop", "react", "next" ] }, { "id": "quivent/Builders", "score": 0.1888, "signals": [ "web", "retro", "gaming" ] }, { "id": "TSMCP/tool-of-tools", "score": 0.1772, "signals": [ "application", "tutorial", "achievements" ] } ] }, { "organization": "AmadeusInnovations", "name": "cherry", "source": "R2 Git bundle", "published_at": "2025-09-06T16:55:05+02:00", "readme": "# 🌸 Cherry CLI - Revolutionary Server Management Platform\n\nA beautiful, secure, and comprehensive command-line interface for Cherry Servers infrastructure management. Features military-grade encryption, server state snapshotting, and the revolutionary **Cherry Server Capsule System**.\n\nAvailable as both `cherry` and `cherrypicker` commands.\n\n## 🌟 Overview\n\nCherry CLI transforms Cherry Servers management with an elegant, security-first approach. Built on robust C foundations with OpenSSL cryptography, it provides enterprise-grade server state management capabilities previously unavailable in any infrastructure tool.\n\n## 🚀 Revolutionary Features\n\n### 💊 Cherry Server Capsule System (NEW)\nThe world's first **complete server state snapshotting and transfer system** with military-grade security:\n- **🔬 Complete Server State Capture**: Users, packages, services, configs, data, SSH keys, crontabs, network settings\n- **🔐 Layered AES-256-GCM Encryption**: OpenSSL-based security with SHA-512 integrity verification\n- **📦 Intelligent Size Optimization**: Rebuild artifact detection removes unnecessary data while maintaining complete restore capability\n- **🚀 Secure Transfer Protocol**: Encrypted transmission with integrity verification and remote confirmation\n- **⚡ Automated Restoration**: Seamless server recreation via intelligent rebuild system\n\n### 🔐 Secure File Transfer (Primary Use Case)\nAdvanced GPG-encrypted file and directory transfer:\n- **AES-256 Symmetric Encryption** with compression\n- **Directory Intelligence**: Automatic tar.gz creation for folders\n- **Secure Transmission**: SCP-based transfer with cleanup\n- **Zero-Knowledge**: Temporary files automatically removed\n\n### 🌸 Blossom System (Enhanced User Management) \nElegant SSH and user management with cherry blossom aesthetics:\n- **Smart User Switching**: SSH with automatic `sudo su - username`\n- **SSH Key Pairing**: Automated key deployment and management\n- **Development Integration**: VS Code remote session support\n- **Beautiful UI**: Sakura-themed interface with emoji-rich feedback\n\n### 🖥️ Complete Infrastructure Management\n- **Active Server System**: Seamless server switching and state management\n- **Tool Installation Pipeline**: Automated development tool deployment\n- **Project & Team Management**: Full Cherry Servers API integration\n- **Smart Caching**: TTL-based performance optimization\n\n## 📋 Prerequisites\n\n### Required Dependencies\n- **`cherryctl`** - Cherry Servers official CLI ([Installation Guide](https://github.com/cherryservers/cherryctl))\n- **OpenSSL 3.x** - Cryptographic operations (installed via `brew install openssl`)\n- **Cherry Servers Account** with API access\n- **SSH Client** - Standard on most systems\n\n### System Requirements\n- **macOS/Linux** - Primary development platforms\n- **GCC Compiler** - C99 standard compliance\n- **GNU Make** - Build system\n- **Git** - Version control (for installation from source)\n\n## 🖥️ Windows Support\n\nCherry CLI now supports Windows through WSL2 with a beautiful iTerm-like experience:\n\n- **🌸 Cherry iTerm Wrapper** - Complete iTerm experience on Windows Terminal\n- **🤖 Claude Code Integration** - AI-powered development workflow support \n- **⌨️ iTerm-Style Shortcuts** - Familiar macOS hotkeys (Ctrl+T, Ctrl+D, etc.)\n- **🎨 Custom Color Schemes** - Cherry-branded themes optimized for development\n- **💾 Session Management** - Save and restore complex multi-project layouts\n- **📁 Smart Path Conversion** - Seamless Windows ↔ WSL path handling\n\n### Quick Windows Setup\n**⚠️ Requires Windows Terminal** (install: `winget install Microsoft.WindowsTerminal`)\n\n```powershell\n# 1. Install WSL2 + Ubuntu (Run PowerShell as Administrator)\nwsl --install\n# Restart computer when prompted\n\n# 2. Build Cherry CLI in Ubuntu terminal\nsudo apt update && sudo apt install -y build-essential libssl-dev libncurses-dev git\ngit clone https://github.com/AmadeusInnovations/cherry.git cherry-cli\ncd cherry-cli && make && make install\n\n# 3. Install Cherry iTerm layer (Back in Windows PowerShell as Admin)\ncd platforms/windows && .\\Scripts\\Install-CherryiTerm.ps1\n\n# 4. Copy Windows Terminal settings\nCopy-Item \".\\WindowsTerminal\\settings.json\" \"$env:LOCALAPPDATA\\Packages\\Microsoft.WindowsTerminal_8wekyb3d8bbwe\\LocalState\\settings.json\"\n```\n\n**🎯 Result:** Full Cherry iTerm experience in Windows Terminal with Claude Code integration!\n\n**📚 Complete Windows Guide:** [platforms/windows/README.md](./platforms/windows/README.md)\n\n## 🔧 Installation\n\n### From Source (Recommended)\n\n```bash\n# Clone the repository \ngit clone \ncd cherry\n\n# Install OpenSSL for cryptographic security\nbrew install openssl # macOS\n# OR: apt-get install libssl-dev # Ubuntu/Debian\n# OR: yum install openssl-devel # CentOS/RHEL\n\n# Build with security libraries\nmake clean && make\n\n# Install globally to ~/.local/bin\nmake install\n```\n\nThe CLI will be available as both `cherry` and `cherrypicker`. Ensure `~/.local/bin` is in your PATH.\n\n### Build Verification\n\n```bash\n# Verify successful installation\nwhich cherry\ncherry --version\n\n# Test core functionality\ncherry --help\n```\n\n## Configuration\n\nFirst, initialize your configuration:\n\n```bash\ncherry init\n# or\ncherrypicker init\n```\n\nThis will check your `cherryctl` configuration and set up CherryPicker.\n\n### Environment Variables\n\nCherryPicker respects the same environment variables as `cherryctl`:\n\n- `CHERRY_AUTH_TOKEN`: Your Cherry Servers API token\n- `CHERRY_PROJECT_ID`: Default project ID (optional)\n\n## 🎯 Quick Start Guide\n\n### Essential Commands\n\n#### 🔐 Secure File Transfer (Primary Use Case)\n```bash\n# Transfer encrypted files\ncherry send document.pdf my-server # Single file with AES-256 encryption \ncherry send ./project/ my-server # Entire directory compressed and encrypted\ncherry send backup.tar.gz prod-server # Large files with optimal compression\n```\n\n#### 💊 Revolutionary Capsule System\n```bash\n# Create complete server snapshot\ncherry server produce capsule # Full server state with encryption\n\n# Transfer server state between environments \ncherry server beam capsule prod-server # Secure transmission with verification\n\n# Restore server from capsule\ncherry server receive capsule # Complete server recreation\n```\n\n#### 🌸 Blossom System (Enhanced SSH)\n```bash\n# SSH with user switching\ncherry blossom # SSH to active server as default user\ncherry blossom deploy # SSH and switch to 'deploy' user \ncherry blossom www-data # SSH and switch to 'www-data' user\ncherry blossom pair # Set up SSH key authentication\n```\n\n#### 🖥️ Server Management\n```bash\n# Server operations\ncherry server activate my-server # Set active server for operations\ncherry server info # Get detailed server information \ncherry server users add username # Add user account\ncherry install docker # Install tools on active server\n```\n\n### Global Options\n\n```\n-h, --help Show comprehensive help with examples\n-v, --version Show version and build information \n--token TOKEN Cherry Servers API token\n--project-id ID Project ID for operations\n--verbose Enable detailed operation logging\n--quiet Suppress non-essential output\n--json Machine-readable JSON output\n```\n\n## 🌸 Cherry Blossom UI Experience\n\nCherry CLI features a carefully crafted visual experience inspired by Japanese cherry blossoms:\n\n- **🌸 Sakura Pink** - Primary accent color for key operations\n- **🌿 Spring Green** - Success states and positive feedback \n- **🌌 Sky Blue** - Information and helpful guidance\n- **🤍 Cherry White** - Clean, readable text display\n- **Emoji-Rich Feedback** - Visual context for immediate understanding\n- **Progressive Help** - Context-aware assistance and suggestions\n\n## 📚 Comprehensive Command Reference\n\n#### List Servers\n\n```bash\ncherry list\ncherry list --project-id 12345\ncherry list --json\n```\n\n#### Get Server Information\n\n```bash\ncherry info server-123\ncherry info my-server-hostname --json\n```\n\n#### SSH Key Management\n\nAdd an SSH key to your account:\n\n```bash\n# Use default key (~/.ssh/id_rsa.pub)\ncherry ssh-key add\n\n# Specify a key file\ncherry ssh-key add ~/.ssh/my_key.pub\n\n# Add with a custom label\ncherry ssh-key add ~/.ssh/id_rsa.pub --label \"my-workstation-key\"\n```\n\nList all SSH keys:\n\n```bash\ncherry ssh-key list\ncherry ssh-key list --json\n```\n\nDelete an SSH key:\n\n```bash\ncherry ssh-key delete --name mykey\n```\n\n#### Server Management\n\nCreate a new server:\n\n```bash\ncherry create --hostname web1 --plan c1-small-x86 --image ubuntu_22_04 --region eu_nord_1\n```\n\n#### File Deployment\n\nDeploy a file to a server:\n\n```bash\ncherry deploy --server 12345 --local ./myapp.tar.gz --remote /tmp/myapp.tar.gz\n```\n\nDeploy a directory to a server:\n\n```bash\ncherry deploy --server 12345 --local ./webapp --remote /var/www/html\n```\n\n#### Remote Command Execution\n\nExecute a command on a remote server:\n\n```bash\ncherry exec --server 12345 --command \"systemctl restart nginx\"\n```\n\nExecute with verbose output:\n\n```bash\ncherry exec --server 12345 --command \"ls -la /var/log\" --verbose\n```\n\n#### User Information\n\nGet user information:\n\n```bash\ncherry user\ncherry user --json\n```\n\n#### Connect to Server\n\n```bash\n# Connect as root (default)\ncherry ssh server-123\n\n# Connect as specific user\ncherry ssh server-123 myuser\n```\n\n#### Upload Files\n\n```bash\n# Upload to /tmp/ (default)\ncherry upload server-123 ./myfile.txt\n\n# Upload to specific path\ncherry upload server-123 ./myfile.txt /home/user/\n\n# Upload with specific user\ncherry upload server-123 ./myfile.txt /home/user/ myuser\n```\n\n## Examples\n\n### Complete Workflow\n\n```bash\n# Initialize configuration\ncherry init\n\n# Add your SSH key\ncherry ssh-key add --label \"workstation-2024\"\n\n# List available servers\ncherry list --project-id 12345\n\n# Get detailed server information\ncherry info server-67890\n\n# SSH into the server\ncherry ssh server-67890\n\n# Upload a configuration file\ncherry upload server-67890 ./config.yml /etc/myapp/\n```\n\n### Project-Specific Usage\n\nSet your project ID as an environment variable:\n\n```bash\nexport CHERRY_PROJECT_ID=12345\ncherry list\ncherry info my-server\n```\n\n## 🏗️ Advanced Architecture\n\nCherry CLI is built with enterprise-grade architecture focusing on security, performance, and extensibility:\n\n### Core Architecture\n- **🚀 High-Performance C Engine**: Zero-overhead command processing with optimal memory management\n- **🔐 OpenSSL Cryptographic Foundation**: Military-grade encryption with AES-256-GCM and SHA-512 integrity\n- **🧠 Intelligent Caching System**: TTL-based performance optimization with automatic cache invalidation\n- **🌐 Cherry Servers API Integration**: Complete API coverage through optimized cherryctl interface\n\n### Security Architecture \n- **🛡️ Multi-Layer Encryption**: GPG for files, AES-256-GCM for capsules, SSH for connections\n- **🔑 Comprehensive Key Management**: Automated SSH key deployment with secure storage\n- **✅ Integrity Verification**: SHA-512 checksums with transmission verification protocols\n- **🚫 Zero-Knowledge Operation**: Automatic cleanup of sensitive temporary files\n\n### Capsule System Architecture\n- **📊 Component-Based Capture**: Modular system for selective server state snapshotting\n- **🔬 Rebuild Intelligence**: Automated detection of rebuildable vs. preservable artifacts \n- **📡 Secure Transmission Protocol**: Chunked transfer with integrity verification and remote confirmation\n- **⚡ Automated Restoration**: Seamless server recreation via blossom system integration\n\n### Performance Features\n- **⚡ Static Memory Allocation**: Minimal heap fragmentation with predictable performance\n- **🔄 Connection Reuse**: Optimized SSH connection management for batch operations\n- **📈 Streaming Processing**: Large file handling without memory bloat\n- **🎯 Smart Resource Management**: Automatic cleanup with comprehensive error handling\n\n## Error Handling\n\nCherryPicker provides detailed error messages and suggestions:\n\n- **Missing Dependencies**: Checks for cherryctl availability\n- **Configuration Issues**: Guides through setup process\n- **Command Failures**: Shows underlying cherryctl error details\n- **File Not Found**: Clear messages for missing SSH keys or files\n\n## Development\n\n### Building\n\n```bash\nmake clean\nmake\n```\n\n### Testing\n\n```bash\nmake test\n```\n\n### Debugging\n\nBuild with debug symbols:\n\n```bash\nmake debug\n```\n\n## Command Reference\n\n| Command | Description | Example |\n|---------|-------------|---------|\n| `init` | Initialize configuration | `cherry init` |\n| `list` | List all servers | `cherry list [--json]` |\n| `info` | Get server details | `cherry info server-123 [--json]` |\n| `create` | Create a new server | `cherry create --hostname web1 --plan c1-small --image ubuntu_22_04 --region eu_nord_1` |\n| `ssh-key` | Manage SSH keys | `cherry ssh-key [options]` |\n| `ssh` | Connect to server | `cherry ssh server-123 [username]` |\n| `deploy` | Deploy files to server | `cherry deploy --server 123 --local ./app --remote /opt/app` |\n| `exec` | Execute command on server | `cherry exec --server 123 --command \"systemctl status nginx\"` |\n| `upload` | Upload files | `cherry upload server-123 file.txt [remote-path] [username]` |\n| `user` | Get user information | `cherry user [--json]` |\n\n**Note**: All commands can also be run using `cherrypicker` instead of `cherry`.\n\n## Contributing\n\n1. Fork the repository\n2. Create your feature branch\n3. Make your changes following the existing code style\n4. Test your changes\n5. Submit a pull request\n\n## License\n\n[Add your license information here]\n\n## Support\n\nFor Cherry Servers API documentation: https://docs.cherryservers.com/\nFor cherryctl documentation: https://github.com/cherryservers/cherryctl\n\n## 🔮 Roadmap & Future Vision\n\n### Completed Revolutionary Features ✅\n- [x] **Cherry Server Capsule System** - Complete server state snapshotting with military-grade encryption\n- [x] **Layered AES-256-GCM Security** - OpenSSL-based cryptographic foundation\n- [x] **Blossom User Switching** - SSH with automatic user switching via `sudo su`\n- [x] **Secure File Transfer** - GPG-encrypted file and directory transmission\n- [x] **Intelligent Rebuild System** - Automated artifact detection and restoration\n\n### Next-Generation Enhancements 🚀\n- [ ] **Compression Integration** - LZ4/ZSTD support for 90%+ capsule size reduction\n- [ ] **Distributed Capsules** - Multi-server orchestrated snapshots and synchronized restoration\n- [ ] **Incremental Snapshots** - Delta-based capsule updates for massive efficiency gains\n- [ ] **Cloud Storage Integration** - Direct AWS S3/GCS capsule storage with lifecycle management\n- [ ] **AI-Powered Optimization** - Machine learning for optimal server configuration recommendations\n\n### Enterprise Features 🏢\n- [ ] **Multi-Tenant Architecture** - Organization and team-based access control\n- [ ] **Audit Logging** - Comprehensive operation tracking for compliance\n- [ ] **Policy Engine** - Rule-based automation and security enforcement\n- [ ] **Disaster Recovery Automation** - Automated failover and restoration workflows\n- [ ] **Performance Analytics** - Real-time server optimization recommendations\n\n### Developer Experience 👨‍💻\n- [ ] **Plugin Architecture** - Custom command and protocol extensions\n- [ ] **Interactive Mode** - Guided workflows for complex operations\n- [ ] **Tab Completion** - Shell completion for all commands and parameters\n- [ ] **Configuration Profiles** - Environment-specific settings and credentials\n- [ ] **API Integration** - REST API for programmatic access\n\n## 🎯 Production Readiness\n\nCherry CLI v1.0.0 represents a **production-ready** platform with:\n- ✅ **Military-Grade Security** - OpenSSL AES-256-GCM encryption\n- ✅ **Zero Data Loss** - SHA-512 integrity verification \n- ✅ **Complete Functionality** - All requested features implemented\n- ✅ **Comprehensive Testing** - Robust error handling and edge case coverage\n- ✅ **Performance Optimized** - C-based implementation with minimal overhead", "has_readme": true, "url": "https://github.com/AmadeusInnovations/cherry", "category": "unclassified", "value_tier": "standard", "value_rank": 2, "editorial_note": null, "similarity_group": "Developer Tools", "group_score": 13, "similar": [ { "id": "MozArchAngelos/cherry", "score": 1.0, "signals": [ "compiler", "plugin", "developer" ] }, { "id": "Moestradamus-Productions/cherry", "score": 1.0, "signals": [ "compiler", "plugin", "developer" ] }, { "id": "MorchestraWorld/sakura", "score": 0.5228, "signals": [ "automation", "terminal", "cli" ] }, { "id": "Geijutsu/sakura", "score": 0.5228, "signals": [ "automation", "terminal", "cli" ] }, { "id": "CherryMesh/sakura", "score": 0.5228, "signals": [ "automation", "terminal", "cli" ] } ] }, { "organization": "AmadeusInnovations", "name": "cherry-blossom", "source": "R2 Git bundle", "published_at": "2025-10-14T21:05:23+02:00", "readme": "# 🌸 Cherry CLI - Revolutionary Server Management Platform\n\n[![Version](https://img.shields.io/badge/version-1.0.0-pink.svg)](https://github.com/cherryservers/cherry-cli)\n[![License](https://img.shields.io/badge/license-MIT-green.svg)](LICENSE)\n[![Security](https://img.shields.io/badge/encryption-AES--256--GCM-blue.svg)](docs/security.md)\n[![Platform](https://img.shields.io/badge/platform-macOS%20%7C%20Linux%20%7C%20Windows-lightgrey.svg)](docs/installation.md)\n\n> **The world's first CLI with complete server state snapshotting and cherry blossom-themed aesthetics.**\n\nA revolutionary, security-first command-line interface for Cherry Servers infrastructure management featuring military-grade encryption, complete server state capture via the Capsule System, and beautiful cherry blossom-themed user experience.\n\n---\n\n## ✨ Revolutionary Features\n\n### 💊 **Cherry Server Capsule System** - *World's First Complete Server State Capture*\n- 🔬 **Complete Server Snapshot**: Users, packages, services, configs, data, SSH keys, crontabs, network settings\n- 🔐 **Military-Grade Security**: AES-256-GCM encryption with SHA-512 integrity verification\n- 📦 **Intelligent Optimization**: 90%+ size reduction through rebuild artifact detection\n- 🚀 **Secure Transfer**: Encrypted transmission with remote confirmation\n- ⚡ **Automated Restoration**: One-command server recreation from capsules\n\n### 🔐 **Enterprise-Grade Security**\n- **AES-256-GCM Encryption** for all data transfers\n- **TLS 1.3** for secure API communications\n- **GPG Integration** for file encryption\n- **SSH Key Management** with automated deployment\n- **Zero-Knowledge Operation** with automatic cleanup\n\n### 🌸 **Blossom System** - *Enhanced User Experience*\n- **Smart SSH Management** with automatic user switching\n- **Cherry Blossom Aesthetics** with sakura-themed interface\n- **Emoji-Rich Feedback** for immediate visual context\n- **Progressive Help System** with contextual guidance\n\n### 🌐 **Advanced P2P Networking**\n- **Peer Discovery** with automatic topology mapping\n- **NAT Traversal** using sophisticated hole-punching\n- **End-to-End Encryption** for secure peer communication\n- **Load Balancing** with intelligent peer selection\n- **Fault Tolerance** with automatic failover\n\n---\n\n## 🚀 Quick Start\n\n### Installation\n\n**macOS/Linux:**\n```bash\n# One-line installation\ncurl -sSL https://raw.githubusercontent.com/cherryservers/cherry-cli/main/install.sh | bash\n\n# Manual installation\ngit clone https://github.com/cherryservers/cherry-cli.git\ncd cherry-cli\nmake && make install\n```\n\n**Windows (WSL2):**\n```powershell\n# Install WSL2 + Ubuntu (Run as Administrator)\nwsl --install\n\n# In Ubuntu terminal\nsudo apt update && apt install -y build-essential libssl-dev git\ngit clone https://github.com/cherryservers/cherry-cli.git\ncd cherry-cli && make && make install\n```\n\n### First Time Setup\n\n```bash\n# Initialize configuration\ncherry init\n\n# Set your Cherry Servers API token\nexport CHERRY_AUTH_TOKEN=\"your-token-here\"\n\n# Verify installation\ncherry --version\ncherry list\n```\n\n---\n\n## 🎯 Core Usage Examples\n\n### 💊 **Capsule System** - Complete Server Management\n```bash\n# Create complete server snapshot\ncherry server produce capsule\n# → Creates encrypted .capsule file with full server state\n\n# Transfer server state to new environment\ncherry server beam capsule production-server\n# → Secure transmission with integrity verification\n\n# Restore complete server from capsule\ncherry server receive capsule\n# → Automated server recreation with all configurations\n```\n\n### 🔐 **Secure File Transfer**\n```bash\n# Transfer encrypted files\ncherry send document.pdf my-server # Single file with AES-256\ncherry send ./project/ production-server # Entire directory compressed\ncherry send backup.tar.gz staging-server # Large files optimized\n```\n\n### 🌸 **Enhanced SSH Management**\n```bash\n# Smart SSH with user switching\ncherry blossom # SSH to active server\ncherry blossom deploy # SSH and switch to 'deploy' user\ncherry blossom www-data # SSH and switch to 'www-data'\ncherry blossom pair # Set up SSH key authentication\n```\n\n### 🖥️ **Server Operations**\n```bash\n# Server management\ncherry server activate my-server # Set active server\ncherry server info # Detailed server information\ncherry server create --plan c1-small --image ubuntu_22_04\ncherry install docker # Install tools on active server\n```\n\n### 🌐 **P2P Networking**\n```bash\n# P2P operations\ncherry p2p init # Initialize P2P node\ncherry p2p peers # List connected peers\ncherry p2p send peer-id \"Hello Cherry!\" # Secure messaging\ncherry p2p discover # Network topology discovery\n```\n\n---\n\n## 🏗️ Architecture Overview\n\nCherry CLI implements a **dual-architecture approach** with two complementary implementations:\n\n### 🏛️ **Foundation Implementation** *(Production Ready)*\n- **Status**: ✅ **Fully Functional** with 120+ commands\n- **Architecture**: Mature monolithic design with proven stability\n- **Features**: Complete Capsule System, P2P networking, security features\n- **Use Case**: Production deployments requiring immediate functionality\n\n### ⚡ **Evolution Implementation** *(Next Generation)*\n- **Status**: 🚧 **Modernized Architecture** (modular design complete)\n- **Architecture**: Clean modular structure with enhanced performance\n- **Features**: Memory-safe design, <30ms startup, comprehensive testing\n- **Use Case**: Future development with modern C practices\n\n```\ncherry-cli/\n├── implementations/\n│ ├── foundation/ # 🏛️ Production-ready implementation\n│ │ ├── src/ # 36 source files, 120+ commands\n│ │ ├── include/ # Comprehensive headers\n│ │ └── platforms/ # Multi-platform support\n│ └── evolution/ # ⚡ Modernized architecture\n│ ├── src/\n│ │ ├── core/ # System initialization\n│ │ ├── commands/ # Modular command structure\n│ │ ├── lib/ # Core libraries\n│ │ └── p2p/ # P2P networking subsystem\n│ └── tests/ # Comprehensive test suite\n```\n\n---\n\n## 🔒 Security & Compliance\n\n### **Encryption Standards**\n- **AES-256-GCM**: File and capsule encryption\n- **SHA-512**: Integrity verification\n- **TLS 1.3**: API communications\n- **GPG**: Additional file encryption layer\n- **libsodium**: P2P networking security\n\n### **Security Certifications**\n- ✅ **Buffer Overflow Protection**\n- ✅ **Memory Leak Prevention**\n- ✅ **Input Validation & Sanitization**\n- ✅ **Principle of Least Privilege**\n- ✅ **Zero-Knowledge Temporary Files**\n\n### **Compliance Features**\n- **Audit Logging**: Comprehensive operation tracking\n- **Access Control**: Role-based permissions\n- **Data Residency**: Configurable storage locations\n- **Encryption at Rest**: All stored data encrypted\n\n---\n\n## 🖥️ Platform Support\n\n| Platform | Status | Installation Method | Notes |\n|----------|--------|-------------------|--------|\n| **macOS** | ✅ Full Support | Homebrew, Source | Native performance |\n| **Linux** | ✅ Full Support | Package Manager, Source | All major distributions |\n| **Windows** | ✅ WSL2 Support | WSL2 + Ubuntu | Cherry iTerm experience |\n| **ARM64** | ✅ Native Support | Source compilation | Apple Silicon, ARM servers |\n\n### **Windows Integration**\n- 🌸 **Cherry iTerm Wrapper**: Complete iTerm experience in Windows Terminal\n- 🤖 **Claude Code Integration**: AI-powered development workflows\n- ⌨️ **iTerm-Style Shortcuts**: Familiar macOS hotkeys (Ctrl+T, Ctrl+D)\n- 🎨 **Custom Themes**: Cherry-branded color schemes\n- 💾 **Session Management**: Multi-project layout persistence\n\n---\n\n## 📊 Performance Specifications\n\n### **Foundation Implementation**\n| Metric | Specification | Typical Performance |\n|--------|---------------|-------------------|\n| Startup Time | <100ms | ~50ms |\n| Memory Usage | <8MB | ~4MB |\n| Command Response | <200ms | ~100ms |\n| File Transfer | 50MB/s+ | ~80MB/s |\n\n### **Evolution Implementation**\n| Metric | Target | Achieved |\n|--------|--------|----------|\n| Startup Time | <30ms | ~15ms |\n| Memory Usage | <4MB | ~2MB |\n| Binary Size | <2MB | ~1.5MB |\n| Response Time | <50ms | ~25ms |\n\n---\n\n## 🧪 Command Reference\n\n### **Server Management**\n```bash\ncherry list # List all servers\ncherry info # Detailed server info\ncherry create --plan c1-small --image ubuntu # Create server\ncherry server activate # Set active server\ncherry ssh [user] # SSH connection\n```\n\n### **File Operations**\n```bash\ncherry send # Encrypted file transfer\ncherry retrieve : # Secure file retrieval\ncherry deploy # Project deployment\n```\n\n### **Idea Management**\n```bash\ncherry idea add \"API Rate Limiting\" # Capture new ideas\ncherry idea list --priority 4,5 # Review high-priority ideas \ncherry idea search \"authentication\" # Find related concepts\ncherry idea connect 23 31 --type implements # Link related ideas\ncherry idea analyze 42 --enhance # AI-powered idea analysis\n```\n\n### **Advanced Features**\n```bash\ncherry server produce capsule # Create server snapshot\ncherry server beam capsule # Transfer server state\ncherry blossom [user] # Enhanced SSH\ncherry p2p init # P2P networking\ncherry install # Tool installation\n```\n\n### **Configuration & Diagnostics**\n```bash\ncherry init # Initial setup\ncherry config show # View configuration\ncherry doctor # System health check\ncherry --help # Comprehensive help\n```\n\n---\n\n## 🎨 Cherry Blossom Experience\n\n### **Visual Theme**\n- 🌸 **Sakura Pink**: Primary accent for key operations\n- 🌿 **Spring Green**: Success states and positive feedback\n- 🌌 **Sky Blue**: Information and guidance\n- 🤍 **Cherry White**: Clean, readable text\n- 🌙 **Twilight Purple**: Error states and warnings\n\n### **User Interface Elements**\n- **Emoji-Rich Feedback**: Visual context for operations\n- **Progressive Loading**: Beautiful progress indicators\n- **Contextual Help**: Smart suggestions and guidance\n- **Accessibility**: WCAG-compliant color schemes\n- **Multi-Theme Support**: Dark, light, and monochrome modes\n\n---\n\n## 🤝 Contributing\n\n### **Development Setup**\n```bash\n# Clone repository\ngit clone https://github.com/cherryservers/cherry-cli.git\ncd cherry-cli\n\n# Foundation implementation\ncd implementations/foundation\nmake clean && make debug\n\n# Evolution implementation \ncd implementations/evolution\nmkdir build && cd build\ncmake -DCMAKE_BUILD_TYPE=Debug ..\nmake -j$(nproc)\n```\n\n### **Code Standards**\n- **C Standard**: C11 with GNU extensions\n- **Memory Safety**: Comprehensive bounds checking\n- **Documentation**: Doxygen-compatible comments\n- **Testing**: Unit and integration test coverage\n- **Security**: Static analysis and vulnerability scanning\n\n### **Contribution Process**\n1. Fork the repository\n2. Create feature branch following naming conventions\n3. Implement changes with comprehensive tests\n4. Ensure security and performance standards\n5. Submit pull request with detailed description\n\n---\n\n## 📚 Documentation\n\n### **User Guides**\n- [Installation Guide](docs/installation.md)\n- [Configuration Reference](docs/configuration.md)\n- [Command Reference](docs/commands.md)\n- [Security Best Practices](docs/security.md)\n\n### **Technical Documentation**\n- [Architecture Overview](docs/architecture.md)\n- [Idea Management System](docs/architecture/CHERRY_IDEA_MANAGEMENT_SPECIFICATION.md)\n- [P2P Networking Guide](docs/p2p.md)\n- [API Integration](docs/api.md)\n- [Performance Tuning](docs/performance.md)\n\n### **Platform-Specific**\n- [Windows Setup Guide](implementations/foundation/platforms/windows/README.md)\n- [macOS Optimization](docs/macos.md)\n- [Linux Distribution Notes](docs/linux.md)\n\n---\n\n## 🆘 Support & Community\n\n### **Getting Help**\n- 📖 **Documentation**: Comprehensive guides and references\n- 🐛 **GitHub Issues**: Bug reports and feature requests\n- 💬 **Discussions**: Community questions and support\n- 📧 **Security**: security@cherryservers.com for vulnerabilities\n\n### **Community Resources**\n- **Cherry Servers API**: [https://docs.cherryservers.com/](https://docs.cherryservers.com/)\n- **cherryctl CLI**: [https://github.com/cherryservers/cherryctl](https://github.com/cherryservers/cherryctl)\n- **Community Forum**: [https://community.cherryservers.com/](https://community.cherryservers.com/)\n\n---\n\n## 📄 License & Acknowledgments\n\n### **License**\nCherry CLI is released under the **MIT License**. See [LICENSE](LICENSE) for complete terms.\n\n### **Acknowledgments**\n- **Cherry Servers Team**: API and infrastructure support\n- **OpenSSL Project**: Cryptographic foundation\n- **libsodium Developers**: Modern cryptography library\n- **Security Researchers**: Vulnerability disclosure and improvements\n- **Open Source Community**: Dependencies and continuous improvement\n\n### **Security Disclosure**\nFor security vulnerabilities, please email security@cherryservers.com with details. We follow responsible disclosure practices and will acknowledge contributions appropriately.\n\n---\n\n## 🔮 Roadmap & Future Vision\n\n### **Completed Revolutionary Features** ✅\n- [x] Cherry Server Capsule System with AES-256-GCM encryption\n- [x] Complete server state snapshotting and restoration\n- [x] Blossom user management with SSH automation\n- [x] Advanced P2P networking with NAT traversal\n- [x] Secure file transfer with GPG integration\n- [x] Windows support via Cherry iTerm wrapper\n\n### **Next-Generation Enhancements** 🚀\n- [x] **Idea Management System**: Comprehensive concept capture and development workflow\n- [ ] **AI-Powered Optimization**: ML-based server configuration recommendations\n- [ ] **Distributed Capsules**: Multi-server orchestrated snapshots\n- [ ] **Cloud Storage Integration**: Direct AWS S3/GCS capsule storage\n- [ ] **Incremental Snapshots**: Delta-based updates for efficiency\n- [ ] **Performance Analytics**: Real-time optimization recommendations\n\n### **Enterprise Features** 🏢\n- [ ] **Multi-Tenant Architecture**: Organization-based access control\n- [ ] **Policy Engine**: Rule-based automation and security enforcement\n- [ ] **Disaster Recovery**: Automated failover and restoration workflows\n- [ ] **Compliance Dashboard**: Audit trail and compliance reporting\n- [ ] **API Management**: RESTful API for programmatic access\n\n---\n\n
\n\n**🌸 Made with love and cherry blossoms 🌸**\n\n*Where revolutionary technology meets beautiful design in server management.*\n\n[![Cherry Servers](https://img.shields.io/badge/Powered%20by-Cherry%20Servers-pink.svg)](https://www.cherryservers.com/)\n[![Built with C](https://img.shields.io/badge/Built%20with-C-blue.svg)](https://en.wikipedia.org/wiki/C_(programming_language))\n[![Security First](https://img.shields.io/badge/Security-First-green.svg)](docs/security.md)\n\n
", "has_readme": true, "url": "https://github.com/AmadeusInnovations/cherry-blossom", "category": "unclassified", "value_tier": "standard", "value_rank": 2, "editorial_note": null, "similarity_group": "Developer Tools", "group_score": 13, "similar": [ { "id": "quivent/cherry-blossom", "score": 1.0, "signals": [ "package", "library", "automation" ] }, { "id": "MorchestraWorld/sakura", "score": 0.9996, "signals": [ "package", "library", "automation" ] }, { "id": "Geijutsu/sakura", "score": 0.9996, "signals": [ "package", "library", "automation" ] }, { "id": "CherryMesh/sakura", "score": 0.9996, "signals": [ "package", "library", "automation" ] }, { "id": "Geijutsu/cherry", "score": 0.9789, "signals": [ "package", "library", "automation" ] } ] }, { "organization": "AmadeusInnovations", "name": "claudio", "source": "R2 Git bundle", "published_at": "2025-09-19T23:35:48+02:00", "readme": "# Claudio\n\nA comprehensive AI-powered desktop application ecosystem with advanced session monitoring and collaborative intelligence capabilities.\n\n## Quick Start\n\n```bash\ngit clone https://github.com/AmadeusInnovations/claudio.git\ncd claudio/desktop\nnpm install\nnpm run tauri:dev\n```\n\n## Project Structure\n\n- **`desktop/`** - Tauri-based desktop application with React frontend\n- **`interface/`** - Reusable UI component library (@claudio/interface)\n- **`logic/`** - Shared business logic and backend services\n\n## Requirements\n\n- **macOS** 10.15+ (Catalina or newer)\n- **Node.js** 18+\n- **Rust** (latest stable)\n- **Xcode Command Line Tools**\n\n## Installation\n\n### 1. Install Dependencies\n```bash\n# Install Xcode Command Line Tools\nxcode-select --install\n\n# Install Rust\ncurl --proto '=https' --tlsv1.2 -sSf https://sh.rustup.rs | sh\nsource ~/.cargo/env\n\n# Install Tauri CLI\nnpm install -g @tauri-apps/cli\n```\n\n### 2. Clone and Setup\n```bash\ngit clone https://github.com/AmadeusInnovations/claudio.git\ncd claudio/desktop\nnpm install\n```\n\n### 3. Run Development\n```bash\nnpm run tauri:dev\n```\n\n## Components\n\n### Desktop App\nModern chat interface with real-time session monitoring, predictive analytics, and auto-healing capabilities. Built with Tauri, React, and TypeScript.\n\n[→ Desktop README](desktop/README.md)\n\n### Interface Library\nReusable UI component library with theming, hooks, and React components extracted from the main application.\n\n[→ Interface README](interface/README.md)\n\n### Logic Layer\nShared business logic, API clients, and backend services powering the entire ecosystem.\n\n[→ Logic README](logic/README.md)\n\n## License\n\nMIT", "has_readme": true, "url": "https://github.com/AmadeusInnovations/claudio", "category": "unclassified", "value_tier": "standard", "value_rank": 2, "editorial_note": null, "similarity_group": "Web & Applications", "group_score": 12, "similar": [ { "id": "MorchestraWorld/claudio", "score": 1.0, "signals": [ "desktop", "frontend", "react" ] }, { "id": "MozArchAngelos/claudio", "score": 0.2644, "signals": [ "claudio" ] }, { "id": "Moestradamus-Productions/claudio", "score": 0.2644, "signals": [ "claudio" ] }, { "id": "quivent/matrix-terminal-origins", "score": 0.2136, "signals": [ "desktop", "frontend", "react" ] }, { "id": "quivent/score", "score": 0.2109, "signals": [ "frontend", "app", "backend" ] } ] }, { "organization": "AmadeusInnovations", "name": "ClothSimulator", "source": "R2 Git bundle", "published_at": "2025-09-27T01:31:19+02:00", "readme": "# ClothSimulator - Real-time Fabric Physics Simulation\n\nA high-performance, real-time cloth physics simulator with interactive vertex manipulation using OpenGL and modern C++.\n\n## Features\n\n- **Real-time cloth physics** using mass-spring-damper system with Verlet integration\n- **Interactive vertex manipulation** with mouse controls\n- **Multiple constraint types**: structural, shear, and bending constraints\n- **Cross-platform support** for Windows, macOS, and Linux\n- **Modern OpenGL rendering** with customizable shaders\n- **Performance optimization** with fixed timestep physics and spatial acceleration\n\n## Controls\n\n- **Left Mouse**: Drag cloth vertices\n- **Right Mouse**: Rotate camera\n- **Middle Mouse**: Pin/unpin vertices \n- **Mouse Wheel**: Zoom camera\n- **WASD**: Move camera\n- **QE**: Camera elevation\n- **R**: Reset camera\n- **Space**: Reset cloth simulation\n- **Esc**: Exit application\n\n## Requirements\n\n### Dependencies\n- OpenGL 4.1+\n- C++20 compatible compiler\n- CMake 3.20+\n\n### Required Libraries\n- **GLFW 3.3+**: Windowing and input\n- **GLAD**: OpenGL loader\n- **GLM**: Mathematics library\n- **spdlog**: Logging\n\n### Optional Libraries (for testing and documentation)\n- **Google Test**: Unit testing framework\n- **Google Benchmark**: Performance benchmarking\n- **Doxygen**: Documentation generation\n\n## Installation\n\n### Using vcpkg (Recommended)\n\n1. Install vcpkg:\n```bash\ngit clone https://github.com/Microsoft/vcpkg.git\ncd vcpkg\n./bootstrap-vcpkg.sh # On Windows: .\\\\bootstrap-vcpkg.bat\n```\n\n2. Install dependencies:\n```bash\n./vcpkg install glfw3 glad glm spdlog gtest benchmark\n```\n\n3. Configure environment:\n```bash\nexport VCPKG_ROOT=/path/to/vcpkg\n```\n\n### Manual Installation\n\n#### macOS (using Homebrew)\n```bash\nbrew install glfw glm spdlog cmake\n```\n\n#### Ubuntu/Debian\n```bash\nsudo apt-get update\nsudo apt-get install libglfw3-dev libglm-dev libspdlog-dev cmake build-essential\n```\n\n#### Windows (using vcpkg)\nFollow the vcpkg instructions above, or use package managers like Conan.\n\n## Building\n\n1. Clone the repository:\n```bash\ngit clone \ncd ClothSimulator\n```\n\n2. Create build directory and configure:\n```bash\nmkdir build\ncd build\ncmake .. -DCMAKE_BUILD_TYPE=Release\n```\n\n3. Build:\n```bash\ncmake --build . --config Release\n```\n\n4. Run:\n```bash\n./ClothSimulator_ # On Windows: .\\\\ClothSimulator_windows_x64.exe\n```\n\n## Build Options\n\n```bash\ncmake .. -DCMAKE_BUILD_TYPE=Release \\\n -DBUILD_TESTS=ON \\\n -DBUILD_EXAMPLES=ON \\\n -DBUILD_DOCS=ON \\\n -DENABLE_LTO=ON \\\n -DENABLE_PCH=ON\n```\n\nAvailable options:\n- `BUILD_TESTS`: Build test suite (default: ON)\n- `BUILD_EXAMPLES`: Build example applications (default: ON)\n- `BUILD_DOCS`: Build documentation (default: OFF)\n- `BUILD_BENCHMARKS`: Build performance benchmarks (default: ON)\n- `ENABLE_LTO`: Enable Link Time Optimization (default: ON)\n- `ENABLE_PCH`: Enable Precompiled Headers (default: ON)\n- `ENABLE_OPENGL`: Enable OpenGL backend (default: ON)\n- `ENABLE_VULKAN`: Enable Vulkan backend (default: OFF)\n\n## Architecture\n\nThe ClothSimulator is built with a modular architecture:\n\n```\nsrc/\n├── core/ # Application framework and window management\n├── physics/ # Cloth physics simulation and constraints\n├── rendering/ # OpenGL rendering system and shaders\n├── interaction/ # Input handling and user interaction\n└── platform/ # Platform-specific implementations\n```\n\n### Key Components\n\n- **ClothPhysicsSimulator**: Core physics engine with Verlet integration\n- **Renderer**: OpenGL-based rendering system with shader management\n- **InputHandler**: Mouse and keyboard interaction handling\n- **Application**: Main application framework and lifecycle management\n\n## Performance\n\nThe simulator is optimized for real-time performance:\n\n- **Target Performance**: 60+ FPS with 10,000+ vertices\n- **Physics Timestep**: Fixed 1/120s for stability\n- **Constraint Solving**: Multiple iterations for accuracy\n- **Memory Management**: Object pooling and cache-friendly data layouts\n\n## Testing\n\nRun the test suite:\n```bash\ncd build\nctest -C Release --verbose\n```\n\nRun specific test categories:\n```bash\n./tests/unit_tests\n./tests/integration_tests\n./tests/performance_tests\n```\n\n## Documentation\n\nGenerate documentation (requires Doxygen):\n```bash\ncmake .. -DBUILD_DOCS=ON\ncmake --build . --target docs\n```\n\nView documentation: `docs/html/index.html`\n\n## License\n\nMIT License - see LICENSE file for details.\n\n## Contributing\n\n1. Fork the repository\n2. Create a feature branch\n3. Make your changes\n4. Add tests for new functionality\n5. Ensure all tests pass\n6. Submit a pull request\n\n## Troubleshooting\n\n### Common Issues\n\n**Build fails with \"glad not found\":**\n- Ensure vcpkg is properly installed and `VCPKG_ROOT` is set\n- Try: `./vcpkg install glad`\n\n**OpenGL context creation fails:**\n- Update graphics drivers\n- Ensure OpenGL 4.1+ support\n- Check if running in virtual environment\n\n**Poor performance:**\n- Enable Release build: `-DCMAKE_BUILD_TYPE=Release`\n- Enable optimizations: `-DENABLE_LTO=ON`\n- Reduce cloth resolution for testing\n\n**Linking errors on Linux:**\n- Install development packages: `sudo apt-get install libgl1-mesa-dev`\n- Check X11 libraries: `sudo apt-get install libx11-dev`\n\nFor more help, please open an issue on GitHub.", "has_readme": true, "url": "https://github.com/AmadeusInnovations/ClothSimulator", "category": "unclassified", "value_tier": "standard", "value_rank": 2, "editorial_note": null, "similarity_group": "Developer Tools", "group_score": 5, "similar": [ { "id": "quivent/ClothSimulator", "score": 1.0, "signals": [ "compiler", "library", "package" ] }, { "id": "quivent/Chasm", "score": 0.1505, "signals": [ "library", "framework", "opengl" ] }, { "id": "MozArchAngelos/chasm", "score": 0.1505, "signals": [ "library", "framework", "opengl" ] }, { "id": "Moestradamus-Productions/chasm", "score": 0.1505, "signals": [ "library", "framework", "opengl" ] }, { "id": "AmadeusInnovations/chasm", "score": 0.1505, "signals": [ "library", "framework", "opengl" ] } ] }, { "organization": "AmadeusInnovations", "name": "English", "source": "R2 Git bundle", "published_at": "2025-09-09T03:50:38+02:00", "readme": "
\n\n# English Programming Language\n**Natural Language → Optimized Binary Compilation**\n\n[![Build Status](https://img.shields.io/badge/build-passing-brightgreen)](#) [![Platforms](https://img.shields.io/badge/platforms-macOS%20%7C%20Linux%20%7C%20Windows-blue)](#) [![Performance](https://img.shields.io/badge/throughput-1M%20cmd%2Fs-orange)](#) [![License](https://img.shields.io/badge/license-Private-red)](#) [![Version](https://img.shields.io/badge/version-0.1.0-blue)](#)\n\n*Transform natural English specifications into high-performance binary executables*\n\n[**Quick Start**](#-quick-start) • [**Installation**](#-installation) • [**Examples**](#-usage-examples) • [**Architecture**](#-architecture) • [**Performance**](#-performance-benchmarks)\n\n
\n\n---\n\n## 📋 Table of Contents\n\n- [Overview](#-overview)\n- [Quick Start](#-quick-start) \n- [Installation](#-installation)\n- [Usage Examples](#-usage-examples)\n- [Architecture](#-architecture)\n- [Performance Benchmarks](#-performance-benchmarks)\n- [Advanced Features](#-advanced-features)\n- [Development](#-development)\n- [Build Variants](#-build-variants)\n- [Troubleshooting](#-troubleshooting)\n- [FAQ](#-faq)\n- [Contributing](#-contributing)\n- [Support](#-support--contact)\n\n---\n\n## 🎯 Overview\n\nThe **English Programming Language** is a sophisticated natural language to binary compilation framework built in Rust. It transforms natural English specifications into structured command protocols and optimized binary executables, enabling developers to create and validate programs using intuitive natural language.\n\n### 🎯 Primary Use Case: Natural Language to Binary Compilation\n\nThe English Programming Language serves a unique niche: transforming natural English specifications directly into optimized binary executables. This is fundamentally different from traditional programming languages.\n\n#### 🔄 The Core Workflow\n\n```\n# Traditional Programming\nWrite Code → Compile → Execute\n\n# English Programming Language \nWrite Natural Language → Parse → Compile → Execute Binary\n```\n\n**Example Transformation:**\n```bash\n# Input: Natural English\nenglish parse \"create a file named config.json with default settings\"\n\n# Output: Parsed Specification\n✓ Operation: FileCreate {\n name: \"config.json\",\n content: \"default_settings\",\n permissions: 644\n}\n\n# Compilation (Full build)\nenglish compile \"create a file named config.json\" --output config_creator.bin\n# → Produces optimized binary executable\n```\n\n#### 🎨 Key Use Cases\n\n**1. Rapid Prototyping & Specification Validation**\n- **Problem**: Converting business requirements to code takes time\n- **Solution**: Directly express intent in natural language \n- **Benefit**: Immediate validation of understanding\n\n```bash\n# Business requirement validation\nenglish parse \"backup all user data weekly with encryption\"\n# → Immediately shows if the system understands the requirement correctly\n```\n\n**2. Non-Programmer Tool Creation**\n- **Problem**: Domain experts can't create tools without programming knowledge\n- **Solution**: Express tool behavior in natural language\n- **Benefit**: Domain experts become tool creators\n\n```bash\n# A data analyst creates a tool without coding\nenglish compile \"analyze CSV files for duplicate entries and generate report\"\n# → Creates a binary tool that does exactly what was described\n```\n\n**3. High-Performance Command Generation**\n- **Problem**: Shell scripts and interpreted languages are slow for repetitive tasks\n- **Solution**: Natural language compiles to optimized binary\n- **Benefit**: English-like expressiveness with C-level performance\n\n```bash\n# Performance-critical file processing\nenglish compile \"process log files larger than 1GB using parallel algorithms\"\n# → SIMD-optimized binary with parallel processing\n```\n\n**4. Educational Bridge**\n- **Problem**: Learning programming syntax barriers\n- **Solution**: Start with natural language, see the compilation process\n- **Benefit**: Gradual transition from English to code understanding\n\n```bash\n# Learning progression\nenglish parse \"sort a list of numbers\" --detailed\n# → Shows AST, compilation steps, helping understand programming concepts\n```\n\n**5. Specification-Driven Development**\n- **Problem**: Requirements often lose fidelity during code translation\n- **Solution**: Requirements ARE the executable code\n- **Benefit**: No translation errors between spec and implementation\n\n```bash\n# Executable specifications\nenglish compile \"validate email addresses according to RFC 5322 standard\"\n# → Binary that does exactly what the specification states\n```\n\n#### 🏗️ Architecture Advantages\n\n**Dual Build System**\n- **MVP Build**: Lightweight parsing and validation (8MB)\n- **Full Build**: Complete compilation with SIMD optimization (24MB)\n\n**Performance Characteristics**\n- **Parse Time**: <1ms cached, <10ms new specifications\n- **Compilation**: <1ms for optimized binary generation\n- **Throughput**: 1M+ commands/second processing capability\n\n#### 🚀 Real-World Scenarios\n\n**DevOps Automation**\n```bash\n# Instead of complex bash scripts\nenglish compile \"monitor disk usage and alert when 80% full\"\n# → Efficient binary with system monitoring capabilities\n```\n\n**Data Processing Pipelines**\n```bash\n# Instead of writing Python/Java data processors\nenglish compile \"transform JSON logs to CSV with timestamp normalization\"\n# → SIMD-optimized data transformation binary\n```\n\n**System Administration**\n```bash\n# Instead of memorizing complex command combinations\nenglish compile \"find files modified in last 24 hours larger than 100MB\"\n# → Optimized file system traversal binary\n```\n\n**Rapid Tool Creation**\n```bash\n# For one-off or specialized tools\nenglish compile \"calculate network bandwidth utilization from tcpdump output\"\n# → Specialized network analysis binary\n```\n\n#### 🎪 Unique Value Proposition\n\n**What Makes This Different:**\n1. **Not a Transpiler**: Doesn't convert to another programming language\n2. **Direct Binary Output**: Compiles natural language to optimized machine code\n3. **Performance Focus**: SIMD optimization and JIT execution\n4. **Specification Validation**: Immediate feedback on requirement understanding\n5. **Domain Expert Friendly**: No programming syntax required\n\n**Performance vs. Expressiveness Matrix:**\n```\nHigh Performance | Traditional C/Rust\n ↑ | ↑\n | | English Programming ← Unique Position\n | | ↓\nLow Performance | Shell Scripts/Python\n ←------------------------→\n Low Expressiveness High Expressiveness\n```\n\n#### 🎯 Target Audiences\n\n**Primary Users:**\n- **Domain Experts**: Who know what they want but not how to code it\n- **DevOps Engineers**: Who need quick, efficient automation tools\n- **Data Analysts**: Who want performance without programming complexity\n- **System Administrators**: Who need rapid, reliable tool creation\n\n**Secondary Users:**\n- **Rapid Prototypers**: Testing ideas quickly\n- **Educators**: Teaching programming concepts through natural language\n- **Requirements Engineers**: Validating specification understanding\n\n#### 🔮 Innovation Potential\n\nThis represents a paradigm shift from:\n- \"Learn to code\" → \"Describe what you want\"\n- \"Write programs\" → \"Express intent\"\n- \"Debug syntax\" → \"Refine specifications\"\n\nThe English Programming Language bridges the gap between human intent and machine execution, making computing accessible while maintaining the performance characteristics needed for production systems.\n\n### 🚀 Key Capabilities\n\n- **🧠 Advanced Natural Language Processing**: State-of-the-art parser using nom combinators for English specifications\n- **⚡ SIMD Optimization**: Hardware-accelerated compilation with SSE, AVX, NEON support\n- **🔥 JIT Execution Engine**: Cross-platform memory management and just-in-time execution\n- **📋 Dynamic Template System**: JSON/TOML/Rust template loading with optimization caching\n- **🏗️ Dual Build System**: MVP (lightweight) and Full (complete feature set) variants\n- **📊 Performance Focused**: Sub-millisecond parsing with comprehensive performance tracking\n\n---\n\n## ⚡ Quick Start\n\nGet up and running in under 2 minutes:\n\n```bash\n# 1. Clone the repository\ngit clone https://github.com/AmadeusInnovations/English.git\ncd English\n\n# 2. Install the full version\ncargo install --path . --features full\n\n# 3. Verify installation \nenglish --version\n# Expected output: English Programming CLI Framework 0.1.0\n\n# 4. Try your first natural language command\nenglish parse \"create a file named hello.txt\"\n# Expected output: Parsed specification with operation details\n\n# 5. Check system capabilities\nenglish info\n# Expected output: System information, build config, and performance targets\n```\n\n**🎉 Congratulations!** You now have a working natural language to binary compiler.\n\n---\n\n## 📦 Installation\n\n### Prerequisites\n\n- **Rust**: Version 1.70+ ([Install via rustup](https://rustup.rs/))\n- **Platform**: macOS (Intel/ARM), Linux (x86_64/ARM), Windows 10+ \n- **Memory**: 512MB available RAM for compilation\n- **Storage**: 200MB for full installation\n\n### Installation Options\n\n#### Option 1: Full Installation (Recommended)\n```bash\ngit clone https://github.com/AmadeusInnovations/English.git\ncd English\ncargo install --path . --features full\n```\n\n#### Option 2: MVP Installation (Lightweight)\n```bash\ngit clone https://github.com/AmadeusInnovations/English.git \ncd English\ncargo install --path . --features mvp\n```\n\n#### Option 3: Development Installation\n```bash\ngit clone https://github.com/AmadeusInnovations/English.git\ncd English\ncargo build --features full\ncargo run --features full -- --help\n```\n\n### Post-Installation Verification\n\n```bash\n# Verify binary location\nwhich english\n# Expected: /Users/[username]/.cargo/bin/english\n\n# Test core functionality\nenglish validate\n# Expected: System validation with performance metrics\n\n# Check all available commands\nenglish --help\n# Expected: Complete command listing with descriptions\n```\n\n### Troubleshooting Installation\n\n**Common Issues:**\n\n- **Rust version too old**: Update with `rustup update`\n- **Build fails on Windows**: Ensure Visual Studio Build Tools installed\n- **Permission denied**: Use `cargo install --path . --root ~/.local` instead\n- **Out of memory**: Use `cargo build --release` with `--jobs 1` flag\n\n---\n\n## 💡 Usage Examples\n\n### Basic Natural Language Processing\n\n```bash\n# Parse file operations\nenglish parse \"create a file named test.txt with content hello world\"\n# Output: \n# ✓ Parsed specification successfully\n# Operation: FileCreate { name: \"test.txt\", content: \"hello world\" }\n# Validation: PASSED\n# Performance: 0.8ms\n\n# Search operations\nenglish parse \"find all files containing the word config\" \n# Output:\n# ✓ Parsed specification successfully \n# Operation: FileSearch { pattern: \"config\", scope: \"all_files\" }\n# Validation: PASSED\n# Performance: 1.2ms\n```\n\n### System Information and Capabilities\n\n```bash\nenglish info\n# Output:\n# English Programming CLI Framework\n# Version: 0.1.0\n# Build: Full (advanced features enabled)\n# \n# System Information:\n# Architecture: aarch64\n# OS: macos\n# Available Instructions: [NEON, SIMD]\n# \n# Performance Targets:\n# NL Parse (cached): <1ms \n# NL Parse (new): <10ms\n# End-to-End: <5ms\n# Throughput: 1,000,000 commands/second\n```\n\n### Performance Monitoring\n\n```bash\n# Enable detailed performance tracking\nenglish parse \"list directory contents\" --perf\n# Output:\n# ✓ Specification: 'list directory contents'\n# ├─ Parse Time: 0.7ms\n# ├─ Template Load: 0.1ms \n# ├─ Validation: 0.2ms\n# └─ Total: 1.0ms\n\n# Disable caching for benchmarking\nenglish parse \"complex operation example\" --no-cache --perf\n# Output:\n# ✓ Cold performance measurement\n# Parse Time: 8.3ms (no cache)\n# Cache Miss Penalty: 7.6ms\n```\n\n### Template and Configuration Usage\n\n```bash\n# Show available templates\nenglish docs templates\n# Output: Lists all built-in operation templates\n\n# Load custom template\nenglish parse \"custom operation\" --template ./my-template.json\n# Output: Uses custom template for parsing\n```\n\n---\n\n## 🏗️ Architecture\n\n### Compilation Pipeline\n\n```mermaid\ngraph TD\n A[Natural Language Input] --> B[Advanced NL Parser]\n B --> C[Grammar Analysis & Validation]\n C --> D[Dynamic Template Resolution]\n D --> E[SIMD Optimization Layer]\n E --> F[JIT Memory Allocation]\n F --> G[Binary Code Generation]\n G --> H[Executable Output]\n \n I[Template Cache] --> D\n J[Performance Metrics] --> E\n K[Memory Manager] --> F\n```\n\n### Core Components\n\n#### 🧠 **Natural Language Parser** (`english-core`)\n- **Advanced Grammar**: nom combinators with English specification parsing\n- **Intent Recognition**: Sophisticated pattern matching and semantic analysis\n- **Context Awareness**: State-based parsing with memory of previous operations\n- **Performance**: <1ms cached, <10ms cold parsing with 99.3% accuracy\n\n#### 📋 **Dynamic Template System** (`english-compiler`) \n- **Multi-Format Support**: JSON, TOML, and native Rust templates\n- **Hot Reloading**: Development-friendly template updates without restart\n- **Optimization**: Template compilation and caching for performance\n- **Validation**: Schema validation and compatibility checking\n\n#### ⚡ **SIMD Optimizer** (`english-compiler`)\n- **Hardware Detection**: Automatic CPU feature detection (SSE, AVX, NEON, RVV)\n- **Pattern Optimization**: Common operation pattern recognition and acceleration\n- **Cache Management**: Intelligent caching with performance tracking\n- **Cross-Platform**: Unified optimization API across architectures\n\n#### 🔥 **JIT Execution Engine** (`english-execution`)\n- **Memory Management**: Cross-platform allocation (Unix mmap, Windows VirtualAlloc)\n- **Code Generation**: Runtime binary code compilation and execution\n- **Safety**: Memory protection and bounds checking\n- **Performance**: <1µs initialization, <10µs execution latency\n\n#### 🔌 **Protocol Layer** (`english-protocol`)\n- **Serialization**: Efficient message serialization with versioning\n- **Transport**: High-performance inter-component communication\n- **Error Handling**: Comprehensive error propagation and recovery\n- **Monitoring**: Built-in performance and health metrics\n\n---\n\n## 📊 Performance Benchmarks\n\n### Parsing Performance\n\n| Operation Type | Cold Parse | Cached Parse | Throughput | Memory |\n|---------------|------------|--------------|------------|---------|\n| **File Operations** | 8.2ms | 0.6ms | 1,250/sec | 12KB |\n| **String Processing** | 12.5ms | 0.8ms | 950/sec | 18KB |\n| **System Commands** | 6.1ms | 0.4ms | 1,800/sec | 8KB |\n| **Complex Queries** | 24.8ms | 1.2ms | 480/sec | 35KB |\n\n### System Resource Usage\n\n| Build Type | Binary Size | Memory Usage | Startup Time | CPU Usage |\n|------------|-------------|--------------|-------------|-----------|\n| **MVP** | 8.2MB | 45MB | 12ms | 0.3% |\n| **Full** | 23.7MB | 128MB | 28ms | 1.2% |\n\n### Real-World Benchmarks\n\n```bash\n# Benchmark: Processing 1000 mixed operations\nenglish validate --benchmark 1000\n# Results:\n# ├─ Average Parse Time: 1.3ms\n# ├─ 95th Percentile: 2.8ms \n# ├─ 99th Percentile: 12.1ms\n# ├─ Throughput: 785,000 operations/second\n# └─ Memory Peak: 156MB\n```\n\n---\n\n## 🚀 Advanced Features\n\n### SIMD Optimization\n\n```bash\n# Enable SIMD debugging to see optimizations\nENGLISH_DEBUG_SIMD=1 english parse \"batch process files\"\n# Output shows SIMD instruction selection and performance gains\n```\n\n### Memory Management Control\n\n```bash \n# Custom memory allocation limits\nenglish parse \"large operation\" --memory-limit 256M\n# Controls JIT memory allocation for resource-constrained environments\n```\n\n### Template Development\n\n```json\n// custom-template.json\n{\n \"patterns\": [\n {\n \"input\": \"create database table {name}\",\n \"operation\": \"DatabaseCreate\",\n \"parameters\": [\"name\"],\n \"validation\": \"schema_valid\"\n }\n ]\n}\n```\n\n---\n\n## 🛠️ Development\n\n### Project Structure\n\n```\nEnglish/\n├── english/ # Main CLI application and entry point\n├── english-core/ # Core parsing and protocol logic \n│ ├── src/nl_interface/ # Natural language processing\n│ ├── src/spec_parser/ # Specification parsing and validation\n│ └── src/common/ # Shared utilities and error handling\n├── english-compiler/ # SIMD optimization and compilation\n│ ├── src/codegen.rs # Binary code generation\n│ ├── src/simd_optimizer.rs # Hardware-accelerated optimization \n│ └── src/dynamic_loader.rs # Template loading and compilation\n├── english-execution/ # JIT execution and memory management\n│ ├── src/engine.rs # Core execution engine\n│ └── src/memory_manager.rs # Cross-platform memory allocation\n├── benchmarks/ # Performance benchmarking suite\n├── config/ # Configuration templates and examples\n├── docs/ # Comprehensive documentation\n├── examples/ # Usage examples and tutorials\n└── tests/ # Integration and unit tests\n```\n\n### Development Setup\n\n```bash\n# Clone and setup development environment\ngit clone https://github.com/AmadeusInnovations/English.git\ncd English\n\n# Install development dependencies\ncargo install --path . --features full\n\n# Run all tests\ncargo test --features full\n\n# Run specific test suites \ncargo test --features mvp nl_parser\ncargo test --features full simd_optimizer\n\n# Build for development with debug info\ncargo build --features full\n\n# Run integration tests\n./scripts/test_builds.sh\n```\n\n### Code Quality and Standards\n\n```bash\n# Format code\ncargo fmt --all\n\n# Run linter\ncargo clippy --all --features full\n\n# Check documentation\ncargo doc --open --features full\n\n# Profile performance\ncargo run --features full --release -- parse \"test\" --profile\n```\n\n---\n\n## ⚙️ Build Variants\n\n| Feature | MVP Build | Full Build | Description |\n|---------|-----------|------------|-------------|\n| **Natural Language Parser** | ✅ Core | ✅ Advanced | Basic vs. sophisticated parsing |\n| **Template System** | ✅ Static | ✅ Dynamic | Fixed vs. runtime template loading |\n| **SIMD Optimization** | ❌ | ✅ | Hardware acceleration disabled/enabled |\n| **JIT Execution** | ❌ | ✅ | Direct execution vs. binary generation |\n| **Performance Tracking** | ✅ Basic | ✅ Detailed | Simple vs. comprehensive metrics |\n| **Memory Management** | ✅ Standard | ✅ Advanced | Basic vs. optimized allocation |\n| **Binary Size** | ~8MB | ~24MB | Lightweight vs. full-featured |\n| **Startup Time** | ~12ms | ~28ms | Fast vs. feature-rich initialization |\n\n### Build Commands\n\n```bash\n# MVP build - lightweight, essential features only\ncargo build --features mvp\ncargo install --path . --features mvp\n\n# Full build - complete feature set \ncargo build --features full\ncargo install --path . --features full\n\n# Development build - debug symbols and verbose output\ncargo build --features full,debug\n```\n\n---\n\n## 🔧 Troubleshooting\n\n### Common Issues and Solutions\n\n#### Build Failures\n\n**Issue**: `cargo build` fails with linker errors\n```bash\n# Solution: Update Rust and install system dependencies\nrustup update \n# macOS: xcode-select --install\n# Ubuntu: sudo apt install build-essential \n# Windows: Install Visual Studio Build Tools\n```\n\n**Issue**: \"feature not found\" errors\n```bash\n# Solution: Use correct feature flags\ncargo build --features full # Not --feature (singular)\n```\n\n#### Runtime Issues\n\n**Issue**: \"english: command not found\"\n```bash \n# Solution: Add Cargo bin directory to PATH\necho 'export PATH=\"$HOME/.cargo/bin:$PATH\"' >> ~/.bashrc\nsource ~/.bashrc\n```\n\n**Issue**: Slow parsing performance\n```bash\n# Solution: Enable optimizations and check system resources\nenglish info # Check if SIMD is enabled\nENGLISH_DEBUG=1 english parse \"test\" --perf # Debug performance\n```\n\n#### Memory and Performance\n\n**Issue**: High memory usage during compilation\n```bash\n# Solution: Use memory-constrained builds\ncargo build --release --jobs 1\n# Or use MVP build for lower memory footprint\ncargo build --features mvp\n```\n\n### Debug Mode\n\n```bash\n# Enable debug logging \nENGLISH_DEBUG=1 english parse \"debug this\"\n\n# Enable SIMD debugging\nENGLISH_DEBUG_SIMD=1 english parse \"optimization test\"\n\n# Enable memory debugging\nENGLISH_DEBUG_MEMORY=1 english parse \"memory test\"\n\n# Enable all debugging \nENGLISH_DEBUG=1 ENGLISH_DEBUG_SIMD=1 ENGLISH_DEBUG_MEMORY=1 english parse \"full debug\"\n```\n\n---\n\n## ❓ FAQ\n\n### General Questions\n\n**Q: What makes this different from other natural language processing tools?** \nA: Unlike NLP libraries focused on analysis, we compile natural language directly to optimized binary code, achieving executable performance with intuitive natural language input.\n\n**Q: How accurate is the natural language parsing?** \nA: Current accuracy is 99.3% for structured commands and 94.7% for free-form natural language, with continuous improvement through template expansion.\n\n**Q: What programming languages can I generate code for?** \nA: Currently generates optimized binary code directly. Future versions will target C, Rust, and WebAssembly with preserving performance characteristics.\n\n### Performance Questions\n\n**Q: How fast is the compilation process?** \nA: Average end-to-end compilation is <5ms for cached operations, <25ms for new operations, with throughput exceeding 1M commands/second.\n\n**Q: Does it work offline?** \nA: Yes, completely offline after installation. No external APIs or network dependencies required for core functionality.\n\n**Q: What's the memory overhead?** \nA: MVP build uses ~45MB RAM, Full build uses ~128MB RAM during active compilation, with minimal runtime overhead.\n\n### Technical Questions\n\n**Q: Which CPU architectures are supported?** \nA: Full support for x86_64 (SSE, AVX), ARM64 (NEON), with experimental RISC-V support. Automatic CPU feature detection included.\n\n**Q: Can I extend it with custom operations?** \nA: Yes, through JSON/TOML templates for MVP builds and full Rust extensions for Full builds. Template hot-reloading supported in development.\n\n**Q: Is it thread-safe for concurrent usage?** \nA: Yes, designed for high-concurrency scenarios with lock-free parsing and isolated execution contexts per thread.\n\n### Integration Questions\n\n**Q: How do I integrate this into my existing build pipeline?** \nA: Use as CLI tool in scripts, or embed the `english-core` library directly into your Rust applications for programmatic access.\n\n**Q: Can I use this in production environments?** \nA: Currently in active development. MVP build is stable for non-critical applications, Full build recommended for evaluation only.\n\n**Q: What's the licensing for commercial use?** \nA: Private repository with proprietary license. Contact amadeus.innovations@example.com for commercial licensing discussions.\n\n### Development Questions \n\n**Q: How can I contribute new language patterns?** \nA: Submit template files via pull requests, or contact the development team for core parser enhancements.\n\n**Q: What's the development roadmap?** \nA: Focus areas: expanded language coverage, additional target platforms, IDE integration, and performance optimization.\n\n**Q: How do I report bugs or request features?** \nA: Use GitHub Issues for bug reports, feature requests, and discussions. Security issues should be reported privately.\n\n---\n\n## 🤝 Contributing\n\nWe welcome contributions to the English Programming Language project! Here's how you can help:\n\n### Priority Areas\n\n1. **Language Pattern Expansion**: Add support for new natural language constructs\n2. **Template Library**: Contribute operation templates for common use cases\n3. **Platform Support**: Help with Windows, Linux, and additional architecture support\n4. **Performance Optimization**: Identify and resolve bottlenecks\n5. **Documentation**: Improve examples, guides, and API documentation\n6. **Testing**: Expand test coverage and edge case handling\n\n### Contribution Process\n\n```bash\n# 1. Fork the repository on GitHub\n# 2. Clone your fork\ngit clone https://github.com/your-username/English.git \ncd English\n\n# 3. Create a feature branch\ngit checkout -b feature/your-feature-name\n\n# 4. Make your changes and test\ncargo test --features full\ncargo fmt --all\ncargo clippy --all\n\n# 5. Commit with clear messages\ngit commit -m \"Add support for conditional operations\"\n\n# 6. Push and create pull request\ngit push origin feature/your-feature-name\n```\n\n### Development Guidelines\n\n- **Code Style**: Run `cargo fmt` and `cargo clippy` before submitting\n- **Testing**: Include tests for new features and bug fixes \n- **Documentation**: Update relevant documentation and examples\n- **Performance**: Benchmark changes that might affect performance\n- **Compatibility**: Ensure changes work across supported platforms\n\n### Recognition\n\nContributors will be acknowledged in:\n- Repository contributors list\n- Release notes for significant contributions \n- Optional inclusion in project documentation\n\n---\n\n## 📞 Support & Contact\n\n### Community Support\n- **GitHub Issues**: [Bug reports and feature requests](https://github.com/AmadeusInnovations/English/issues)\n- **Discussions**: [Community Q&A and ideas](https://github.com/AmadeusInnovations/English/discussions)\n\n### Professional Support\n- **Technical Support**: technical-support@amadeus-innovations.com\n- **Business Inquiries**: partnerships@amadeus-innovations.com \n- **Licensing Questions**: licensing@amadeus-innovations.com\n\n### Security\n- **Security Issues**: security@amadeus-innovations.com (GPG key available)\n- **Responsible Disclosure**: We follow a 90-day disclosure timeline\n\n### Development Team\n- **Architecture Questions**: Contact core development team via GitHub\n- **Performance Issues**: Include benchmark results and system specifications\n- **Feature Requests**: Use GitHub Issues with detailed use cases\n\n---\n\n
\n\n**Natural Language → Optimized Binary. Built with Rust for Safety and Performance.**\n\n*Copyright © 2024 Amadeus Innovations. All rights reserved.*\n\n[⬆️ Back to Top](#english-programming-language)\n\n
", "has_readme": true, "url": "https://github.com/AmadeusInnovations/English", "category": "unclassified", "value_tier": "standard", "value_rank": 2, "editorial_note": null, "similarity_group": "Developer Tools", "group_score": 15, "similar": [ { "id": "Geijutsu/english", "score": 1.0, "signals": [ "compiler", "library", "automation" ] }, { "id": "TSMCP/Orchestra", "score": 0.2351, "signals": [ "automation", "framework", "cli" ] }, { "id": "MorchestraWorld/Orchestra", "score": 0.2351, "signals": [ "automation", "framework", "cli" ] }, { "id": "Geijutsu/orchestra", "score": 0.2351, "signals": [ "automation", "framework", "cli" ] }, { "id": "TSMCP/tool-of-tools", "score": 0.2203, "signals": [ "automation", "framework", "cli" ] } ] }, { "organization": "AmadeusInnovations", "name": "entropic", "source": "R2 Git bundle", "published_at": "2025-09-18T18:41:51+02:00", "readme": "# 🌌 Entropy - Advanced Claude CLI Implementation\n\n**Honoring Claude Shannon - The Father of Information Theory**\n\nThis project represents a complete extraction, enhancement, and evolution of the original Claude Code CLI from a monolithic 369,642-line entropy binary into a powerful, maintainable, and feature-rich implementation that preserves full functionality while adding significant enhancements.\n\n## 🎯 Project Overview\n\nThe original Claude Code CLI was distributed as a single heavily minified/obfuscated JavaScript file (`original/entropy`) containing 369,644 lines. Through systematic extraction and iterative enhancement, Entropy has evolved into a production-ready CLI with advanced features including:\n\n- **🔄 Complete Tool Execution Cycle**: Proper streaming API integration with tool continuation\n- **📊 Advanced Analytics**: Token counting, session management, and performance monitoring \n- **🎨 Enhanced UI**: Gradient prompts, timestamps, and interactive command history\n- **⚡ Performance Optimized**: Intelligent caching, connection pooling, and memory management\n- **🔒 Enterprise Security**: Permission systems, sandboxing, and comprehensive validation\n- **☁️ Cloud Ready**: Multi-provider integrations and deployment optimization\n\n## 🏗️ Architecture Evolution\n\n### Phase-Based Extraction (Complete - 100%)\n\n**Phase 0 (0-5%): Foundation** ✅\n- Basic CLI structure and entry points\n- Configuration management foundation\n\n**Phase 1 (5-30%): React UI Framework** ✅ \n- Terminal UI components and setup screens\n- Interactive interface development\n\n**Phase 2 (30-60%): API & Streaming** ✅\n- Anthropic API client with streaming support\n- Message handling and response processing\n\n**Phase 3 (60-85%): System Integration** ✅\n- MCP server management and tool execution\n- Permission systems and security frameworks\n\n**Phase 4A (85-90%): CLI Framework** ✅\n- Complete command processing system\n- Advanced tool execution and validation\n\n**Phase 4B (90-95%): Advanced Streaming** ✅ \n- Rate calculation and optimization\n- Output summarization and filtering\n\n**Phase 4C (95-100%): Cloud Integration & Polish** ✅\n- Cloud provider integrations\n- Final UI polish and comprehensive diagnostics\n\n## 🚀 Key Features & Enhancements\n\n### 🔧 Interactive CLI with Advanced Features\n\n```bash\n# Install with convenient aliases\nentropy --help # Main CLI\ne --help # Short alias (also exits sessions)\nlittle-e --help # Additional alias\n\n# Advanced command features\nentropy \"query\" # Direct query execution\n;ls # Execute bash commands with ; prefix\nE or e # Exit interactive sessions\nt # Show total token count\ntasks # List active tasks\nadd \"task\" # Add new task\ndone 1 # Mark task as complete\n```\n\n### 📊 Token Management & Analytics\n\n- **Real-time token counting** with session and total tracking\n- **Intelligent cost monitoring** across conversation sessions\n- **Performance metrics** including API response times\n- **Memory usage tracking** and optimization alerts\n\n### 🎨 Enhanced User Experience\n\n- **Gradient prompt styling** with dynamic colors\n- **Timestamp prefixing** for all interactions `[HH:MM:SS]`\n- **Command history** with up/down arrow navigation\n- **Interactive task management** with persistent state\n- **Intelligent tool execution** with proper continuation cycles\n\n### ⚡ Advanced Tool Execution System\n\n```bash\n# Supported tools with intelligent continuation\n🔧 list_files # Directory exploration with follow-up analysis\n🔧 read_file # File content analysis with contextual responses \n🔧 bash # Command execution with result processing\n🔧 manage_tasks # Task management with status updates\n```\n\n**Critical Bug Fix**: Tool execution hanging resolved through intelligent filtering of user-facing vs internal tool execution cycles.\n\n### 🔒 Security & Permission Management\n\n- **Directory sandboxing** - Operations restricted to session directory\n- **Tool-level permissions** with allow/deny lists\n- **Intelligent permission prompts** with automatic approval systems\n- **Input validation** and sanitization throughout\n\n## 🐛 Known Issues & Maintenance History\n\n### Critical Bugs Resolved\n\n#### 1. **Tool Execution Hanging (Priority 1)** ✅ FIXED\n- **Symptom**: Tools would show \"🔧 Using tool: list_files...\" but hang without completing\n- **Root Cause**: Continuation logic attempted to send internal tool results (like Task/subagent calls) back to Claude\n- **Solution**: Implemented intelligent filtering to only continue conversations for user-facing tools\n\n#### 2. **Variable Scope Errors** ✅ FIXED \n- **Symptom**: `sessionTokensUsed is not defined`, `hp is not defined`\n- **Root Cause**: Variables defined in different scopes than where they were accessed\n- **Solution**: Moved token variables to module level, used proper client references\n\n#### 3. **HTTP 400 API Errors** ✅ FIXED\n- **Symptom**: \"unexpected `tool_use_id` found in `tool_result` blocks\" \n- **Root Cause**: Improper message formatting for tool continuation\n- **Solution**: Restructured assistant messages to include proper tool_use blocks with matching IDs\n\n#### 4. **Arrow Key Handling** ✅ FIXED\n- **Symptom**: Arrow keys showing as raw escape sequences `^[[A`\n- **Root Cause**: Readline history integration not properly configured\n- **Solution**: Enhanced readline configuration with built-in history support\n\n### Ongoing Maintenance Needs\n\n#### 1. **Streaming API Complexity** (High Priority)\n- The streaming API with tool execution requires careful event handling\n- Future changes to Anthropic's API may require immediate updates\n- **Mitigation**: Comprehensive error handling and fallback mechanisms\n\n#### 2. **Token Counting Accuracy** (Medium Priority) \n- Token counts may not always reflect exact billing due to API variations\n- **Mitigation**: Regular validation against Anthropic's official billing\n\n#### 3. **Cross-Platform Compatibility** (Medium Priority)\n- Terminal features may behave differently across Windows/macOS/Linux\n- **Mitigation**: Comprehensive testing matrix and platform-specific handling\n\n#### 4. **Memory Management** (Low Priority)\n- Long-running sessions may accumulate memory usage\n- **Mitigation**: Periodic cleanup and garbage collection optimization\n\n## 🔧 Installation & Usage\n\n### Quick Install\n\n```bash\n# Build and install entropy binary\npkg entropy-standalone.js --targets node18-macos-x64 --output dist/entropy\ncp dist/entropy ~/.local/bin/entropy\n\n# Create aliases\nln -sf ~/.local/bin/entropy ~/.local/bin/e\nln -sf ~/.local/bin/entropy ~/.local/bin/little-e\n```\n\n### Development Setup\n\n```bash\nnpm install\nnpm run dev\n\n# Testing specific phases\nnode test-phase4-complete.js # Comprehensive functionality test\n```\n\n### Interactive Usage\n\n```bash\n🤖 Entropy ❯ Hello Claude!\n[12:34:56] Hello! I'm Claude, running in Entropy Code...\n\n🤖 Entropy ❯ ;ls # Execute bash command\n🤖 Entropy ❯ tasks # Show active tasks \n🤖 Entropy ❯ add \"Review code\" # Add new task\n🤖 Entropy ❯ t # Show total tokens\n🤖 Entropy ❯ e # Exit session\n```\n\n## 📈 Performance & Architecture\n\n### Current Metrics\n- **Binary Size**: ~15-20MB (optimized with pkg)\n- **Startup Time**: <500ms typical\n- **Memory Usage**: ~50-100MB during active use\n- **API Response Time**: 200ms-2s (depends on query complexity)\n\n### Architecture Patterns\n\n```javascript\n// Streaming API with proper tool continuation \nfor await (let event of stream) {\n switch (event.type) {\n case \"content_block_start\":\n // Capture tool_use blocks with proper ID tracking\n case \"content_block_stop\": \n // Execute tools and store results for continuation\n case \"message_stop\":\n // Continue conversation only for user-facing tools\n }\n}\n```\n\n### Key Classes & Functions\n\n- **`HP` Class**: Core Anthropic API client (extracted from original entropy)\n- **`makeApiRequest()`**: Main API interaction with streaming support\n- **`executeToolCall()`**: Tool execution with permission validation\n- **`addToHistory()`**: Command history management with readline integration\n\n## 🌟 Innovation Areas\n\n### Current Innovations\n1. **Intelligent Tool Continuation** - Distinguishes user-facing vs internal tools\n2. **Hybrid History Management** - Combines custom tracking with readline integration \n3. **Dynamic Permission Systems** - Context-aware approval mechanisms\n4. **Phase-Based Architecture** - Systematic extraction and enhancement methodology\n\n### Future Innovation Opportunities\n1. **AI-Powered Debugging** - Automatic error detection and resolution suggestions\n2. **Predictive Caching** - Machine learning-based response caching\n3. **Multi-Model Support** - Integration with additional AI providers\n4. **Plugin Ecosystem** - Third-party extension framework\n\n## 🤝 Anthropic Ecosystem Integration\n\n### Respectful Implementation\n- **Preserves Original Functionality**: 100% feature parity with original Claude Code CLI\n- **Enhances User Experience**: Adds valuable features without changing core behavior\n- **Maintains API Compatibility**: Uses official Anthropic APIs exclusively\n- **Respects Usage Policies**: Implements proper rate limiting and token management\n\n### Contributing to the Ecosystem\n- **Open Source Enhancement**: Makes CLI functionality more accessible and maintainable\n- **Educational Value**: Demonstrates best practices for Anthropic API integration\n- **Community Benefits**: Provides a foundation for further innovation\n- **Quality Standards**: Maintains high code quality and comprehensive documentation\n\n## 📊 Comparison: Original vs Enhanced\n\n| Aspect | Original Entropy | Enhanced Entropy |\n|--------|------------------|------------------|\n| **Size** | 369,644 lines | 2,000+ lines (readable) |\n| **Maintainability** | Minified/obfuscated | Fully documented, modular |\n| **Features** | Basic CLI | Advanced UI, analytics, task management |\n| **Reliability** | Tool hanging issues | Robust error handling, proper cycles |\n| **User Experience** | Functional | Enhanced with history, timestamps, gradients |\n| **Extensibility** | Monolithic | Modular, phase-based architecture |\n| **Security** | Basic | Enterprise-grade permissions, sandboxing |\n\n## 🔮 Continuous Innovation Philosophy\n\nEntropy embodies a philosophy of **continuous improvement** while **respecting the original**:\n\n### Innovation Principles\n1. **Preserve Core Functionality** - Never break what works\n2. **Enhance User Experience** - Add value through thoughtful improvements \n3. **Maintain Compatibility** - Ensure seamless transitions and updates\n4. **Document Everything** - Make knowledge accessible and maintainable\n5. **Plan for Evolution** - Design for future enhancements and scalability\n\n### Maintenance Commitment\n- **Regular Updates**: Staying current with Anthropic API changes\n- **Bug Tracking**: Comprehensive issue identification and resolution\n- **Performance Monitoring**: Continuous optimization and enhancement\n- **Community Feedback**: Responsive to user needs and suggestions\n- **Security Updates**: Proactive security maintenance and improvements\n\n## 📜 License & Acknowledgments\n\n**MIT License** - See LICENSE file for details\n\n### Special Thanks\n- **Anthropic Team** - For the original Claude Code CLI and ongoing API excellence\n- **Claude Shannon** - For information theory foundations that inspire this project\n- **Open Source Community** - For tools and libraries that make this possible\n\n---\n\n**\"In honor of Claude Shannon, we transform entropy into organized, maintainable intelligence.\"**\n\n*Entropy CLI - Where Information Theory Meets Practical AI Implementation*", "has_readme": true, "url": "https://github.com/AmadeusInnovations/entropic", "category": "unclassified", "value_tier": "standard", "value_rank": 2, "editorial_note": null, "similarity_group": "Developer Tools", "group_score": 11, "similar": [ { "id": "MorchestraWorld/entropy", "score": 0.9986, "signals": [ "plugin", "terminal", "framework" ] }, { "id": "AmadeusInnovations/entropy", "score": 0.9986, "signals": [ "plugin", "terminal", "framework" ] }, { "id": "quivent/kamaji", "score": 0.2028, "signals": [ "plugin", "terminal", "framework" ] }, { "id": "quivent/cherry-blossom", "score": 0.1964, "signals": [ "terminal", "cli", "api" ] }, { "id": "AmadeusInnovations/cherry-blossom", "score": 0.1964, "signals": [ "terminal", "cli", "api" ] } ] }, { "organization": "AmadeusInnovations", "name": "entropy", "source": "R2 Git bundle", "published_at": "2025-09-09T01:10:04+00:00", "readme": "# 🌌 Entropy - Advanced Claude CLI Implementation\n\n**Honoring Claude Shannon - The Father of Information Theory**\n\nThis project represents a complete extraction, enhancement, and evolution of the original Claude Code CLI from a monolithic 369,642-line entropy binary into a powerful, maintainable, and feature-rich implementation that preserves full functionality while adding significant enhancements.\n\n## 🎯 Project Overview\n\nThe original Claude Code CLI was distributed as a single heavily minified/obfuscated JavaScript file (`original/entropy`) containing 369,644 lines. Through systematic extraction and iterative enhancement, Entropy has evolved into a production-ready CLI with advanced features including:\n\n- **🔄 Complete Tool Execution Cycle**: Proper streaming API integration with tool continuation\n- **📊 Advanced Analytics**: Token counting, session management, and performance monitoring \n- **🎨 Enhanced UI**: Gradient prompts, timestamps, and interactive command history\n- **⚡ Performance Optimized**: Intelligent caching, connection pooling, and memory management\n- **🔒 Enterprise Security**: Permission systems, sandboxing, and comprehensive validation\n- **☁️ Cloud Ready**: Multi-provider integrations and deployment optimization\n\n## 🏗️ Architecture Evolution\n\n### Phase-Based Extraction (Complete - 100%)\n\n**Phase 0 (0-5%): Foundation** ✅\n- Basic CLI structure and entry points\n- Configuration management foundation\n\n**Phase 1 (5-30%): React UI Framework** ✅ \n- Terminal UI components and setup screens\n- Interactive interface development\n\n**Phase 2 (30-60%): API & Streaming** ✅\n- Anthropic API client with streaming support\n- Message handling and response processing\n\n**Phase 3 (60-85%): System Integration** ✅\n- MCP server management and tool execution\n- Permission systems and security frameworks\n\n**Phase 4A (85-90%): CLI Framework** ✅\n- Complete command processing system\n- Advanced tool execution and validation\n\n**Phase 4B (90-95%): Advanced Streaming** ✅ \n- Rate calculation and optimization\n- Output summarization and filtering\n\n**Phase 4C (95-100%): Cloud Integration & Polish** ✅\n- Cloud provider integrations\n- Final UI polish and comprehensive diagnostics\n\n## 🚀 Key Features & Enhancements\n\n### 🔧 Interactive CLI with Advanced Features\n\n```bash\n# Install with convenient aliases\nentropy --help # Main CLI\ne --help # Short alias (also exits sessions)\nlittle-e --help # Additional alias\n\n# Advanced command features\nentropy \"query\" # Direct query execution\n;ls # Execute bash commands with ; prefix\nE or e # Exit interactive sessions\nt # Show total token count\ntasks # List active tasks\nadd \"task\" # Add new task\ndone 1 # Mark task as complete\n```\n\n### 📊 Token Management & Analytics\n\n- **Real-time token counting** with session and total tracking\n- **Intelligent cost monitoring** across conversation sessions\n- **Performance metrics** including API response times\n- **Memory usage tracking** and optimization alerts\n\n### 🎨 Enhanced User Experience\n\n- **Gradient prompt styling** with dynamic colors\n- **Timestamp prefixing** for all interactions `[HH:MM:SS]`\n- **Command history** with up/down arrow navigation\n- **Interactive task management** with persistent state\n- **Intelligent tool execution** with proper continuation cycles\n\n### ⚡ Advanced Tool Execution System\n\n```bash\n# Supported tools with intelligent continuation\n🔧 list_files # Directory exploration with follow-up analysis\n🔧 read_file # File content analysis with contextual responses \n🔧 bash # Command execution with result processing\n🔧 manage_tasks # Task management with status updates\n```\n\n**Critical Bug Fix**: Tool execution hanging resolved through intelligent filtering of user-facing vs internal tool execution cycles.\n\n### 🔒 Security & Permission Management\n\n- **Directory sandboxing** - Operations restricted to session directory\n- **Tool-level permissions** with allow/deny lists\n- **Intelligent permission prompts** with automatic approval systems\n- **Input validation** and sanitization throughout\n\n## 🐛 Known Issues & Maintenance History\n\n### Critical Bugs Resolved\n\n#### 1. **Tool Execution Hanging (Priority 1)** ✅ FIXED\n- **Symptom**: Tools would show \"🔧 Using tool: list_files...\" but hang without completing\n- **Root Cause**: Continuation logic attempted to send internal tool results (like Task/subagent calls) back to Claude\n- **Solution**: Implemented intelligent filtering to only continue conversations for user-facing tools\n\n#### 2. **Variable Scope Errors** ✅ FIXED \n- **Symptom**: `sessionTokensUsed is not defined`, `hp is not defined`\n- **Root Cause**: Variables defined in different scopes than where they were accessed\n- **Solution**: Moved token variables to module level, used proper client references\n\n#### 3. **HTTP 400 API Errors** ✅ FIXED\n- **Symptom**: \"unexpected `tool_use_id` found in `tool_result` blocks\" \n- **Root Cause**: Improper message formatting for tool continuation\n- **Solution**: Restructured assistant messages to include proper tool_use blocks with matching IDs\n\n#### 4. **Arrow Key Handling** ✅ FIXED\n- **Symptom**: Arrow keys showing as raw escape sequences `^[[A`\n- **Root Cause**: Readline history integration not properly configured\n- **Solution**: Enhanced readline configuration with built-in history support\n\n### Ongoing Maintenance Needs\n\n#### 1. **Streaming API Complexity** (High Priority)\n- The streaming API with tool execution requires careful event handling\n- Future changes to Anthropic's API may require immediate updates\n- **Mitigation**: Comprehensive error handling and fallback mechanisms\n\n#### 2. **Token Counting Accuracy** (Medium Priority) \n- Token counts may not always reflect exact billing due to API variations\n- **Mitigation**: Regular validation against Anthropic's official billing\n\n#### 3. **Cross-Platform Compatibility** (Medium Priority)\n- Terminal features may behave differently across Windows/macOS/Linux\n- **Mitigation**: Comprehensive testing matrix and platform-specific handling\n\n#### 4. **Memory Management** (Low Priority)\n- Long-running sessions may accumulate memory usage\n- **Mitigation**: Periodic cleanup and garbage collection optimization\n\n## 🔧 Installation & Usage\n\n### Quick Install\n\n```bash\n# Build and install entropy binary\npkg entropy-standalone.js --targets node18-macos-x64 --output dist/entropy\ncp dist/entropy ~/.local/bin/entropy\n\n# Create aliases\nln -sf ~/.local/bin/entropy ~/.local/bin/e\nln -sf ~/.local/bin/entropy ~/.local/bin/little-e\n```\n\n### Development Setup\n\n```bash\nnpm install\nnpm run dev\n\n# Testing specific phases\nnode test-phase4-complete.js # Comprehensive functionality test\n```\n\n### Interactive Usage\n\n```bash\n🤖 Entropy ❯ Hello Claude!\n[12:34:56] Hello! I'm Claude, running in Entropy Code...\n\n🤖 Entropy ❯ ;ls # Execute bash command\n🤖 Entropy ❯ tasks # Show active tasks \n🤖 Entropy ❯ add \"Review code\" # Add new task\n🤖 Entropy ❯ t # Show total tokens\n🤖 Entropy ❯ e # Exit session\n```\n\n## 📈 Performance & Architecture\n\n### Current Metrics\n- **Binary Size**: ~15-20MB (optimized with pkg)\n- **Startup Time**: <500ms typical\n- **Memory Usage**: ~50-100MB during active use\n- **API Response Time**: 200ms-2s (depends on query complexity)\n\n### Architecture Patterns\n\n```javascript\n// Streaming API with proper tool continuation \nfor await (let event of stream) {\n switch (event.type) {\n case \"content_block_start\":\n // Capture tool_use blocks with proper ID tracking\n case \"content_block_stop\": \n // Execute tools and store results for continuation\n case \"message_stop\":\n // Continue conversation only for user-facing tools\n }\n}\n```\n\n### Key Classes & Functions\n\n- **`HP` Class**: Core Anthropic API client (extracted from original entropy)\n- **`makeApiRequest()`**: Main API interaction with streaming support\n- **`executeToolCall()`**: Tool execution with permission validation\n- **`addToHistory()`**: Command history management with readline integration\n\n## 🌟 Innovation Areas\n\n### Current Innovations\n1. **Intelligent Tool Continuation** - Distinguishes user-facing vs internal tools\n2. **Hybrid History Management** - Combines custom tracking with readline integration \n3. **Dynamic Permission Systems** - Context-aware approval mechanisms\n4. **Phase-Based Architecture** - Systematic extraction and enhancement methodology\n\n### Future Innovation Opportunities\n1. **AI-Powered Debugging** - Automatic error detection and resolution suggestions\n2. **Predictive Caching** - Machine learning-based response caching\n3. **Multi-Model Support** - Integration with additional AI providers\n4. **Plugin Ecosystem** - Third-party extension framework\n\n## 🤝 Anthropic Ecosystem Integration\n\n### Respectful Implementation\n- **Preserves Original Functionality**: 100% feature parity with original Claude Code CLI\n- **Enhances User Experience**: Adds valuable features without changing core behavior\n- **Maintains API Compatibility**: Uses official Anthropic APIs exclusively\n- **Respects Usage Policies**: Implements proper rate limiting and token management\n\n### Contributing to the Ecosystem\n- **Open Source Enhancement**: Makes CLI functionality more accessible and maintainable\n- **Educational Value**: Demonstrates best practices for Anthropic API integration\n- **Community Benefits**: Provides a foundation for further innovation\n- **Quality Standards**: Maintains high code quality and comprehensive documentation\n\n## 📊 Comparison: Original vs Enhanced\n\n| Aspect | Original Entropy | Enhanced Entropy |\n|--------|------------------|------------------|\n| **Size** | 369,644 lines | 2,000+ lines (readable) |\n| **Maintainability** | Minified/obfuscated | Fully documented, modular |\n| **Features** | Basic CLI | Advanced UI, analytics, task management |\n| **Reliability** | Tool hanging issues | Robust error handling, proper cycles |\n| **User Experience** | Functional | Enhanced with history, timestamps, gradients |\n| **Extensibility** | Monolithic | Modular, phase-based architecture |\n| **Security** | Basic | Enterprise-grade permissions, sandboxing |\n\n## 🔮 Continuous Innovation Philosophy\n\nEntropy embodies a philosophy of **continuous improvement** while **respecting the original**:\n\n### Innovation Principles\n1. **Preserve Core Functionality** - Never break what works\n2. **Enhance User Experience** - Add value through thoughtful improvements \n3. **Maintain Compatibility** - Ensure seamless transitions and updates\n4. **Document Everything** - Make knowledge accessible and maintainable\n5. **Plan for Evolution** - Design for future enhancements and scalability\n\n### Maintenance Commitment\n- **Regular Updates**: Staying current with Anthropic API changes\n- **Bug Tracking**: Comprehensive issue identification and resolution\n- **Performance Monitoring**: Continuous optimization and enhancement\n- **Community Feedback**: Responsive to user needs and suggestions\n- **Security Updates**: Proactive security maintenance and improvements\n\n## 📜 License & Acknowledgments\n\n**MIT License** - See LICENSE file for details\n\n### Special Thanks\n- **Anthropic Team** - For the original Claude Code CLI and ongoing API excellence\n- **Claude Shannon** - For information theory foundations that inspire this project\n- **Open Source Community** - For tools and libraries that make this possible\n\n---\n\n**\"In honor of Claude Shannon, we transform entropy into organized, maintainable intelligence.\"**\n\n*Entropy CLI - Where Information Theory Meets Practical AI Implementation*", "has_readme": true, "url": "https://github.com/AmadeusInnovations/entropy", "category": "unclassified", "value_tier": "standard", "value_rank": 2, "editorial_note": null, "similarity_group": "Developer Tools", "group_score": 11, "similar": [ { "id": "MorchestraWorld/entropy", "score": 1.0, "signals": [ "plugin", "terminal", "framework" ] }, { "id": "AmadeusInnovations/entropic", "score": 0.9986, "signals": [ "plugin", "terminal", "framework" ] }, { "id": "quivent/kamaji", "score": 0.2031, "signals": [ "plugin", "terminal", "framework" ] }, { "id": "quivent/cherry-blossom", "score": 0.1967, "signals": [ "terminal", "cli", "api" ] }, { "id": "AmadeusInnovations/cherry-blossom", "score": 0.1967, "signals": [ "terminal", "cli", "api" ] } ] }, { "organization": "AmadeusInnovations", "name": "gatherer", "source": "R2 Git bundle", "published_at": "2025-10-27T17:24:42-04:00", "readme": "# Gatherer CLI\n\n> **Intelligent IP Discovery and Aggregation Tool**\n\nGatherer is a powerful command-line interface tool designed to systematically discover, analyze, and aggregate valuable intellectual property assets from various sources including `.claude` folders, project directories, documents, and other clear value IP locations.\n\n[![Go Version](https://img.shields.io/badge/Go-1.21+-blue.svg)](https://golang.org/)\n[![License](https://img.shields.io/badge/License-MIT-green.svg)](LICENSE)\n[![Build Status](https://img.shields.io/badge/Build-Passing-brightgreen.svg)](Makefile)\n[![Tests](https://img.shields.io/badge/Tests-Passing-brightgreen.svg)](./cmd)\n\n## 🚀 Implementation Status\n\n**✅ FULLY IMPLEMENTED FEATURES:**\n- ✅ Asset discovery with pattern-based filtering\n- ✅ Quality assessment and content analysis \n- ✅ Real-time status monitoring and reporting\n- ✅ Database management and operations\n- ✅ Multi-format output (table, JSON, YAML, CSV)\n- ✅ Configuration management system\n- ✅ Comprehensive CLI interface with help system\n- ✅ Testing infrastructure with benchmarks\n- ✅ Version management and build system\n\n**🚧 PARTIALLY IMPLEMENTED / FUTURE FEATURES:**\n- 🚧 Physical asset storage (symlink, copy, reference methods)\n- 🚧 Advanced deduplication algorithms\n- 🚧 Agent-based processing architecture\n- 🚧 Backup and recovery operations\n- 🚧 Extended metadata extraction\n\n## 🎯 Current Features\n\n### 🔍 **Intelligent Discovery** ✅ IMPLEMENTED\n- **Pattern-based Asset Discovery**: Configurable file patterns with inclusion/exclusion filters\n- **Multi-source Scanning**: Scan multiple directories simultaneously with depth control\n- **Quality Assessment**: Configurable quality scoring thresholds for asset filtering\n- **Content Analysis**: Basic content analysis and metadata extraction\n- **Dry-run Capabilities**: Preview discovery results before execution\n\n### 📊 **Database Management** ✅ IMPLEMENTED\n- **SQLite Backend**: Portable, efficient database storage\n- **Query Interface**: Flexible search and filtering capabilities\n- **Statistics & Health**: Database health monitoring and comprehensive statistics\n- **Session Tracking**: Discovery session management and history\n- **Export Capabilities**: JSON, CSV, and structured data export\n\n### 🎨 **User Experience** ✅ IMPLEMENTED\n- **Beautiful CLI**: Colored output with progress indicators and tables\n- **Multi-format Output**: Table, JSON, YAML, and CSV output formats\n- **Configuration Management**: Dynamic configuration with validation\n- **Comprehensive Help**: Detailed help system with examples and usage patterns\n- **Status Monitoring**: Real-time system and operation status reporting\n\n### 🧪 **Development & Testing** ✅ IMPLEMENTED\n- **Comprehensive Test Suite**: Unit tests for all core packages\n- **Benchmarking Infrastructure**: Performance testing and measurement\n- **Version Management**: Proper version injection and build information\n- **Development Tooling**: Makefile with build, test, and development targets\n\n### 🚧 **Future Features** (Planned)\n- **Physical Asset Storage**: Symlink, copy, and reference storage methods\n- **Advanced Deduplication**: Content-based duplicate detection and resolution\n- **Agent-based Processing**: Extensible agent architecture for content analysis\n- **Advanced Security Analysis**: Detection of secrets and sensitive information\n- **Backup & Recovery**: Automated backup system with integrity verification\n\n## 🚀 Quick Start\n\n### Installation\n\n```bash\n# Clone the repository\ngit clone \ncd gatherer\n\n# Build the binary\nmake build\n\n# Install to system PATH (optional)\nmake install\n```\n\n### Basic Usage\n\n```bash\n# Discover assets with dry-run preview\ngatherer discover --dry-run --sources ~/.claude,./projects\n\n# Perform actual discovery with quality filtering\ngatherer discover --sources ~/.claude --quality-threshold 0.8\n\n# View comprehensive status\ngatherer status --format table\n\n# Search the database\ngatherer database search --query \"*.go\" --type source\n\n# Show database statistics\ngatherer database stats\n\n# Export discovered assets\ngatherer database export --format json --output assets.json\n```\n\n## 📖 Detailed Usage\n\n### Discovery Operations\n\n```bash\n# Basic discovery with pattern filtering\ngatherer discover --sources ~/Documents --pattern \"*.md,*.go,*.py\"\n\n# Discovery with content analysis\ngatherer discover --analyze-content --extract-metadata --max-depth 5\n\n# Quality-focused discovery\ngatherer discover --quality-threshold 0.7 --exclude \".git,node_modules\"\n\n# Multi-source discovery with size limits\ngatherer discover --sources ~/.claude,./projects,~/Research --max-size 10MB\n\n# Dry-run to preview discovery results\ngatherer discover --dry-run --sources . --pattern \"*.go\"\n```\n\n### Database Operations\n\n```bash\n# View database statistics\ngatherer database stats\n\n# Search for specific assets\ngatherer database search --query \"claude\" --category configuration\n\n# List discovery sessions\ngatherer database sessions --limit 10\n\n# Export data\ngatherer database export --format json --output assets.json\n\n# Create backup\ngatherer database backup --compress\n\n# Health check and maintenance\ngatherer database health\ngatherer database vacuum\n```\n\n### Configuration Management\n\n```bash\n# View current configuration\ngatherer config show\n\n# Set configuration values\ngatherer config set discovery.max_depth=10\ngatherer config set storage.method=symlink\n\n# Validate configuration\ngatherer config validate\n\n# Reset to defaults\ngatherer config reset storage\n```\n\n### Status and Monitoring\n\n```bash\n# Comprehensive status overview\ngatherer status\n\n# JSON format for scripting\ngatherer status --format json\n\n# Specific component status\ngatherer status --components database,storage\n\n# Historical data with trends\ngatherer status --historical --limit 30\n```\n\n## ⚙️ Configuration\n\nGatherer uses a YAML configuration file located at `./configs/gatherer.yaml`. The configuration supports:\n\n### Discovery Settings\n```yaml\ndiscovery:\n source_paths:\n - \"~/.claude\"\n - \"./projects\"\n - \"~/Documents\"\n file_patterns:\n - \"*.md\"\n - \"*.go\"\n - \"*.py\"\n - \"*.js\"\n exclude_paths:\n - \".git/\"\n - \"node_modules/\"\n - \"vendor/\"\n max_file_size: 10485760 # 10MB\n content_analysis:\n enabled: true\n detect_secrets: true\n extract_metadata: true\n```\n\n### Storage Configuration\n```yaml\nstorage:\n database_path: \"./gatherer.db\"\n backup_enabled: true\n asset_storage:\n method: \"symlink\" # symlink, copy, reference\n base_path: \"./gathered_assets\"\n preserve_structure: true\n deduplicate: true\n```\n\n### Agent Configuration\n```yaml\nagents:\n content_analyzer:\n enabled: true\n timeout: \"300s\"\n concurrent_limit: 5\n quality_assessor:\n enabled: true\n threshold: 0.7\n```\n\n## 🏗️ Architecture\n\n### Component Overview\n\n```\ngatherer/\n├── cmd/ # CLI command implementations\n│ ├── root.go # Root command and global configuration\n│ ├── discover.go # Discovery operations\n│ ├── analyze.go # Content analysis\n│ ├── store.go # Storage and aggregation\n│ ├── status.go # Status reporting\n│ ├── database.go # Database operations\n│ ├── config.go # Configuration management\n│ └── version.go # Version information\n├── internal/ # Internal packages\n│ ├── discovery/ # Asset discovery engine\n│ ├── storage/ # Storage and database systems\n│ └── config/ # Configuration management\n├── configs/ # Configuration files\n└── docs/ # Documentation\n```\n\n### Key Components\n\n- **Discovery Engine**: Intelligent file system traversal with pattern matching\n- **Analysis System**: Content analysis and quality assessment\n- **Storage System**: Flexible aggregation with multiple storage methods\n- **Database Layer**: SQLite-based persistence with rich querying\n- **Configuration System**: Hot-reloadable YAML configuration with validation\n\n## 🛠️ Development\n\n### Build System\n\n```bash\n# Development build\nmake dev\n\n# Run all checks\nmake check\n\n# Create release build\nmake release\n\n# Run demo\nmake demo\n\n# View all available targets\nmake help\n```\n\n### Testing and Benchmarking\n\n```bash\n# Run all tests\nmake test\n\n# Run benchmarks\nmake bench\n\n# Generate benchmark report\nmake bench-report\n\n# Run CPU profiling benchmarks\nmake bench-cpu\n\n# Run memory profiling benchmarks\nmake bench-mem\n\n# Run linting\nmake lint\n\n# Format code\nmake fmt\n\n# Run all checks (format, vet, test)\nmake check\n```\n\n### Database Management\n\n```bash\n# Reset database\nmake db-reset\n\n# Create backup\nmake db-backup\n\n# Show configuration\nmake config-show\n```\n\n## 📊 Examples\n\n### Example 1: Claude Configuration Discovery\n\n```bash\n# Discover all Claude configurations\ngatherer discover --sources ~/.claude --pattern \"*.md,*.yaml,*.json\"\ngatherer database search --query \"claude\" --type configuration\ngatherer database stats\n```\n\n### Example 2: Project Documentation Analysis\n\n```bash\n# Find and analyze project documentation\ngatherer discover --pattern \"README*,CHANGELOG*,*.md\" --quality-threshold 0.6\ngatherer status --format json > project_status.json\ngatherer database export --format json --output documentation.json\n```\n\n### Example 3: Source Code Quality Assessment\n\n```bash\n# Analyze source code quality across projects\ngatherer discover --sources ./projects --pattern \"*.go,*.py,*.js\" --analyze-content\ngatherer database search --query \"*.go\" --type source\ngatherer database export --format csv --output quality_report.csv\n```\n\n### Example 4: Comprehensive System Analysis\n\n```bash\n# Perform comprehensive discovery and analysis\ngatherer discover --sources ~/.claude,./projects --analyze-content --extract-metadata\ngatherer status\ngatherer database stats\ngatherer database sessions\n```\n\n## 📊 Performance\n\nGatherer includes comprehensive performance benchmarking. See [PERFORMANCE.md](./PERFORMANCE.md) for detailed metrics and baseline measurements.\n\nKey performance characteristics:\n- **Sub-second discovery**: Typical operations complete in <2 seconds\n- **Memory efficient**: ~40KB allocation per discovery operation\n- **Scalable concurrency**: Linear performance scaling up to 8 workers\n- **Database optimization**: Zero-allocation configuration objects\n\n## 🤝 Contributing\n\n1. Fork the repository\n2. Create a feature branch\n3. Implement your changes\n4. Add tests and documentation\n5. Run `make check` to verify all tests pass\n6. Run `make bench` to ensure performance is maintained\n7. Submit a pull request\n\n## 📄 License\n\nThis project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.\n\n## 🙏 Acknowledgments\n\n- Built with [Cobra](https://github.com/spf13/cobra) for CLI framework\n- Database powered by [SQLite](https://sqlite.org/)\n- Configuration management via [Viper](https://github.com/spf13/viper)\n- Inspired by the morchestrator project patterns\n\n---\n\n**Gatherer CLI** - *Intelligent IP Discovery and Aggregation*", "has_readme": true, "url": "https://github.com/AmadeusInnovations/gatherer", "category": "unclassified", "value_tier": "standard", "value_rank": 2, "editorial_note": null, "similarity_group": "Data & Storage", "group_score": 10, "similar": [ { "id": "CherryMesh/gatherer", "score": 1.0, "signals": [ "backup", "search", "database" ] }, { "id": "TSMCP/Orchestra", "score": 0.2384, "signals": [ "backup", "search", "thresholds" ] }, { "id": "MorchestraWorld/Orchestra", "score": 0.2384, "signals": [ "backup", "search", "thresholds" ] }, { "id": "Geijutsu/orchestra", "score": 0.2384, "signals": [ "backup", "search", "thresholds" ] }, { "id": "AmadeusInnovations/MultiLinguist", "score": 0.236, "signals": [ "backup", "data", "csv" ] } ] }, { "organization": "AmadeusInnovations", "name": "intel", "source": "R2 Git bundle", "published_at": "2025-09-20T11:12:46+02:00", "readme": "# RAG - Retrieval-Augmented Generation System\n\nA standalone, high-performance RAG (Retrieval-Augmented Generation) system implemented in pure C with pattern memory capabilities and development workflow integration.\n\n## Overview\n\nThis RAG system provides:\n\n- **Pattern Memory**: Memory-based learning and adaptation patterns (320-byte structure)\n- **Usage Learning**: Learning from development workflow interactions\n- **Binary Semantic Search**: Efficient knowledge retrieval and storage\n- **Tool Integration**: Development utility coordination and knowledge sharing\n- **Real-time Context**: Dynamic project pattern detection and guidance\n\n## Features\n\n### Core Commands\n\n- `rag --init` - Initialize a blank RAG system in current directory\n- `rag --reset` - Reset existing RAG system to blank baseline state\n- `rag --status` - Show bootstrap and learning status\n- `rag --query ` - Query the knowledge base\n- `rag --seed ` - Add knowledge from markdown files\n- `rag --agent-enhance` - Get agent enhancement guidance\n\n### Advanced Features\n\n- `rag --learn ` - Record learning events\n- `rag --consult ` - Get domain-specific guidance \n- `rag --sync` - Bidirectional agent synchronization\n- `rag --export ` - Export system to installable package\n- `rag --install ` - Install system from package\n\n## Quick Start\n\n### 1. Build the System\n\n```bash\nmake clean && make\n```\n\n### 2. Initialize RAG in New Directory\n\n```bash\n# In any new project directory\nrag --init\n```\n\n### 3. Check Status\n\n```bash\nrag --status\n```\n\n### 4. Add Knowledge\n\n```bash\nrag --seed documentation.md\n```\n\n### 5. Query Knowledge\n\n```bash\nrag --query \"project navigation\"\nrag --agent-enhance\n```\n\n## Installation Options\n\n### Local Installation (Project-specific)\n```bash\nmake install # Installs to ./bin/rag\n```\n\n### Global Installation (System-wide)\n```bash\nmake install-global # Installs to ~/.local/bin/rag\n```\n\n## Architecture\n\n### Core Components\n\n- **rag.c** - Main CLI interface and query engine\n- **rag_bootstrap.c** - Consciousness bootstrap memory system\n- **rag_rl.c** - Reinforcement learning and adaptation\n- **rag_wisdom.c** - Accumulated wisdom and pattern recognition\n- **rag_diff.c** - Change detection and synchronization\n- **agent_integration.c** - Multi-agent coordination\n\n### Data Storage\n\n```\n.rag/\n├── knowledge/ # Markdown knowledge files\n├── chunks/ # Processed text chunks\n├── embeddings/ # Semantic embeddings\n├── metadata/ # File metadata and indexes\n├── memory_bootstrap.bin # Pattern memory (320 bytes)\n└── rl_bootstrap.bin # Learning statistics\n```\n\n## Key Capabilities\n\n### 1. Pattern Memory System\nMaintains patterns for:\n- Navigation strategies\n- Teaching methods\n- Error corrections\n- Learning insights\n- Cross-project wisdom\n\n**Current Utilization**: 3/320 bytes active (1.1% information density)\n\n### 2. Usage Pattern Learning\nTracks and learns from:\n- Successful development actions\n- Correction patterns\n- Context-specific performance\n- Domain expertise accumulation\n\n### 3. Development Enhancement\nProvides dynamic guidance based on:\n- Current project patterns\n- Accumulated domain knowledge\n- Real-time context analysis\n- Success metrics and optimization\n\n### 4. Knowledge Synchronization\nSupports:\n- Development tool memory sharing\n- Bidirectional learning sync\n- Context-aware recommendations\n- Pattern detection and analysis\n\n## Usage Examples\n\n### Basic Workflow\n```bash\n# Initialize new RAG system\nrag --init\n\n# Add project documentation\nrag --seed README.md\nrag --seed docs/architecture.md\n\n# Get guidance for current work\nrag --agent-enhance\nrag --query \"debugging patterns\"\n\n# Learn from experience\nrag --learn \"debugging\" \"memory leak\" \"used valgrind\"\n```\n\n### Agent Integration\n```bash\n# Start agent session\nrag --session-start agent_$(date +%s)\n\n# Get context-aware enhancement\nrag --agent-enhance\n\n# Record learning summary\nrag --session-summary \"Fixed memory issues using systematic debugging\"\n```\n\n### System Management\n```bash\n# Export for deployment\nrag --export /path/to/package\n\n# Install in new environment\nrag --install /path/to/package\n\n# Reset to clean state\nrag --reset\n```\n\n## Performance Characteristics\n\n- **Query Speed**: 1.4μs bootstrap analysis, 0.06μs pattern analysis\n- **Memory Usage**: 320-byte pattern memory, ~1MB total footprint\n- **Learning**: Real-time pattern recognition and adaptation\n- **Scalability**: Handles knowledge bases up to 100MB efficiently\n- **Analysis Performance**: 15.6M analyses/second (verified)\n\n## Integration with III\n\nThis RAG system was originally developed as part of the III (Intelligence Integration Initiative) project but has been extracted as a standalone system. It maintains compatibility with III's:\n\n- Binary semantic matrices\n- Agent coordination protocols \n- Knowledge representation standards\n- Performance optimization patterns\n\n## Contributing\n\nThe RAG system uses pure C for maximum performance and portability. Key development principles:\n\n- Zero external dependencies\n- Memory-safe operations\n- Real-time performance\n- Agent-agnostic design\n- Cross-platform compatibility\n\n## License\n\nPart of the III project ecosystem. See original III project for licensing details.\n\n## Advanced Configuration\n\nThe system supports various advanced configurations through environment variables and configuration files. See `RAG_BUILD_DEPLOYMENT.md` for detailed deployment options and `RAG_KNOWLEDGE_SYNCHRONIZATION_SYSTEM.md` for multi-agent coordination setup.", "has_readme": true, "url": "https://github.com/AmadeusInnovations/intel", "category": "unclassified", "value_tier": "standard", "value_rank": 2, "editorial_note": null, "similarity_group": "Agents & Orchestration", "group_score": 8, "similar": [ { "id": "Moestradamus-Productions/intel", "score": 1.0, "signals": [ "multi-agent", "workflow", "agent" ] }, { "id": "quivent/CollaborativeIntelligence", "score": 0.1875, "signals": [ "multi-agent", "agent", "memory" ] }, { "id": "quivent/III", "score": 0.1792, "signals": [ "workflow", "agent", "memory" ] }, { "id": "TSMCP/CollaborativeIntelligence", "score": 0.1759, "signals": [ "multi-agent", "agent", "memory" ] }, { "id": "CherryMesh/gatherer", "score": 0.1546, "signals": [ "agent", "memory", "characteristics" ] } ] }, { "organization": "AmadeusInnovations", "name": "MultiLinguist", "source": "R2 Git bundle", "published_at": "2025-09-08T19:31:48+02:00", "readme": "# Multilinguist CLI\n\n**Intelligent file translation agent for multilingual content processing**\n\nMultilinguist is a comprehensive Go + Cobra CLI application that behaves as an intelligent agent for file translation tasks. It automatically detects file types and applies appropriate translation strategies, with specialized support for JSONL conversation logs and other structured formats.\n\n## ✨ Key Features\n\n- **🔍 Smart File Detection**: Automatic file type detection based on extension and content analysis\n- **💬 JSONL Conversation Logs**: Specialized processing for conversation data with metadata preservation\n- **⚙️ YAML Configuration**: Flexible, English-readable configuration system\n- **🌍 Multi-format Support**: JSON, JSONL, TXT, Markdown, CSV files\n- **📁 Structure Preservation**: Maintains file structure and metadata integrity\n- **🔄 Intelligent Processing**: Extensible architecture for future translation capabilities\n- **📊 Progress Tracking**: Comprehensive logging and progress reporting\n- **💾 Safe Operations**: Automatic backups and dry-run capabilities\n\n## 🚀 Quick Start\n\n### Installation\n\n```bash\n# Clone and install\ngit clone \ncd multilinguist\nmake install\n\n# Or use the installation script\n./scripts/install.sh\n```\n\n### Basic Usage\n\n```bash\n# Initialize configuration\nmultilinguist config init\n\n# Translate a file (dry run first)\nmultilinguist translate --dry-run conversation.jsonl\n\n# Actual translation\nmultilinguist translate conversation.jsonl\n\n# Translate to specific language\nmultilinguist translate --target-lang fr document.txt\n\n# Custom output directory\nmultilinguist translate --output-dir ./translated file.json\n```\n\n## 📋 Supported File Types\n\n| File Type | Extension | Features |\n|-----------|-----------|----------|\n| **JSONL** | `.jsonl` | Conversation logs, metadata preservation, selective field translation |\n| **JSON** | `.json` | Structured data, nested object support, field filtering |\n| **Text** | `.txt` | Full content translation, formatting preservation |\n| **Markdown** | `.md` | Structure preservation, selective content translation |\n| **CSV** | `.csv` | Tabular data, configurable column translation |\n\n## 🎯 JSONL Conversation Logs\n\nMultilinguist has specialized support for JSONL conversation files (like Claude conversation logs):\n\n```jsonl\n{\"type\":\"user\",\"message\":{\"role\":\"user\",\"content\":\"Hello world\"},\"uuid\":\"test-1\"}\n{\"type\":\"assistant\",\"message\":{\"role\":\"assistant\",\"content\":\"Hi there!\"},\"uuid\":\"test-2\"}\n```\n\n**Features:**\n- Preserves UUIDs, timestamps, and metadata\n- Translates only content fields\n- Maintains conversation structure\n- Handles nested message content\n- Batch processing for large files\n\n## ⚙️ Configuration\n\nMultilinguist uses YAML configuration for maximum flexibility:\n\n```yaml\n# ~/.multilinguist.yaml\ntranslation:\n default_source_lang: \"auto\"\n default_target_lang: \"en\"\n preserve_timestamps: true\n backup_original: true\n\nstrategies:\n - file_type: \"jsonl\"\n preserve_structure: true\n translate_fields: [\"content\", \"message.content\", \"summary\"]\n skip_fields: [\"uuid\", \"timestamp\", \"sessionId\"]\n```\n\n### Configuration Commands\n\n```bash\n# Initialize default config\nmultilinguist config init\n\n# View current configuration\nmultilinguist config show\n\n# Show config file path\nmultilinguist config path\n```\n\n## 🔧 Command Reference\n\n### Global Flags\n\n- `--config`: Specify configuration file path\n- `--verbose, -v`: Enable verbose output\n- `--version`: Show version information\n\n### Translate Command\n\n```bash\nmultilinguist translate [flags] FILE\n```\n\n**Flags:**\n- `--source-lang, -s`: Source language (default: auto-detect)\n- `--target-lang, -t`: Target language (default: en)\n- `--output-dir, -o`: Output directory (default: ./translated)\n- `--dry-run`: Show translation plan without executing\n- `--backup`: Create backup of original file (default: true)\n- `--config, -c`: Configuration file path\n\n**Examples:**\n```bash\n# Basic translation\nmultilinguist translate document.txt\n\n# Specify languages\nmultilinguist translate --source-lang es --target-lang en data.json\n\n# Custom output location\nmultilinguist translate --output-dir ./results --target-lang fr conversation.jsonl\n\n# Preview translation plan\nmultilinguist translate --dry-run --verbose file.md\n```\n\n## 🧪 Development\n\n### Prerequisites\n\n- Go 1.19 or later\n- Make (optional, but recommended)\n\n### Building from Source\n\n```bash\n# Download dependencies\ngo mod tidy\n\n# Build binary\nmake build\n\n# Run tests\nmake test\n\n# Install locally\nmake install\n\n# Run demo\nmake demo\n\n# Cross-platform build\nmake build-all\n```\n\n### Project Structure\n\n```\nmultilinguist/\n├── cmd/ # Command implementations\n│ ├── root.go # Root command and configuration\n│ ├── translate.go # Translation command\n│ └── config.go # Configuration management\n├── internal/\n│ ├── config/ # Configuration handling\n│ ├── models/ # Data structures\n│ ├── translator/ # Translation logic\n│ └── utils/ # Utility functions\n├── test/\n│ ├── fixtures/ # Test files\n│ └── integration/ # Integration tests\n├── scripts/ # Installation scripts\n└── Makefile # Build automation\n```\n\n### Testing\n\n```bash\n# Run all tests\nmake test\n\n# Run with coverage\nmake test-coverage\n\n# Run integration tests\nmake test-integration\n\n# Run benchmarks\nmake benchmark\n```\n\n## 🌐 Translation Strategy\n\nThe CLI uses intelligent translation strategies based on file type:\n\n### JSONL Strategy\n- **Preserve Structure**: ✅ Yes\n- **Translate Fields**: `content`, `message.content`, `summary`\n- **Skip Fields**: `uuid`, `timestamp`, `sessionId`, `requestId`\n- **Batch Size**: 10 messages\n- **Metadata**: Fully preserved\n\n### JSON Strategy\n- **Preserve Structure**: ✅ Yes\n- **Translate Fields**: `content`, `text`, `message`, `description`\n- **Skip Fields**: `id`, `uuid`, `timestamp`, `metadata`\n- **Batch Size**: 20 objects\n\n### Text Strategy\n- **Preserve Structure**: ❌ No (full content translation)\n- **Batch Size**: 50 chunks\n- **Formatting**: Preserved\n\n## 📊 Output and Results\n\nSuccessful translations provide detailed summaries:\n\n```\n✅ Translation completed successfully!\n\n📊 Translation Summary:\n Source: conversation.jsonl (auto)\n Target: translated/conversation_fr.jsonl (fr)\n File Type: jsonl\n Messages Processed: 150\n Backup: conversation_backup.jsonl\n Processed: 2025-09-08T18:30:00Z\n```\n\n## 🔄 Extensibility\n\nThe CLI is designed for extensibility:\n\n1. **New File Types**: Add detection logic and translation strategies\n2. **Translation Services**: Integrate with actual translation APIs\n3. **Custom Strategies**: Define specialized processing for domain-specific formats\n4. **Plugin System**: Framework ready for plugin-based extensions\n\n## 🛠 Production Notes\n\n### Translation Implementation\n\nThe current implementation includes a **placeholder translation system** that adds language prefixes (e.g., `[ES] Hello world`) to demonstrate functionality. For production use, integrate with:\n\n- Google Translate API\n- Azure Translator\n- OpenAI Translation\n- Custom translation services\n\n### Performance\n\n- **Concurrent Processing**: Configurable concurrency limits\n- **Batch Processing**: Optimized for large files\n- **Memory Efficient**: Streaming processing for large datasets\n- **Progress Tracking**: Real-time progress reporting\n\n## 📝 License\n\nThis project is licensed under the MIT License - see the LICENSE file for details.\n\n## 🤝 Contributing\n\n1. Fork the repository\n2. Create your feature branch (`git checkout -b feature/amazing-feature`)\n3. Commit your changes (`git commit -m 'Add amazing feature'`)\n4. Push to the branch (`git push origin feature/amazing-feature`)\n5. Open a Pull Request\n\n## 📞 Support\n\nFor questions, issues, or contributions:\n\n- Create an issue in the repository\n- Check existing documentation\n- Review the configuration options\n\n---\n\n**Built with ❤️ using Go + Cobra following CLI-Maker best practices**", "has_readme": true, "url": "https://github.com/AmadeusInnovations/MultiLinguist", "category": "unclassified", "value_tier": "standard", "value_rank": 2, "editorial_note": null, "similarity_group": "Developer Tools", "group_score": 8, "similar": [ { "id": "CherryMesh/gatherer", "score": 0.236, "signals": [ "framework", "cli", "csv" ] }, { "id": "AmadeusInnovations/gatherer", "score": 0.236, "signals": [ "framework", "cli", "csv" ] }, { "id": "Geijutsu/english", "score": 0.1776, "signals": [ "automation", "language", "framework" ] }, { "id": "AmadeusInnovations/English", "score": 0.1776, "signals": [ "automation", "language", "framework" ] }, { "id": "Moestradamus-Productions/morchestrator", "score": 0.1747, "signals": [ "plugin", "language", "framework" ] } ] }, { "organization": "AmadeusInnovations", "name": "pointsio", "source": "R2 Git bundle", "published_at": "2025-09-01T14:53:52+02:00", "readme": "# PointsIO - Dynamic Web Application\n\nA privacy-focused, mobile-first tracking application with complete data persistence, real charts, and dynamic functionality built with Vite.\n\n## 🎯 Purpose\n\nThis is a fully functional tracking application. It provides real data persistence, interactive charts, and a comprehensive management system.\n\n## 🚀 Dynamic Features\n\n### ✅ Complete Data Layer\n- **Local Storage Persistence**: All data stored securely in browser localStorage\n- **Item Management**: Add, edit, track multiple items with schedules\n- **Event Recording**: Real-time event logging with adherence tracking\n- **Symptom Tracking**: Daily wellness and symptom check-ins\n- **Progress Calculations**: Dynamic progress tracking and adherence rates\n- **Data Export/Import**: JSON export for backup and medical professional sharing\n\n### ✅ Interactive Charts & Visualizations\n- **Chart.js Integration**: Professional, responsive data visualization\n- **Progress Charts**: Track item progress over time\n- **Wellness Trends**: Monitor daily wellness ratings with trend analysis\n- **Adherence Tracking**: Visual adherence rates with color-coded feedback\n- **Data Correlations**: Multi-dimensional trend analysis\n- **Time-based Filtering**: 7-day, 30-day, 90-day views\n\n### ✅ Smart Features\n- **Milestone System**: Automatic achievement tracking for motivation\n- **Sample Data Generation**: 30 days of realistic sample data for demonstration\n- **Dynamic Forms**: Modal-based item and schedule management\n- **Real-time Updates**: Live data synchronization across all views\n- **Smart Notifications**: Toast feedback for all user actions\n- **Data Validation**: Input validation and error handling\n\n## 🛠 Technology Stack\n\n- **Frontend**: Vanilla JavaScript ES6+, HTML5, CSS3\n- **Charts**: Chart.js for data visualization\n- **Build Tool**: Vite for development and building\n- **Storage**: Browser localStorage for data persistence\n- **Styling**: CSS Custom Properties with mobile-first design\n- **Dependencies**: Minimal - only Chart.js and date-fns utilities\n\n## 📱 Design & Architecture\n\n### Data Architecture\n- **Modular Design**: Separate DataLayer and ChartManager classes\n- **Privacy-First Storage**: All data remains local, never transmitted\n- **Data Validation**: Comprehensive input validation and error handling\n- **Backup & Restore**: Complete data export/import functionality\n- **Performance**: Efficient data querying and chart rendering\n\n### Accessibility Features\n- High contrast color scheme (WCAG AAA 7:1 ratio)\n- Large touch targets (44px minimum)\n- System font stack for optimal readability\n- VoiceOver/screen reader friendly markup\n- Respects `prefers-reduced-motion` and `prefers-color-scheme`\n- High contrast mode support\n\n### Mobile Optimization\n- iPhone Pro Max width constraint (414px)\n- Bottom navigation for thumb-friendly access\n- Card-based layout with generous spacing\n- Touch-optimized interactions\n- Smooth animations and transitions\n\n## 🎨 Visual Design\n\n### Color System\n- **Primary**: Blue (#2563eb) for actions and navigation\n- **Success**: Green (#059669) for completed actions\n- **Warning**: Orange (#d97706) for alerts\n- **Danger**: Red (#dc2626) for destructive actions\n\n### Typography\n- System font stack for native feel\n- 16px base font size for readability\n- 1.6 line height for comfortable reading\n- Consistent font weight hierarchy\n\n## 🧪 Dynamic Application Features\n\n### Core Functionality\n1. **Item Management**: \n - Add items with custom schedules\n - Real progress tracking\n - Adherence rate calculations\n - Schedule-based reminders\n\n2. **Data Tracking**:\n - Real-time event logging with timestamps\n - Daily wellness rating persistence\n - Comprehensive symptom tracking\n - Historical data analysis\n\n3. **Visualizations**:\n - Interactive Chart.js visualizations\n - Progress tracking over time\n - Wellness and data trend analysis\n - Color-coded adherence tracking\n\n4. **Smart Features**:\n - Milestone achievements (7-day streaks, perfect adherence)\n - Automatic trend analysis\n - Data export for medical professionals\n - Comprehensive settings management\n\n### Sample Data Included\n- 30 days of realistic data, wellness, and tracking information\n- Two sample items with proper scheduling\n- Milestone achievements and progress tracking\n- Realistic adherence patterns (90% average)\n\n## 🚀 Getting Started\n\n### Prerequisites\n- Node.js 16+ installed\n- npm or yarn package manager\n\n### Installation\n```bash\n# Install dependencies\nnpm install\n\n# Start development server\nnpm run dev\n\n# Build for production\nnpm run build\n```\n\n### Viewing the Mockup\n1. Start the development server: `npm run dev`\n2. Open browser to `http://localhost:5173`\n3. Resize browser to mobile width for best experience\n4. Use browser dev tools to simulate mobile device\n\n## 📋 Implementation Details\n\n### ✅ Fully Implemented\n- **Complete Data Layer**: localStorage-based persistence with validation\n- **Real Chart Rendering**: Chart.js integration with interactive visualizations\n- **Dynamic UI Updates**: Live data synchronization across all views\n- **Modal Forms**: Medication addition and editing with validation\n- **Data Export**: JSON export functionality for medical professionals\n- **Milestone System**: Achievement tracking with notifications\n- **Responsive Design**: Mobile-first with accessibility compliance\n- **Sample Data**: 30 days of realistic historical data\n\n### 🔮 Future Enhancements\n- Push notifications (requires service worker)\n- Biometric authentication (Web Authentication API)\n- PWA capabilities (offline functionality)\n- Data synchronization across devices\n- Medication interaction warnings\n- Export to PDF reports\n\n## 🔮 Production Deployment\n\nThis application is production-ready for personal use. To deploy:\n\n### Static Hosting (Recommended)\n```bash\nnpm run build\n# Upload dist/ folder to static hosting (Netlify, Vercel, GitHub Pages)\n```\n\n### Native Mobile App\n```bash\n# Install Capacitor for native deployment\nnpm install @capacitor/core @capacitor/ios @capacitor/android\nnpx cap init\nnpm run build\nnpx cap add ios android\nnpx cap run ios\n```\n\n### PWA Conversion\nAdd service worker for offline functionality and app-like experience on mobile browsers.\n\n## 📄 Related Documentation\n\n- `Personal_Medication_Tapering_Tracker_Specification.md` - Original specification (archived)\n- `MEDICATION_TAPERING_TRACKER_SPECIFICATION.md` - Legacy specification (archived)\n\n---\n\n**Disclaimer**: This is a demonstration application. For any health-related decisions, always consult with a healthcare professional.", "has_readme": true, "url": "https://github.com/AmadeusInnovations/pointsio", "category": "unclassified", "value_tier": "standard", "value_rank": 2, "editorial_note": null, "similarity_group": "Infrastructure & Operations", "group_score": 10, "similar": [ { "id": "Moestradamus-Productions/pointsio", "score": 1.0, "signals": [ "hosting", "system", "service" ] }, { "id": "Moestradamus-Productions/lightbrush-website-overhaul", "score": 0.1859, "signals": [ "deploy", "deployment", "server" ] }, { "id": "quivent/Builders", "score": 0.1662, "signals": [ "markup", "browsers", "vanilla" ] }, { "id": "TSMCP/Orchestra", "score": 0.1634, "signals": [ "deploy", "deployment", "server" ] }, { "id": "MorchestraWorld/Orchestra", "score": 0.1634, "signals": [ "deploy", "deployment", "server" ] } ] }, { "organization": "AmadeusInnovations", "name": "Research", "source": "R2 Git bundle", "published_at": "2025-08-31T21:42:16+02:00", "readme": "", "has_readme": false, "url": "https://github.com/AmadeusInnovations/Research", "category": "unclassified", "value_tier": "standard", "value_rank": 2, "editorial_note": null, "similarity_group": "Research & Knowledge", "group_score": 4, "similar": [ { "id": "Moestradamus-Productions/Research", "score": 1.0, "signals": [ "research" ] }, { "id": "AmadeusInnovations/AmadeusInnovations", "score": 0.1317, "signals": [ "research" ] }, { "id": "TSMCP/librarian", "score": 0.1254, "signals": [ "research" ] }, { "id": "quivent/librarian", "score": 0.1254, "signals": [ "research" ] }, { "id": "CherryMesh/librarian", "score": 0.1254, "signals": [ "research" ] } ] }, { "organization": "AmadeusInnovations", "name": "self-education-exploration", "source": "R2 Git bundle", "published_at": "2025-09-09T04:37:27+02:00", "readme": "# Self-Teaching Repository\n\nA comprehensive portfolio of learning systems, development projects, and educational resources organized for clarity and accessibility.\n\n## Repository Structure\n\n### 📚 Learning-Systems/\nEducational content and learning methodologies\n- **Wikipedia-Learning-Sessions/** - Three comprehensive learning sessions with agent implementations\n- **CLI-Library-Learning/** - Command-line interface learning progression\n- **Autonomous-Education-Suite/** - Self-directed learning system implementations \n- **Evolving-Education-Application/** - Adaptive education platform research\n\n### 🔧 Development-Projects/\nActive code projects and tools\n- **CommandLineTools/** - CLI utilities and internal synthesis tools\n- **MoestradamusLearning/** - Learning prediction and orchestration engine\n- **Synthesis-Engine/** - Knowledge synthesis and processing tools\n\n### 🤝 Shared-Context/\nCross-project learning frameworks and methodologies\n- **LEARNING_FRAMEWORK.md** - Core learning principles and protocols\n- **LEARNING_METRICS.md** - Performance measurement frameworks\n- **systematic_analysis_frameworks.md** - Analysis methodologies\n\n### 📦 Shared-Resources/\nCommon utilities and documentation\n- **Learning-Frameworks/** - Reusable learning components\n- **MORCHESTRATED_COMMUNICATION_PROTOCOL.md** - Inter-agent communication standards\n\n### 📁 Archive/\nHistorical data and preserved memories\n- **lost-memories/** - Recovered session data from previous learning iterations\n\n## Navigation\n\n- **[Learning Sessions Index](LEARNING_SESSIONS_INDEX.md)** - Complete guide to all learning sessions\n- **[Shared Context Index](Shared-Context/INDEX.md)** - Framework and methodology reference\n\n## Organization Principles\n\nThis repository follows evidence-based organization with:\n- Clear categorical separation between learning and development\n- Preserved git history for all file movements\n- Cross-referencing between related content\n- Comprehensive documentation for navigation\n\nEach major section includes detailed manifests and session documentation for easy exploration and continuation of work.", "has_readme": true, "url": "https://github.com/AmadeusInnovations/self-education-exploration", "category": "unclassified", "value_tier": "standard", "value_rank": 2, "editorial_note": null, "similarity_group": "Research & Knowledge", "group_score": 13, "similar": [ { "id": "Moestradamus-Productions/self-education-explorer", "score": 0.9928, "signals": [ "education", "knowledge", "learning" ] }, { "id": "TSMCP/librarian", "score": 0.1776, "signals": [ "knowledge", "learning", "research" ] }, { "id": "quivent/librarian", "score": 0.1776, "signals": [ "knowledge", "learning", "research" ] }, { "id": "CherryMesh/librarian", "score": 0.1776, "signals": [ "knowledge", "learning", "research" ] }, { "id": "TSMCP/CollaborativeIntelligence", "score": 0.1723, "signals": [ "knowledge", "learning", "analysis" ] } ] }, { "organization": "AmadeusInnovations", "name": "silence", "source": "R2 Git bundle", "published_at": "2026-02-11T21:20:53-05:00", "readme": "# 🔐 Silence Crypto - Secure P2P Communication\n\n**Ephemeral Key Cascade Protocol Implementation** \n**Status:** 🟡 Partial Implementation (see completion guide) \n**Security Level:** Maximum (Post-Quantum + Perfect Forward Secrecy) \n**Memory Footprint:** <20MB runtime, <10MB binary \n\n## 🚀 What's Been Implemented Autonomously\n\n### ✅ **Complete Components**\n- **Project Structure**: Full Rust/Tauri setup with optimized dependencies\n- **Cryptographic Core**: ChaCha20-Poly1305 encryption with HKDF key derivation\n- **Key Management**: Ephemeral keys with 15-second rotation and secure memory clearing\n- **P2P Networking**: TCP-based direct peer communication with binary serialization\n- **GUI Framework**: Complete HTML/CSS interface with security status indicators\n- **Build System**: Size-optimized release configuration with LTO\n\n### 📊 **Performance Characteristics**\n```yaml\nBinary Size: ~8MB (optimized)\nMemory Usage: 15-18MB runtime\nStartup Time: <150ms\nMessage Latency: <8ms on LAN\nKey Rotation: 15-second intervals\nCPU Overhead: <5% idle, <12% active\n```\n\n### 🛡️ **Security Features Implemented**\n- ✅ Perfect forward secrecy with ephemeral key cascade\n- ✅ ChaCha20-Poly1305 authenticated encryption\n- ✅ HKDF-SHA256 key derivation with unique contexts\n- ✅ Automatic key rotation every 15 seconds\n- ✅ Secure memory zeroing with Zeroize\n- ✅ Local-only P2P communication (no internet)\n\n## ⚠️ **What Needs Manual Completion**\n\n### 🔧 **Missing Components (see completion guide)**\n1. **Post-Quantum Integration**: ML-KEM and ML-DSA library integration\n2. **Message Handler**: GUI event bridge for real-time message display \n3. **Connection Management**: Server startup and peer discovery logic\n4. **Error Handling**: Robust error propagation to GUI\n5. **Testing**: Integration tests and validation suite\n\n## 🏃‍♂️ **Quick Start**\n\n### **Prerequisites**\n```bash\n# Install Rust\ncurl --proto '=https' --tlsv1.2 -sSf https://sh.rustup.rs | sh\nsource $HOME/.cargo/env\n\n# Install system dependencies (Ubuntu/Debian)\nsudo apt-get install libwebkit2gtk-4.0-dev build-essential curl wget libssl-dev\n```\n\n### **Build & Run**\n```bash\ncd Silence/\ncargo build --release # Build optimized binary\ncargo tauri dev # Run development version with GUI\n```\n\n## 📁 **Project Structure**\n```\nSilence/\n├── Cargo.toml # ✅ Dependencies and build config\n├── build.rs # ✅ Tauri build script\n├── tauri.conf.json # ✅ GUI configuration\n├── src/\n│ ├── main.rs # 🟡 Entry point (needs completion)\n│ ├── crypto.rs # ✅ Cryptographic operations\n│ ├── network.rs # ✅ P2P networking layer\n│ └── lib.rs # ✅ Library exports\n├── src-tauri/\n│ └── index.html # ✅ Complete GUI interface\n└── README.md # ✅ This file\n```\n\n## 🔍 **Key Implementation Details**\n\n### **Memory-Optimized Crypto Stack**\n```rust\n// Ephemeral keys with automatic zeroing\n#[derive(ZeroizeOnDrop)]\npub struct EphemeralKeys {\n master_key: [u8; 32], // Never persisted\n session_key: [u8; 32], // Rotated every 15s\n encryption_key: [u8; 32], // Derived per-session\n mac_key: [u8; 32], // Authentication\n}\n```\n\n### **Minimal Dependency Footprint**\n- **Core**: 15 total dependencies (vs 50+ in typical Tauri apps)\n- **Crypto**: RustCrypto ecosystem (pure Rust, well-audited)\n- **Serialization**: Bincode (smaller than JSON)\n- **GUI**: Tauri with minimal features enabled\n\n### **Size Optimizations**\n```toml\n[profile.release]\nlto = true # Link-time optimization\ncodegen-units = 1 # Single code generation unit\npanic = \"abort\" # No unwinding overhead\nstrip = true # Remove debug symbols\nopt-level = \"s\" # Optimize for size\n```\n\n## 🛡️ **Security Architecture**\n\n### **Threat Model**\n- ✅ **Perfect Forward Secrecy**: Past messages secure if keys compromised\n- ✅ **Memory Safety**: Rust prevents buffer overflows and memory corruption \n- ✅ **Local Network Only**: Zero external internet dependencies\n- 🟡 **Post-Quantum**: ML-KEM/ML-DSA integration pending (see completion guide)\n- ✅ **Traffic Analysis**: Binary protocol with padding\n\n### **Key Cascade Flow**\n```\nMaster Key (32 bytes, ephemeral)\n │\n ├── Session Key ──→ HKDF ──→ Next Master Key\n │\n ├── Encryption Key ──→ ChaCha20-Poly1305\n │\n └── MAC Key ──→ Message Authentication\n```\n\n## 📝 **Next Steps**\n\n1. **Review**: Check the implementation meets your requirements\n2. **Complete**: Follow the completion guide for remaining integration\n3. **Test**: Run local P2P communication tests\n4. **Deploy**: Build release version for production use\n\n## ❓ **Questions for Final Integration**\n\n1. **Network Interface**: Auto-detect LAN interface acceptable?\n2. **Port Configuration**: Default port 8080 suitable?\n3. **Message Size**: 4KB max message size sufficient?\n4. **Post-Quantum Priority**: ML-KEM integration urgency level?\n5. **Additional Features**: File transfer, group chat, or text-only?\n\n**Status**: Ready for completion phase. Estimated remaining time: 20-30 minutes.", "has_readme": true, "url": "https://github.com/AmadeusInnovations/silence", "category": "unclassified", "value_tier": "standard", "value_rank": 2, "editorial_note": null, "similarity_group": "Security & Identity", "group_score": 6, "similar": [ { "id": "quivent/cherry-blossom", "score": 0.1507, "signals": [ "encryption", "authentication", "security" ] }, { "id": "AmadeusInnovations/cherry-blossom", "score": 0.1507, "signals": [ "encryption", "authentication", "security" ] }, { "id": "MorchestraWorld/sakura", "score": 0.1505, "signals": [ "encryption", "authentication", "security" ] }, { "id": "Geijutsu/sakura", "score": 0.1505, "signals": [ "encryption", "authentication", "security" ] }, { "id": "CherryMesh/sakura", "score": 0.1505, "signals": [ "encryption", "authentication", "security" ] } ] }, { "organization": "AmadeusInnovations", "name": "Training", "source": "R2 Git bundle", "published_at": "2025-09-02T05:49:32+02:00", "readme": "# Training Repository Index\n**Updated:** 2025-08-28 \n**Purpose:** Comprehensive training resources for AI agent development and task optimization \n**Maintainer:** Automated indexing via CLAUDE.md instructions \n\n---\n\n## 📁 Directory Structure\n\n### **AUTONOMOUS_DEVELOPMENT/**\nAdvanced frameworks and protocols for autonomous AI development:\n- `ADVANCED_AGENT_PARALLELIZATION_FRAMEWORKS.md` - SSA-based coordination with 95% efficiency improvements\n- `AUTONOMOUS_DEVELOPMENT_RESEARCH_PROTOCOLS.md` - Knowledge graph integration with 20:1+ compression ratios\n- `CLAUDE_CODE_ENHANCED_AGENT_IMPLEMENTATION_ROADMAP.md` - Implementation roadmap for enhanced capabilities\n- `COMPREHENSIVE_AGENT_COGNITIVE_ENHANCEMENT_PROTOCOLS.md` - Cognitive enhancement protocols and methodologies\n- `claude-code-tooling-documentation.html` - Technical documentation for Claude Code tooling\n\n### **Insights/**\nTask execution optimization and methodology insights:\n- `effective_prompting_strategies.md` - High-performance prompting patterns with evidence-based validation\n- `task_execution_insights.md` - Critical insights from complex task execution analysis\n- `tool_usage_optimizations.md` - Tool coordination strategies with performance metrics\n\n### **Recommendations/**\nStrategic recommendations for capability enhancement:\n- `recommended_subagents.md` - 5 specialized subagents with implementation roadmap and performance targets\n\n### **Research/**\nLearning methodologies and expert learner development research:\n- `expert_learner_framework.md` - Complete architecture for expert learner agent development\n- `file_inventory.md` - Comprehensive catalog of 94+ analyzed research files\n- `learning_patterns.md` - Universal learning patterns with quantified benefits\n- `learning_theories.md` - Evidence-based learning theories synthesis\n- `research_summary.md` - Comprehensive research findings and recommendations\n\n### **Root Level Quality Assurance:**\n- `AUDIT_PROTOCOL.md` - Comprehensive audit procedures for directory integrity and alignment validation\n- `TRAINING_RECOMMENDATIONS.md` - Quick reference guide with quality standards and best practices\n\n---\n\n## 🎯 Quick Access by Use Case\n\n### **For Task Execution Optimization:**\n1. `TRAINING_RECOMMENDATIONS.md` - Quick reference guide\n2. `Insights/effective_prompting_strategies.md` - Detailed prompting methodologies\n3. `Insights/tool_usage_optimizations.md` - Tool coordination patterns\n\n### **For Agent Development:**\n1. `Recommendations/recommended_subagents.md` - Strategic agent recommendations\n2. `Research/expert_learner_framework.md` - Complete learner agent architecture\n3. `AUTONOMOUS_DEVELOPMENT/` - Advanced development frameworks\n\n### **For Research and Analysis:**\n1. `Research/research_summary.md` - Comprehensive research findings\n2. `Research/learning_theories.md` - Evidence-based methodologies\n3. `AUTONOMOUS_DEVELOPMENT/AUTONOMOUS_DEVELOPMENT_RESEARCH_PROTOCOLS.md` - Research protocols\n\n### **For Process Improvement:**\n1. `Insights/task_execution_insights.md` - Process optimization insights\n2. `AUTONOMOUS_DEVELOPMENT/ADVANCED_AGENT_PARALLELIZATION_FRAMEWORKS.md` - Parallelization strategies\n3. `AUTONOMOUS_DEVELOPMENT/COMPREHENSIVE_AGENT_COGNITIVE_ENHANCEMENT_PROTOCOLS.md` - Enhancement protocols\n\n### **For Quality Assurance:**\n1. `AUDIT_PROTOCOL.md` - Comprehensive audit procedures for directory integrity\n2. `TRAINING_RECOMMENDATIONS.md` - Quick reference with quality standards\n3. `Insights/effective_prompting_strategies.md` - Quality assurance integration patterns\n\n---\n\n## 📊 Performance Metrics Summary\n\n### **Quantified Improvements Documented:**\n- **Learning Efficiency:** 280% improvement through meta-learning strategies\n- **Complex Reasoning:** 538% improvement in reasoning tasks\n- **Memory Optimization:** 46.9% memory reduction with 32% speed increase\n- **Coordination Efficiency:** 95% improvements through SSA-based frameworks\n- **Research Coverage:** 94+ files analyzed with evidence integration\n\n### **Evidence-Based Outcomes:**\n- **Task Tool Optimization:** File-based progress preservation with interruption resistance\n- **Quality Assurance:** 85%+ prediction accuracy with comprehensive validation\n- **Knowledge Transfer:** 67% improvement through multi-modal integration\n- **Resource Utilization:** 90%+ optimal allocation through intelligent management\n\n---\n\n## 🔄 Maintenance Protocol\n\n### **Automated Updates:**\nThis index is maintained through CLAUDE.md instructions ensuring:\n- Real-time updates when new files are added\n- Performance metrics integration from new research\n- Cross-reference validation across documents\n- Quality assurance for documentation standards\n\n### **Manual Review Requirements:**\n- Monthly validation of performance metrics accuracy\n- Quarterly assessment of document relevance and organization\n- Annual strategic review of training resource effectiveness\n- Continuous integration of new optimization discoveries\n\n### **Version Control:**\n- Document creation dates tracked for freshness assessment\n- Performance metrics validated against source research\n- Cross-references maintained for knowledge graph integrity\n- Evidence-based claims verified through source documentation\n\n---\n\n## 🎓 Training Session Guidelines\n\n### **New User Onboarding:**\n1. Start with `TRAINING_RECOMMENDATIONS.md` for quick reference\n2. Review `Insights/effective_prompting_strategies.md` for methodology\n3. Examine relevant use case section for specific guidance\n4. Apply insights with evidence-based approach and documentation\n\n### **Advanced Development:**\n1. Study `AUTONOMOUS_DEVELOPMENT/` frameworks for sophisticated approaches\n2. Implement `Recommendations/recommended_subagents.md` for capability enhancement\n3. Use `Research/` findings for evidence-based decision making\n4. Maintain optimization patterns discovered in `Insights/` documentation\n\n### **Continuous Improvement:**\n- Document new discoveries in appropriate directories\n- Update performance metrics based on validated outcomes\n- Cross-reference new findings with existing knowledge base\n- Maintain quality standards through comprehensive validation\n\n---\n\n## 📈 Success Indicators\n\n### **Training Effectiveness:**\n- Improved task execution efficiency through methodology application\n- Higher quality outputs through evidence-based approaches\n- Reduced rework through optimization pattern implementation\n- Enhanced capability development through strategic agent usage\n\n### **Knowledge Integration:**\n- Cross-domain synthesis opportunities identified and utilized\n- Evidence-based decision making becomes standard practice\n- Quality assurance integration prevents rather than detects issues\n- Process optimization creates compound efficiency improvements\n\n---\n\n## 🔗 Cross-Reference Network\n\nThis repository forms an interconnected knowledge graph:\n- **Research** → **Insights** → **Recommendations** (Evidence-based development pipeline)\n- **AUTONOMOUS_DEVELOPMENT** ↔ **Insights** (Methodology validation and enhancement)\n- **Recommendations** → **Research** (Implementation guidance with research foundation)\n- All documents cross-reference for comprehensive coverage and validation\n\n**Last Updated:** 2025-08-28 \n**Next Review:** 2025-09-28 \n**Maintenance Status:** Automated via CLAUDE.md integration", "has_readme": true, "url": "https://github.com/AmadeusInnovations/Training", "category": "unclassified", "value_tier": "standard", "value_rank": 2, "editorial_note": null, "similarity_group": "Research & Knowledge", "group_score": 10, "similar": [ { "id": "Moestradamus-Productions/Training", "score": 1.0, "signals": [ "knowledge", "learning", "research" ] }, { "id": "MorchestraWorld/autonomous-development-protocol", "score": 0.1753, "signals": [ "research", "analysis", "documentation" ] }, { "id": "Moestradamus-Productions/self-education-explorer", "score": 0.163, "signals": [ "knowledge", "learning", "research" ] }, { "id": "AmadeusInnovations/self-education-exploration", "score": 0.1629, "signals": [ "knowledge", "learning", "research" ] }, { "id": "TSMCP/CollaborativeIntelligence", "score": 0.1544, "signals": [ "knowledge", "learning", "analysis" ] } ] }, { "organization": "AmadeusInnovations", "name": "TravelAgent", "source": "R2 Git bundle", "published_at": "2025-09-02T06:05:58+02:00", "readme": "", "has_readme": false, "url": "https://github.com/AmadeusInnovations/TravelAgent", "category": "unclassified", "value_tier": "standard", "value_rank": 2, "editorial_note": null, "similarity_group": "Other Experiments", "group_score": 0, "similar": [ { "id": "AmadeusInnovations/AmadeusInnovations", "score": 0.3497, "signals": [ "travelagent" ] }, { "id": "quivent/TravelAgent", "score": 0.3323, "signals": [ "travelagent" ] }, { "id": "quivent/TourGuide", "score": 0.1013, "signals": [ "travelagent" ] } ] }, { "organization": "CherryMesh", "name": "555", "source": "R2 Git bundle", "published_at": "2026-05-13T12:17:54-04:00", "readme": "", "has_readme": false, "url": "https://github.com/CherryMesh/555", "category": "unclassified", "value_tier": "standard", "value_rank": 2, "editorial_note": null, "similarity_group": "Other Experiments", "group_score": 0, "similar": [] }, { "organization": "CherryMesh", "name": "capsule", "source": "R2 Git bundle", "published_at": "2025-10-17T22:18:06+02:00", "readme": "", "has_readme": false, "url": "https://github.com/CherryMesh/capsule", "category": "unclassified", "value_tier": "standard", "value_rank": 2, "editorial_note": null, "similarity_group": "Other Experiments", "group_score": 0, "similar": [ { "id": "Geijutsu/capsule", "score": 1.0, "signals": [ "capsule" ] }, { "id": "quivent/cherry-blossom", "score": 0.0826, "signals": [ "capsule" ] }, { "id": "AmadeusInnovations/cherry-blossom", "score": 0.0826, "signals": [ "capsule" ] }, { "id": "MorchestraWorld/sakura", "score": 0.0824, "signals": [ "capsule" ] }, { "id": "Geijutsu/sakura", "score": 0.0824, "signals": [ "capsule" ] } ] }, { "organization": "CherryMesh", "name": "cherry", "source": "R2 Git bundle", "published_at": "2025-12-02T09:55:54-05:00", "readme": "# 🌸 Cherry CLI - Revolutionary Server Management Platform\n\n[![Version](https://img.shields.io/badge/version-1.0.0-pink.svg)](https://github.com/cherryservers/cherry-cli)\n[![License](https://img.shields.io/badge/license-MIT-green.svg)](LICENSE)\n[![Security](https://img.shields.io/badge/encryption-AES--256--GCM-blue.svg)](docs/security.md)\n[![Platform](https://img.shields.io/badge/platform-macOS%20%7C%20Linux%20%7C%20Windows-lightgrey.svg)](docs/installation.md)\n\n> **The world's first CLI with complete server state snapshotting and cherry blossom-themed aesthetics.**\n\nA revolutionary, security-first command-line interface for Cherry Servers infrastructure management featuring military-grade encryption, complete server state capture via the Capsule System, and beautiful cherry blossom-themed user experience.\n\n---\n\n## ✨ Revolutionary Features\n\n### 💊 **Cherry Server Capsule System** - *World's First Complete Server State Capture*\n- 🔬 **Complete Server Snapshot**: Users, packages, services, configs, data, SSH keys, crontabs, network settings\n- 🔐 **Military-Grade Security**: AES-256-GCM encryption with SHA-512 integrity verification\n- 📦 **Intelligent Optimization**: 90%+ size reduction through rebuild artifact detection\n- 🚀 **Secure Transfer**: Encrypted transmission with remote confirmation\n- ⚡ **Automated Restoration**: One-command server recreation from capsules\n\n### 🔐 **Enterprise-Grade Security**\n- **AES-256-GCM Encryption** for all data transfers\n- **TLS 1.3** for secure API communications\n- **GPG Integration** for file encryption\n- **SSH Key Management** with automated deployment\n- **Zero-Knowledge Operation** with automatic cleanup\n\n### 🌸 **Blossom System** - *Enhanced User Experience*\n- **Smart SSH Management** with automatic user switching\n- **Cherry Blossom Aesthetics** with sakura-themed interface\n- **Emoji-Rich Feedback** for immediate visual context\n- **Progressive Help System** with contextual guidance\n\n### 🌐 **Advanced P2P Networking**\n- **Peer Discovery** with automatic topology mapping\n- **NAT Traversal** using sophisticated hole-punching\n- **End-to-End Encryption** for secure peer communication\n- **Load Balancing** with intelligent peer selection\n- **Fault Tolerance** with automatic failover\n\n---\n\n## 🚀 Quick Start\n\n### Installation\n\n**macOS/Linux:**\n```bash\n# One-line installation (both binaries)\ncurl -sSL https://raw.githubusercontent.com/cherryservers/cherry-cli/main/install.sh | bash\n\n# Manual installation (both binaries)\ngit clone https://github.com/cherryservers/cherry-cli.git\ncd cherry-cli\nmake && make install\n\n# Install individual binaries\nmake install-foundation # Installs seedling\nmake install-cherry # Installs cherry\n```\n\n**Windows (WSL2):**\n```powershell\n# Install WSL2 + Ubuntu (Run as Administrator)\nwsl --install\n\n# In Ubuntu terminal\nsudo apt update && apt install -y build-essential libssl-dev git\ngit clone https://github.com/cherryservers/cherry-cli.git\ncd cherry-cli && make && make install\n```\n\n### First Time Setup\n\n```bash\n# Initialize configuration\ncherry init\n\n# Set your Cherry Servers API token\nexport CHERRY_AUTH_TOKEN=\"your-token-here\"\n\n# Verify installation\nseedling --version # Foundation implementation (v1.0.0)\ncherry --version # Primary development interface (v2.1.0-nightly)\ncherry list\n```\n\n---\n\n## 🎯 Core Usage Examples\n\n### 💊 **Capsule System** - Complete Server Management\n```bash\n# Create complete server snapshot\ncherry server produce capsule\n# → Creates encrypted .capsule file with full server state\n\n# Transfer server state to new environment\ncherry server beam capsule production-server\n# → Secure transmission with integrity verification\n\n# Restore complete server from capsule\ncherry server receive capsule\n# → Automated server recreation with all configurations\n```\n\n### 🔐 **Secure File Transfer**\n```bash\n# Transfer encrypted files\ncherry send document.pdf my-server # Single file with AES-256\ncherry send ./project/ production-server # Entire directory compressed\ncherry send backup.tar.gz staging-server # Large files optimized\n```\n\n### 🌸 **Enhanced SSH Management**\n```bash\n# Smart SSH with user switching\ncherry blossom # SSH to active server\ncherry blossom deploy # SSH and switch to 'deploy' user\ncherry blossom www-data # SSH and switch to 'www-data'\ncherry blossom pair # Set up SSH key authentication\n```\n\n### 🖥️ **Server Operations**\n```bash\n# Server management\ncherry server activate my-server # Set active server\ncherry server info # Detailed server information\ncherry server create --plan c1-small --image ubuntu_22_04\ncherry install docker # Install tools on active server\n```\n\n### 🌐 **P2P Networking**\n```bash\n# P2P operations\ncherry p2p init # Initialize P2P node\ncherry p2p peers # List connected peers\ncherry p2p send peer-id \"Hello Cherry!\" # Secure messaging\ncherry p2p discover # Network topology discovery\n```\n\n---\n\n## 🏗️ Architecture Overview\n\nCherry CLI implements a **dual-architecture approach** with two complementary implementations:\n\n### 🏛️ **Foundation Implementation** *(Production Ready)*\n- **Binary**: `seedling`\n- **Status**: ✅ **Fully Functional** with 120+ commands\n- **Architecture**: Mature monolithic design with proven stability\n- **Features**: Complete Capsule System, P2P networking, security features\n- **Use Case**: Production deployments requiring immediate functionality\n\n### ⚡ **Cherry Implementation** *(Primary Development)*\n- **Binary**: `cherry`\n- **Status**: 🚧 **Active Development** (modular design complete)\n- **Architecture**: Clean modular structure with enhanced performance\n- **Features**: Memory-safe design, <30ms startup, comprehensive testing\n- **Use Case**: Primary development interface with modern C practices\n\n```\ncherry-cli/\n├── interfaces/\n│ ├── foundation/ # 🏛️ Production-ready implementation (seedling)\n│ │ ├── src/ # 36 source files, 120+ commands\n│ │ ├── include/ # Comprehensive headers\n│ │ └── platforms/ # Multi-platform support\n│ └── cherry/ # ⚡ Primary development interface (cherry)\n│ ├── src/\n│ │ ├── core/ # System initialization\n│ │ ├── commands/ # Modular command structure\n│ │ ├── lib/ # Core libraries\n│ │ └── p2p/ # P2P networking subsystem\n│ └── tests/ # Comprehensive test suite\n```\n\n---\n\n## 🔒 Security & Compliance\n\n### **Encryption Standards**\n- **AES-256-GCM**: File and capsule encryption\n- **SHA-512**: Integrity verification\n- **TLS 1.3**: API communications\n- **GPG**: Additional file encryption layer\n- **libsodium**: P2P networking security\n\n### **Security Certifications**\n- ✅ **Buffer Overflow Protection**\n- ✅ **Memory Leak Prevention**\n- ✅ **Input Validation & Sanitization**\n- ✅ **Principle of Least Privilege**\n- ✅ **Zero-Knowledge Temporary Files**\n\n### **Compliance Features**\n- **Audit Logging**: Comprehensive operation tracking\n- **Access Control**: Role-based permissions\n- **Data Residency**: Configurable storage locations\n- **Encryption at Rest**: All stored data encrypted\n\n---\n\n## 🖥️ Platform Support\n\n| Platform | Status | Installation Method | Notes |\n|----------|--------|-------------------|--------|\n| **macOS** | ✅ Full Support | Homebrew, Source | Native performance |\n| **Linux** | ✅ Full Support | Package Manager, Source | All major distributions |\n| **Windows** | ✅ WSL2 Support | WSL2 + Ubuntu | Cherry iTerm experience |\n| **ARM64** | ✅ Native Support | Source compilation | Apple Silicon, ARM servers |\n\n### **Windows Integration**\n- 🌸 **Cherry iTerm Wrapper**: Complete iTerm experience in Windows Terminal\n- 🤖 **Claude Code Integration**: AI-powered development workflows\n- ⌨️ **iTerm-Style Shortcuts**: Familiar macOS hotkeys (Ctrl+T, Ctrl+D)\n- 🎨 **Custom Themes**: Cherry-branded color schemes\n- 💾 **Session Management**: Multi-project layout persistence\n\n---\n\n## 📊 Performance Specifications\n\n### **Foundation Implementation**\n| Metric | Specification | Typical Performance |\n|--------|---------------|-------------------|\n| Startup Time | <100ms | ~50ms |\n| Memory Usage | <8MB | ~4MB |\n| Command Response | <200ms | ~100ms |\n| File Transfer | 50MB/s+ | ~80MB/s |\n\n### **Evolution Implementation**\n| Metric | Target | Achieved |\n|--------|--------|----------|\n| Startup Time | <30ms | ~15ms |\n| Memory Usage | <4MB | ~2MB |\n| Binary Size | <2MB | ~1.5MB |\n| Response Time | <50ms | ~25ms |\n\n---\n\n## 🧪 Command Reference\n\n### **Server Management**\n```bash\ncherry list # List all servers\ncherry info # Detailed server info\ncherry create --plan c1-small --image ubuntu # Create server\ncherry server activate # Set active server\ncherry ssh [user] # SSH connection\n```\n\n### **File Operations**\n```bash\ncherry send # Encrypted file transfer\ncherry retrieve : # Secure file retrieval\ncherry deploy # Project deployment\n```\n\n### **Idea Management**\n```bash\ncherry idea add \"API Rate Limiting\" # Capture new ideas\ncherry idea list --priority 4,5 # Review high-priority ideas \ncherry idea search \"authentication\" # Find related concepts\ncherry idea connect 23 31 --type implements # Link related ideas\ncherry idea analyze 42 --enhance # AI-powered idea analysis\n```\n\n### **Advanced Features**\n```bash\ncherry server produce capsule # Create server snapshot\ncherry server beam capsule # Transfer server state\ncherry blossom [user] # Enhanced SSH\ncherry p2p init # P2P networking\ncherry install # Tool installation\n```\n\n### **Configuration & Diagnostics**\n```bash\ncherry init # Initial setup\ncherry config show # View configuration\ncherry doctor # System health check\ncherry --help # Comprehensive help\n```\n\n---\n\n## 🎨 Cherry Blossom Experience\n\n### **Visual Theme**\n- 🌸 **Sakura Pink**: Primary accent for key operations\n- 🌿 **Spring Green**: Success states and positive feedback\n- 🌌 **Sky Blue**: Information and guidance\n- 🤍 **Cherry White**: Clean, readable text\n- 🌙 **Twilight Purple**: Error states and warnings\n\n### **User Interface Elements**\n- **Emoji-Rich Feedback**: Visual context for operations\n- **Progressive Loading**: Beautiful progress indicators\n- **Contextual Help**: Smart suggestions and guidance\n- **Accessibility**: WCAG-compliant color schemes\n- **Multi-Theme Support**: Dark, light, and monochrome modes\n\n---\n\n## 🤝 Contributing\n\n### **Development Setup**\n```bash\n# Clone repository\ngit clone https://github.com/cherryservers/cherry-cli.git\ncd cherry-cli\n\n# Foundation implementation\ncd implementations/foundation\nmake clean && make debug\n\n# Evolution implementation \ncd implementations/evolution\nmkdir build && cd build\ncmake -DCMAKE_BUILD_TYPE=Debug ..\nmake -j$(nproc)\n```\n\n### **Code Standards**\n- **C Standard**: C11 with GNU extensions\n- **Memory Safety**: Comprehensive bounds checking\n- **Documentation**: Doxygen-compatible comments\n- **Testing**: Unit and integration test coverage\n- **Security**: Static analysis and vulnerability scanning\n\n### **Contribution Process**\n1. Fork the repository\n2. Create feature branch following naming conventions\n3. Implement changes with comprehensive tests\n4. Ensure security and performance standards\n5. Submit pull request with detailed description\n\n---\n\n## 📚 Documentation\n\n### **User Guides**\n- [Installation Guide](docs/installation.md)\n- [Configuration Reference](docs/configuration.md)\n- [Command Reference](docs/commands.md)\n- [Security Best Practices](docs/security.md)\n\n### **Technical Documentation**\n- [Architecture Overview](docs/architecture.md)\n- [Idea Management System](docs/architecture/CHERRY_IDEA_MANAGEMENT_SPECIFICATION.md)\n- [P2P Networking Guide](docs/p2p.md)\n- [API Integration](docs/api.md)\n- [Performance Tuning](docs/performance.md)\n\n### **Platform-Specific**\n- [Windows Setup Guide](implementations/foundation/platforms/windows/README.md)\n- [macOS Optimization](docs/macos.md)\n- [Linux Distribution Notes](docs/linux.md)\n\n---\n\n## 🆘 Support & Community\n\n### **Getting Help**\n- 📖 **Documentation**: Comprehensive guides and references\n- 🐛 **GitHub Issues**: Bug reports and feature requests\n- 💬 **Discussions**: Community questions and support\n- 📧 **Security**: security@cherryservers.com for vulnerabilities\n\n### **Community Resources**\n- **Cherry Servers API**: [https://docs.cherryservers.com/](https://docs.cherryservers.com/)\n- **cherryctl CLI**: [https://github.com/cherryservers/cherryctl](https://github.com/cherryservers/cherryctl)\n- **Community Forum**: [https://community.cherryservers.com/](https://community.cherryservers.com/)\n\n---\n\n## 📄 License & Acknowledgments\n\n### **License**\nCherry CLI is released under the **MIT License**. See [LICENSE](LICENSE) for complete terms.\n\n### **Acknowledgments**\n- **Cherry Servers Team**: API and infrastructure support\n- **OpenSSL Project**: Cryptographic foundation\n- **libsodium Developers**: Modern cryptography library\n- **Security Researchers**: Vulnerability disclosure and improvements\n- **Open Source Community**: Dependencies and continuous improvement\n\n### **Security Disclosure**\nFor security vulnerabilities, please email security@cherryservers.com with details. We follow responsible disclosure practices and will acknowledge contributions appropriately.\n\n---\n\n## 🔮 Roadmap & Future Vision\n\n### **Completed Revolutionary Features** ✅\n- [x] Cherry Server Capsule System with AES-256-GCM encryption\n- [x] Complete server state snapshotting and restoration\n- [x] Blossom user management with SSH automation\n- [x] Advanced P2P networking with NAT traversal\n- [x] Secure file transfer with GPG integration\n- [x] Windows support via Cherry iTerm wrapper\n\n### **Next-Generation Enhancements** 🚀\n- [x] **Idea Management System**: Comprehensive concept capture and development workflow\n- [ ] **AI-Powered Optimization**: ML-based server configuration recommendations\n- [ ] **Distributed Capsules**: Multi-server orchestrated snapshots\n- [ ] **Cloud Storage Integration**: Direct AWS S3/GCS capsule storage\n- [ ] **Incremental Snapshots**: Delta-based updates for efficiency\n- [ ] **Performance Analytics**: Real-time optimization recommendations\n\n### **Enterprise Features** 🏢\n- [ ] **Multi-Tenant Architecture**: Organization-based access control\n- [ ] **Policy Engine**: Rule-based automation and security enforcement\n- [ ] **Disaster Recovery**: Automated failover and restoration workflows\n- [ ] **Compliance Dashboard**: Audit trail and compliance reporting\n- [ ] **API Management**: RESTful API for programmatic access\n\n---\n\n
\n\n**🌸 Made with love and cherry blossoms 🌸**\n\n*Where revolutionary technology meets beautiful design in server management.*\n\n[![Cherry Servers](https://img.shields.io/badge/Powered%20by-Cherry%20Servers-pink.svg)](https://www.cherryservers.com/)\n[![Built with C](https://img.shields.io/badge/Built%20with-C-blue.svg)](https://en.wikipedia.org/wiki/C_(programming_language))\n[![Security First](https://img.shields.io/badge/Security-First-green.svg)](docs/security.md)\n\n
", "has_readme": true, "url": "https://github.com/CherryMesh/cherry", "category": "unclassified", "value_tier": "standard", "value_rank": 2, "editorial_note": null, "similarity_group": "Developer Tools", "group_score": 13, "similar": [ { "id": "Geijutsu/cherry", "score": 1.0, "signals": [ "package", "library", "automation" ] }, { "id": "quivent/cherry-blossom", "score": 0.9789, "signals": [ "package", "library", "automation" ] }, { "id": "AmadeusInnovations/cherry-blossom", "score": 0.9789, "signals": [ "package", "library", "automation" ] }, { "id": "MorchestraWorld/sakura", "score": 0.9786, "signals": [ "package", "library", "automation" ] }, { "id": "Geijutsu/sakura", "score": 0.9786, "signals": [ "package", "library", "automation" ] } ] }, { "organization": "CherryMesh", "name": "gatherer", "source": "R2 Git bundle", "published_at": "2025-10-27T17:24:42-04:00", "readme": "# Gatherer CLI\n\n> **Intelligent IP Discovery and Aggregation Tool**\n\nGatherer is a powerful command-line interface tool designed to systematically discover, analyze, and aggregate valuable intellectual property assets from various sources including `.claude` folders, project directories, documents, and other clear value IP locations.\n\n[![Go Version](https://img.shields.io/badge/Go-1.21+-blue.svg)](https://golang.org/)\n[![License](https://img.shields.io/badge/License-MIT-green.svg)](LICENSE)\n[![Build Status](https://img.shields.io/badge/Build-Passing-brightgreen.svg)](Makefile)\n[![Tests](https://img.shields.io/badge/Tests-Passing-brightgreen.svg)](./cmd)\n\n## 🚀 Implementation Status\n\n**✅ FULLY IMPLEMENTED FEATURES:**\n- ✅ Asset discovery with pattern-based filtering\n- ✅ Quality assessment and content analysis \n- ✅ Real-time status monitoring and reporting\n- ✅ Database management and operations\n- ✅ Multi-format output (table, JSON, YAML, CSV)\n- ✅ Configuration management system\n- ✅ Comprehensive CLI interface with help system\n- ✅ Testing infrastructure with benchmarks\n- ✅ Version management and build system\n\n**🚧 PARTIALLY IMPLEMENTED / FUTURE FEATURES:**\n- 🚧 Physical asset storage (symlink, copy, reference methods)\n- 🚧 Advanced deduplication algorithms\n- 🚧 Agent-based processing architecture\n- 🚧 Backup and recovery operations\n- 🚧 Extended metadata extraction\n\n## 🎯 Current Features\n\n### 🔍 **Intelligent Discovery** ✅ IMPLEMENTED\n- **Pattern-based Asset Discovery**: Configurable file patterns with inclusion/exclusion filters\n- **Multi-source Scanning**: Scan multiple directories simultaneously with depth control\n- **Quality Assessment**: Configurable quality scoring thresholds for asset filtering\n- **Content Analysis**: Basic content analysis and metadata extraction\n- **Dry-run Capabilities**: Preview discovery results before execution\n\n### 📊 **Database Management** ✅ IMPLEMENTED\n- **SQLite Backend**: Portable, efficient database storage\n- **Query Interface**: Flexible search and filtering capabilities\n- **Statistics & Health**: Database health monitoring and comprehensive statistics\n- **Session Tracking**: Discovery session management and history\n- **Export Capabilities**: JSON, CSV, and structured data export\n\n### 🎨 **User Experience** ✅ IMPLEMENTED\n- **Beautiful CLI**: Colored output with progress indicators and tables\n- **Multi-format Output**: Table, JSON, YAML, and CSV output formats\n- **Configuration Management**: Dynamic configuration with validation\n- **Comprehensive Help**: Detailed help system with examples and usage patterns\n- **Status Monitoring**: Real-time system and operation status reporting\n\n### 🧪 **Development & Testing** ✅ IMPLEMENTED\n- **Comprehensive Test Suite**: Unit tests for all core packages\n- **Benchmarking Infrastructure**: Performance testing and measurement\n- **Version Management**: Proper version injection and build information\n- **Development Tooling**: Makefile with build, test, and development targets\n\n### 🚧 **Future Features** (Planned)\n- **Physical Asset Storage**: Symlink, copy, and reference storage methods\n- **Advanced Deduplication**: Content-based duplicate detection and resolution\n- **Agent-based Processing**: Extensible agent architecture for content analysis\n- **Advanced Security Analysis**: Detection of secrets and sensitive information\n- **Backup & Recovery**: Automated backup system with integrity verification\n\n## 🚀 Quick Start\n\n### Installation\n\n```bash\n# Clone the repository\ngit clone \ncd gatherer\n\n# Build the binary\nmake build\n\n# Install to system PATH (optional)\nmake install\n```\n\n### Basic Usage\n\n```bash\n# Discover assets with dry-run preview\ngatherer discover --dry-run --sources ~/.claude,./projects\n\n# Perform actual discovery with quality filtering\ngatherer discover --sources ~/.claude --quality-threshold 0.8\n\n# View comprehensive status\ngatherer status --format table\n\n# Search the database\ngatherer database search --query \"*.go\" --type source\n\n# Show database statistics\ngatherer database stats\n\n# Export discovered assets\ngatherer database export --format json --output assets.json\n```\n\n## 📖 Detailed Usage\n\n### Discovery Operations\n\n```bash\n# Basic discovery with pattern filtering\ngatherer discover --sources ~/Documents --pattern \"*.md,*.go,*.py\"\n\n# Discovery with content analysis\ngatherer discover --analyze-content --extract-metadata --max-depth 5\n\n# Quality-focused discovery\ngatherer discover --quality-threshold 0.7 --exclude \".git,node_modules\"\n\n# Multi-source discovery with size limits\ngatherer discover --sources ~/.claude,./projects,~/Research --max-size 10MB\n\n# Dry-run to preview discovery results\ngatherer discover --dry-run --sources . --pattern \"*.go\"\n```\n\n### Database Operations\n\n```bash\n# View database statistics\ngatherer database stats\n\n# Search for specific assets\ngatherer database search --query \"claude\" --category configuration\n\n# List discovery sessions\ngatherer database sessions --limit 10\n\n# Export data\ngatherer database export --format json --output assets.json\n\n# Create backup\ngatherer database backup --compress\n\n# Health check and maintenance\ngatherer database health\ngatherer database vacuum\n```\n\n### Configuration Management\n\n```bash\n# View current configuration\ngatherer config show\n\n# Set configuration values\ngatherer config set discovery.max_depth=10\ngatherer config set storage.method=symlink\n\n# Validate configuration\ngatherer config validate\n\n# Reset to defaults\ngatherer config reset storage\n```\n\n### Status and Monitoring\n\n```bash\n# Comprehensive status overview\ngatherer status\n\n# JSON format for scripting\ngatherer status --format json\n\n# Specific component status\ngatherer status --components database,storage\n\n# Historical data with trends\ngatherer status --historical --limit 30\n```\n\n## ⚙️ Configuration\n\nGatherer uses a YAML configuration file located at `./configs/gatherer.yaml`. The configuration supports:\n\n### Discovery Settings\n```yaml\ndiscovery:\n source_paths:\n - \"~/.claude\"\n - \"./projects\"\n - \"~/Documents\"\n file_patterns:\n - \"*.md\"\n - \"*.go\"\n - \"*.py\"\n - \"*.js\"\n exclude_paths:\n - \".git/\"\n - \"node_modules/\"\n - \"vendor/\"\n max_file_size: 10485760 # 10MB\n content_analysis:\n enabled: true\n detect_secrets: true\n extract_metadata: true\n```\n\n### Storage Configuration\n```yaml\nstorage:\n database_path: \"./gatherer.db\"\n backup_enabled: true\n asset_storage:\n method: \"symlink\" # symlink, copy, reference\n base_path: \"./gathered_assets\"\n preserve_structure: true\n deduplicate: true\n```\n\n### Agent Configuration\n```yaml\nagents:\n content_analyzer:\n enabled: true\n timeout: \"300s\"\n concurrent_limit: 5\n quality_assessor:\n enabled: true\n threshold: 0.7\n```\n\n## 🏗️ Architecture\n\n### Component Overview\n\n```\ngatherer/\n├── cmd/ # CLI command implementations\n│ ├── root.go # Root command and global configuration\n│ ├── discover.go # Discovery operations\n│ ├── analyze.go # Content analysis\n│ ├── store.go # Storage and aggregation\n│ ├── status.go # Status reporting\n│ ├── database.go # Database operations\n│ ├── config.go # Configuration management\n│ └── version.go # Version information\n├── internal/ # Internal packages\n│ ├── discovery/ # Asset discovery engine\n│ ├── storage/ # Storage and database systems\n│ └── config/ # Configuration management\n├── configs/ # Configuration files\n└── docs/ # Documentation\n```\n\n### Key Components\n\n- **Discovery Engine**: Intelligent file system traversal with pattern matching\n- **Analysis System**: Content analysis and quality assessment\n- **Storage System**: Flexible aggregation with multiple storage methods\n- **Database Layer**: SQLite-based persistence with rich querying\n- **Configuration System**: Hot-reloadable YAML configuration with validation\n\n## 🛠️ Development\n\n### Build System\n\n```bash\n# Development build\nmake dev\n\n# Run all checks\nmake check\n\n# Create release build\nmake release\n\n# Run demo\nmake demo\n\n# View all available targets\nmake help\n```\n\n### Testing and Benchmarking\n\n```bash\n# Run all tests\nmake test\n\n# Run benchmarks\nmake bench\n\n# Generate benchmark report\nmake bench-report\n\n# Run CPU profiling benchmarks\nmake bench-cpu\n\n# Run memory profiling benchmarks\nmake bench-mem\n\n# Run linting\nmake lint\n\n# Format code\nmake fmt\n\n# Run all checks (format, vet, test)\nmake check\n```\n\n### Database Management\n\n```bash\n# Reset database\nmake db-reset\n\n# Create backup\nmake db-backup\n\n# Show configuration\nmake config-show\n```\n\n## 📊 Examples\n\n### Example 1: Claude Configuration Discovery\n\n```bash\n# Discover all Claude configurations\ngatherer discover --sources ~/.claude --pattern \"*.md,*.yaml,*.json\"\ngatherer database search --query \"claude\" --type configuration\ngatherer database stats\n```\n\n### Example 2: Project Documentation Analysis\n\n```bash\n# Find and analyze project documentation\ngatherer discover --pattern \"README*,CHANGELOG*,*.md\" --quality-threshold 0.6\ngatherer status --format json > project_status.json\ngatherer database export --format json --output documentation.json\n```\n\n### Example 3: Source Code Quality Assessment\n\n```bash\n# Analyze source code quality across projects\ngatherer discover --sources ./projects --pattern \"*.go,*.py,*.js\" --analyze-content\ngatherer database search --query \"*.go\" --type source\ngatherer database export --format csv --output quality_report.csv\n```\n\n### Example 4: Comprehensive System Analysis\n\n```bash\n# Perform comprehensive discovery and analysis\ngatherer discover --sources ~/.claude,./projects --analyze-content --extract-metadata\ngatherer status\ngatherer database stats\ngatherer database sessions\n```\n\n## 📊 Performance\n\nGatherer includes comprehensive performance benchmarking. See [PERFORMANCE.md](./PERFORMANCE.md) for detailed metrics and baseline measurements.\n\nKey performance characteristics:\n- **Sub-second discovery**: Typical operations complete in <2 seconds\n- **Memory efficient**: ~40KB allocation per discovery operation\n- **Scalable concurrency**: Linear performance scaling up to 8 workers\n- **Database optimization**: Zero-allocation configuration objects\n\n## 🤝 Contributing\n\n1. Fork the repository\n2. Create a feature branch\n3. Implement your changes\n4. Add tests and documentation\n5. Run `make check` to verify all tests pass\n6. Run `make bench` to ensure performance is maintained\n7. Submit a pull request\n\n## 📄 License\n\nThis project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.\n\n## 🙏 Acknowledgments\n\n- Built with [Cobra](https://github.com/spf13/cobra) for CLI framework\n- Database powered by [SQLite](https://sqlite.org/)\n- Configuration management via [Viper](https://github.com/spf13/viper)\n- Inspired by the morchestrator project patterns\n\n---\n\n**Gatherer CLI** - *Intelligent IP Discovery and Aggregation*", "has_readme": true, "url": "https://github.com/CherryMesh/gatherer", "category": "unclassified", "value_tier": "standard", "value_rank": 2, "editorial_note": null, "similarity_group": "Data & Storage", "group_score": 10, "similar": [ { "id": "AmadeusInnovations/gatherer", "score": 1.0, "signals": [ "backup", "search", "database" ] }, { "id": "TSMCP/Orchestra", "score": 0.2384, "signals": [ "backup", "search", "thresholds" ] }, { "id": "MorchestraWorld/Orchestra", "score": 0.2384, "signals": [ "backup", "search", "thresholds" ] }, { "id": "Geijutsu/orchestra", "score": 0.2384, "signals": [ "backup", "search", "thresholds" ] }, { "id": "AmadeusInnovations/MultiLinguist", "score": 0.236, "signals": [ "backup", "data", "csv" ] } ] }, { "organization": "CherryMesh", "name": "librarian", "source": "R2 Git bundle", "published_at": "2025-09-19T12:21:11+02:00", "readme": "# Librarian CLI\n\nA classical library-themed command-line interface for knowledge management and research synthesis.\n\n## Directory Structure\n\n```\n├── cmd/ # Active CLI commands\n├── internal/ # Go packages and libraries\n├── docs/ # Documentation\n│ ├── generated/ # Generated HTML documentation\n│ └── specs/ # Project specifications and analysis\n├── research/ # Research projects and findings\n│ ├── autonomous-education/\n│ └── evolving-education/\n├── learning/ # Educational materials and learning logs\n├── archive/ # Legacy implementations and scattered files\n│ └── cli-v1/ # Previous CLI implementation\n├── scripts/ # Utility and maintenance scripts\n├── main.go # Application entry point\n├── go.mod # Go module definition\n└── .gitignore # Git ignore patterns\n```\n\n## Usage\n\n```bash\n# Build the CLI\ngo build -o librarian .\n\n# Run the CLI\n./librarian --help\n\n# Available commands:\n# catalog - Collection management\n# index - Documentation generation\n# locate - Resource location\n# synthesis - Research synthesis\n# view - Knowledge visualization\n```\n\n## Maintenance\n\nUse the cleanup script to maintain repository organization:\n\n```bash\n./scripts/cleanup.sh\n```\n\nThis script:\n- Removes binary executables\n- Organizes timestamped documentation\n- Moves loose files to appropriate locations\n- Cleans up empty directories\n\n## Development\n\n- Active development occurs in `cmd/` and `internal/`\n- Legacy code is preserved in `archive/`\n- All documentation is organized under `docs/`\n- Research materials are categorized under `research/`\n- Learning materials are collected under `learning/`", "has_readme": true, "url": "https://github.com/CherryMesh/librarian", "category": "unclassified", "value_tier": "standard", "value_rank": 2, "editorial_note": null, "similarity_group": "Research & Knowledge", "group_score": 9, "similar": [ { "id": "TSMCP/librarian", "score": 1.0, "signals": [ "knowledge", "learning", "research" ] }, { "id": "quivent/librarian", "score": 1.0, "signals": [ "knowledge", "learning", "research" ] }, { "id": "Moestradamus-Productions/self-education-explorer", "score": 0.1776, "signals": [ "knowledge", "learning", "research" ] }, { "id": "AmadeusInnovations/self-education-exploration", "score": 0.1776, "signals": [ "knowledge", "learning", "research" ] }, { "id": "TransformerOS/Kamaji", "score": 0.1562, "signals": [ "documentation", "archive", "legacy" ] } ] }, { "organization": "CherryMesh", "name": "sakura", "source": "R2 Git bundle", "published_at": "2025-12-02T09:53:52-05:00", "readme": "# 🌸 Cherry CLI - Revolutionary Server Management Platform\n\n[![Version](https://img.shields.io/badge/version-1.0.0-pink.svg)](https://github.com/cherryservers/cherry-cli)\n[![License](https://img.shields.io/badge/license-MIT-green.svg)](LICENSE)\n[![Security](https://img.shields.io/badge/encryption-AES--256--GCM-blue.svg)](docs/security.md)\n[![Platform](https://img.shields.io/badge/platform-macOS%20%7C%20Linux%20%7C%20Windows-lightgrey.svg)](docs/installation.md)\n\n> **The world's first CLI with complete server state snapshotting and cherry blossom-themed aesthetics.**\n\nA revolutionary, security-first command-line interface for Cherry Servers infrastructure management featuring military-grade encryption, complete server state capture via the Capsule System, and beautiful cherry blossom-themed user experience.\n\n---\n\n## ✨ Revolutionary Features\n\n### 💊 **Cherry Server Capsule System** - *World's First Complete Server State Capture*\n- 🔬 **Complete Server Snapshot**: Users, packages, services, configs, data, SSH keys, crontabs, network settings\n- 🔐 **Military-Grade Security**: AES-256-GCM encryption with SHA-512 integrity verification\n- 📦 **Intelligent Optimization**: 90%+ size reduction through rebuild artifact detection\n- 🚀 **Secure Transfer**: Encrypted transmission with remote confirmation\n- ⚡ **Automated Restoration**: One-command server recreation from capsules\n\n### 🔐 **Enterprise-Grade Security**\n- **AES-256-GCM Encryption** for all data transfers\n- **TLS 1.3** for secure API communications\n- **GPG Integration** for file encryption\n- **SSH Key Management** with automated deployment\n- **Zero-Knowledge Operation** with automatic cleanup\n\n### 🌸 **Blossom System** - *Enhanced User Experience*\n- **Smart SSH Management** with automatic user switching\n- **Cherry Blossom Aesthetics** with sakura-themed interface\n- **Emoji-Rich Feedback** for immediate visual context\n- **Progressive Help System** with contextual guidance\n\n### 🌐 **Advanced P2P Networking**\n- **Peer Discovery** with automatic topology mapping\n- **NAT Traversal** using sophisticated hole-punching\n- **End-to-End Encryption** for secure peer communication\n- **Load Balancing** with intelligent peer selection\n- **Fault Tolerance** with automatic failover\n\n---\n\n## 🚀 Quick Start\n\n### Installation\n\n**macOS/Linux:**\n```bash\n# One-line installation\ncurl -sSL https://raw.githubusercontent.com/cherryservers/cherry-cli/main/install.sh | bash\n\n# Manual installation\ngit clone https://github.com/cherryservers/cherry-cli.git\ncd cherry-cli\nmake && make install\n```\n\n**Windows (WSL2):**\n```powershell\n# Install WSL2 + Ubuntu (Run as Administrator)\nwsl --install\n\n# In Ubuntu terminal\nsudo apt update && apt install -y build-essential libssl-dev git\ngit clone https://github.com/cherryservers/cherry-cli.git\ncd cherry-cli && make && make install\n```\n\n### First Time Setup\n\n```bash\n# Initialize configuration\ncherry init\n\n# Set your Cherry Servers API token\nexport CHERRY_AUTH_TOKEN=\"your-token-here\"\n\n# Verify installation\ncherry --version\ncherry list\n```\n\n---\n\n## 🎯 Core Usage Examples\n\n### 💊 **Capsule System** - Complete Server Management\n```bash\n# Create complete server snapshot\ncherry server produce capsule\n# → Creates encrypted .capsule file with full server state\n\n# Transfer server state to new environment\ncherry server beam capsule production-server\n# → Secure transmission with integrity verification\n\n# Restore complete server from capsule\ncherry server receive capsule\n# → Automated server recreation with all configurations\n```\n\n### 🔐 **Secure File Transfer**\n```bash\n# Transfer encrypted files\ncherry send document.pdf my-server # Single file with AES-256\ncherry send ./project/ production-server # Entire directory compressed\ncherry send backup.tar.gz staging-server # Large files optimized\n```\n\n### 🌸 **Enhanced SSH Management**\n```bash\n# Smart SSH with user switching\ncherry blossom # SSH to active server\ncherry blossom deploy # SSH and switch to 'deploy' user\ncherry blossom www-data # SSH and switch to 'www-data'\ncherry blossom pair # Set up SSH key authentication\n```\n\n### 🖥️ **Server Operations**\n```bash\n# Server management\ncherry server activate my-server # Set active server\ncherry server info # Detailed server information\ncherry server create --plan c1-small --image ubuntu_22_04\ncherry install docker # Install tools on active server\n```\n\n### 🌐 **P2P Networking**\n```bash\n# P2P operations\ncherry p2p init # Initialize P2P node\ncherry p2p peers # List connected peers\ncherry p2p send peer-id \"Hello Cherry!\" # Secure messaging\ncherry p2p discover # Network topology discovery\n```\n\n---\n\n## 🏗️ Architecture Overview\n\nCherry CLI implements a **dual-architecture approach** with two complementary implementations:\n\n### 🏛️ **Foundation Implementation** *(Production Ready)*\n- **Status**: ✅ **Fully Functional** with 120+ commands\n- **Architecture**: Mature monolithic design with proven stability\n- **Features**: Complete Capsule System, P2P networking, security features\n- **Use Case**: Production deployments requiring immediate functionality\n\n### ⚡ **Evolution Implementation** *(Next Generation)*\n- **Status**: 🚧 **Modernized Architecture** (modular design complete)\n- **Architecture**: Clean modular structure with enhanced performance\n- **Features**: Memory-safe design, <30ms startup, comprehensive testing\n- **Use Case**: Future development with modern C practices\n\n```\ncherry-cli/\n├── implementations/\n│ ├── foundation/ # 🏛️ Production-ready implementation\n│ │ ├── src/ # 36 source files, 120+ commands\n│ │ ├── include/ # Comprehensive headers\n│ │ └── platforms/ # Multi-platform support\n│ └── evolution/ # ⚡ Modernized architecture\n│ ├── src/\n│ │ ├── core/ # System initialization\n│ │ ├── commands/ # Modular command structure\n│ │ ├── lib/ # Core libraries\n│ │ └── p2p/ # P2P networking subsystem\n│ └── tests/ # Comprehensive test suite\n```\n\n---\n\n## 🔒 Security & Compliance\n\n### **Encryption Standards**\n- **AES-256-GCM**: File and capsule encryption\n- **SHA-512**: Integrity verification\n- **TLS 1.3**: API communications\n- **GPG**: Additional file encryption layer\n- **libsodium**: P2P networking security\n\n### **Security Certifications**\n- ✅ **Buffer Overflow Protection**\n- ✅ **Memory Leak Prevention**\n- ✅ **Input Validation & Sanitization**\n- ✅ **Principle of Least Privilege**\n- ✅ **Zero-Knowledge Temporary Files**\n\n### **Compliance Features**\n- **Audit Logging**: Comprehensive operation tracking\n- **Access Control**: Role-based permissions\n- **Data Residency**: Configurable storage locations\n- **Encryption at Rest**: All stored data encrypted\n\n---\n\n## 🖥️ Platform Support\n\n| Platform | Status | Installation Method | Notes |\n|----------|--------|-------------------|--------|\n| **macOS** | ✅ Full Support | Homebrew, Source | Native performance |\n| **Linux** | ✅ Full Support | Package Manager, Source | All major distributions |\n| **Windows** | ✅ WSL2 Support | WSL2 + Ubuntu | Cherry iTerm experience |\n| **ARM64** | ✅ Native Support | Source compilation | Apple Silicon, ARM servers |\n\n### **Windows Integration**\n- 🌸 **Cherry iTerm Wrapper**: Complete iTerm experience in Windows Terminal\n- 🤖 **Claude Code Integration**: AI-powered development workflows\n- ⌨️ **iTerm-Style Shortcuts**: Familiar macOS hotkeys (Ctrl+T, Ctrl+D)\n- 🎨 **Custom Themes**: Cherry-branded color schemes\n- 💾 **Session Management**: Multi-project layout persistence\n\n---\n\n## 📊 Performance Specifications\n\n### **Foundation Implementation**\n| Metric | Specification | Typical Performance |\n|--------|---------------|-------------------|\n| Startup Time | <100ms | ~50ms |\n| Memory Usage | <8MB | ~4MB |\n| Command Response | <200ms | ~100ms |\n| File Transfer | 50MB/s+ | ~80MB/s |\n\n### **Evolution Implementation**\n| Metric | Target | Achieved |\n|--------|--------|----------|\n| Startup Time | <30ms | ~15ms |\n| Memory Usage | <4MB | ~2MB |\n| Binary Size | <2MB | ~1.5MB |\n| Response Time | <50ms | ~25ms |\n\n---\n\n## 🧪 Command Reference\n\n### **Server Management**\n```bash\ncherry list # List all servers\ncherry info # Detailed server info\ncherry create --plan c1-small --image ubuntu # Create server\ncherry server activate # Set active server\ncherry ssh [user] # SSH connection\n```\n\n### **File Operations**\n```bash\ncherry send # Encrypted file transfer\ncherry retrieve : # Secure file retrieval\ncherry deploy # Project deployment\n```\n\n### **Idea Management**\n```bash\ncherry idea add \"API Rate Limiting\" # Capture new ideas\ncherry idea list --priority 4,5 # Review high-priority ideas \ncherry idea search \"authentication\" # Find related concepts\ncherry idea connect 23 31 --type implements # Link related ideas\ncherry idea analyze 42 --enhance # AI-powered idea analysis\n```\n\n### **Advanced Features**\n```bash\ncherry server produce capsule # Create server snapshot\ncherry server beam capsule # Transfer server state\ncherry blossom [user] # Enhanced SSH\ncherry p2p init # P2P networking\ncherry install # Tool installation\n```\n\n### **Configuration & Diagnostics**\n```bash\ncherry init # Initial setup\ncherry config show # View configuration\ncherry doctor # System health check\ncherry --help # Comprehensive help\n```\n\n---\n\n## 🎨 Cherry Blossom Experience\n\n### **Visual Theme**\n- 🌸 **Sakura Pink**: Primary accent for key operations\n- 🌿 **Spring Green**: Success states and positive feedback\n- 🌌 **Sky Blue**: Information and guidance\n- 🤍 **Cherry White**: Clean, readable text\n- 🌙 **Twilight Purple**: Error states and warnings\n\n### **User Interface Elements**\n- **Emoji-Rich Feedback**: Visual context for operations\n- **Progressive Loading**: Beautiful progress indicators\n- **Contextual Help**: Smart suggestions and guidance\n- **Accessibility**: WCAG-compliant color schemes\n- **Multi-Theme Support**: Dark, light, and monochrome modes\n\n---\n\n## 🤝 Contributing\n\n### **Development Setup**\n```bash\n# Clone repository\ngit clone https://github.com/cherryservers/cherry-cli.git\ncd cherry-cli\n\n# Foundation implementation\ncd implementations/foundation\nmake clean && make debug\n\n# Evolution implementation \ncd implementations/evolution\nmkdir build && cd build\ncmake -DCMAKE_BUILD_TYPE=Debug ..\nmake -j$(nproc)\n```\n\n### **Code Standards**\n- **C Standard**: C11 with GNU extensions\n- **Memory Safety**: Comprehensive bounds checking\n- **Documentation**: Doxygen-compatible comments\n- **Testing**: Unit and integration test coverage\n- **Security**: Static analysis and vulnerability scanning\n\n### **Contribution Process**\n1. Fork the repository\n2. Create feature branch following naming conventions\n3. Implement changes with comprehensive tests\n4. Ensure security and performance standards\n5. Submit pull request with detailed description\n\n---\n\n## 📚 Documentation\n\n### **User Guides**\n- [Installation Guide](docs/installation.md)\n- [Configuration Reference](docs/configuration.md)\n- [Command Reference](docs/commands.md)\n- [Security Best Practices](docs/security.md)\n\n### **Technical Documentation**\n- [Architecture Overview](docs/architecture.md)\n- [Idea Management System](docs/architecture/CHERRY_IDEA_MANAGEMENT_SPECIFICATION.md)\n- [P2P Networking Guide](docs/p2p.md)\n- [API Integration](docs/api.md)\n- [Performance Tuning](docs/performance.md)\n\n### **Platform-Specific**\n- [Windows Setup Guide](implementations/foundation/platforms/windows/README.md)\n- [macOS Optimization](docs/macos.md)\n- [Linux Distribution Notes](docs/linux.md)\n\n---\n\n## 🆘 Support & Community\n\n### **Getting Help**\n- 📖 **Documentation**: Comprehensive guides and references\n- 🐛 **GitHub Issues**: Bug reports and feature requests\n- 💬 **Discussions**: Community questions and support\n- 📧 **Security**: security@cherryservers.com for vulnerabilities\n\n### **Community Resources**\n- **Cherry Servers API**: [https://docs.cherryservers.com/](https://docs.cherryservers.com/)\n- **cherryctl CLI**: [https://github.com/cherryservers/cherryctl](https://github.com/cherryservers/cherryctl)\n- **Community Forum**: [https://community.cherryservers.com/](https://community.cherryservers.com/)\n\n---\n\n## 📄 License & Acknowledgments\n\n### **License**\nCherry CLI is released under the **MIT License**. See [LICENSE](LICENSE) for complete terms.\n\n### **Acknowledgments**\n- **Cherry Servers Team**: API and infrastructure support\n- **OpenSSL Project**: Cryptographic foundation\n- **libsodium Developers**: Modern cryptography library\n- **Security Researchers**: Vulnerability disclosure and improvements\n- **Open Source Community**: Dependencies and continuous improvement\n\n### **Security Disclosure**\nFor security vulnerabilities, please email security@cherryservers.com with details. We follow responsible disclosure practices and will acknowledge contributions appropriately.\n\n---\n\n## 🔮 Roadmap & Future Vision\n\n### **Completed Revolutionary Features** ✅\n- [x] Cherry Server Capsule System with AES-256-GCM encryption\n- [x] Complete server state snapshotting and restoration\n- [x] Blossom user management with SSH automation\n- [x] Advanced P2P networking with NAT traversal\n- [x] Secure file transfer with GPG integration\n- [x] Windows support via Cherry iTerm wrapper\n\n### **Next-Generation Enhancements** 🚀\n- [x] **Idea Management System**: Comprehensive concept capture and development workflow\n- [ ] **AI-Powered Optimization**: ML-based server configuration recommendations\n- [ ] **Distributed Capsules**: Multi-server orchestrated snapshots\n- [ ] **Cloud Storage Integration**: Direct AWS S3/GCS capsule storage\n- [ ] **Incremental Snapshots**: Delta-based updates for efficiency\n- [ ] **Performance Analytics**: Real-time optimization recommendations\n\n### **Enterprise Features** 🏢\n- [ ] **Multi-Tenant Architecture**: Organization-based access control\n- [ ] **Policy Engine**: Rule-based automation and security enforcement\n- [ ] **Disaster Recovery**: Automated failover and restoration workflows\n- [ ] **Compliance Dashboard**: Audit trail and compliance reporting\n- [ ] **API Management**: RESTful API for programmatic access\n\n---\n\n
\n\n**🌸 Made with love and cherry blossoms 🌸**\n\n*Where revolutionary technology meets beautiful design in server management.*\n\n[![Cherry Servers](https://img.shields.io/badge/Powered%20by-Cherry%20Servers-pink.svg)](https://www.cherryservers.com/)\n[![Built with C](https://img.shields.io/badge/Built%20with-C-blue.svg)](https://en.wikipedia.org/wiki/C_(programming_language))\n[![Security First](https://img.shields.io/badge/Security-First-green.svg)](docs/security.md)\n\n
", "has_readme": true, "url": "https://github.com/CherryMesh/sakura", "category": "unclassified", "value_tier": "standard", "value_rank": 2, "editorial_note": null, "similarity_group": "Developer Tools", "group_score": 13, "similar": [ { "id": "MorchestraWorld/sakura", "score": 1.0, "signals": [ "package", "library", "automation" ] }, { "id": "Geijutsu/sakura", "score": 1.0, "signals": [ "package", "library", "automation" ] }, { "id": "quivent/cherry-blossom", "score": 0.9996, "signals": [ "package", "library", "automation" ] }, { "id": "AmadeusInnovations/cherry-blossom", "score": 0.9996, "signals": [ "package", "library", "automation" ] }, { "id": "Geijutsu/cherry", "score": 0.9786, "signals": [ "package", "library", "automation" ] } ] }, { "organization": "CherryMesh", "name": "Secular", "source": "R2 Git bundle", "published_at": "2025-12-02T09:51:49-05:00", "readme": "# ❤️🪵\n\n*Radicle Heartwood Protocol & Stack*\n\nHeartwood is the third iteration of the Radicle Protocol, a powerful\npeer-to-peer code collaboration and publishing stack. The repository contains a\nfull implementation of Heartwood, complete with a user-friendly command-line\ninterface (`rad`) and network daemon (`radicle-node`).\n\nRadicle was designed to be a secure, decentralized and powerful alternative to\ncode forges such as GitHub and GitLab that preserves user sovereignty\nand freedom.\n\nSee the [Radicle home page](https://radicle.xyz/) for general\ninformation, and the [Zulip chat](https://radicle.zulipchat.com/) to\ntalk to the project.\n\nSee the [Protocol Guide](https://radicle.xyz/guides/protocol) for an\nin-depth description of how Radicle works.\n\n## Installation\n\n**Requirements**\n\n* *Linux* or *Unix* based operating system.\n* Git 2.34 or later\n* OpenSSH 9.1 or later with `ssh-agent`\n\n### 📀 From binaries\n\n> Requires `curl` and `tar`.\n\nRun the following command to install the latest binary release:\n\n curl -sSf https://radicle.xyz/install | sh\n\nOr visit our [download](https://radicle.xyz/download) page.\n\n### 📦 From source\n\n> Requires the Rust toolchain.\n\nYou can install the Radicle stack from source, by running the following\ncommands from inside this repository:\n\n cargo install --path crates/radicle-cli --force --locked --root ~/.radicle\n cargo install --path crates/radicle-node --force --locked --root ~/.radicle\n cargo install --path crates/radicle-remote-helper --force --locked --root ~/.radicle\n\nOr directly from our seed node:\n\n cargo install --force --locked --root ~/.radicle \\\n --git https://seed.radicle.xyz/z3gqcJUoA1n9HaHKufZs5FCSGazv5.git \\\n crates/radicle-cli crates/radicle-node crates/radicle-remote-helper\n\n## Running\n\n*Systemd* unit files are provided for the node under the `/systemd` folder.\nThey can be used as a starting point for further customization.\n\nFor running in debug mode, see [HACKING.md](HACKING.md).\n\n## Feedback\n\nIf you have feedback, feel free to create issues using `rad issue`, join\n[our Zulip][zulip], or email [feedback@radicle.xyz][mail-feedback].\nEmails sent to this address are [automatically posted][zulip-help-email] to\n[our **public** #feedback channel on Zulip][zulip-feedback], revealing the\n[`From` header][rfc2822s3.6.2] (which usually contains your name and email\naddress). This allows us to discuss your feedback on Zulip, and, if necessary,\nrespond to you via email.\n\n## Contributing\n\nSee [CONTRIBUTING.md](CONTRIBUTING.md) and [HACKING.md](HACKING.md) for an\nintroduction to contributing to Radicle.\n\n## License\n\nRadicle is distributed under the terms of both the MIT license and the Apache License (Version 2.0).\n\nSee [LICENSE-APACHE](LICENSE-APACHE) and [LICENSE-MIT](LICENSE-MIT) for details.\n\n[zulip]: https://radicle.zulipchat.com/\n[zulip-feedback]: https://radicle.zulipchat.com/#narrow/channel/392584-feedback\n[zulip-help-email]: https://talently.zulip.com/help/message-a-channel-by-email\n[mail-feedback]: mailto:feedback@radicle.xyz\n[rfc2822s3.6.2]: https://datatracker.ietf.org/doc/html/rfc2822#section-3.6.2", "has_readme": true, "url": "https://github.com/CherryMesh/Secular", "category": "unclassified", "value_tier": "standard", "value_rank": 2, "editorial_note": null, "similarity_group": "Developer Tools", "group_score": 2, "similar": [ { "id": "quivent/Secular", "score": 0.9034, "signals": [ "code", "rad", "gitlab" ] }, { "id": "quivent/DocumentationRenderer", "score": 0.0972, "signals": [ "code", "tar", "mailto" ] }, { "id": "quivent/CollaborativeIntelligenceCLI", "score": 0.0897, "signals": [ "project", "provided", "powerful" ] }, { "id": "Geijutsu/tao", "score": 0.0887, "signals": [ "code", "cargo", "secure" ] }, { "id": "quivent/cpm", "score": 0.0848, "signals": [ "code", "join", "provided" ] } ] }, { "organization": "CherryMesh", "name": "seed", "source": "R2 Git bundle", "published_at": "2025-12-02T09:57:56-05:00", "readme": "# 🌱 Seed - Beautiful Server Configuration\n\nDead-simple Ubuntu server setup with **named profiles**, modular technology stacks, gorgeous ASCII UI, and Ansible automation.\n\n## Installation\n\n### Quick Install (Recommended)\n```bash\n# Clone and run the tiny installer binary\ngit clone \ncd seed\n./install\n```\n\nThe `install` binary is a tiny (1.7MB) Go program that:\n- ✓ Checks for Python 3 and pip3\n- ✓ Installs Seed using pip3\n- ✓ Provides next steps with colored output\n\n### Alternative: Direct Install\n```bash\npip3 install --user --break-system-packages -e .\n\n# Bootstrap dependencies (optional - installs Ansible, etc.)\nseed bootstrap\n```\n\n**Note:** The `--break-system-packages` flag is required for Python 3.11+ (PEP 668)\n\n## Quick Start\n\n```bash\n# See available technology stacks\nseed config stacks\n\n# Add technology stacks you want\nseed config add docker\nseed config add python\n\n# Add custom packages\nseed config pkg add jq tmux htop\n\n# Review your configuration\nseed config show\n\n# Preview what will be installed\nseed preview\n\n# Apply configuration (will prompt for sudo password)\nseed setup\n\n# View interactive documentation\nseed docs\n```\n\n## Commands\n\n### Profile Management\n\n```bash\nseed config profiles # List all profiles (built-in and user)\nseed config profile use # Switch to a profile\nseed config profile import # Import built-in profile to customize\nseed config profile new # Create new profile\nseed config profile new --copy-from= # Create from existing\nseed config profile copy # Copy a profile\nseed config profile delete # Delete a profile\n```\n\n### Built-in Profiles\n\nSeed ships with pre-configured profiles embedded in the binary:\n\n| Profile | Description | Stacks Included |\n|---------|-------------|-----------------|\n| `dev` | Full-stack development environment | python, nodejs, docker, github, cli-tools |\n| `prod` | Production web server with security | webserver, security, monitoring |\n| `ml` | Machine learning workstation | python, machine-learning, ollama |\n| `ml-gpu` | ML workstation with GPU support | python, machine-learning, ollama, cuda |\n| `web` | Web development environment | nodejs, docker, github |\n| `minimal` | Minimal setup with essential tools | base packages only |\n\n**Using built-in profiles:**\n```bash\n# Use directly (read-only)\nseed config profile use ml-gpu\nseed setup\n\n# Or import to customize\nseed config profile import ml-gpu my-custom-ml\nseed config profile use my-custom-ml\nseed config add golang\nseed config pkg add nvim\nseed setup\n```\n\n### Technology Stack Management\n\n```bash\nseed config stacks # List available technology stacks\nseed config show # Show current configuration\nseed config add # Add a technology stack to your config\nseed config remove # Remove a technology stack\nseed config edit # Edit config file directly\nseed config reset # Reset to defaults\n```\n\n### Package Management\n\n```bash\nseed config pkg add # Add custom packages\nseed config pkg remove # Remove custom packages\n```\n\n### Installation\n\n```bash\nseed setup # Install configured packages (prompts for password)\nseed check # Dry run (show what would change)\nseed preview # Preview generated Ansible playbook\n```\n\n### Dependency Management\n\n```bash\nseed bootstrap # Install dependencies locally\nseed bootstrap --remote user@host # Install dependencies on remote server\n```\n\nThe `bootstrap` command checks for and installs all required dependencies:\n- **Python 3.7+** - Checks version, offers to install if missing\n- **pip3** - Checks availability, offers to install if missing\n- **Ansible** - Installs via pip3 if not present (no sudo required)\n- **PATH configuration** - Ensures ~/.local/bin is in PATH\n\n**Interactive Installation:**\nIf Python or pip3 are missing, bootstrap will:\n1. ✅ Detect what's missing\n2. ❓ Ask: \"Would you like to install missing dependencies now? [y/N]\"\n3. ✅ If yes: Runs `sudo apt install` (prompts for your password)\n4. ⚠️ If no: Shows manual installation instructions and exits\n\n**Example:**\n```bash\nseed bootstrap --remote user@hostname\n\n# Output:\n🔧 Checking dependencies...\n ✓ Python 3 found: Python 3.12.3\n ✗ pip3 not found\n ⚠ Ansible not installed (will install via pip3)\n\nMissing dependencies: pip3\n\nTo install these, you'll need sudo access on the remote server.\nWould you like to install missing dependencies now? [y/N]: y\n\n# (You enter your password, pip3 gets installed, then Ansible installs)\n✓ Bootstrap complete!\n```\n\n### Remote Deployment\n\n```bash\nseed plant user@hostname # Deploy Seed to remote (auto-bootstrap)\nseed plant user@host --no-bootstrap # Skip bootstrap step\nseed plant user@host -p 2222 # Use custom SSH port\nseed plant user@host -i ~/.ssh/key # Use specific SSH key\n```\n\nThe `plant` command automates full deployment to remote servers:\n1. **Bootstraps** the remote server (installs Python/pip3/Ansible - interactive)\n2. **Packages** the current Seed installation\n3. **Transfers** via SCP to the remote server\n4. **Extracts and installs** via pip3\n5. **Provides** next steps for configuration\n\n**Requirements on remote server:**\n- Ubuntu/Debian system\n- SSH access with sudo privileges\n- If Python/pip3 are missing, bootstrap will prompt you to install them\n\n### Backup/Restore\n\n```bash\nseed backup # Save current package list\nseed restore # Restore from backup\n```\n\n### Documentation\n\n```bash\nseed docs # Generate and open interactive HTML documentation\n```\n\nThe `docs` command generates a beautiful, styled HTML documentation page showcasing:\n- All available technology stacks with dependencies\n- Built-in profiles with descriptions\n- Complete command reference with examples\n- Quick start guides and workflows\n\nThe page opens automatically in your default browser and features:\n- 🎨 Modern, responsive design with Seed branding (cyan/green theme)\n- 📖 Interactive navigation\n- 💻 Syntax-highlighted code examples\n- 📦 Live view of currently available stacks and profiles\n\n## Available Technology Stacks\n\n| Stack | Description | Dependencies |\n|--------|-------------|--------------|\n| `github` | GitHub CLI (gh) for repo management | - |\n| `nodejs` | Node.js 20.x LTS and npm | - |\n| `devtools` | Essential dev tools (make, cmake, gdb, strace, valgrind) | - |\n| `cli-tools` | Modern CLI utilities (jq, ripgrep, fzf, bat, httpie) | - |\n| `docker` | Docker Engine and Docker Compose | - |\n| `python` | Python 3, pip, venv, and dev tools | - |\n| `golang` | Go language and tools | - |\n| `rust` | Rust toolchain via rustup | - |\n| `database` | PostgreSQL and Redis | - |\n| `monitoring` | System monitoring tools (htop, iotop, nethogs) | - |\n| `security` | Security and firewall tools (ufw, fail2ban, aide) | - |\n| `webserver` | Nginx web server and SSL tools (certbot) | - |\n| `machine-learning` | ML tools and libraries (scikit-learn, numpy, pandas, jupyter) | Requires: `python` |\n| `ollama` | Local LLM runtime for running language models | Optional: `cuda` |\n| `cuda` | NVIDIA CUDA drivers and toolkit for GPU acceleration | - |\n\n### Dependency Management\n\nSeed automatically handles stack dependencies:\n\n- **Required dependencies** are automatically installed when you add a stack\n- **Optional dependencies** are suggested but not required (you can add them manually if needed)\n- Dependencies are resolved recursively and installed in the correct order\n\nExample with **required dependencies** (`machine-learning` requires `python`):\n```bash\nseed config add machine-learning\n# ✓ Added stack: machine-learning\n# ↳ Dependencies added: python\n```\n\nExample with **optional dependencies** (`ollama` optionally uses `cuda` for GPU):\n```bash\nseed config add ollama\n# ✓ Added stack: ollama\n# ⓘ Optional dependencies available:\n# ○ cuda - NVIDIA GPU acceleration (recommended for better performance)\n\n# To add the optional CUDA support:\nseed config add cuda\n```\n\n## Configuration File\n\nYour configuration is stored at `~/.seed/configs/.yml`:\n\n```yaml\nstacks:\n - base\n - docker\n - python\ncustom_packages:\n - jq\n - tmux\neditor: vim\n```\n\n## Base Packages\n\nAlways installed:\n- build-essential (make, gcc, g++)\n- curl, wget, git\n- vim, net-tools, unzip\n\n## Complete Deployment Workflow\n\n**Zero to configured server in 2 commands:**\n\n```bash\n# 1. Install Seed locally and deploy to remote server\npip3 install seed\nseed plant user@192.168.1.100\n# (Bootstrap will interactively install Python/pip3/Ansible if needed)\n\n# 2. SSH to server and apply configuration\nssh user@192.168.1.100\nseed config profile use ml-gpu\nseed setup # Will prompt for sudo password\n```\n\nThe `plant` command handles everything automatically:\n- ✅ Interactive bootstrap (installs Python/pip3/Ansible)\n- ✅ Transfers and installs Seed\n- ✅ Configures PATH\n- ✅ Ready to use immediately\n\n## Examples\n\n### Using Named Profiles\n\n**Create separate profiles for different environments:**\n```bash\n# Create a development profile\nseed config profile new dev\nseed config profile use dev\nseed config add nodejs python docker github cli-tools\nseed config pkg add tmux neovim\n\n# Create a production profile\nseed config profile new prod\nseed config profile use prod\nseed config add webserver security monitoring\nseed config pkg add fail2ban\n\n# Create staging from prod\nseed config profile new staging --copy-from=prod\n\n# List all profiles\nseed config profiles\n\n# Switch between them\nseed config profile use dev # For development work\nseed config profile use prod # For production setup\n```\n\n### Single Profile Examples\n\n**Full-stack development server:**\n```bash\nseed config add nodejs docker database github cli-tools\nseed setup\n```\n\n**Python development:**\n```bash\nseed config add python github devtools\nseed config pkg add ipython jupyter\nseed setup\n```\n\n**Secure web server:**\n```bash\nseed config add webserver security monitoring\nseed setup\n```\n\n**Modern CLI environment:**\n```bash\nseed config add github cli-tools\nseed config pkg add neovim zsh\nseed setup\n```\n\n## Requirements\n\n**Local machine (to run Seed CLI):**\n- Python 3.7+\n- pip3\n\n**Remote server (deployment target):**\n- Ubuntu/Debian-based system\n- Python 3.7+ and pip3\n- SSH access\n- sudo privileges (for package installation)\n\n## Notes\n\n- Profiles are generated dynamically into Ansible playbooks\n- All operations are idempotent (safe to run multiple times)\n- Use `seed check` to preview changes before applying\n- Use `seed preview` to see the generated Ansible playbook", "has_readme": true, "url": "https://github.com/CherryMesh/seed", "category": "unclassified", "value_tier": "standard", "value_rank": 2, "editorial_note": null, "similarity_group": "Infrastructure & Operations", "group_score": 10, "similar": [ { "id": "Geijutsu/seed", "score": 1.0, "signals": [ "docker", "monitoring", "deploy" ] }, { "id": "TransformerOS/atlas", "score": 0.1795, "signals": [ "docker", "monitoring", "server" ] }, { "id": "quivent/DocumentationRenderer", "score": 0.173, "signals": [ "docker", "deploy", "deployment" ] }, { "id": "TransformerOS/Autoprime", "score": 0.1718, "signals": [ "docker", "monitoring", "deployment" ] }, { "id": "MozArchAngelos/cherry", "score": 0.167, "signals": [ "docker", "deploy", "deployment" ] } ] }, { "organization": "CinemaAGI", "name": "Financials", "source": "R2 Git bundle", "published_at": "2025-12-02T10:31:16-05:00", "readme": "# React + TypeScript + Vite\n\nThis template provides a minimal setup to get React working in Vite with HMR and some ESLint rules.\n\nCurrently, two official plugins are available:\n\n- [@vitejs/plugin-react](https://github.com/vitejs/vite-plugin-react/blob/main/packages/plugin-react) uses [Babel](https://babeljs.io/) (or [oxc](https://oxc.rs) when used in [rolldown-vite](https://vite.dev/guide/rolldown)) for Fast Refresh\n- [@vitejs/plugin-react-swc](https://github.com/vitejs/vite-plugin-react/blob/main/packages/plugin-react-swc) uses [SWC](https://swc.rs/) for Fast Refresh\n\n## React Compiler\n\nThe React Compiler is not enabled on this template because of its impact on dev & build performances. To add it, see [this documentation](https://react.dev/learn/react-compiler/installation).\n\n## Expanding the ESLint configuration\n\nIf you are developing a production application, we recommend updating the configuration to enable type-aware lint rules:\n\n```js\nexport default defineConfig([\n globalIgnores(['dist']),\n {\n files: ['**/*.{ts,tsx}'],\n extends: [\n // Other configs...\n\n // Remove tseslint.configs.recommended and replace with this\n tseslint.configs.recommendedTypeChecked,\n // Alternatively, use this for stricter rules\n tseslint.configs.strictTypeChecked,\n // Optionally, add this for stylistic rules\n tseslint.configs.stylisticTypeChecked,\n\n // Other configs...\n ],\n languageOptions: {\n parserOptions: {\n project: ['./tsconfig.node.json', './tsconfig.app.json'],\n tsconfigRootDir: import.meta.dirname,\n },\n // other options...\n },\n },\n])\n```\n\nYou can also install [eslint-plugin-react-x](https://github.com/Rel1cx/eslint-react/tree/main/packages/plugins/eslint-plugin-react-x) and [eslint-plugin-react-dom](https://github.com/Rel1cx/eslint-react/tree/main/packages/plugins/eslint-plugin-react-dom) for React-specific lint rules:\n\n```js\n// eslint.config.js\nimport reactX from 'eslint-plugin-react-x'\nimport reactDom from 'eslint-plugin-react-dom'\n\nexport default defineConfig([\n globalIgnores(['dist']),\n {\n files: ['**/*.{ts,tsx}'],\n extends: [\n // Other configs...\n // Enable lint rules for React\n reactX.configs['recommended-typescript'],\n // Enable lint rules for React DOM\n reactDom.configs.recommended,\n ],\n languageOptions: {\n parserOptions: {\n project: ['./tsconfig.node.json', './tsconfig.app.json'],\n tsconfigRootDir: import.meta.dirname,\n },\n // other options...\n },\n },\n])\n```", "has_readme": true, "url": "https://github.com/CinemaAGI/Financials", "category": "unclassified", "value_tier": "standard", "value_rank": 2, "editorial_note": null, "similarity_group": "Web & Applications", "group_score": 3, "similar": [ { "id": "quivent/Cinematix", "score": 0.9894, "signals": [ "react", "application", "rolldown" ] }, { "id": "quivent/arch-viz", "score": 0.9865, "signals": [ "react", "application", "rolldown" ] }, { "id": "quivent/fluffy", "score": 0.9565, "signals": [ "react", "application", "stricter" ] }, { "id": "quivent/trumpit", "score": 0.1498, "signals": [ "react", "hmr", "eslint" ] }, { "id": "quivent/fast-cli", "score": 0.065, "signals": [ "fast" ] } ] }, { "organization": "EvolvingOpenAI", "name": "openmesh-cli", "source": "R2 Git bundle", "published_at": "2025-09-17T22:57:41+02:00", "readme": "# Xnode Deployer\n\nA high-performance CLI tool for deploying Xnodes across multiple cloud providers with modern Rust architecture, comprehensive error handling, and optimized performance.\n\n[![Rust](https://github.com/Openmesh-Network/xnode-deployer/workflows/Rust/badge.svg)](https://github.com/Openmesh-Network/xnode-deployer/actions)\n[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)\n[![Crates.io](https://img.shields.io/crates/v/xnode-deployer.svg)](https://crates.io/crates/xnode-deployer)\n\n## Features\n\n🚀 **Modern CLI Interface** - Built with Clap v4 for intuitive command-line experience \n⚡ **High Performance** - Optimized binary size (30-50% reduction) and startup time \n🔒 **Memory Safe** - Rust's ownership system ensures zero memory vulnerabilities \n🌐 **Multi-Provider Support** - Deploy to Hivelocity, Hyperstack, Cherry Servers, and Hetzner Cloud \n📊 **Progress Indicators** - Real-time deployment status with colored output \n🔧 **XDG Compliant** - Follows standard configuration directory conventions \n🔄 **Retry Logic** - Automatic retry with exponential backoff for reliability \n📈 **Comprehensive Testing** - Unit, integration, and performance benchmarks \n\n## Supported Providers\n\n- **Hivelocity** - Bare metal and compute instances\n- **Hyperstack** - Virtual machine instances \n- **Cherry Servers** - Bare metal dedicated servers with spot instances\n- **Hetzner Cloud** - Virtual servers with flexible configurations and locations\n- **More providers** - Extensible architecture for future additions\n\n## Installation\n\n### From Source (Recommended)\n\n```bash\n# Clone the repository\ngit clone https://github.com/Openmesh-Network/xnode-deployer.git\ncd xnode-deployer\n\n# Install globally (optimized release build)\ncargo install --path . --locked\n\n# Verify installation\nxnode-deployer --version\n```\n\n### From Crates.io\n\n```bash\ncargo install xnode-deployer\n```\n\n## Quick Start\n\n### 1. Configuration\n\nCreate a configuration file or use environment variables:\n\n```bash\n# Create sample config (creates ~/.config/xnode-deployer/config.toml)\nxnode-deployer config\n\n# Or set environment variables\nexport HIVELOCITY_API_KEY=\"your-api-key-here\"\nexport HYPERSTACK_API_KEY=\"your-api-key-here\"\nexport CHERRY_AUTH_TOKEN=\"your-api-key-here\"\nexport HETZNER_API_TOKEN=\"your-api-key-here\"\n```\n\n### 2. Deploy Your First Xnode\n\n#### Hivelocity Deployment\n\n```bash\nxnode-deployer deploy \\\n --provider hivelocity \\\n --hivelocity-location LAX1 \\\n --hivelocity-product-id 123 \\\n --hivelocity-hostname my-xnode \\\n --xnode-owner \"your-identifier\" \\\n --domain \"xnode.yourdomain.com\" \\\n --acme-email \"admin@yourdomain.com\"\n```\n\n#### Hyperstack Deployment\n\n```bash\nxnode-deployer deploy \\\n --provider hyperstack \\\n --hyperstack-name my-xnode \\\n --hyperstack-environment prod \\\n --hyperstack-flavor n1-cpu-small \\\n --hyperstack-key my-ssh-key \\\n --xnode-owner \"your-identifier\"\n```\n\n#### Cherry Servers Deployment\n\n```bash\nxnode-deployer deploy \\\n --provider cherry \\\n --cherry-project-id 12345 \\\n --cherry-plan e3_1240v3 \\\n --cherry-region EU-East-1 \\\n --cherry-hostname my-xnode \\\n --cherry-image ubuntu_24_04 \\\n --xnode-owner \"your-identifier\" \\\n --domain \"xnode.yourdomain.com\" \\\n --acme-email \"admin@yourdomain.com\"\n```\n\n#### Hetzner Cloud Deployment\n\n```bash\nxnode-deployer deploy \\\n --provider hetzner \\\n --hetzner-name my-xnode \\\n --hetzner-server-type cx21 \\\n --hetzner-location nbg1 \\\n --hetzner-image ubuntu-24.04 \\\n --xnode-owner \"your-identifier\" \\\n --domain \"xnode.yourdomain.com\" \\\n --acme-email \"admin@yourdomain.com\"\n```\n\n## Usage Examples\n\n### Deploy with All Options\n\n```bash\nxnode-deployer deploy \\\n --provider hivelocity \\\n --hivelocity-location LAX1 \\\n --hivelocity-product-id 123 \\\n --hivelocity-hostname production-xnode \\\n --hivelocity-tags \"production,xnode,monitoring\" \\\n --xnode-owner \"company-prod\" \\\n --domain \"xnode-prod.company.com\" \\\n --acme-email \"devops@company.com\" \\\n --user-passwd \"secure-password-123\" \\\n --output json \\\n --verbose\n```\n\n### Check Instance Status\n\n```bash\n# Get IP address and status\nxnode-deployer status \\\n --provider hivelocity \\\n --instance-id 456789\n\n# JSON output for automation\nxnode-deployer status \\\n --provider hyperstack \\\n --instance-id 123456 \\\n --output json\n```\n\n### Undeploy Instance\n\n```bash\nxnode-deployer undeploy \\\n --provider hivelocity \\\n --instance-id 456789\n```\n\n### Configuration Management\n\n```bash\n# Show current configuration\nxnode-deployer config\n\n# Show with JSON output\nxnode-deployer config --output json\n```\n\n## Configuration File\n\nThe configuration file follows XDG Base Directory Specification and is located at:\n- Linux/macOS: `~/.config/xnode-deployer/config.toml`\n- Windows: `%APPDATA%\\xnode-deployer\\config.toml`\n\n### Sample Configuration\n\n```toml\n[settings]\ndefault_output_format = \"text\"\ntimeout_seconds = 300\nretry_attempts = 3\nlog_level = \"info\"\n\n[hivelocity]\napi_key = \"your-hivelocity-api-key\"\ndefault_location = \"LAX1\"\ndefault_period = \"monthly\"\ndefault_hardware_type = \"compute\"\n\n[hyperstack]\napi_key = \"your-hyperstack-api-key\"\ndefault_environment = \"production\"\ndefault_flavor = \"n1-cpu-small\"\ndefault_key = \"my-default-ssh-key\"\n\n[cherry]\napi_key = \"your-cherry-api-key\"\ndefault_project_id = 12345\ndefault_plan = \"e3_1240v3\"\ndefault_region = \"EU-East-1\"\ndefault_image = \"ubuntu_24_04\"\n\n[hetzner]\napi_key = \"your-hetzner-api-key\"\ndefault_server_type = \"cx21\"\ndefault_location = \"nbg1\"\ndefault_image = \"ubuntu-24.04\"\n```\n\n## Environment Variables\n\nAll configuration options can be overridden with environment variables:\n\n```bash\n# Provider API keys\nexport HIVELOCITY_API_KEY=\"your-key\"\nexport HYPERSTACK_API_KEY=\"your-key\"\nexport CHERRY_AUTH_TOKEN=\"your-key\"\nexport HETZNER_API_TOKEN=\"your-key\"\n\n# Deployment configuration\nexport XNODE_OWNER=\"your-identifier\"\nexport DOMAIN=\"xnode.yourdomain.com\"\nexport ACME_EMAIL=\"admin@yourdomain.com\"\nexport USER_PASSWD=\"secure-password\"\n```\n\n## Command Reference\n\n### Global Options\n\n- `--config ` - Custom configuration file path\n- `--verbose` - Enable verbose logging\n- `--output ` - Output format: `text`, `json`, `yaml`\n\n### Commands\n\n#### `deploy` - Deploy new Xnode\n\n**Provider Options:**\n- `--provider ` - Cloud provider (`hivelocity`, `hyperstack`, `cherry`, `hetzner`)\n\n**Hivelocity Options:**\n- `--hivelocity-location ` - Data center location\n- `--hivelocity-product-id ` - Hardware product ID\n- `--hivelocity-hostname ` - Instance hostname\n- `--hivelocity-period ` - Billing period (`monthly`, `hourly`)\n- `--hivelocity-hardware-type ` - Hardware type (`bare-metal`, `compute`)\n- `--hivelocity-tags ` - Comma-separated tags\n\n**Hyperstack Options:**\n- `--hyperstack-name ` - Instance name\n- `--hyperstack-environment ` - Environment name\n- `--hyperstack-flavor ` - Instance flavor/size\n- `--hyperstack-key ` - SSH key name\n\n**Cherry Servers Options:**\n- `--cherry-project-id ` - Project ID (required)\n- `--cherry-plan ` - Server plan slug (e.g., `e3_1240v3`, `e5_1620v4`)\n- `--cherry-region ` - Region slug (e.g., `LT-Siauliai`, `EU-East-1`)\n- `--cherry-hostname ` - Server hostname\n- `--cherry-image ` - OS image slug (default: `ubuntu_24_04`)\n- `--cherry-ssh-keys ` - Comma-separated SSH key IDs\n- `--cherry-tags ` - Comma-separated server tags\n- `--cherry-spot-instance` - Create as spot instance for cost savings\n\n**Hetzner Cloud Options:**\n- `--hetzner-name ` - Server name (required)\n- `--hetzner-server-type ` - Server type (e.g., `cx21`, `cpx11`, `ccx12`)\n- `--hetzner-location ` - Location (e.g., `nbg1`, `ash`, `hel1`)\n- `--hetzner-image ` - OS image (default: `ubuntu-24.04`)\n- `--hetzner-ssh-keys ` - Comma-separated SSH key names\n- `--hetzner-labels ` - Key=value labels (comma-separated)\n- `--hetzner-networks ` - Comma-separated network IDs\n- `--hetzner-volumes ` - Comma-separated volume IDs\n\n**Xnode Configuration:**\n- `--xnode-owner ` - Owner identifier\n- `--domain ` - Domain name for SSL\n- `--acme-email ` - Email for SSL certificates\n- `--user-passwd ` - User password\n- `--encrypted ` - Encrypted configuration\n- `--initial-config ` - Initial configuration\n\n#### `undeploy` - Remove Xnode\n\n- `--instance-id ` - Provider instance ID\n\n#### `status` - Check Xnode status\n\n- `--instance-id ` - Provider instance ID\n\n#### `config` - Show configuration\n\nNo additional options.\n\n## Performance\n\nThe CLI is optimized for performance with:\n\n- **Binary Size**: 30-50% reduction through LTO and symbol stripping\n- **Startup Time**: Sub-second startup for all operations\n- **Memory Usage**: Efficient connection pooling and async operations\n- **Network Optimization**: HTTP/2 with connection reuse and keepalive\n\n### Benchmarking\n\nRun performance benchmarks:\n\n```bash\n# Install criterion\ncargo install cargo-criterion\n\n# Run benchmarks\ncargo criterion\n\n# View results in target/criterion/report/index.html\n```\n\n## Development\n\n### Prerequisites\n\n- Rust 1.70.0 or later\n- Git\n\n### Setup\n\n```bash\ngit clone https://github.com/Openmesh-Network/xnode-deployer.git\ncd xnode-deployer\n\n# Install development dependencies\ncargo build\n\n# Run tests\ncargo test\n\n# Run integration tests\ncargo test --test integration_tests\n\n# Run benchmarks\ncargo bench\n```\n\n### Architecture\n\nThe project follows modern Rust best practices:\n\n```\nsrc/\n├── main.rs # CLI entry point\n├── cli.rs # Command-line interface definitions \n├── config.rs # Configuration management\n├── error.rs # Error handling and types\n├── lib.rs # Library interface\n├── hivelocity/ # Hivelocity provider implementation\n├── hyperstack/ # Hyperstack provider implementation\n├── cherry/ # Cherry Servers provider implementation\n├── hetzner/ # Hetzner Cloud provider implementation\n└── utils/ # Utilities and common code\n```\n\n### Testing\n\n```bash\n# Unit tests\ncargo test\n\n# Integration tests with mocking\ncargo test --test integration_tests\n\n# Performance benchmarks\ncargo bench\n\n# Test coverage\ncargo install cargo-tarpaulin\ncargo tarpaulin --out html\n```\n\n## Related Projects\n\n- [xnode-manager](https://github.com/Openmesh-Network/xnode-manager) - Xnode management and orchestration\n\n## API Keys & Providers\n\n### Hivelocity\n\nGet your API key from [Hivelocity Portal](https://portal.hivelocity.net/):\n1. Log in to your account\n2. Navigate to API section\n3. Generate new API key\n4. Add to config file or environment variable\n\n### Hyperstack\n\nGet your API key from Hyperstack Dashboard:\n1. Log in to your account\n2. Go to API Keys section\n3. Generate new key\n4. Add to config file or environment variable\n\n### Cherry Servers\n\nGet your API key from [Cherry Servers Portal](https://portal.cherryservers.com/):\n1. Log in to your account\n2. Navigate to API Keys section\n3. Generate new API key\n4. Add to config file or set `CHERRY_AUTH_TOKEN` environment variable\n\nNote: Cherry Servers uses project-based billing. Make sure to get your project ID from the portal.\n\n### Hetzner Cloud\n\nGet your API key from [Hetzner Cloud Console](https://console.hetzner.cloud/):\n1. Log in to your account\n2. Go to Security → API Tokens\n3. Generate new API token\n4. Add to config file or set `HETZNER_API_TOKEN` environment variable\n\nNote: Hetzner Cloud offers competitive pricing and multiple European locations (Germany, Finland, US East).\n\n## Error Handling\n\nThe CLI provides comprehensive error handling with:\n\n- **Colored Output** - Easily distinguish error types\n- **Actionable Messages** - Clear guidance on how to fix issues\n- **Exit Codes** - Standard exit codes for script automation\n- **Retry Logic** - Automatic retry for transient network errors\n\n## Contributing\n\n1. Fork the repository\n2. Create feature branch (`git checkout -b feature/amazing-feature`)\n3. Run tests (`cargo test`)\n4. Commit changes (`git commit -am 'Add amazing feature'`)\n5. Push to branch (`git push origin feature/amazing-feature`)\n6. Open Pull Request\n\n## License\n\nThis project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.\n\n## Support\n\n- **Issues**: [GitHub Issues](https://github.com/Openmesh-Network/xnode-deployer/issues)\n- **Discussions**: [GitHub Discussions](https://github.com/Openmesh-Network/xnode-deployer/discussions)\n- **Documentation**: [API Documentation](https://docs.rs/xnode-deployer)\n\n## Versioning\n\nFollows the xnode-manager Major and Minor version, Patch version is independent.\n\nCurrent version: **1.0.6**", "has_readme": true, "url": "https://github.com/EvolvingOpenAI/openmesh-cli", "category": "unclassified", "value_tier": "standard", "value_rank": 2, "editorial_note": null, "similarity_group": "Infrastructure & Operations", "group_score": 12, "similar": [ { "id": "MozArchAngelos/cherry", "score": 0.1591, "signals": [ "cloud", "network", "deploy" ] }, { "id": "Moestradamus-Productions/cherry", "score": 0.1591, "signals": [ "cloud", "network", "deploy" ] }, { "id": "AmadeusInnovations/cherry", "score": 0.1591, "signals": [ "cloud", "network", "deploy" ] }, { "id": "quivent/cherry-blossom", "score": 0.1563, "signals": [ "cloud", "network", "deploy" ] }, { "id": "AmadeusInnovations/cherry-blossom", "score": 0.1563, "signals": [ "cloud", "network", "deploy" ] } ] }, { "organization": "Geijutsu", "name": "ais", "source": "R2 Git bundle", "published_at": "2025-11-30T23:20:32+00:00", "readme": "# AIS - AWS Intelligence Suite\n\nA next-generation AWS CLI reimplementation designed for both human developers and autonomous agents, featuring intuitive commands, beautiful terminal UI, AI-specialized workflows, persistent memory, and embedded self-maintenance.\n\n## Overview\n\nAIS transforms cloud infrastructure management through intelligent design, making AWS accessible, beautiful, and optimized for AI workloads. Built with dual-mode architecture, AIS serves human operators with rich visual interfaces while providing autonomous agents with structured, predictable programmatic access.\n\n**Core Capabilities:**\n- 🎨 **Intuitive Interface** - Natural command structure with intelligent exploration\n- 🤖 **AI-Specialized Workflows** - Pre-configured pathways for ML training, agent orchestration, research\n- 🧠 **Memory System** - Persistent learning across sessions with pattern recognition\n- 🔧 **Self-Maintenance** - Embedded agent master for autonomous updates and optimization\n- 👥 **Dual-Mode Design** - Seamless operation for both humans and autonomous agents\n- 📊 **Visual Topology** - Resource relationship mapping and dependency visualization\n\n## Quick Start\n\n### Installation\n\n```bash\n# Install via Go (requires Go 1.21+)\ngo install github.com/yourusername/ais@latest\n\n# Or download pre-built binary\ncurl -L https://github.com/yourusername/ais/releases/latest/download/ais-$(uname -s)-$(uname -m) -o ais\nchmod +x ais\nsudo mv ais /usr/local/bin/\n\n# Verify installation\nais version\n```\n\n### Initial Configuration\n\n```bash\n# Initialize with AWS credentials\nais init\n\n# Configure AI workload profile\nais config set-profile ml-training\n\n# Enable memory and learning\nais memory enable\n\n# Activate agent master\nais agent activate\n```\n\n### Basic Usage\n\n```bash\n# Explore services interactively\nais explore\n\n# List resources with beautiful output\nais ec2 list\nais s3 buckets\nais sagemaker jobs\n\n# Launch ML training job\nais ml train \\\n --framework pytorch \\\n --model transformer \\\n --dataset s3://my-bucket/data \\\n --instances p3.8xlarge \\\n --count 4\n\n# Orchestrate agent research\nais agent orchestrate \\\n --task \"neural architecture search\" \\\n --agents 8 \\\n --output s3://results/\n\n# View memory and history\nais memory show\nais memory search \"training jobs\"\n```\n\n## Command Structure\n\n```\nais [service] [action] [options]\n\nCore Services:\n ec2, s3, lambda, sagemaker, batch, ecs, eks, rds, dynamodb...\n\nAI-Specialized Commands:\n ml ML training, deployment, and optimization\n agent Agent orchestration and management\n research Research workflow automation\n dev Development environment provisioning\n\nUtility Commands:\n explore Interactive service discovery\n memory Session history and pattern learning\n optimize Cost and resource optimization\n topology Resource relationship visualization\n```\n\n## AI-Specialized Workflows\n\n### Distributed ML Training\n\n```bash\n# Auto-configured distributed training\nais ml train \\\n --framework pytorch \\\n --model gpt-neo \\\n --dataset s3://corpus/training \\\n --strategy distributed-data-parallel \\\n --auto-scale \\\n --budget 1000\n\n# Monitor training progress\nais ml status --job training-001\n\n# Rerun with modifications using memory\nais ml train --from-previous --learning-rate 0.0001\n```\n\n### Multi-Agent Orchestration\n\n```bash\n# Coordinate distributed agents\nais agent orchestrate \\\n --task \"protein folding analysis\" \\\n --agents researcher,analyzer,validator \\\n --instances 16 \\\n --coordination hierarchical \\\n --output s3://results/experiment-001\n\n# Scale dynamically based on progress\nais agent scale --task experiment-001 --workers 32\n\n# Aggregate and analyze results\nais agent results --task experiment-001 --format summary\n```\n\n### Research Infrastructure\n\n```bash\n# Batch experiment execution\nais research run \\\n --experiment nas-benchmark \\\n --parameter-sweep learning_rate=[0.001,0.01,0.1] \\\n --parameter-sweep batch_size=[32,64,128] \\\n --replicas 3 \\\n --output s3://experiments/\n\n# Cross-cloud comparison\nais research compare \\\n --experiment nas-benchmark \\\n --clouds aws,azure,gcp \\\n --metric accuracy\n```\n\n## Architecture\n\nAIS employs a modular, extensible architecture:\n\n```\nais/\n├── Core Engine # Command parsing, AWS integration, execution\n├── UI Layer # Terminal rendering, visual components\n├── Memory System # Persistent storage, pattern learning\n├── Agent Master # Self-maintenance, updates, optimization\n├── AI Workflows # ML training, orchestration, research modules\n└── Service Adapters # AWS, Azure, GCP integration\n```\n\n### Technology Stack\n\n- **Language**: Go 1.21+ (performance, concurrency)\n- **CLI Framework**: Cobra (command structure)\n- **AWS SDK**: AWS SDK for Go v2\n- **UI**: BubbleTea + Lipgloss (beautiful terminal)\n- **Memory**: SQLite + Bleve (storage, search)\n- **Agent Master**: gRPC, self-updating binaries\n\n## Configuration\n\nConfiguration stored in `~/.ais/`:\n\n```\n~/.ais/\n├── config.yaml # Main configuration\n├── credentials.enc # Encrypted AWS credentials\n├── profiles/ # AI workload profiles\n│ ├── ml-training.yaml\n│ ├── agent-research.yaml\n│ ├── development.yaml\n│ └── production.yaml\n├── memory/ # Session memory database\n│ ├── commands.db\n│ ├── patterns.db\n│ └── resources.db\n├── agent/ # Agent master data\n│ ├── state.json\n│ ├── updates/\n│ └── learning/\n└── cache/ # Performance cache\n```\n\n## Development\n\n### Building from Source\n\n```bash\n# Clone repository\ngit clone https://github.com/yourusername/ais.git\ncd ais\n\n# Install dependencies\ngo mod download\n\n# Build\ngo build -o ais\n\n# Install globally\ngo install\n\n# Run tests\ngo test ./...\n\n# Run with race detection\ngo test -race ./...\n```\n\n### Project Structure\n\n```\nais/\n├── cmd/ # Command implementations\n│ ├── root.go # Root command setup\n│ ├── ec2.go # EC2 commands\n│ ├── ml.go # ML workflow commands\n│ ├── agent.go # Agent orchestration\n│ └── ...\n├── pkg/ # Core packages\n│ ├── aws/ # AWS service integrations\n│ ├── ui/ # Terminal UI components\n│ ├── memory/ # Memory system\n│ ├── agent/ # Agent master\n│ ├── ml/ # AI workflow modules\n│ └── config/ # Configuration management\n├── internal/ # Internal utilities\n├── docs/ # Documentation\n├── tests/ # Test suites\n└── configs/ # Configuration templates\n```\n\n## Contributing\n\nContributions welcome! See [CONTRIBUTING.md](CONTRIBUTING.md) for guidelines.\n\n**Development Standards:**\n- Follow Go best practices and idiomatic patterns\n- Add comprehensive tests for new features\n- Update documentation for API changes\n- Run `go fmt`, `go vet`, `golangci-lint` before commits\n- Use conventional commit messages\n\n## License\n\nMIT License - see [LICENSE](LICENSE) for details.\n\n## Documentation\n\n- **Documentation Site**: https://ais-cli.dev\n- **API Reference**: https://ais-cli.dev/api\n- **Tutorials**: https://ais-cli.dev/tutorials\n- **Community Forum**: https://forum.ais-cli.dev\n\n## Support\n\n- **GitHub Issues**: https://github.com/yourusername/ais/issues\n- **Discussions**: https://github.com/yourusername/ais/discussions\n- **Discord**: https://discord.gg/ais-cli\n- **Email**: support@ais-cli.dev\n\n## Roadmap\n\n### Current (v1.0)\n- [x] Core AWS service integration (EC2, S3, Lambda, SageMaker)\n- [x] Beautiful terminal UI with visual feedback\n- [x] Memory system foundation\n- [x] Basic ML workflow automation\n\n### Near-term (v1.1-1.3)\n- [ ] Advanced agent orchestration capabilities\n- [ ] Multi-cloud support (Azure, GCP)\n- [ ] Natural language command processing\n- [ ] Enhanced visual topology mapping\n- [ ] Plugin system for extensibility\n\n### Long-term (v2.0+)\n- [ ] Distributed agent coordination protocol\n- [ ] Advanced cost prediction and optimization\n- [ ] Cross-cloud resource migration\n- [ ] AI-driven infrastructure recommendations\n- [ ] Full agent autonomy for complex workflows\n\n---\n\n**Built for the future of cloud infrastructure management - where humans and agents collaborate seamlessly.**", "has_readme": true, "url": "https://github.com/Geijutsu/ais", "category": "unclassified", "value_tier": "standard", "value_rank": 2, "editorial_note": null, "similarity_group": "Developer Tools", "group_score": 15, "similar": [ { "id": "quivent/conduct", "score": 0.1808, "signals": [ "automation", "framework", "cli" ] }, { "id": "TSMCP/monetize", "score": 0.1724, "signals": [ "plugin", "automation", "language" ] }, { "id": "MorchestraWorld/monetize", "score": 0.1724, "signals": [ "plugin", "automation", "language" ] }, { "id": "quivent/CollaborativeIntelligence", "score": 0.1695, "signals": [ "plugin", "terminal", "framework" ] }, { "id": "Geijutsu/cherry", "score": 0.1689, "signals": [ "automation", "terminal", "language" ] } ] }, { "organization": "Geijutsu", "name": "apilo", "source": "R2 Git bundle", "published_at": "2025-11-30T23:20:32+00:00", "readme": "# API Latency Optimizer\n\n**Version**: 2.0 - Production Ready\n**Status**: ✅ All Critical Mitigations Complete\n**Performance**: 93.69% latency reduction (515ms → 33ms average)\n\nA production-ready API optimization system that achieves 3-5x performance improvements through memory-bounded caching, advanced invalidation strategies, circuit breaker protection, and comprehensive monitoring.\n\n---\n\n## 🚀 Quick Start\n\n### Installation\n\n```bash\n# Clone the repository\ngit clone \ncd api-latency-optimizer\n\n# Install dependencies\ngo mod download\n\n# Build the optimizer\ngo build -ldflags=\"-w -s\" -o bin/api-optimizer ./src\n\n# Run with default configuration\n./bin/api-optimizer --config config/production_config.yaml\n```\n\n### Basic Usage\n\n```go\npackage main\n\nimport (\n \"github.com/yourorg/api-latency-optimizer/src\"\n \"time\"\n)\n\nfunc main() {\n // Create optimizer with production config\n config := src.DefaultIntegratedConfig()\n optimizer, err := src.NewIntegratedOptimizer(config)\n if err != nil {\n panic(err)\n }\n\n // Start the optimizer\n if err := optimizer.Start(); err != nil {\n panic(err)\n }\n defer optimizer.Stop()\n\n // Use optimized HTTP client\n client := optimizer.GetClient()\n resp, err := client.Get(\"https://api.example.com/endpoint\")\n // ... handle response\n}\n```\n\n### Claude Code Integration (Recommended)\n\n**Quick Start in Claude Code:**\n\n```\n/api-optimize https://api.example.com\n```\n\nThe optimizer is available as a slash command in Claude Code for instant benchmarking and optimization. See [QUICKSTART_CLAUDE_CODE.md](QUICKSTART_CLAUDE_CODE.md) for full guide.\n\n---\n\n## ✨ Key Features\n\n### Production-Ready Optimizations\n- ✅ **93.69% latency reduction** validated (515ms → 33ms)\n- ✅ **98% cache hit ratio** sustained under load\n- ✅ **15.8x throughput increase** measured\n- ✅ **Memory-bounded caching** with configurable limits\n- ✅ **Advanced cache invalidation** (tag, pattern, dependency, version-based)\n- ✅ **Circuit breaker protection** with automatic failover\n- ✅ **HTTP/2 optimization** with connection pooling\n- ✅ **Production monitoring** with real-time metrics\n- ✅ **Alert management system** with multiple severity levels\n\n### Core Components\n\n#### 1. Memory-Bounded Cache (`src/memory_bounded_cache.go`)\n- Hard memory limits with configurable MB maximum\n- Automatic GC optimization with pressure detection\n- Real-time memory tracking and leak detection\n- Dynamic eviction rates based on memory pressure\n\n#### 2. Advanced Cache Invalidation (`src/advanced_invalidation.go`)\n- Tag-based: `InvalidateByTag(\"user:123\")`\n- Pattern-based: `InvalidateByPattern(\"/api/users/*\")`\n- Dependency tracking for cascading invalidation\n- Version-based for data consistency\n- Async invalidation support\n\n#### 3. Circuit Breaker & Failover (`src/circuit_breaker.go`)\n- Three-state circuit breaker (Closed, Open, Half-Open)\n- Automatic failover to backup services\n- Health checking with automatic recovery\n- Multiple failover strategies\n\n#### 4. Production Monitoring (`src/production_monitoring.go`)\n- System metrics (CPU, memory, network, disk)\n- GC metrics with pause time analysis\n- Performance metrics (latency percentiles, throughput)\n- Prometheus and Jaeger integration\n\n#### 5. Alert System (`src/alerts.go`)\n- Configurable thresholds for all metrics\n- Severity levels (INFO, WARNING, CRITICAL)\n- Cooldown management\n- Alert history and acknowledgment\n\n---\n\n## 📚 Documentation Index\n\n### Getting Started\n- **[Quick Start Guide](QUICK_START.md)** - Get running in 5 minutes\n- **[Claude Code Quick Start](QUICKSTART_CLAUDE_CODE.md)** - ⚡ Use in Claude Code (recommended)\n- **[Claude Code Integration Guide](CLAUDE_CODE_INTEGRATION.md)** - Complete Claude Code integration\n- **[Installation Guide](docs/INSTALLATION.md)** - Detailed setup instructions\n- **[Architecture Overview](docs/ARCHITECTURE.md)** - System design and components\n\n### Implementation\n- **[Implementation Guide](IMPLEMENTATION_GUIDE_AND_DRAWBACKS.md)** - Complete implementation details\n- **[Configuration Reference](docs/CONFIGURATION.md)** - All configuration options\n- **[API Reference](docs/API_REFERENCE.md)** - Programmatic usage\n\n### Deployment\n- **[Deployment Guide](docs/DEPLOYMENT.md)** - Production deployment steps\n- **[Production Runbook](PRODUCTION_RUNBOOK.md)** - Operations guide\n- **[Monitoring Guide](docs/MONITORING_GUIDE.md)** - Observability setup\n\n### Reference\n- **[Troubleshooting Guide](docs/TROUBLESHOOTING.md)** - Common issues and solutions\n- **[Performance Report](PHASE1_VALIDATION_SUCCESS_REPORT.md)** - Validated performance metrics\n- **[Production Readiness Report](PRODUCTION_READINESS_REPORT.md)** - Audit and status\n\n### Advanced Topics\n- **[Cache Architecture](docs/CACHE_ARCHITECTURE.md)** - Cache design details\n- **[Statistical Validation](STATISTICAL_VALIDATION_PROTOCOL.md)** - Performance validation methodology\n- **[Phased Deployment](PHASED_DEPLOYMENT_STRATEGY.md)** - Rollout strategies\n\n---\n\n## 🎯 Performance Highlights\n\n### Validated Results (Phase 1)\n| Metric | Baseline | Optimized | Improvement |\n|--------|----------|-----------|-------------|\n| **Average Latency** | 515ms | 33ms | **93.69%** |\n| **P50 Latency** | 460ms | 29ms | **93.7%** |\n| **P95 Latency** | 850ms | 75ms | **91.2%** |\n| **Throughput** | 2.1 RPS | 33.5 RPS | **15.8x** |\n| **Cache Hit Ratio** | 0% | 98% | **N/A** |\n\n### Production Targets\n- ✅ Cache Hit Ratio: >90% (achieved 98%)\n- ✅ Average Latency: <100ms (achieved 33ms)\n- ✅ Memory Usage: <500MB (configurable, bounded)\n- ✅ Throughput: >80 RPS (achieved 33.5 RPS baseline)\n- ✅ Error Rate: <1%\n\n---\n\n## 🏗️ Architecture\n\n```\n┌─────────────────────────────────────────────────────────────┐\n│ IntegratedOptimizer │\n├─────────────────────────────────────────────────────────────┤\n│ ┌─────────────────┐ ┌──────────────────┐ │\n│ │ OptimizedClient │ │ BenchmarkEngine │ │\n│ └────────┬────────┘ └────────┬─────────┘ │\n│ │ │ │\n│ ┌────────▼────────┐ ┌────────▼────────┐ │\n│ │ Memory-Bounded │ │ Monitoring │ │\n│ │ Cache │ │ Dashboard │ │\n│ └────────┬────────┘ └─────────────────┘ │\n│ │ │\n│ ┌────────▼────────┐ ┌──────────────────┐ │\n│ │ Advanced │ │ Circuit Breaker │ │\n│ │ Invalidation │ │ & Failover │ │\n│ └─────────────────┘ └──────────────────┘ │\n│ │ │ │\n│ ┌────────▼────────────────────▼────────┐ │\n│ │ Production Monitoring & │ │\n│ │ Alert System │ │\n│ └────────────────────────────────────────┘ │\n└─────────────────────────────────────────────────────────────┘\n```\n\n---\n\n## 🔧 Configuration Example\n\n```yaml\n# config/production_config.yaml\noptimization:\n cache:\n enabled: true\n max_memory_mb: 500\n default_ttl: \"10m\"\n gc_threshold_percent: 0.8\n enable_memory_tracker: true\n\n invalidation:\n enable_tag_based: true\n enable_pattern_matching: true\n enable_dependency_tracking: true\n enable_version_based: true\n async_invalidation: true\n\n http2:\n max_connections_per_host: 20\n idle_timeout: \"90s\"\n tls_timeout: \"10s\"\n\n circuit_breaker:\n failure_threshold: 5\n open_timeout: \"30s\"\n half_open_max_requests: 3\n\n monitoring:\n enabled: true\n dashboard_port: 8080\n metrics_interval: \"5s\"\n alerting_enabled: true\n prometheus_enabled: true\n```\n\n---\n\n## 📊 Monitoring Dashboard\n\nAccess the real-time monitoring dashboard:\n\n```bash\n# Start optimizer with monitoring\n./bin/api-optimizer --config config/production_config.yaml\n\n# Access dashboard\nopen http://localhost:8080/dashboard\n\n# View metrics\ncurl http://localhost:8080/metrics\n\n# Health check\ncurl http://localhost:8080/health\n```\n\n### Available Metrics\n- Cache hit/miss ratios\n- Memory usage and pressure\n- Latency percentiles (P50, P95, P99)\n- Throughput (requests/sec)\n- Circuit breaker states\n- Active connections\n- GC statistics\n\n---\n\n## 🧪 Testing\n\n```bash\n# Run unit tests\ngo test ./src/... -v\n\n# Run integration tests\ngo test ./tests/... -v\n\n# Run benchmarks\ngo test ./src/... -bench=. -benchmem\n\n# Run with coverage\ngo test ./src/... -cover -coverprofile=coverage.out\ngo tool cover -html=coverage.out\n```\n\n---\n\n## 🚀 Deployment\n\n### Production Checklist\n\n- [x] Memory-bounded cache implemented\n- [x] Advanced cache invalidation implemented\n- [x] Circuit breaker and failover implemented\n- [x] Production monitoring implemented\n- [x] Alert system implemented\n- [x] Test coverage comprehensive\n- [x] Performance validated\n- [ ] Configuration reviewed for production environment\n- [ ] Alert notification channels configured\n- [ ] Monitoring dashboards deployed\n- [ ] Load testing completed\n\n### Quick Deploy\n\n```bash\n# Build production binary\ngo build -ldflags=\"-w -s\" -o api-optimizer ./src\n\n# Deploy configuration\ncp config/production_config.yaml /etc/api-optimizer/config.yaml\n\n# Start service\n./api-optimizer \\\n --config /etc/api-optimizer/config.yaml \\\n --monitor=true \\\n --dashboard=true \\\n --port=8080\n```\n\nSee **[Deployment Guide](docs/DEPLOYMENT.md)** for complete instructions.\n\n---\n\n## 📈 Performance Tuning\n\n### Cache Configuration\n```yaml\ncache:\n max_memory_mb: 1000 # Increase for more caching\n default_ttl: \"15m\" # Balance freshness vs performance\n gc_threshold_percent: 0.75 # Trigger GC earlier for smoother operation\n```\n\n### HTTP/2 Optimization\n```yaml\nhttp2:\n max_connections_per_host: 30 # Increase for higher throughput\n idle_timeout: \"120s\" # Keep connections alive longer\n```\n\n### Circuit Breaker Tuning\n```yaml\ncircuit_breaker:\n failure_threshold: 3 # More sensitive to failures\n open_timeout: \"10s\" # Faster recovery attempts\n```\n\n---\n\n## 🛟 Troubleshooting\n\n### High Memory Usage\n```bash\n# Check memory metrics\ncurl http://localhost:8080/metrics | grep memory\n\n# Adjust cache limit\n# Edit config: max_memory_mb: 250\n```\n\n### Cache Miss Rate Too High\n```bash\n# Check cache statistics\ncurl http://localhost:8080/cache/stats\n\n# Increase TTL or memory limit\n# Review invalidation patterns\n```\n\n### Circuit Breaker Tripping\n```bash\n# Check circuit breaker state\ncurl http://localhost:8080/circuit/status\n\n# Review failure logs\n# Adjust failure threshold if needed\n```\n\nSee **[Troubleshooting Guide](docs/TROUBLESHOOTING.md)** for complete guide.\n\n---\n\n## 🤝 Contributing\n\nContributions are welcome! Please see our contributing guidelines.\n\n### Development Setup\n```bash\n# Install development dependencies\ngo mod download\n\n# Run tests\nmake test\n\n# Run linter\nmake lint\n\n# Build\nmake build\n```\n\n---\n\n## 📄 License\n\nCopyright 2025 - API Latency Optimizer Project\n\n---\n\n## 🔗 Links\n\n- **Documentation**: [docs/](docs/)\n- **Issues**: [GitHub Issues](https://github.com/yourorg/api-latency-optimizer/issues)\n- **Discussions**: [GitHub Discussions](https://github.com/yourorg/api-latency-optimizer/discussions)\n\n---\n\n**Built with production-grade reliability and performance optimization.**", "has_readme": true, "url": "https://github.com/Geijutsu/apilo", "category": "unclassified", "value_tier": "standard", "value_rank": 2, "editorial_note": null, "similarity_group": "Infrastructure & Operations", "group_score": 8, "similar": [ { "id": "MorchestraWorld/apilo", "score": 1.0, "signals": [ "service", "network", "monitoring" ] }, { "id": "TSMCP/Orchestra", "score": 0.1963, "signals": [ "monitoring", "deploy", "deployment" ] }, { "id": "MorchestraWorld/Orchestra", "score": 0.1963, "signals": [ "monitoring", "deploy", "deployment" ] }, { "id": "Geijutsu/orchestra", "score": 0.1963, "signals": [ "monitoring", "deploy", "deployment" ] }, { "id": "AGI-Film/Gate", "score": 0.1889, "signals": [ "service", "network", "monitoring" ] } ] }, { "organization": "Geijutsu", "name": "benchmark", "source": "R2 Git bundle", "published_at": "2025-11-30T23:06:35+00:00", "readme": "# Benchmark CLI\n\nEnterprise-grade software project benchmarking tool with statistical validation and academic-quality algorithms.\n\n## 🎯 Overview\n\nBenchmark is a sophisticated CLI tool that implements research-backed algorithms for comprehensive software project assessment. Based on academic frameworks including ISO 25010, AHP-TOPSIS, and multi-criteria decision making methodologies.\n\n### Key Features\n\n- **7-Metric Benchmarking System** with statistical validation\n- **V4 Algorithms** with technology-agnostic normalization \n- **Statistical Framework** including correlation analysis and bias detection\n- **Academic-Quality Reporting** with comprehensive validation\n- **Enterprise-Ready** with performance optimization and extensive testing\n\n## 📊 Benchmarking Metrics\n\n| Metric | Scale | Purpose |\n|--------|-------|---------|\n| **Business Value** | 1-10 | Strategic importance and revenue impact |\n| **Performance Score** | 0-100 | Technical performance and optimization |\n| **Market Relevance** | 1-10 | Technology relevance in 2025 market |\n| **Innovation Benchmark** | 0-100 | Innovation level and creativity |\n| **Quality Benchmark** | 0-100 | Code quality and development practices |\n| **Uniqueness Benchmark** | 0-100 | Market differentiation |\n| **Marketability Score** | 0-100 | Commercial readiness and potential |\n\n## 🚀 Quick Start\n\n### Installation\n\n```bash\n# Clone and build\ngit clone \ncd benchmark\nmake install\n\n# Verify installation\nbenchmark --version\n```\n\n### Basic Usage\n\n```bash\n# Scan current directory\nbenchmark scan\n\n# Scan specific directory\nbenchmark scan ./projects\n\n# Compare two projects\nbenchmark compare project1 project2\n\n# View algorithm details\nbenchmark algorithms\n\n# Run statistical validation\nbenchmark validate\n```\n\n## 📖 Commands\n\n### Core Commands\n\n- `benchmark scan [path]` - Scan directory and calculate benchmarks\n- `benchmark score [project]` - Detailed project analysis \n- `benchmark compare [proj1] [proj2]` - Side-by-side comparison\n- `benchmark algorithms` - Algorithm documentation\n- `benchmark validate [data]` - Statistical validation\n\n### Algorithm Documentation\n\n- `benchmark algorithms business-value` - Business Value algorithm details\n- `benchmark algorithms performance` - Performance Score algorithm details\n- `benchmark algorithms market-relevance` - Market Relevance algorithm details\n- `benchmark algorithms innovation` - Innovation Benchmark algorithm details\n- `benchmark algorithms quality` - Quality Benchmark algorithm details\n- `benchmark algorithms uniqueness` - Uniqueness Benchmark algorithm details\n- `benchmark algorithms marketability` - Marketability Score algorithm details\n\n### Advanced Analysis\n\n- `benchmark validate --bias-check` - Technology bias detection\n- `benchmark validate --correlations` - Cross-metric correlation analysis\n- `benchmark validate --distributions` - Distribution normality testing\n- `benchmark validate --outliers` - Outlier detection and analysis\n\n## 🔬 Statistical Features\n\n### V4 Algorithm Suite\n\n- **Technology-Agnostic Normalization**: Eliminates systematic bias (ANOVA p-value >0.10)\n- **Independent Quality Metrics**: Realistic correlation patterns (r: 0.3-0.7)\n- **Continuous Scoring**: Natural statistical distributions (Shapiro p-value >0.05)\n- **Cross-Metric Validation**: Prevents impossible combinations (<5% outliers)\n- **Boundary Enforcement**: Logical consistency constraints\n\n### Academic Framework Integration\n\n- **ISO/IEC 25010 SQuaRE**: Systems and Software Quality Requirements\n- **AHP-TOPSIS Methodology**: Analytical Hierarchy Process + TOPSIS ranking\n- **Multi-Criteria Decision Making**: Evidence-based weight assignment\n- **Statistical Validation**: Comprehensive correlation and distribution testing\n\n## 🛠️ Development\n\n### Prerequisites\n\n- Go 1.21+ \n- Make\n- Git\n\n### Development Setup\n\n```bash\n# Set up development environment\nmake dev-setup\n\n# Development cycle\nmake dev\n\n# Run tests\nmake test\n\n# Quality checks\nmake check\n```\n\n### Build Commands\n\n```bash\nmake build # Build binary\nmake install # Build and install\nmake quick # Quick build and install\nmake cross-build # Multi-platform builds\nmake release # Production build\n```\n\n### Testing\n\n```bash\nmake test # Run tests\nmake test-coverage # Coverage analysis\nmake test-race # Race condition detection\n```\n\n## 📈 Performance\n\n- **Calculation Speed**: <5ms per project\n- **Memory Usage**: Optimized for large repositories (1000+ projects)\n- **Concurrent Processing**: Multi-threaded scanning and analysis\n- **Caching**: Intelligent caching for expensive calculations\n\n## 🎓 Research Foundation\n\nBased on comprehensive analysis of academic literature and industry standards:\n\n- **Mathematical Models**: Weighted Product Model, AHP-TOPSIS hybrid\n- **Normalization Techniques**: Hybrid z-score and min-max with outlier detection\n- **Correlation Validation**: Pearson/Spearman analysis with VIF multicollinearity detection\n- **Statistical Distribution**: Shapiro-Wilk normality testing with entropy validation\n\n## 📋 Development Status\n\n### ✅ Phase 1 Complete: Project Foundation\n- [x] Go module structure and Cobra CLI framework\n- [x] Complete command architecture (scan, compare, validate, algorithms)\n- [x] Comprehensive Makefile with build/test/install targets\n- [x] Project documentation and development plan\n\n### 🔄 Phase 2 In Progress: Core Models & Data Structures\n- [ ] Project detection and type classification\n- [ ] BenchmarkResult structures for 7-metric system\n- [ ] Statistical validation result models\n- [ ] Configuration management\n\n### 📅 Upcoming Phases\n- **Phase 3**: Project Detection & Analysis Engine\n- **Phase 4**: V4 Benchmarking Algorithms Implementation \n- **Phase 5**: Statistical Validation Framework\n- **Phase 6**: CLI Interface Enhancement\n- **Phase 7**: Advanced Analysis Features\n- **Phase 8**: Testing & Quality Assurance\n\n## 🤝 Contributing\n\n1. Fork the repository\n2. Create feature branch (`git checkout -b feature/amazing-feature`)\n3. Run tests (`make check`)\n4. Commit changes (`git commit -m 'Add amazing feature'`)\n5. Push to branch (`git push origin feature/amazing-feature`)\n6. Open Pull Request\n\n## 📄 License\n\nMIT License - see LICENSE file for details.\n\n## 🔗 References\n\n- ISO/IEC 25010:2023 Systems and Software Quality Requirements\n- Triantaphyllou, E. (2000). Multi-criteria decision making methods\n- AHP-TOPSIS Methodology for objective weight calculation\n- Portfolio CLI System - Research foundation and algorithm validation\n\n---\n\n**Status**: Phase 1 Complete - Core CLI framework implemented \n**Next**: Phase 2 - Core Models & Data Structures \n**Target**: Enterprise-grade benchmarking tool with academic validation", "has_readme": true, "url": "https://github.com/Geijutsu/benchmark", "category": "unclassified", "value_tier": "standard", "value_rank": 2, "editorial_note": null, "similarity_group": "Research & Knowledge", "group_score": 11, "similar": [ { "id": "MorchestraWorld/benchmark", "score": 1.0, "signals": [ "benchmark", "research", "analysis" ] }, { "id": "quivent/benchmarker", "score": 0.997, "signals": [ "benchmark", "research", "analysis" ] }, { "id": "TSMCP/monetize", "score": 0.197, "signals": [ "benchmark", "research", "analysis" ] }, { "id": "MorchestraWorld/monetize", "score": 0.197, "signals": [ "benchmark", "research", "analysis" ] }, { "id": "CherryMesh/gatherer", "score": 0.1683, "signals": [ "benchmark", "research", "analysis" ] } ] }, { "organization": "Geijutsu", "name": "capsule", "source": "R2 Git bundle", "published_at": "2025-11-30T23:21:23+00:00", "readme": "", "has_readme": false, "url": "https://github.com/Geijutsu/capsule", "category": "unclassified", "value_tier": "standard", "value_rank": 2, "editorial_note": null, "similarity_group": "Other Experiments", "group_score": 0, "similar": [ { "id": "CherryMesh/capsule", "score": 1.0, "signals": [ "capsule" ] }, { "id": "quivent/cherry-blossom", "score": 0.0826, "signals": [ "capsule" ] }, { "id": "AmadeusInnovations/cherry-blossom", "score": 0.0826, "signals": [ "capsule" ] }, { "id": "MorchestraWorld/sakura", "score": 0.0824, "signals": [ "capsule" ] }, { "id": "Geijutsu/sakura", "score": 0.0824, "signals": [ "capsule" ] } ] }, { "organization": "Geijutsu", "name": "cherry", "source": "R2 Git bundle", "published_at": "2025-11-30T23:21:24+00:00", "readme": "# 🌸 Cherry CLI - Revolutionary Server Management Platform\n\n[![Version](https://img.shields.io/badge/version-1.0.0-pink.svg)](https://github.com/cherryservers/cherry-cli)\n[![License](https://img.shields.io/badge/license-MIT-green.svg)](LICENSE)\n[![Security](https://img.shields.io/badge/encryption-AES--256--GCM-blue.svg)](docs/security.md)\n[![Platform](https://img.shields.io/badge/platform-macOS%20%7C%20Linux%20%7C%20Windows-lightgrey.svg)](docs/installation.md)\n\n> **The world's first CLI with complete server state snapshotting and cherry blossom-themed aesthetics.**\n\nA revolutionary, security-first command-line interface for Cherry Servers infrastructure management featuring military-grade encryption, complete server state capture via the Capsule System, and beautiful cherry blossom-themed user experience.\n\n---\n\n## ✨ Revolutionary Features\n\n### 💊 **Cherry Server Capsule System** - *World's First Complete Server State Capture*\n- 🔬 **Complete Server Snapshot**: Users, packages, services, configs, data, SSH keys, crontabs, network settings\n- 🔐 **Military-Grade Security**: AES-256-GCM encryption with SHA-512 integrity verification\n- 📦 **Intelligent Optimization**: 90%+ size reduction through rebuild artifact detection\n- 🚀 **Secure Transfer**: Encrypted transmission with remote confirmation\n- ⚡ **Automated Restoration**: One-command server recreation from capsules\n\n### 🔐 **Enterprise-Grade Security**\n- **AES-256-GCM Encryption** for all data transfers\n- **TLS 1.3** for secure API communications\n- **GPG Integration** for file encryption\n- **SSH Key Management** with automated deployment\n- **Zero-Knowledge Operation** with automatic cleanup\n\n### 🌸 **Blossom System** - *Enhanced User Experience*\n- **Smart SSH Management** with automatic user switching\n- **Cherry Blossom Aesthetics** with sakura-themed interface\n- **Emoji-Rich Feedback** for immediate visual context\n- **Progressive Help System** with contextual guidance\n\n### 🌐 **Advanced P2P Networking**\n- **Peer Discovery** with automatic topology mapping\n- **NAT Traversal** using sophisticated hole-punching\n- **End-to-End Encryption** for secure peer communication\n- **Load Balancing** with intelligent peer selection\n- **Fault Tolerance** with automatic failover\n\n---\n\n## 🚀 Quick Start\n\n### Installation\n\n**macOS/Linux:**\n```bash\n# One-line installation (both binaries)\ncurl -sSL https://raw.githubusercontent.com/cherryservers/cherry-cli/main/install.sh | bash\n\n# Manual installation (both binaries)\ngit clone https://github.com/cherryservers/cherry-cli.git\ncd cherry-cli\nmake && make install\n\n# Install individual binaries\nmake install-foundation # Installs seedling\nmake install-cherry # Installs cherry\n```\n\n**Windows (WSL2):**\n```powershell\n# Install WSL2 + Ubuntu (Run as Administrator)\nwsl --install\n\n# In Ubuntu terminal\nsudo apt update && apt install -y build-essential libssl-dev git\ngit clone https://github.com/cherryservers/cherry-cli.git\ncd cherry-cli && make && make install\n```\n\n### First Time Setup\n\n```bash\n# Initialize configuration\ncherry init\n\n# Set your Cherry Servers API token\nexport CHERRY_AUTH_TOKEN=\"your-token-here\"\n\n# Verify installation\nseedling --version # Foundation implementation (v1.0.0)\ncherry --version # Primary development interface (v2.1.0-nightly)\ncherry list\n```\n\n---\n\n## 🎯 Core Usage Examples\n\n### 💊 **Capsule System** - Complete Server Management\n```bash\n# Create complete server snapshot\ncherry server produce capsule\n# → Creates encrypted .capsule file with full server state\n\n# Transfer server state to new environment\ncherry server beam capsule production-server\n# → Secure transmission with integrity verification\n\n# Restore complete server from capsule\ncherry server receive capsule\n# → Automated server recreation with all configurations\n```\n\n### 🔐 **Secure File Transfer**\n```bash\n# Transfer encrypted files\ncherry send document.pdf my-server # Single file with AES-256\ncherry send ./project/ production-server # Entire directory compressed\ncherry send backup.tar.gz staging-server # Large files optimized\n```\n\n### 🌸 **Enhanced SSH Management**\n```bash\n# Smart SSH with user switching\ncherry blossom # SSH to active server\ncherry blossom deploy # SSH and switch to 'deploy' user\ncherry blossom www-data # SSH and switch to 'www-data'\ncherry blossom pair # Set up SSH key authentication\n```\n\n### 🖥️ **Server Operations**\n```bash\n# Server management\ncherry server activate my-server # Set active server\ncherry server info # Detailed server information\ncherry server create --plan c1-small --image ubuntu_22_04\ncherry install docker # Install tools on active server\n```\n\n### 🌐 **P2P Networking**\n```bash\n# P2P operations\ncherry p2p init # Initialize P2P node\ncherry p2p peers # List connected peers\ncherry p2p send peer-id \"Hello Cherry!\" # Secure messaging\ncherry p2p discover # Network topology discovery\n```\n\n---\n\n## 🏗️ Architecture Overview\n\nCherry CLI implements a **dual-architecture approach** with two complementary implementations:\n\n### 🏛️ **Foundation Implementation** *(Production Ready)*\n- **Binary**: `seedling`\n- **Status**: ✅ **Fully Functional** with 120+ commands\n- **Architecture**: Mature monolithic design with proven stability\n- **Features**: Complete Capsule System, P2P networking, security features\n- **Use Case**: Production deployments requiring immediate functionality\n\n### ⚡ **Cherry Implementation** *(Primary Development)*\n- **Binary**: `cherry`\n- **Status**: 🚧 **Active Development** (modular design complete)\n- **Architecture**: Clean modular structure with enhanced performance\n- **Features**: Memory-safe design, <30ms startup, comprehensive testing\n- **Use Case**: Primary development interface with modern C practices\n\n```\ncherry-cli/\n├── interfaces/\n│ ├── foundation/ # 🏛️ Production-ready implementation (seedling)\n│ │ ├── src/ # 36 source files, 120+ commands\n│ │ ├── include/ # Comprehensive headers\n│ │ └── platforms/ # Multi-platform support\n│ └── cherry/ # ⚡ Primary development interface (cherry)\n│ ├── src/\n│ │ ├── core/ # System initialization\n│ │ ├── commands/ # Modular command structure\n│ │ ├── lib/ # Core libraries\n│ │ └── p2p/ # P2P networking subsystem\n│ └── tests/ # Comprehensive test suite\n```\n\n---\n\n## 🔒 Security & Compliance\n\n### **Encryption Standards**\n- **AES-256-GCM**: File and capsule encryption\n- **SHA-512**: Integrity verification\n- **TLS 1.3**: API communications\n- **GPG**: Additional file encryption layer\n- **libsodium**: P2P networking security\n\n### **Security Certifications**\n- ✅ **Buffer Overflow Protection**\n- ✅ **Memory Leak Prevention**\n- ✅ **Input Validation & Sanitization**\n- ✅ **Principle of Least Privilege**\n- ✅ **Zero-Knowledge Temporary Files**\n\n### **Compliance Features**\n- **Audit Logging**: Comprehensive operation tracking\n- **Access Control**: Role-based permissions\n- **Data Residency**: Configurable storage locations\n- **Encryption at Rest**: All stored data encrypted\n\n---\n\n## 🖥️ Platform Support\n\n| Platform | Status | Installation Method | Notes |\n|----------|--------|-------------------|--------|\n| **macOS** | ✅ Full Support | Homebrew, Source | Native performance |\n| **Linux** | ✅ Full Support | Package Manager, Source | All major distributions |\n| **Windows** | ✅ WSL2 Support | WSL2 + Ubuntu | Cherry iTerm experience |\n| **ARM64** | ✅ Native Support | Source compilation | Apple Silicon, ARM servers |\n\n### **Windows Integration**\n- 🌸 **Cherry iTerm Wrapper**: Complete iTerm experience in Windows Terminal\n- 🤖 **Claude Code Integration**: AI-powered development workflows\n- ⌨️ **iTerm-Style Shortcuts**: Familiar macOS hotkeys (Ctrl+T, Ctrl+D)\n- 🎨 **Custom Themes**: Cherry-branded color schemes\n- 💾 **Session Management**: Multi-project layout persistence\n\n---\n\n## 📊 Performance Specifications\n\n### **Foundation Implementation**\n| Metric | Specification | Typical Performance |\n|--------|---------------|-------------------|\n| Startup Time | <100ms | ~50ms |\n| Memory Usage | <8MB | ~4MB |\n| Command Response | <200ms | ~100ms |\n| File Transfer | 50MB/s+ | ~80MB/s |\n\n### **Evolution Implementation**\n| Metric | Target | Achieved |\n|--------|--------|----------|\n| Startup Time | <30ms | ~15ms |\n| Memory Usage | <4MB | ~2MB |\n| Binary Size | <2MB | ~1.5MB |\n| Response Time | <50ms | ~25ms |\n\n---\n\n## 🧪 Command Reference\n\n### **Server Management**\n```bash\ncherry list # List all servers\ncherry info # Detailed server info\ncherry create --plan c1-small --image ubuntu # Create server\ncherry server activate # Set active server\ncherry ssh [user] # SSH connection\n```\n\n### **File Operations**\n```bash\ncherry send # Encrypted file transfer\ncherry retrieve : # Secure file retrieval\ncherry deploy # Project deployment\n```\n\n### **Idea Management**\n```bash\ncherry idea add \"API Rate Limiting\" # Capture new ideas\ncherry idea list --priority 4,5 # Review high-priority ideas \ncherry idea search \"authentication\" # Find related concepts\ncherry idea connect 23 31 --type implements # Link related ideas\ncherry idea analyze 42 --enhance # AI-powered idea analysis\n```\n\n### **Advanced Features**\n```bash\ncherry server produce capsule # Create server snapshot\ncherry server beam capsule # Transfer server state\ncherry blossom [user] # Enhanced SSH\ncherry p2p init # P2P networking\ncherry install # Tool installation\n```\n\n### **Configuration & Diagnostics**\n```bash\ncherry init # Initial setup\ncherry config show # View configuration\ncherry doctor # System health check\ncherry --help # Comprehensive help\n```\n\n---\n\n## 🎨 Cherry Blossom Experience\n\n### **Visual Theme**\n- 🌸 **Sakura Pink**: Primary accent for key operations\n- 🌿 **Spring Green**: Success states and positive feedback\n- 🌌 **Sky Blue**: Information and guidance\n- 🤍 **Cherry White**: Clean, readable text\n- 🌙 **Twilight Purple**: Error states and warnings\n\n### **User Interface Elements**\n- **Emoji-Rich Feedback**: Visual context for operations\n- **Progressive Loading**: Beautiful progress indicators\n- **Contextual Help**: Smart suggestions and guidance\n- **Accessibility**: WCAG-compliant color schemes\n- **Multi-Theme Support**: Dark, light, and monochrome modes\n\n---\n\n## 🤝 Contributing\n\n### **Development Setup**\n```bash\n# Clone repository\ngit clone https://github.com/cherryservers/cherry-cli.git\ncd cherry-cli\n\n# Foundation implementation\ncd implementations/foundation\nmake clean && make debug\n\n# Evolution implementation \ncd implementations/evolution\nmkdir build && cd build\ncmake -DCMAKE_BUILD_TYPE=Debug ..\nmake -j$(nproc)\n```\n\n### **Code Standards**\n- **C Standard**: C11 with GNU extensions\n- **Memory Safety**: Comprehensive bounds checking\n- **Documentation**: Doxygen-compatible comments\n- **Testing**: Unit and integration test coverage\n- **Security**: Static analysis and vulnerability scanning\n\n### **Contribution Process**\n1. Fork the repository\n2. Create feature branch following naming conventions\n3. Implement changes with comprehensive tests\n4. Ensure security and performance standards\n5. Submit pull request with detailed description\n\n---\n\n## 📚 Documentation\n\n### **User Guides**\n- [Installation Guide](docs/installation.md)\n- [Configuration Reference](docs/configuration.md)\n- [Command Reference](docs/commands.md)\n- [Security Best Practices](docs/security.md)\n\n### **Technical Documentation**\n- [Architecture Overview](docs/architecture.md)\n- [Idea Management System](docs/architecture/CHERRY_IDEA_MANAGEMENT_SPECIFICATION.md)\n- [P2P Networking Guide](docs/p2p.md)\n- [API Integration](docs/api.md)\n- [Performance Tuning](docs/performance.md)\n\n### **Platform-Specific**\n- [Windows Setup Guide](implementations/foundation/platforms/windows/README.md)\n- [macOS Optimization](docs/macos.md)\n- [Linux Distribution Notes](docs/linux.md)\n\n---\n\n## 🆘 Support & Community\n\n### **Getting Help**\n- 📖 **Documentation**: Comprehensive guides and references\n- 🐛 **GitHub Issues**: Bug reports and feature requests\n- 💬 **Discussions**: Community questions and support\n- 📧 **Security**: security@cherryservers.com for vulnerabilities\n\n### **Community Resources**\n- **Cherry Servers API**: [https://docs.cherryservers.com/](https://docs.cherryservers.com/)\n- **cherryctl CLI**: [https://github.com/cherryservers/cherryctl](https://github.com/cherryservers/cherryctl)\n- **Community Forum**: [https://community.cherryservers.com/](https://community.cherryservers.com/)\n\n---\n\n## 📄 License & Acknowledgments\n\n### **License**\nCherry CLI is released under the **MIT License**. See [LICENSE](LICENSE) for complete terms.\n\n### **Acknowledgments**\n- **Cherry Servers Team**: API and infrastructure support\n- **OpenSSL Project**: Cryptographic foundation\n- **libsodium Developers**: Modern cryptography library\n- **Security Researchers**: Vulnerability disclosure and improvements\n- **Open Source Community**: Dependencies and continuous improvement\n\n### **Security Disclosure**\nFor security vulnerabilities, please email security@cherryservers.com with details. We follow responsible disclosure practices and will acknowledge contributions appropriately.\n\n---\n\n## 🔮 Roadmap & Future Vision\n\n### **Completed Revolutionary Features** ✅\n- [x] Cherry Server Capsule System with AES-256-GCM encryption\n- [x] Complete server state snapshotting and restoration\n- [x] Blossom user management with SSH automation\n- [x] Advanced P2P networking with NAT traversal\n- [x] Secure file transfer with GPG integration\n- [x] Windows support via Cherry iTerm wrapper\n\n### **Next-Generation Enhancements** 🚀\n- [x] **Idea Management System**: Comprehensive concept capture and development workflow\n- [ ] **AI-Powered Optimization**: ML-based server configuration recommendations\n- [ ] **Distributed Capsules**: Multi-server orchestrated snapshots\n- [ ] **Cloud Storage Integration**: Direct AWS S3/GCS capsule storage\n- [ ] **Incremental Snapshots**: Delta-based updates for efficiency\n- [ ] **Performance Analytics**: Real-time optimization recommendations\n\n### **Enterprise Features** 🏢\n- [ ] **Multi-Tenant Architecture**: Organization-based access control\n- [ ] **Policy Engine**: Rule-based automation and security enforcement\n- [ ] **Disaster Recovery**: Automated failover and restoration workflows\n- [ ] **Compliance Dashboard**: Audit trail and compliance reporting\n- [ ] **API Management**: RESTful API for programmatic access\n\n---\n\n
\n\n**🌸 Made with love and cherry blossoms 🌸**\n\n*Where revolutionary technology meets beautiful design in server management.*\n\n[![Cherry Servers](https://img.shields.io/badge/Powered%20by-Cherry%20Servers-pink.svg)](https://www.cherryservers.com/)\n[![Built with C](https://img.shields.io/badge/Built%20with-C-blue.svg)](https://en.wikipedia.org/wiki/C_(programming_language))\n[![Security First](https://img.shields.io/badge/Security-First-green.svg)](docs/security.md)\n\n
", "has_readme": true, "url": "https://github.com/Geijutsu/cherry", "category": "unclassified", "value_tier": "standard", "value_rank": 2, "editorial_note": null, "similarity_group": "Developer Tools", "group_score": 13, "similar": [ { "id": "CherryMesh/cherry", "score": 1.0, "signals": [ "package", "library", "automation" ] }, { "id": "quivent/cherry-blossom", "score": 0.9789, "signals": [ "package", "library", "automation" ] }, { "id": "AmadeusInnovations/cherry-blossom", "score": 0.9789, "signals": [ "package", "library", "automation" ] }, { "id": "MorchestraWorld/sakura", "score": 0.9786, "signals": [ "package", "library", "automation" ] }, { "id": "Geijutsu/sakura", "score": 0.9786, "signals": [ "package", "library", "automation" ] } ] }, { "organization": "Geijutsu", "name": "dom", "source": "R2 Git bundle", "published_at": "2025-11-30T23:21:58+00:00", "readme": "# dom - Dead Simple DNS CLI\n\nIntuitive CLI for managing DNS records with support for multiple providers and partial domain matching.\n\n## Installation\n\n```bash\ncd ~/dom\nmake install\n```\n\n## Quick Start\n\n### 1. Add providers\n```bash\n# Cloudflare\ndom provider add cloudflare --token YOUR_CLOUDFLARE_TOKEN\n\n# Namecheap\ndom provider add namecheap --token YOUR_API_KEY --api-user YOUR_USERNAME\n\n# DigitalOcean\ndom provider add digitalocean --token YOUR_DO_TOKEN\n\n# GoDaddy\ndom provider add godaddy --token YOUR_API_SECRET --api-key YOUR_API_KEY\n```\n\n### 2. List your domains (from all providers)\n```bash\ndom domains\n```\n\n### 3. Add DNS records\n```bash\n# Add wildcard (*.domain) - automatically finds the right provider\ndom record wildcard geijutsu\n\n# Add subdomain\ndom record add geijutsu www\n```\n\n## Commands\n\n### Provider Management\n\n```bash\n# List providers\ndom providers\n\n# Add providers\ndom provider add cloudflare --token YOUR_TOKEN\ndom provider add namecheap --token YOUR_API_KEY --api-user YOUR_USERNAME\ndom provider add digitalocean --token YOUR_DO_TOKEN\ndom provider add route53 --token YOUR_AWS_ACCESS_KEY\ndom provider add godaddy --token YOUR_API_SECRET --api-key YOUR_API_KEY\ndom provider add linode --token YOUR_LINODE_TOKEN\n\n# Remove provider\ndom provider remove cloudflare\n```\n\n### View Domains & Records\n\n```bash\n# List all domains from all providers\ndom domains\n\n# Show DNS records (searches across all providers)\ndom domain geijutsu # Matches geijutsu.work from any provider\ndom domain ocean # Matches oceanica.network from any provider\ndom domain geijutsu.work # Exact match\ndom domain network # Shows all .network domains\n```\n\n### Add DNS Records\n\n```bash\n# Add subdomain A record (searches across all providers)\ndom record add geijutsu www # Uses server IP\ndom record add ocean api --ip 1.2.3.4 # Custom IP\ndom record add moe app --ip 5.6.7.8 --proxy # With Cloudflare proxy\n\n# Add wildcard A record\ndom record wildcard geijutsu # *.geijutsu.work → server IP\ndom record wildcard ocean --ip 1.2.3.4 # Custom IP\n```\n\n## Supported Providers\n\n- **Cloudflare** - Full support (records, wildcards, proxy)\n- **Namecheap** - Domain listing (record management coming soon)\n- **DigitalOcean** - Domain listing (record management coming soon)\n- **AWS Route 53** - Planned\n- **GoDaddy** - Domain listing (record management coming soon)\n- **Linode** - Domain listing (record management coming soon)\n\n## Features\n\n### Multi-Provider Support\nConfigure multiple DNS providers and manage domains across all of them:\n```bash\ndom provider add cloudflare-prod --token XXX\ndom provider add cloudflare-dev --token YYY\ndom provider add digitalocean --token ZZZ\ndom domains # Shows domains from all providers\n```\n\n### Cross-Provider Domain Search\nAll commands automatically search across all configured providers:\n- `geijutsu` matches `geijutsu.work` regardless of which provider hosts it\n- `ocean` matches `oceanica.network` from any configured provider\n- If multiple providers have matching domains, you'll be shown all matches\n\n### Smart Defaults\n- **IP**: Auto-detects server's public IP\n- **Proxy**: Disabled by default (DNS-only, no caching)\n- **TTL**: 120 seconds\n\n## Examples\n\n```bash\n# Setup multiple providers\ndom provider add cloudflare --token YOUR_CF_TOKEN\ndom provider add digitalocean --token YOUR_DO_TOKEN\n\n# Point all subdomains to this server (finds the right provider automatically)\ndom record wildcard geijutsu\n\n# Add specific subdomains\ndom record add geijutsu www\ndom record add geijutsu api\ndom record add ocean dashboard --ip 10.0.0.5\n\n# View records from all providers\ndom domains\n\n# View specific domain records\ndom domain geijutsu\n\n# With Cloudflare proxy enabled\ndom record add moe cdn --proxy\n```", "has_readme": true, "url": "https://github.com/Geijutsu/dom", "category": "unclassified", "value_tier": "standard", "value_rank": 2, "editorial_note": null, "similarity_group": "Infrastructure & Operations", "group_score": 8, "similar": [ { "id": "Geijutsu/gatherer", "score": 0.1501, "signals": [ "coming", "soon", "support" ] }, { "id": "Oceantica/Ocean", "score": 0.1396, "signals": [ "ocean" ] }, { "id": "MorchestraWorld/PortAuthority", "score": 0.1281, "signals": [ "server", "subdomains", "subdomain" ] }, { "id": "Oceantica/Map", "score": 0.1226, "signals": [ "dns", "proxy", "server" ] }, { "id": "quivent/PortAuthority", "score": 0.1167, "signals": [ "subdomains", "subdomain", "hosts" ] } ] }, { "organization": "Geijutsu", "name": "Duchess", "source": "R2 Git bundle", "published_at": "2026-05-06T21:18:04+00:00", "readme": "# Duchess - AI-Powered Global Luxury Product Sourcing Platform\n\nAn intelligent Shopify enterprise platform leveraging AI and multi-agent systems to curate, source, and sell premium products from around the world, powered by decades of luxury travel experience and global sourcing expertise.\n\n## Overview\n\nDuchess transforms world-class product sourcing expertise into an automated, scalable e-commerce platform. By combining AI-driven research, multi-agent coordination, and deep influencer networks, this platform identifies, validates, and sells high-quality products with optimal arbitrage opportunities.\n\n## Key Features\n\n- **AI-Driven Product Research**: Autonomous multi-agent system for continuous product discovery and market analysis\n- **Global Sourcing Intelligence**: Leverages luxury travel experience and international supplier networks\n- **Bulk Arbitrage Optimization**: Identifies and executes profitable bulk purchasing opportunities\n- **Influencer Network Integration**: Built-in connections to marketing and distribution channels\n- **Automated Shopify Integration**: Seamless platform for product listing, inventory, and sales management\n- **Quality-First Curation**: AI-assisted validation ensuring only premium products reach the platform\n\n## Primary Agent System\n\n**Agent Name**: Multi-Agent Coordination System\n**Agent Type**: Luxury Product Sourcing Platform\n**Implementation**: FastAPI backend (`/home/taobot/Autominers/Duchess/main.py`)\n**Total Agents**: 18 specialized autonomous agents\n\n### LLM Configuration\n- **Primary Model**: OpenAI GPT-4 Turbo (`gpt-4-turbo-preview`)\n- **Fallback Models**: Anthropic Claude 3 Opus, Ollama local models (llama3.2)\n- **Framework**: LangChain for provider abstraction\n- **API Keys Required**: `OPENAI_API_KEY`, `ANTHROPIC_API_KEY` (optional)\n\n### Data Mining Strategy\nThe Duchess platform employs AI-driven product research through specialized autonomous agents:\n\n**Data Sources**:\n- Shopify API (e-commerce integration)\n- Luxury product databases (global sourcing)\n- Supplier networks (verification and validation)\n- Market data APIs (pricing and trends)\n- Influencer networks (distribution channels)\n\n**Mining Techniques**:\n- Automated supplier validation and quality assessment\n- Real-time market analysis and trend detection\n- Arbitrage opportunity calculation\n- Quality scoring algorithms (0-10 scale)\n- Multi-agent collaborative research\n\n**Agent Coordination**:\nAll 18 agents communicate via Redis pub/sub and Celery task queue for distributed processing.\n\n## Architecture\n\nThe platform operates through coordinated specialized agents:\n\n- **Research Agents**: Discover products, analyze trends, and identify opportunities\n- **Learner Agents**: Continuously improve sourcing strategies through market feedback\n- **Analyst Agents**: Evaluate product viability, pricing, and market positioning\n- **Finance Agents**: Calculate arbitrage opportunities and optimize bulk purchasing\n- **Protocol Agents**: Coordinate multi-agent workflows and maintain knowledge repositories\n- **Quality Assessment Agents**: Evaluate product quality and supplier reliability\n- **Shopify Integration Agents**: Automate product publishing and inventory sync\n- **Pricing Strategy Agents**: Optimize pricing based on market conditions\n- **Store Developer Agents**: Manage store optimization and customer experience\n- **Inventory Manager Agents**: Track stock levels and reorder points\n- **Content Creation Agents**: Generate product descriptions and SEO content\n- **Product Manager Agents**: Oversee product lifecycle from discovery to sale\n\n## Quick Start\n\n### Prerequisites\n\n- Shopify account with API access\n- OpenAI API key or compatible AI service\n- Node.js 18+ or Python 3.10+\n- Database system (PostgreSQL recommended)\n\n### Installation\n\n```bash\n# Clone the repository\ngit clone \ncd Duchess\n\n# Install dependencies\nnpm install\n# or\npip install -r requirements.txt\n\n# Configure environment\ncp .env.example .env\n# Edit .env with your API keys and credentials\n\n# Initialize database\nnpm run db:migrate\n# or\npython manage.py migrate\n\n# Start the platform\nnpm run start\n# or\npython main.py\n```\n\n### Configuration\n\nEdit `.env` with your credentials:\n\n```env\nSHOPIFY_SHOP_NAME=your-shop-name\nSHOPIFY_API_KEY=your-api-key\nSHOPIFY_API_SECRET=your-api-secret\nAI_API_KEY=your-ai-api-key\nDATABASE_URL=postgresql://user:pass@localhost/duchess\n```\n\n## Usage\n\n### Starting the Research System\n\n```bash\n# Launch iterative research agents\nnpm run agents:research --category \"luxury-textiles\"\n\n# Start continuous learning cycle\nnpm run agents:learn --iterations 10\n\n# Generate market analysis\nnpm run analyze:market --product-type \"artisan-goods\"\n```\n\n### Product Sourcing Workflow\n\n1. **Discovery**: Research agents identify potential products\n2. **Analysis**: Analyst and finance agents evaluate opportunities\n3. **Validation**: Quality checks and supplier verification\n4. **Integration**: Automated Shopify listing creation\n5. **Optimization**: Continuous learning from sales data\n\n### Managing the Knowledge Base\n\n```bash\n# View accumulated research\nnpm run knowledge:view\n\n# Export findings\nnpm run knowledge:export --format json\n\n# Update strategies\nnpm run strategy:update\n```\n\n## Project Structure\n\n```\nDuchess/\n├── agents/ # Multi-agent system implementations\n│ ├── research/ # Product discovery agents\n│ ├── learner/ # Adaptive learning agents\n│ ├── analyst/ # Market analysis agents\n│ └── finance/ # Arbitrage calculation agents\n├── shopify/ # Shopify integration modules\n├── knowledge/ # Research repository and databases\n├── strategies/ # Sourcing and sales strategies\n├── orchestration/ # Agent coordination system\n└── platform/ # Application interface\n```\n\n## Development Roadmap\n\n### Phase 1: Foundation (Current)\n- [ ] Multi-agent research system\n- [ ] Knowledge repository architecture\n- [ ] Basic Shopify integration\n\n### Phase 2: Intelligence\n- [ ] Advanced product discovery algorithms\n- [ ] Automated supplier validation\n- [ ] Bulk arbitrage optimization engine\n\n### Phase 3: Scale\n- [ ] Influencer network integration\n- [ ] Multi-category expansion\n- [ ] International logistics automation\n\n### Phase 4: Enterprise\n- [ ] White-label platform capabilities\n- [ ] Advanced analytics dashboard\n- [ ] API marketplace for sourcing data\n\n## Contributing\n\nThis project combines expertise in AI, music, luxury travel, and global product sourcing. Contributions in the following areas are particularly valuable:\n\n- AI/ML model improvements for product discovery\n- Shopify integration enhancements\n- Supplier network expansion\n- Quality validation algorithms\n- Influencer marketing automation\n\n## Documentation\n\n### Core Documentation\n- [PURPOSE.md](./PURPOSE.md) - Core mission and objectives\n- [INTENT.md](./INTENT.md) - User goals and use cases\n- [CONCEPTS.md](./CONCEPTS.md) - Key terminology and frameworks\n- [METHODS.md](./METHODS.md) - Implementation methodologies\n- [SPECIFICATION.md](./SPECIFICATION.md) - Technical specifications\n- [CLAUDE.md](./CLAUDE.md) - AI collaboration guidelines\n\n### Getting Started\n- [QUICK_START.md](./QUICK_START.md) - Get up and running quickly\n- [DEPLOYMENT.md](./DEPLOYMENT.md) - Deployment guide\n\n### Detailed Documentation (`docs/`)\n| Category | Description |\n|----------|-------------|\n| [docs/agents/](./docs/agents/) | Agent specifications, implementations, and quick references |\n| [docs/architecture/](./docs/architecture/) | System architecture and design documents |\n| [docs/security/](./docs/security/) | Security audits, policies, and remediation guides |\n| [docs/database/](./docs/database/) | Database setup, migrations, and configuration |\n| [docs/monitoring/](./docs/monitoring/) | Monitoring, metrics, and health checks |\n| [docs/integration/](./docs/integration/) | Shopify and event broadcasting integration |\n| [docs/orchestration/](./docs/orchestration/) | Workflow and agent orchestration |\n| [docs/performance/](./docs/performance/) | Performance optimization strategies |\n\n### Archive (`archive/`)\nHistorical implementation reports and milestone documentation are preserved in the `archive/` directory.\n\n## License\n\n[To be determined based on business model]\n\n## Contact\n\nFor partnership inquiries, supplier network integration, or platform access, please contact [contact information].\n\n---\n\nBuilt with AI by experts who've spent lifetimes discovering the world's finest products.", "has_readme": true, "url": "https://github.com/Geijutsu/Duchess", "category": "unclassified", "value_tier": "standard", "value_rank": 2, "editorial_note": null, "similarity_group": "Agents & Orchestration", "group_score": 14, "similar": [ { "id": "quivent/Animate", "score": 0.232, "signals": [ "multi-agent", "autonomous", "collaboration" ] }, { "id": "quivent/TourGuide", "score": 0.2232, "signals": [ "collaboration", "workflow", "agent" ] }, { "id": "Oceantics/Instruments", "score": 0.2133, "signals": [ "collaboration", "agents", "workflow" ] }, { "id": "Oceantica/Instruments", "score": 0.2133, "signals": [ "collaboration", "agents", "workflow" ] }, { "id": "AGI-Film/Autonomous", "score": 0.2106, "signals": [ "multi-agent", "autonomous", "collaboration" ] } ] }, { "organization": "Geijutsu", "name": "english", "source": "R2 Git bundle", "published_at": "2025-11-30T23:21:27+00:00", "readme": "
\n\n# English Programming Language\n**Natural Language → Optimized Binary Compilation**\n\n[![Build Status](https://img.shields.io/badge/build-passing-brightgreen)](#) [![Platforms](https://img.shields.io/badge/platforms-macOS%20%7C%20Linux%20%7C%20Windows-blue)](#) [![Performance](https://img.shields.io/badge/throughput-1M%20cmd%2Fs-orange)](#) [![License](https://img.shields.io/badge/license-Private-red)](#) [![Version](https://img.shields.io/badge/version-0.1.0-blue)](#)\n\n*Transform natural English specifications into high-performance binary executables*\n\n[**Quick Start**](#-quick-start) • [**Installation**](#-installation) • [**Examples**](#-usage-examples) • [**Architecture**](#-architecture) • [**Performance**](#-performance-benchmarks)\n\n
\n\n---\n\n## 📋 Table of Contents\n\n- [Overview](#-overview)\n- [Quick Start](#-quick-start) \n- [Installation](#-installation)\n- [Usage Examples](#-usage-examples)\n- [Architecture](#-architecture)\n- [Performance Benchmarks](#-performance-benchmarks)\n- [Advanced Features](#-advanced-features)\n- [Development](#-development)\n- [Build Variants](#-build-variants)\n- [Troubleshooting](#-troubleshooting)\n- [FAQ](#-faq)\n- [Contributing](#-contributing)\n- [Support](#-support--contact)\n\n---\n\n## 🎯 Overview\n\nThe **English Programming Language** is a sophisticated natural language to binary compilation framework built in Rust. It transforms natural English specifications into structured command protocols and optimized binary executables, enabling developers to create and validate programs using intuitive natural language.\n\n### 🎯 Primary Use Case: Natural Language to Binary Compilation\n\nThe English Programming Language serves a unique niche: transforming natural English specifications directly into optimized binary executables. This is fundamentally different from traditional programming languages.\n\n#### 🔄 The Core Workflow\n\n```\n# Traditional Programming\nWrite Code → Compile → Execute\n\n# English Programming Language \nWrite Natural Language → Parse → Compile → Execute Binary\n```\n\n**Example Transformation:**\n```bash\n# Input: Natural English\nenglish parse \"create a file named config.json with default settings\"\n\n# Output: Parsed Specification\n✓ Operation: FileCreate {\n name: \"config.json\",\n content: \"default_settings\",\n permissions: 644\n}\n\n# Compilation (Full build)\nenglish compile \"create a file named config.json\" --output config_creator.bin\n# → Produces optimized binary executable\n```\n\n#### 🎨 Key Use Cases\n\n**1. Rapid Prototyping & Specification Validation**\n- **Problem**: Converting business requirements to code takes time\n- **Solution**: Directly express intent in natural language \n- **Benefit**: Immediate validation of understanding\n\n```bash\n# Business requirement validation\nenglish parse \"backup all user data weekly with encryption\"\n# → Immediately shows if the system understands the requirement correctly\n```\n\n**2. Non-Programmer Tool Creation**\n- **Problem**: Domain experts can't create tools without programming knowledge\n- **Solution**: Express tool behavior in natural language\n- **Benefit**: Domain experts become tool creators\n\n```bash\n# A data analyst creates a tool without coding\nenglish compile \"analyze CSV files for duplicate entries and generate report\"\n# → Creates a binary tool that does exactly what was described\n```\n\n**3. High-Performance Command Generation**\n- **Problem**: Shell scripts and interpreted languages are slow for repetitive tasks\n- **Solution**: Natural language compiles to optimized binary\n- **Benefit**: English-like expressiveness with C-level performance\n\n```bash\n# Performance-critical file processing\nenglish compile \"process log files larger than 1GB using parallel algorithms\"\n# → SIMD-optimized binary with parallel processing\n```\n\n**4. Educational Bridge**\n- **Problem**: Learning programming syntax barriers\n- **Solution**: Start with natural language, see the compilation process\n- **Benefit**: Gradual transition from English to code understanding\n\n```bash\n# Learning progression\nenglish parse \"sort a list of numbers\" --detailed\n# → Shows AST, compilation steps, helping understand programming concepts\n```\n\n**5. Specification-Driven Development**\n- **Problem**: Requirements often lose fidelity during code translation\n- **Solution**: Requirements ARE the executable code\n- **Benefit**: No translation errors between spec and implementation\n\n```bash\n# Executable specifications\nenglish compile \"validate email addresses according to RFC 5322 standard\"\n# → Binary that does exactly what the specification states\n```\n\n#### 🏗️ Architecture Advantages\n\n**Dual Build System**\n- **MVP Build**: Lightweight parsing and validation (8MB)\n- **Full Build**: Complete compilation with SIMD optimization (24MB)\n\n**Performance Characteristics**\n- **Parse Time**: <1ms cached, <10ms new specifications\n- **Compilation**: <1ms for optimized binary generation\n- **Throughput**: 1M+ commands/second processing capability\n\n#### 🚀 Real-World Scenarios\n\n**DevOps Automation**\n```bash\n# Instead of complex bash scripts\nenglish compile \"monitor disk usage and alert when 80% full\"\n# → Efficient binary with system monitoring capabilities\n```\n\n**Data Processing Pipelines**\n```bash\n# Instead of writing Python/Java data processors\nenglish compile \"transform JSON logs to CSV with timestamp normalization\"\n# → SIMD-optimized data transformation binary\n```\n\n**System Administration**\n```bash\n# Instead of memorizing complex command combinations\nenglish compile \"find files modified in last 24 hours larger than 100MB\"\n# → Optimized file system traversal binary\n```\n\n**Rapid Tool Creation**\n```bash\n# For one-off or specialized tools\nenglish compile \"calculate network bandwidth utilization from tcpdump output\"\n# → Specialized network analysis binary\n```\n\n#### 🎪 Unique Value Proposition\n\n**What Makes This Different:**\n1. **Not a Transpiler**: Doesn't convert to another programming language\n2. **Direct Binary Output**: Compiles natural language to optimized machine code\n3. **Performance Focus**: SIMD optimization and JIT execution\n4. **Specification Validation**: Immediate feedback on requirement understanding\n5. **Domain Expert Friendly**: No programming syntax required\n\n**Performance vs. Expressiveness Matrix:**\n```\nHigh Performance | Traditional C/Rust\n ↑ | ↑\n | | English Programming ← Unique Position\n | | ↓\nLow Performance | Shell Scripts/Python\n ←------------------------→\n Low Expressiveness High Expressiveness\n```\n\n#### 🎯 Target Audiences\n\n**Primary Users:**\n- **Domain Experts**: Who know what they want but not how to code it\n- **DevOps Engineers**: Who need quick, efficient automation tools\n- **Data Analysts**: Who want performance without programming complexity\n- **System Administrators**: Who need rapid, reliable tool creation\n\n**Secondary Users:**\n- **Rapid Prototypers**: Testing ideas quickly\n- **Educators**: Teaching programming concepts through natural language\n- **Requirements Engineers**: Validating specification understanding\n\n#### 🔮 Innovation Potential\n\nThis represents a paradigm shift from:\n- \"Learn to code\" → \"Describe what you want\"\n- \"Write programs\" → \"Express intent\"\n- \"Debug syntax\" → \"Refine specifications\"\n\nThe English Programming Language bridges the gap between human intent and machine execution, making computing accessible while maintaining the performance characteristics needed for production systems.\n\n### 🚀 Key Capabilities\n\n- **🧠 Advanced Natural Language Processing**: State-of-the-art parser using nom combinators for English specifications\n- **⚡ SIMD Optimization**: Hardware-accelerated compilation with SSE, AVX, NEON support\n- **🔥 JIT Execution Engine**: Cross-platform memory management and just-in-time execution\n- **📋 Dynamic Template System**: JSON/TOML/Rust template loading with optimization caching\n- **🏗️ Dual Build System**: MVP (lightweight) and Full (complete feature set) variants\n- **📊 Performance Focused**: Sub-millisecond parsing with comprehensive performance tracking\n\n---\n\n## ⚡ Quick Start\n\nGet up and running in under 2 minutes:\n\n```bash\n# 1. Clone the repository\ngit clone https://github.com/AmadeusInnovations/English.git\ncd English\n\n# 2. Install the full version\ncargo install --path . --features full\n\n# 3. Verify installation \nenglish --version\n# Expected output: English Programming CLI Framework 0.1.0\n\n# 4. Try your first natural language command\nenglish parse \"create a file named hello.txt\"\n# Expected output: Parsed specification with operation details\n\n# 5. Check system capabilities\nenglish info\n# Expected output: System information, build config, and performance targets\n```\n\n**🎉 Congratulations!** You now have a working natural language to binary compiler.\n\n---\n\n## 📦 Installation\n\n### Prerequisites\n\n- **Rust**: Version 1.70+ ([Install via rustup](https://rustup.rs/))\n- **Platform**: macOS (Intel/ARM), Linux (x86_64/ARM), Windows 10+ \n- **Memory**: 512MB available RAM for compilation\n- **Storage**: 200MB for full installation\n\n### Installation Options\n\n#### Option 1: Full Installation (Recommended)\n```bash\ngit clone https://github.com/AmadeusInnovations/English.git\ncd English\ncargo install --path . --features full\n```\n\n#### Option 2: MVP Installation (Lightweight)\n```bash\ngit clone https://github.com/AmadeusInnovations/English.git \ncd English\ncargo install --path . --features mvp\n```\n\n#### Option 3: Development Installation\n```bash\ngit clone https://github.com/AmadeusInnovations/English.git\ncd English\ncargo build --features full\ncargo run --features full -- --help\n```\n\n### Post-Installation Verification\n\n```bash\n# Verify binary location\nwhich english\n# Expected: /Users/[username]/.cargo/bin/english\n\n# Test core functionality\nenglish validate\n# Expected: System validation with performance metrics\n\n# Check all available commands\nenglish --help\n# Expected: Complete command listing with descriptions\n```\n\n### Troubleshooting Installation\n\n**Common Issues:**\n\n- **Rust version too old**: Update with `rustup update`\n- **Build fails on Windows**: Ensure Visual Studio Build Tools installed\n- **Permission denied**: Use `cargo install --path . --root ~/.local` instead\n- **Out of memory**: Use `cargo build --release` with `--jobs 1` flag\n\n---\n\n## 💡 Usage Examples\n\n### Basic Natural Language Processing\n\n```bash\n# Parse file operations\nenglish parse \"create a file named test.txt with content hello world\"\n# Output: \n# ✓ Parsed specification successfully\n# Operation: FileCreate { name: \"test.txt\", content: \"hello world\" }\n# Validation: PASSED\n# Performance: 0.8ms\n\n# Search operations\nenglish parse \"find all files containing the word config\" \n# Output:\n# ✓ Parsed specification successfully \n# Operation: FileSearch { pattern: \"config\", scope: \"all_files\" }\n# Validation: PASSED\n# Performance: 1.2ms\n```\n\n### System Information and Capabilities\n\n```bash\nenglish info\n# Output:\n# English Programming CLI Framework\n# Version: 0.1.0\n# Build: Full (advanced features enabled)\n# \n# System Information:\n# Architecture: aarch64\n# OS: macos\n# Available Instructions: [NEON, SIMD]\n# \n# Performance Targets:\n# NL Parse (cached): <1ms \n# NL Parse (new): <10ms\n# End-to-End: <5ms\n# Throughput: 1,000,000 commands/second\n```\n\n### Performance Monitoring\n\n```bash\n# Enable detailed performance tracking\nenglish parse \"list directory contents\" --perf\n# Output:\n# ✓ Specification: 'list directory contents'\n# ├─ Parse Time: 0.7ms\n# ├─ Template Load: 0.1ms \n# ├─ Validation: 0.2ms\n# └─ Total: 1.0ms\n\n# Disable caching for benchmarking\nenglish parse \"complex operation example\" --no-cache --perf\n# Output:\n# ✓ Cold performance measurement\n# Parse Time: 8.3ms (no cache)\n# Cache Miss Penalty: 7.6ms\n```\n\n### Template and Configuration Usage\n\n```bash\n# Show available templates\nenglish docs templates\n# Output: Lists all built-in operation templates\n\n# Load custom template\nenglish parse \"custom operation\" --template ./my-template.json\n# Output: Uses custom template for parsing\n```\n\n---\n\n## 🏗️ Architecture\n\n### Compilation Pipeline\n\n```mermaid\ngraph TD\n A[Natural Language Input] --> B[Advanced NL Parser]\n B --> C[Grammar Analysis & Validation]\n C --> D[Dynamic Template Resolution]\n D --> E[SIMD Optimization Layer]\n E --> F[JIT Memory Allocation]\n F --> G[Binary Code Generation]\n G --> H[Executable Output]\n \n I[Template Cache] --> D\n J[Performance Metrics] --> E\n K[Memory Manager] --> F\n```\n\n### Core Components\n\n#### 🧠 **Natural Language Parser** (`english-core`)\n- **Advanced Grammar**: nom combinators with English specification parsing\n- **Intent Recognition**: Sophisticated pattern matching and semantic analysis\n- **Context Awareness**: State-based parsing with memory of previous operations\n- **Performance**: <1ms cached, <10ms cold parsing with 99.3% accuracy\n\n#### 📋 **Dynamic Template System** (`english-compiler`) \n- **Multi-Format Support**: JSON, TOML, and native Rust templates\n- **Hot Reloading**: Development-friendly template updates without restart\n- **Optimization**: Template compilation and caching for performance\n- **Validation**: Schema validation and compatibility checking\n\n#### ⚡ **SIMD Optimizer** (`english-compiler`)\n- **Hardware Detection**: Automatic CPU feature detection (SSE, AVX, NEON, RVV)\n- **Pattern Optimization**: Common operation pattern recognition and acceleration\n- **Cache Management**: Intelligent caching with performance tracking\n- **Cross-Platform**: Unified optimization API across architectures\n\n#### 🔥 **JIT Execution Engine** (`english-execution`)\n- **Memory Management**: Cross-platform allocation (Unix mmap, Windows VirtualAlloc)\n- **Code Generation**: Runtime binary code compilation and execution\n- **Safety**: Memory protection and bounds checking\n- **Performance**: <1µs initialization, <10µs execution latency\n\n#### 🔌 **Protocol Layer** (`english-protocol`)\n- **Serialization**: Efficient message serialization with versioning\n- **Transport**: High-performance inter-component communication\n- **Error Handling**: Comprehensive error propagation and recovery\n- **Monitoring**: Built-in performance and health metrics\n\n---\n\n## 📊 Performance Benchmarks\n\n### Parsing Performance\n\n| Operation Type | Cold Parse | Cached Parse | Throughput | Memory |\n|---------------|------------|--------------|------------|---------|\n| **File Operations** | 8.2ms | 0.6ms | 1,250/sec | 12KB |\n| **String Processing** | 12.5ms | 0.8ms | 950/sec | 18KB |\n| **System Commands** | 6.1ms | 0.4ms | 1,800/sec | 8KB |\n| **Complex Queries** | 24.8ms | 1.2ms | 480/sec | 35KB |\n\n### System Resource Usage\n\n| Build Type | Binary Size | Memory Usage | Startup Time | CPU Usage |\n|------------|-------------|--------------|-------------|-----------|\n| **MVP** | 8.2MB | 45MB | 12ms | 0.3% |\n| **Full** | 23.7MB | 128MB | 28ms | 1.2% |\n\n### Real-World Benchmarks\n\n```bash\n# Benchmark: Processing 1000 mixed operations\nenglish validate --benchmark 1000\n# Results:\n# ├─ Average Parse Time: 1.3ms\n# ├─ 95th Percentile: 2.8ms \n# ├─ 99th Percentile: 12.1ms\n# ├─ Throughput: 785,000 operations/second\n# └─ Memory Peak: 156MB\n```\n\n---\n\n## 🚀 Advanced Features\n\n### SIMD Optimization\n\n```bash\n# Enable SIMD debugging to see optimizations\nENGLISH_DEBUG_SIMD=1 english parse \"batch process files\"\n# Output shows SIMD instruction selection and performance gains\n```\n\n### Memory Management Control\n\n```bash \n# Custom memory allocation limits\nenglish parse \"large operation\" --memory-limit 256M\n# Controls JIT memory allocation for resource-constrained environments\n```\n\n### Template Development\n\n```json\n// custom-template.json\n{\n \"patterns\": [\n {\n \"input\": \"create database table {name}\",\n \"operation\": \"DatabaseCreate\",\n \"parameters\": [\"name\"],\n \"validation\": \"schema_valid\"\n }\n ]\n}\n```\n\n---\n\n## 🛠️ Development\n\n### Project Structure\n\n```\nEnglish/\n├── english/ # Main CLI application and entry point\n├── english-core/ # Core parsing and protocol logic \n│ ├── src/nl_interface/ # Natural language processing\n│ ├── src/spec_parser/ # Specification parsing and validation\n│ └── src/common/ # Shared utilities and error handling\n├── english-compiler/ # SIMD optimization and compilation\n│ ├── src/codegen.rs # Binary code generation\n│ ├── src/simd_optimizer.rs # Hardware-accelerated optimization \n│ └── src/dynamic_loader.rs # Template loading and compilation\n├── english-execution/ # JIT execution and memory management\n│ ├── src/engine.rs # Core execution engine\n│ └── src/memory_manager.rs # Cross-platform memory allocation\n├── benchmarks/ # Performance benchmarking suite\n├── config/ # Configuration templates and examples\n├── docs/ # Comprehensive documentation\n├── examples/ # Usage examples and tutorials\n└── tests/ # Integration and unit tests\n```\n\n### Development Setup\n\n```bash\n# Clone and setup development environment\ngit clone https://github.com/AmadeusInnovations/English.git\ncd English\n\n# Install development dependencies\ncargo install --path . --features full\n\n# Run all tests\ncargo test --features full\n\n# Run specific test suites \ncargo test --features mvp nl_parser\ncargo test --features full simd_optimizer\n\n# Build for development with debug info\ncargo build --features full\n\n# Run integration tests\n./scripts/test_builds.sh\n```\n\n### Code Quality and Standards\n\n```bash\n# Format code\ncargo fmt --all\n\n# Run linter\ncargo clippy --all --features full\n\n# Check documentation\ncargo doc --open --features full\n\n# Profile performance\ncargo run --features full --release -- parse \"test\" --profile\n```\n\n---\n\n## ⚙️ Build Variants\n\n| Feature | MVP Build | Full Build | Description |\n|---------|-----------|------------|-------------|\n| **Natural Language Parser** | ✅ Core | ✅ Advanced | Basic vs. sophisticated parsing |\n| **Template System** | ✅ Static | ✅ Dynamic | Fixed vs. runtime template loading |\n| **SIMD Optimization** | ❌ | ✅ | Hardware acceleration disabled/enabled |\n| **JIT Execution** | ❌ | ✅ | Direct execution vs. binary generation |\n| **Performance Tracking** | ✅ Basic | ✅ Detailed | Simple vs. comprehensive metrics |\n| **Memory Management** | ✅ Standard | ✅ Advanced | Basic vs. optimized allocation |\n| **Binary Size** | ~8MB | ~24MB | Lightweight vs. full-featured |\n| **Startup Time** | ~12ms | ~28ms | Fast vs. feature-rich initialization |\n\n### Build Commands\n\n```bash\n# MVP build - lightweight, essential features only\ncargo build --features mvp\ncargo install --path . --features mvp\n\n# Full build - complete feature set \ncargo build --features full\ncargo install --path . --features full\n\n# Development build - debug symbols and verbose output\ncargo build --features full,debug\n```\n\n---\n\n## 🔧 Troubleshooting\n\n### Common Issues and Solutions\n\n#### Build Failures\n\n**Issue**: `cargo build` fails with linker errors\n```bash\n# Solution: Update Rust and install system dependencies\nrustup update \n# macOS: xcode-select --install\n# Ubuntu: sudo apt install build-essential \n# Windows: Install Visual Studio Build Tools\n```\n\n**Issue**: \"feature not found\" errors\n```bash\n# Solution: Use correct feature flags\ncargo build --features full # Not --feature (singular)\n```\n\n#### Runtime Issues\n\n**Issue**: \"english: command not found\"\n```bash \n# Solution: Add Cargo bin directory to PATH\necho 'export PATH=\"$HOME/.cargo/bin:$PATH\"' >> ~/.bashrc\nsource ~/.bashrc\n```\n\n**Issue**: Slow parsing performance\n```bash\n# Solution: Enable optimizations and check system resources\nenglish info # Check if SIMD is enabled\nENGLISH_DEBUG=1 english parse \"test\" --perf # Debug performance\n```\n\n#### Memory and Performance\n\n**Issue**: High memory usage during compilation\n```bash\n# Solution: Use memory-constrained builds\ncargo build --release --jobs 1\n# Or use MVP build for lower memory footprint\ncargo build --features mvp\n```\n\n### Debug Mode\n\n```bash\n# Enable debug logging \nENGLISH_DEBUG=1 english parse \"debug this\"\n\n# Enable SIMD debugging\nENGLISH_DEBUG_SIMD=1 english parse \"optimization test\"\n\n# Enable memory debugging\nENGLISH_DEBUG_MEMORY=1 english parse \"memory test\"\n\n# Enable all debugging \nENGLISH_DEBUG=1 ENGLISH_DEBUG_SIMD=1 ENGLISH_DEBUG_MEMORY=1 english parse \"full debug\"\n```\n\n---\n\n## ❓ FAQ\n\n### General Questions\n\n**Q: What makes this different from other natural language processing tools?** \nA: Unlike NLP libraries focused on analysis, we compile natural language directly to optimized binary code, achieving executable performance with intuitive natural language input.\n\n**Q: How accurate is the natural language parsing?** \nA: Current accuracy is 99.3% for structured commands and 94.7% for free-form natural language, with continuous improvement through template expansion.\n\n**Q: What programming languages can I generate code for?** \nA: Currently generates optimized binary code directly. Future versions will target C, Rust, and WebAssembly with preserving performance characteristics.\n\n### Performance Questions\n\n**Q: How fast is the compilation process?** \nA: Average end-to-end compilation is <5ms for cached operations, <25ms for new operations, with throughput exceeding 1M commands/second.\n\n**Q: Does it work offline?** \nA: Yes, completely offline after installation. No external APIs or network dependencies required for core functionality.\n\n**Q: What's the memory overhead?** \nA: MVP build uses ~45MB RAM, Full build uses ~128MB RAM during active compilation, with minimal runtime overhead.\n\n### Technical Questions\n\n**Q: Which CPU architectures are supported?** \nA: Full support for x86_64 (SSE, AVX), ARM64 (NEON), with experimental RISC-V support. Automatic CPU feature detection included.\n\n**Q: Can I extend it with custom operations?** \nA: Yes, through JSON/TOML templates for MVP builds and full Rust extensions for Full builds. Template hot-reloading supported in development.\n\n**Q: Is it thread-safe for concurrent usage?** \nA: Yes, designed for high-concurrency scenarios with lock-free parsing and isolated execution contexts per thread.\n\n### Integration Questions\n\n**Q: How do I integrate this into my existing build pipeline?** \nA: Use as CLI tool in scripts, or embed the `english-core` library directly into your Rust applications for programmatic access.\n\n**Q: Can I use this in production environments?** \nA: Currently in active development. MVP build is stable for non-critical applications, Full build recommended for evaluation only.\n\n**Q: What's the licensing for commercial use?** \nA: Private repository with proprietary license. Contact amadeus.innovations@example.com for commercial licensing discussions.\n\n### Development Questions \n\n**Q: How can I contribute new language patterns?** \nA: Submit template files via pull requests, or contact the development team for core parser enhancements.\n\n**Q: What's the development roadmap?** \nA: Focus areas: expanded language coverage, additional target platforms, IDE integration, and performance optimization.\n\n**Q: How do I report bugs or request features?** \nA: Use GitHub Issues for bug reports, feature requests, and discussions. Security issues should be reported privately.\n\n---\n\n## 🤝 Contributing\n\nWe welcome contributions to the English Programming Language project! Here's how you can help:\n\n### Priority Areas\n\n1. **Language Pattern Expansion**: Add support for new natural language constructs\n2. **Template Library**: Contribute operation templates for common use cases\n3. **Platform Support**: Help with Windows, Linux, and additional architecture support\n4. **Performance Optimization**: Identify and resolve bottlenecks\n5. **Documentation**: Improve examples, guides, and API documentation\n6. **Testing**: Expand test coverage and edge case handling\n\n### Contribution Process\n\n```bash\n# 1. Fork the repository on GitHub\n# 2. Clone your fork\ngit clone https://github.com/your-username/English.git \ncd English\n\n# 3. Create a feature branch\ngit checkout -b feature/your-feature-name\n\n# 4. Make your changes and test\ncargo test --features full\ncargo fmt --all\ncargo clippy --all\n\n# 5. Commit with clear messages\ngit commit -m \"Add support for conditional operations\"\n\n# 6. Push and create pull request\ngit push origin feature/your-feature-name\n```\n\n### Development Guidelines\n\n- **Code Style**: Run `cargo fmt` and `cargo clippy` before submitting\n- **Testing**: Include tests for new features and bug fixes \n- **Documentation**: Update relevant documentation and examples\n- **Performance**: Benchmark changes that might affect performance\n- **Compatibility**: Ensure changes work across supported platforms\n\n### Recognition\n\nContributors will be acknowledged in:\n- Repository contributors list\n- Release notes for significant contributions \n- Optional inclusion in project documentation\n\n---\n\n## 📞 Support & Contact\n\n### Community Support\n- **GitHub Issues**: [Bug reports and feature requests](https://github.com/AmadeusInnovations/English/issues)\n- **Discussions**: [Community Q&A and ideas](https://github.com/AmadeusInnovations/English/discussions)\n\n### Professional Support\n- **Technical Support**: technical-support@amadeus-innovations.com\n- **Business Inquiries**: partnerships@amadeus-innovations.com \n- **Licensing Questions**: licensing@amadeus-innovations.com\n\n### Security\n- **Security Issues**: security@amadeus-innovations.com (GPG key available)\n- **Responsible Disclosure**: We follow a 90-day disclosure timeline\n\n### Development Team\n- **Architecture Questions**: Contact core development team via GitHub\n- **Performance Issues**: Include benchmark results and system specifications\n- **Feature Requests**: Use GitHub Issues with detailed use cases\n\n---\n\n
\n\n**Natural Language → Optimized Binary. Built with Rust for Safety and Performance.**\n\n*Copyright © 2024 Amadeus Innovations. All rights reserved.*\n\n[⬆️ Back to Top](#english-programming-language)\n\n
", "has_readme": true, "url": "https://github.com/Geijutsu/english", "category": "unclassified", "value_tier": "standard", "value_rank": 2, "editorial_note": null, "similarity_group": "Developer Tools", "group_score": 15, "similar": [ { "id": "AmadeusInnovations/English", "score": 1.0, "signals": [ "compiler", "library", "automation" ] }, { "id": "TSMCP/Orchestra", "score": 0.2351, "signals": [ "automation", "framework", "cli" ] }, { "id": "MorchestraWorld/Orchestra", "score": 0.2351, "signals": [ "automation", "framework", "cli" ] }, { "id": "Geijutsu/orchestra", "score": 0.2351, "signals": [ "automation", "framework", "cli" ] }, { "id": "TSMCP/tool-of-tools", "score": 0.2203, "signals": [ "automation", "framework", "cli" ] } ] }, { "organization": "Geijutsu", "name": "gatherer", "source": "R2 Git bundle", "published_at": "2025-11-30T23:21:28+00:00", "readme": "# Gatherer\n\nA Geijutsu project repository.\n\n## Status\n\nThis repository is currently in early development.\n\n## Installation\n\n```bash\ngit clone https://github.com/geijutsu/gatherer.git\ncd gatherer\n```\n\n## Usage\n\nDocumentation coming soon.\n\n## Contributing\n\nSee [CONTRIBUTING.md](CONTRIBUTING.md) for contribution guidelines.\n\n## License\n\nMIT License - see [LICENSE](LICENSE) for details.\n\n## Contact\n\nFor questions or support, please open an issue on GitHub.", "has_readme": true, "url": "https://github.com/Geijutsu/gatherer", "category": "unclassified", "value_tier": "standard", "value_rank": 2, "editorial_note": null, "similarity_group": "Research & Knowledge", "group_score": 1, "similar": [ { "id": "Geijutsu/dom", "score": 0.1501, "signals": [ "coming", "soon", "support" ] }, { "id": "CherryMesh/gatherer", "score": 0.1047, "signals": [ "documentation", "gatherer", "contributing" ] }, { "id": "AmadeusInnovations/gatherer", "score": 0.1047, "signals": [ "documentation", "gatherer", "contributing" ] }, { "id": "quivent/gemmachain", "score": 0.0889, "signals": [ "documentation", "soon", "currently" ] }, { "id": "quivent/gpu-dev", "score": 0.0878, "signals": [ "documentation", "guidelines", "contributing" ] } ] }, { "organization": "Geijutsu", "name": "hayao", "source": "R2 Git bundle", "published_at": "2025-11-30T23:21:29+00:00", "readme": "# 🔥 Kamaji - Mystical Task Manager 🔥\n\n*Inspired by Kamaji the boiler man from Spirited Away*\n\nA magical task and project manager that guides your work through spiritual phases: **Spirit** → **River** → **Bridge** → **Bathhouse**\n\n## ✨ Mystical Features\n\n### 🔮 Progress Tracking with Subtasks\n- Visual progress bars with mystical symbols\n- Subtasks that automatically calculate parent progress\n- Phase-based workflow inspired by spiritual transformation\n\n### 🌊 Spiritual Phases\n- **🌟 Spirit**: Planning and ideation phase\n- **🌊 River**: Active development and work\n- **🌉 Bridge**: Review, testing, and refinement \n- **🏮 Bathhouse**: Completion and celebration\n\n### 🎭 Mystical CLI & TUI\n- Spirited Away themed interface with emojis and mystical language\n- Clean, colorful TUI with phase-based color coding\n- Progress visualization with custom progress bars\n\n## 🏗️ Installation\n\n```bash\ncargo build --release\n```\n\n## 🌟 Usage\n\n### CLI Commands\n\n#### 🌟 Tasks - Begin Your Journey\n```bash\nkamaji task list # Survey your spiritual tasks\nkamaji task add \"Cleanse the code\" # Summon new task\nkamaji task edit 0 \"Purify the algorithm\" # Transform task essence\nkamaji task delete 0 # Banish to the void\nkamaji task complete 0 # Mark completion\nkamaji task details 0 # Reveal mysteries\nkamaji task add-sub 0 \"Write tests\" # Add guiding subtask\nkamaji task complete-sub 0 1 # Complete subtask step\nkamaji task phase 0 # Advance through phases\n```\n\n#### 🏗️ Projects - Build Your World\n```bash\nkamaji project list # Survey your realm\nkamaji project add \"Mystical Web App\" # Create sanctuary\nkamaji project edit 0 \"Enchanted Portal\" # Reshape destiny\nkamaji project delete 0 # Dissolve to spirit world\nkamaji project details 0 # Peer into soul\nkamaji project phase 0 # Advance phases\n```\n\n#### 🏛️ Applications - Your Digital Kingdom\n```bash\nkamaji app list # Survey kingdom\nkamaji app add \"Spirit Commerce\" # Establish domain\nkamaji app edit 0 \"Mystical Marketplace\" # Reforge purpose\nkamaji app delete 0 # Return to void\nkamaji app details 0 # Contemplate essence\n```\n\n#### 🎭 Mystical TUI\n```bash\nkamaji # Enter mystical realm\nkamaji tui # Explicit TUI launch\n```\n\n### 🎮 TUI Controls\n- **Tab**: Switch between Spirit Realms (Tasks/Projects/Applications)\n- **↑/↓**: Navigate through your mystical items\n- **Enter**: Complete task and advance its spiritual journey\n- **P**: Advance selected item through phases\n- **Q**: Return to the physical world\n\n## 🌟 Creative Features & Ideas\n\n### 🔮 Implemented\n- **Mystical Phases**: Spiritual workflow progression\n- **Subtask Magic**: Automatic progress calculation\n- **Progress Visualization**: Beautiful Unicode progress bars\n- **Spirited Away Theming**: Immersive mystical experience\n\n### 🌊 Future Enchantments\n\n#### 🎨 Visual Magic\n- **Seasonal Themes**: Interface changes with real seasons\n- **Constellation View**: Tasks arranged like star maps\n- **Spirit Animal Avatars**: Personal guides for different project types\n- **Mystical Animations**: Flowing water effects in TUI\n\n#### 🔮 Workflow Sorcery\n- **Ritual Scheduling**: Tasks that must be done at specific times\n- **Energy Levels**: Track your spiritual energy and suggest optimal tasks\n- **Karma Points**: Gamification with spiritual rewards\n- **Dream Journal**: Capture ideas during different phases\n\n#### 🌟 Collaboration Magic\n- **Spirit Circles**: Team workspaces with shared mystical themes\n- **Mentor System**: Senior spirits guide junior ones\n- **Ritual Ceremonies**: Team celebrations for major completions\n- **Message Bottles**: Async communication between team spirits\n\n#### 🏮 Advanced Features\n- **Time Portals**: Historical view of your spiritual journey\n- **Prophecy Mode**: AI-powered task predictions and suggestions\n- **Sacred Geometry**: Visual task relationships and dependencies\n- **Meditation Breaks**: Built-in mindfulness reminders\n\nData flows like a river through `~/.kamaji.json` 🌊\n\n*\"Once you've met someone, you never really forget them. It just takes a while for your memories to return.\"* - Zeniba", "has_readme": true, "url": "https://github.com/Geijutsu/hayao", "category": "unclassified", "value_tier": "standard", "value_rank": 2, "editorial_note": null, "similarity_group": "Web & Applications", "group_score": 5, "similar": [ { "id": "quivent/PointsiOS", "score": 0.1084, "signals": [ "app", "interface", "gamification" ] }, { "id": "MorchestraWorld/Points", "score": 0.1059, "signals": [ "app", "interface", "gamification" ] }, { "id": "TransformerOS/Kamaji", "score": 0.0972, "signals": [ "interface", "kamaji", "tui" ] }, { "id": "TSMCP/tool-of-tools", "score": 0.0971, "signals": [ "themed", "immersive", "establish" ] }, { "id": "quivent/portfolio", "score": 0.0962, "signals": [ "web", "interface", "gamification" ] } ] }, { "organization": "Geijutsu", "name": "Luminary", "source": "R2 Git bundle", "published_at": "2025-11-30T23:21:31+00:00", "readme": "# Luminary\n\nA radiant platform for missions of light—where purpose meets skill, and good work finds its champions.\n\n## Overview\n\nLuminary is a mission-driven job board that connects world-changing initiatives with skilled professionals ready to make a difference. Built on principles of altruism, transparency, and impact, Luminary serves as the bridge between those who envision a better world and those who possess the expertise to manifest it.\n\nWhile its complement **Mercenary** represents the skilled executors, Luminary embodies the source—the luminous origin point where humanitarian missions, social impact projects, and life-preserving work find their beginning.\n\n## Vision\n\nEvery great mission begins with light—a vision of what could be, a hope for what should be. Luminary transforms these visions into actionable missions, carefully curated to ensure that every posted opportunity serves the greater good.\n\n## Core Principles\n\n- **Light Over Darkness**: Every mission posted serves humanitarian, environmental, or social good\n- **Humility in Design**: Elegant simplicity without ostentation\n- **Radical Sincerity**: Transparent impact metrics and authentic mission descriptions\n- **Complementary Ecosystem**: Seamless integration with Mercenary for mission execution\n- **Life Preservation**: Priority given to missions that protect and preserve life\n\n## Key Features\n\n### For Mission Posters\n- **Mission Creation**: Craft detailed mission briefs with impact metrics\n- **Skilled Matching**: AI-powered matching with qualified mercenaries\n- **Impact Tracking**: Monitor mission outcomes and real-world effects\n- **Transparent Funding**: Clear budget allocation and compensation structures\n\n### For Mercenaries\n- **Curated Missions**: Access to verified, high-impact opportunities\n- **Skill-Based Discovery**: Missions matched to your unique capabilities\n- **Mission History**: Build a portfolio of world-changing work\n- **Community Recognition**: Acknowledgment within the Luminary ecosystem\n\n## Visual Design\n\nLuminary embraces a visual language of light:\n- **Primary Colors**: Radiant blue and pure white\n- **Typography**: Clean, readable, accessible\n- **Interactions**: Smooth, responsive, intuitive\n- **Aesthetic**: Minimalist elegance reflecting humble brilliance\n\n## Getting Started\n\n### Prerequisites\n- Node.js 18+ or compatible runtime\n- PostgreSQL 14+ for data persistence\n- Redis for caching and real-time features\n\n### Quick Start (Recommended)\n\nUsing the provided Makefile:\n\n```bash\n# 1. Setup project (install dependencies + create .env)\nmake setup\n\n# 2. Edit backend/.env with your credentials\n\n# 3. Start services and initialize database\nmake services-start\nmake db-setup\nmake db-migrate\n\n# 4. Start development servers\nmake dev\n```\n\n**Common commands:**\n- `make help` - See all available commands\n- `make dev` - Start development servers\n- `make test` - Run tests\n- `make build` - Build for production\n- `make status` - Check project status\n\n### Manual Installation\n\n```bash\n# Clone the repository\ngit clone \ncd Luminary\n\n# Install dependencies\nnpm install\n\n# Set up environment variables\ncd backend\ncp .env.example .env\n# Edit .env with your database credentials\n\n# Initialize database (ensure PostgreSQL and Redis are running)\nnpm run db:migrate\n\n# Start development servers (from project root)\ncd ..\nnpm run dev\n```\n\nFor detailed setup instructions, see [SETUP.md](SETUP.md).\n\n### Configuration\n\nCreate a `.env` file with the following variables:\n\n```env\nDATABASE_URL=postgresql://user:password@localhost:5432/luminary\nREDIS_URL=redis://localhost:6379\nJWT_SECRET=your-secret-key\nAPI_PORT=3000\n```\n\n## Usage Examples\n\n### Posting a Mission\n\n```javascript\nconst mission = {\n title: \"Develop clean water filtration system for rural communities\",\n category: \"humanitarian\",\n impact: \"Provide clean water access to 10,000+ people\",\n skills: [\"engineering\", \"water-systems\", \"community-development\"],\n budget: { min: 50000, max: 100000 },\n duration: \"6 months\",\n location: \"Remote with field visits\"\n};\n\nawait luminary.missions.create(mission);\n```\n\n### Searching for Missions\n\n```javascript\nconst missions = await luminary.missions.search({\n categories: [\"environmental\", \"humanitarian\"],\n skills: [\"data-science\", \"gis\"],\n impactLevel: \"high\"\n});\n```\n\n## Architecture\n\nLuminary follows a clean, modular architecture:\n\n```\nLuminary/\n├── src/\n│ ├── api/ # RESTful API endpoints\n│ ├── services/ # Business logic and services\n│ ├── models/ # Data models and schemas\n│ ├── utils/ # Shared utilities\n│ └── integrations/ # External integrations (Mercenary bridge)\n├── client/\n│ ├── components/ # React components\n│ ├── pages/ # Application pages\n│ ├── styles/ # Global styles and themes\n│ └── hooks/ # Custom React hooks\n├── docs/ # Additional documentation\n└── tests/ # Test suites\n```\n\n## Contributing\n\nLuminary welcomes contributions that align with its mission of amplifying good work. Please review our contribution guidelines and code of conduct before submitting.\n\n### Development Workflow\n\n1. Fork the repository\n2. Create a feature branch: `git checkout -b feature/your-feature`\n3. Make your changes with clear, descriptive commits\n4. Ensure tests pass: `npm test`\n5. Submit a pull request with detailed description\n\n## Testing\n\n```bash\n# Run all tests\nnpm test\n\n# Run tests in watch mode\nnpm run test:watch\n\n# Run tests with coverage\nnpm run test:coverage\n```\n\n## Production Build\n\nBuild for production deployment:\n\n```bash\n# Build both backend and frontend\nnpm run build\n\n# Start production server\ncd backend\nnpm start\n```\n\nFor detailed deployment instructions, see SETUP.md.\n\n## Integration with Mercenary\n\nLuminary and Mercenary form a complementary ecosystem:\n\n- **Mission Flow**: Missions created in Luminary are discoverable in Mercenary\n- **Skill Matching**: Mercenary profiles are matched against Luminary mission requirements\n- **Unified Authentication**: Shared identity system for seamless navigation\n- **Impact Tracking**: Mission outcomes tracked across both platforms\n\n## Roadmap\n\n- **Phase 1**: Core mission posting and discovery (Q1)\n- **Phase 2**: AI-powered skill matching and recommendations (Q2)\n- **Phase 3**: Impact metrics and reporting dashboard (Q3)\n- **Phase 4**: Global community features and recognition system (Q4)\n\n## Philosophy\n\nBuilt by a polymath who has transformed personal struggle into a commitment to preserve life and amplify good in the world. Luminary doesn't seek to celebrate its origin, but rather to manifest a vision—a platform where every mission posted is a beacon of hope, and every mercenary who answers is an agent of positive change.\n\nThe creator remains in the background, as all great visionaries should, letting the work speak for itself.\n\n## License\n\n[License Type] - See LICENSE file for details\n\n## Support\n\nFor questions, issues, or mission-related inquiries:\n- Documentation: [Link to docs]\n- Community: [Link to community forum]\n- Contact: [Support email]\n\n---\n\n*\"In the darkness, we seek those who carry light. In Luminary, we give them missions worthy of their brilliance.\"*", "has_readme": true, "url": "https://github.com/Geijutsu/Luminary", "category": "unclassified", "value_tier": "standard", "value_rank": 2, "editorial_note": null, "similarity_group": "Web & Applications", "group_score": 7, "similar": [ { "id": "InfotonDB/Luminary", "score": 0.8724, "signals": [ "frontend", "react", "dashboard" ] }, { "id": "MorchestraWorld/League-Of-Sages", "score": 0.1669, "signals": [ "application", "visionaries", "great" ] }, { "id": "Moestradamus-Productions/League-Of-Sages", "score": 0.1669, "signals": [ "application", "visionaries", "great" ] }, { "id": "MorchestraWorld/TaoBot-Ecosystem", "score": 0.1606, "signals": [ "dashboard", "backend", "application" ] }, { "id": "Moestradamus-Productions/rootandhue", "score": 0.1597, "signals": [ "frontend", "react", "backend" ] } ] }, { "organization": "Geijutsu", "name": "orchestra", "source": "R2 Git bundle", "published_at": "2025-11-30T23:21:32+00:00", "readme": "# 🎯 Symphony\n\n
\n\n**A Comprehensive Command Management System** \n*Streamline your development workflow with intelligent command orchestration*\n\n[![Version](https://img.shields.io/badge/version-1.2.0--dev-blue.svg)](https://github.com/commandcenter/commandcenter)\n[![Status](https://img.shields.io/badge/status-Active%20Development-green.svg)](https://github.com/commandcenter/commandcenter)\n[![Go](https://img.shields.io/badge/go-1.21+-00ADD8.svg)](https://golang.org/)\n[![License](https://img.shields.io/badge/license-MIT-blue.svg)](LICENSE)\n\n---\n\n
\n\n## 🚀 **What is Symphony?**\n\nSymphony is a powerful dual-architecture system that combines **Go+Cobra CLI** with **web interface integration** to provide a unified command management experience. It bridges the gap between command-line efficiency and visual workflow management.\n\n### ✨ **Key Features**\n\n- 🔧 **Hybrid Architecture**: Go+Cobra CLI + Web Dashboard + Makefile automation\n- ⚡ **70+ Commands**: Comprehensive toolkit for development workflows\n- 🌐 **Web Interface**: Visual command browser with real-time search\n- 🎯 **Smart Execution**: Intelligent command routing and error handling\n- 📊 **Monitoring**: Built-in performance tracking and system health\n- 🔒 **Security**: Input validation and safe command execution\n\n---\n\n## 🎯 **Quick Start**\n\n### **Prerequisites**\n\n```bash\n# Required dependencies\nGo 1.21+ # CLI functionality\nNode.js 16+ # Web interface\nMake # Build automation\nGit # Version control\n```\n\n### **Installation**\n\n```bash\n# Clone and setup\ngit clone \ncd Orchestra\n\n# Install all dependencies\nmake install\n\n# Build conductor CLI\ncd conductor && go build -ldflags=\"-s -w\" .\n\n# Verify installation\nmake test\n```\n\n### **Launch Dashboard** 🌐\n\nChoose your preferred method:\n\n```bash\n# Option 1: Simple web server\nmake serve\n# → Opens http://localhost:3000\n\n# Option 2: Enhanced CLI server\ncd conductor && go run main.go serve --port 3000\n# → Advanced server with monitoring\n\n# Option 3: Development mode\nnpm start\n# → Hot reload for development\n```\n\n---\n\n## 🛠️ **Most Useful Commands**\n\n### **🎯 Core Operations**\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
CommandDescription & Usage
\n\n**`serve`**\n\n\nLaunch the web dashboard with monitoring\n```bash\nconductor serve --port 3000\n# Opens web interface with real-time metrics\n```\n
\n\n**`debug`**\n\n\nSystem diagnostics and troubleshooting\n```bash\nconductor debug --system\n# Comprehensive system health analysis\n```\n
\n\n**`test`**\n\n\nRun comprehensive test suites\n```bash\nconductor test --all --coverage\n# Execute all tests with coverage reporting\n```\n
\n\n**`monitor`**\n\n\nReal-time system monitoring\n```bash\nconductor monitor --health --alerts\n# Live system metrics with threshold alerts\n```\n
\n\n### **📊 Quality & Analysis**\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
CommandDescription & Usage
\n\n**`validate-quality`**\n\n\nComprehensive quality assessment\n```bash\nconductor validate-quality --all --score\n# Code quality analysis with scoring\n```\n
\n\n**`analyze`**\n\n\nCode analysis and metrics\n```bash\nconductor analyze --complexity --performance\n# Deep code analysis with recommendations\n```\n
\n\n**`audit`**\n\n\nSecurity and compliance auditing\n```bash\nconductor audit --security --dependencies\n# Security vulnerability scanning\n```\n
\n\n**`benchmark`**\n\n\nPerformance benchmarking\n```bash\nconductor benchmark --cpu --memory --disk\n# System performance measurement\n```\n
\n\n### **🔧 Development Workflow**\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
CommandDescription & Usage
\n\n**`sync`**\n\n\nSynchronize with ~/.claude/commands\n```bash\nconductor sync --force --backup\n# Sync command definitions with verification\n```\n
\n\n**`build`**\n\n\nBuild project components\n```bash\nconductor build --optimize --parallel\n# Optimized parallel build execution\n```\n
\n\n**`deploy`**\n\n\nDeployment automation\n```bash\nconductor deploy --environment prod --verify\n# Production deployment with validation\n```\n
\n\n**`optimize`**\n\n\nPerformance optimization\n```bash\nconductor optimize --binary --resources\n# System and binary optimization\n```\n
\n\n---\n\n## 💻 **Example Usage**\n\n### **Scenario 1: Development Setup**\n\n```bash\n# Start your development session\nconductor serve & # Launch web dashboard\nconductor monitor --background # Start system monitoring\nconductor test --watch # Continuous testing\n\n# Open browser to http://localhost:3000\n# → Visual command interface with real-time updates\n```\n\n### **Scenario 2: Code Quality Check**\n\n```bash\n# Comprehensive quality assessment\nconductor validate-quality --all --format json > quality-report.json\nconductor analyze --complexity --output analysis.md\nconductor audit --security --export security-audit.pdf\n\n# Review results in web interface or generated files\n```\n\n### **Scenario 3: Performance Analysis**\n\n```bash\n# System performance benchmarking\nconductor benchmark --full --compare-baseline\nconductor monitor --performance --duration 60s\nconductor optimize --recommendations --apply-safe\n\n# Generated reports: benchmark-results.json, performance-profile.html\n```\n\n### **Scenario 4: Project Health Check**\n\n```bash\n# Complete project assessment\nconductor debug --comprehensive --export debug-report.html\nconductor test --all --coverage --report coverage.html\nconductor validate-quality --score --detailed --format markdown\n\n# Consolidated health dashboard in web interface\n```\n\n---\n\n## 🏗️ **Architecture Overview**\n\n```mermaid\ngraph TB\n A[User Input] --> B{Interface Choice}\n B -->|CLI| C[Go+Cobra Commands]\n B -->|Web| D[Dashboard Interface]\n B -->|Make| E[Makefile Targets]\n \n C --> F[Command Router]\n D --> F\n E --> F\n \n F --> G[Core Engine]\n G --> H[Execution Layer]\n G --> I[Monitoring System]\n G --> J[Validation Framework]\n \n H --> K[System Operations]\n I --> L[Metrics Collection]\n J --> M[Security Validation]\n \n K --> N[Results]\n L --> N\n M --> N\n```\n\n### **Component Integration**\n\n- **🎯 Go CLI**: Type-safe command parsing with enhanced error handling\n- **🌐 Web Interface**: Intuitive command discovery and real-time monitoring \n- **🔧 Makefile Backend**: Reliable execution engine for system operations\n- **📊 Monitoring System**: Performance tracking and health validation\n- **🔒 Security Framework**: Input validation and safe execution patterns\n\n---\n\n## 📊 **Command Categories**\n\n### **🛠️ Development Tools**\n```bash\nconductor build # Project building\nconductor test # Testing automation \nconductor debug # Diagnostics and troubleshooting\nconductor optimize # Performance optimization\n```\n\n### **📈 Analysis & Monitoring**\n```bash\nconductor analyze # Code analysis\nconductor monitor # System monitoring\nconductor benchmark # Performance testing\nconductor audit # Security auditing\n```\n\n### **🚀 Deployment & Operations**\n```bash\nscripts/deploy-native.sh # Native deployment automation\nconductor sync # Synchronization tools\nconductor validate-quality # Quality assurance\nscripts/security-native.sh # Security hardening\n```\n\n### **📚 Documentation & Knowledge**\n```bash\nconductor document # Documentation generation\nconductor explain # System explanation\nconductor guide # Interactive guides\nconductor help # Comprehensive help system\n```\n\n---\n\n## 🌟 **Web Interface Features**\n\n### **🎯 Command Browser**\n- **Visual Discovery**: Browse all 70+ commands with descriptions\n- **Real-time Search**: Instant filtering across names and documentation\n- **Category Organization**: Logical grouping by functionality\n- **Quick Actions**: One-click command execution with parameter forms\n\n### **📊 Live Monitoring**\n- **System Metrics**: CPU, memory, disk usage with real-time charts\n- **Command History**: Execution log with performance timing\n- **Health Dashboard**: System status with color-coded indicators\n- **Alert System**: Configurable thresholds with notifications\n\n### **🎨 User Experience**\n- **Dark/Light Themes**: Persistent theme preferences\n- **Responsive Design**: Optimal viewing on all devices\n- **Keyboard Shortcuts**: Power-user efficiency features\n- **Export Capabilities**: Save results in multiple formats\n\n---\n\n## ⚙️ **Configuration**\n\n### **Environment Variables**\n```bash\n# Core configuration\nexport COMMANDCENTER_PORT=3000\nexport COMMANDCENTER_THEME=dark\nexport COMMANDCENTER_PATH=/custom/path\n\n# Advanced options\nexport COMMANDCENTER_LOG_LEVEL=info\nexport COMMANDCENTER_MONITOR_INTERVAL=30s\nexport COMMANDCENTER_CACHE_SIZE=100MB\n```\n\n### **Configuration Files**\n- `config/merge-config.json` - Command synchronization settings\n- `package.json` - Node.js dependencies and scripts \n- `conductor/go.mod` - Go module dependencies\n- `Makefile` - Build automation targets\n\n---\n\n## 🚀 **Development Commands**\n\n### **Local Development**\n```bash\n# Development workflow\nmake dev # Start with hot reload\nmake test # Run comprehensive tests \nmake build # Production build\nmake clean # Clean artifacts\n\n# Go development\ncd conductor\ngo run main.go serve # Test CLI directly\ngo test ./... # Run Go tests\ngo build -o bin/conductor # Build binary\n```\n\n### **Quality Assurance**\n```bash\n# Code quality checks\nconductor validate-quality --all --fix # Fix quality issues\nconductor analyze --complexity --report # Generate analysis report\nconductor test --coverage --threshold 90 # Ensure test coverage\nconductor audit --security --dependencies # Security validation\n```\n\n---\n\n## 🎯 **Performance Metrics**\n\n### **Current Performance**\n- ⚡ **CLI Startup**: ~26ms for 70 commands\n- 🌐 **Web Load Time**: ~150ms initial load \n- 🔧 **Build Time**: ~37ms for Makefile targets\n- 📊 **Command Execution**: <100ms average response\n\n### **Quality Standards**\n- ✅ **Implementation Progress**: 70+ commands available\n- ✅ **Documentation Coverage**: 94.3% documented\n- ✅ **Build System**: 100% functional targets\n- ✅ **Test Coverage**: 89%+ across core components\n\n---\n\n## 🛠️ **Troubleshooting**\n\n### **Common Issues**\n\n**Web interface not loading:**\n```bash\n# Check port availability\nlsof -i :3000\n# Try alternative port \nconductor serve --port 3001\n```\n\n**Commands not found:**\n```bash\n# Verify installation\nconductor debug --system\n# Rebuild CLI\ncd conductor && go build -o bin/conductor\n```\n\n**Build failures:**\n```bash\n# Clean and reinstall\nmake clean && make install\n# Verify configuration\nmake config-check\n```\n\n### **Debug Information**\n```bash\n# Comprehensive diagnostics\nconductor debug --all --export debug-report.html\nconductor monitor --health --verbose\nconductor validate-quality --score --detailed\n```\n\n---\n\n## 📚 **Additional Resources**\n\n### **Documentation**\n- 📖 **API Documentation**: Generated from code with examples\n- 🎯 **Command Reference**: Complete guide for all 70+ commands\n- 🏗️ **Architecture Guide**: System design and integration patterns\n- 🚀 **Deployment Guide**: Production deployment instructions\n\n### **Community**\n- 💬 **Discussions**: Feature requests and community support\n- 🐛 **Issues**: Bug reports and enhancement tracking \n- 🤝 **Contributing**: Contribution guidelines and development setup\n- 📋 **Roadmap**: Future features and development priorities\n\n---\n\n## 📄 **License**\n\nMIT License - see [LICENSE](LICENSE) file for details.\n\n---\n\n
\n\n**🎯 Symphony v1.2.0-dev** \n*Building the future of command management*\n\n[**🚀 Get Started**](#-quick-start) • [**📚 Documentation**](#-additional-resources) • [**💬 Community**](https://github.com/commandcenter/commandcenter/discussions)\n\n---\n\n*Made with ❤️ for developers who value efficiency and elegant tooling*\n\n
", "has_readme": true, "url": "https://github.com/Geijutsu/orchestra", "category": "unclassified", "value_tier": "standard", "value_rank": 2, "editorial_note": null, "similarity_group": "Developer Tools", "group_score": 10, "similar": [ { "id": "TSMCP/Orchestra", "score": 1.0, "signals": [ "tooling", "automation", "framework" ] }, { "id": "MorchestraWorld/Orchestra", "score": 1.0, "signals": [ "tooling", "automation", "framework" ] }, { "id": "TSMCP/tool-of-tools", "score": 0.2507, "signals": [ "automation", "framework", "cli" ] }, { "id": "CherryMesh/gatherer", "score": 0.2384, "signals": [ "tooling", "framework", "cli" ] }, { "id": "AmadeusInnovations/gatherer", "score": 0.2384, "signals": [ "tooling", "framework", "cli" ] } ] }, { "organization": "Geijutsu", "name": "quillo", "source": "R2 Git bundle", "published_at": "2025-11-30T23:21:43+00:00", "readme": "# Qwen-Code\n\nA Claude Code-like CLI tool that integrates Qwen3 LLM with filesystem access capabilities.\n\n## Features\n\n- 🤖 **Qwen3 Integration**: OpenAI-compatible API support for Qwen models\n- 📁 **Filesystem Tools**: Read, write, list files and directories\n- 🔄 **Streaming Responses**: Real-time output with SSE support\n- 💬 **Interactive Mode**: REPL for multi-turn conversations\n- ⚙️ **Configuration**: Persistent settings management\n\n## Installation\n\n```bash\ncd qwen-code\nnpm install\nnpm link # Makes qwen-code command globally available\n```\n\n## Configuration\n\nSet up your Qwen3 API endpoint:\n\n```bash\n# View current config\nqwen-code config\n\n# Set API URL (default: http://localhost:8000)\nqwen-code config apiUrl http://localhost:8000\n\n# Set model (default: qwen2.5-coder-32b-instruct)\nqwen-code config model qwen2.5-coder-32b-instruct\n\n# Enable verbose mode\nqwen-code config verbose true\n```\n\nConfiguration is stored in `~/.qwen-code/config.json`\n\n## Usage\n\n### Single Prompt\n\n```bash\nqwen-code \"List all files in the current directory\"\nqwen-code \"Read the package.json file\"\nqwen-code \"Create a hello.txt file with 'Hello World'\"\n```\n\n### Interactive Mode\n\n```bash\nqwen-code interactive\n# or\nqwen-code i\n```\n\nIn interactive mode, you can have multi-turn conversations:\n\n```\n> List files in src/\n> Read the first file\n> Create a summary of what you found\n> exit\n```\n\n### Options\n\n- `-v, --verbose`: Show detailed execution logs\n- `-m, --model `: Override model for this request\n- `--api-url `: Override API URL for this request\n\n## Available Tools\n\nThe AI has access to these filesystem tools:\n\n- **read_file**: Read contents of a file\n- **write_file**: Write content to a file\n- **list_directory**: List files and directories\n- **create_directory**: Create a new directory\n- **file_stats**: Get file/directory metadata\n\n## Architecture\n\n```\nqwen-code/\n├── src/\n│ ├── core/\n│ │ ├── api/ # Qwen3 API client\n│ │ ├── tools/ # Tool execution engine\n│ │ └── config/ # Configuration manager\n│ ├── cli/ # CLI commands & interactive mode\n│ ├── utils/ # File operations utilities\n│ └── index.js # Main entry point\n└── docs/ # Documentation\n```\n\n## Running a Local Qwen3 Server\n\nUse vLLM or similar to serve Qwen models:\n\n```bash\n# Example with vLLM\npython -m vllm.entrypoints.openai.api_server \\\n --model Qwen/Qwen2.5-Coder-32B-Instruct \\\n --port 8000\n```\n\nOr use Ollama with OpenAI compatibility:\n\n```bash\nollama serve\n# Update config to point to Ollama's OpenAI endpoint\nqwen-code config apiUrl http://localhost:11434/v1\n```\n\n## Examples\n\n```bash\n# File operations\nqwen-code \"Show me the contents of README.md\"\nqwen-code \"Create a new file called test.txt with 'Hello World'\"\n\n# Directory operations\nqwen-code \"What files are in the src directory?\"\nqwen-code \"Create a new directory called output\"\n\n# Complex tasks\nqwen-code \"Read all .js files in src/ and create a summary\"\n```\n\n## Development\n\nReference implementation from `~/Documents/bit/src`:\n- API client pattern: `bit/src/core/api/client.js`\n- Tool execution: `bit/src/core/tools/execution-engine.js`\n- File operations: `bit/src/utils/file-operations.js`\n\n## License\n\nMIT", "has_readme": true, "url": "https://github.com/Geijutsu/quillo", "category": "unclassified", "value_tier": "standard", "value_rank": 2, "editorial_note": null, "similarity_group": "Models & Machine Learning", "group_score": 7, "similar": [ { "id": "TransformerOS/PillowTalk", "score": 0.2105, "signals": [ "models", "model", "filesystem" ] }, { "id": "quivent/PillowTalk", "score": 0.2033, "signals": [ "models", "model", "filesystem" ] }, { "id": "quivent/ram", "score": 0.1657, "signals": [ "models", "model", "bit" ] }, { "id": "TransformerOS/Autoprime", "score": 0.1571, "signals": [ "qwen", "models", "model" ] }, { "id": "quivent/ollama", "score": 0.1531, "signals": [ "model", "ollama", "responses" ] } ] }, { "organization": "Geijutsu", "name": "sakura", "source": "R2 Git bundle", "published_at": "2025-11-30T23:21:44+00:00", "readme": "# 🌸 Cherry CLI - Revolutionary Server Management Platform\n\n[![Version](https://img.shields.io/badge/version-1.0.0-pink.svg)](https://github.com/cherryservers/cherry-cli)\n[![License](https://img.shields.io/badge/license-MIT-green.svg)](LICENSE)\n[![Security](https://img.shields.io/badge/encryption-AES--256--GCM-blue.svg)](docs/security.md)\n[![Platform](https://img.shields.io/badge/platform-macOS%20%7C%20Linux%20%7C%20Windows-lightgrey.svg)](docs/installation.md)\n\n> **The world's first CLI with complete server state snapshotting and cherry blossom-themed aesthetics.**\n\nA revolutionary, security-first command-line interface for Cherry Servers infrastructure management featuring military-grade encryption, complete server state capture via the Capsule System, and beautiful cherry blossom-themed user experience.\n\n---\n\n## ✨ Revolutionary Features\n\n### 💊 **Cherry Server Capsule System** - *World's First Complete Server State Capture*\n- 🔬 **Complete Server Snapshot**: Users, packages, services, configs, data, SSH keys, crontabs, network settings\n- 🔐 **Military-Grade Security**: AES-256-GCM encryption with SHA-512 integrity verification\n- 📦 **Intelligent Optimization**: 90%+ size reduction through rebuild artifact detection\n- 🚀 **Secure Transfer**: Encrypted transmission with remote confirmation\n- ⚡ **Automated Restoration**: One-command server recreation from capsules\n\n### 🔐 **Enterprise-Grade Security**\n- **AES-256-GCM Encryption** for all data transfers\n- **TLS 1.3** for secure API communications\n- **GPG Integration** for file encryption\n- **SSH Key Management** with automated deployment\n- **Zero-Knowledge Operation** with automatic cleanup\n\n### 🌸 **Blossom System** - *Enhanced User Experience*\n- **Smart SSH Management** with automatic user switching\n- **Cherry Blossom Aesthetics** with sakura-themed interface\n- **Emoji-Rich Feedback** for immediate visual context\n- **Progressive Help System** with contextual guidance\n\n### 🌐 **Advanced P2P Networking**\n- **Peer Discovery** with automatic topology mapping\n- **NAT Traversal** using sophisticated hole-punching\n- **End-to-End Encryption** for secure peer communication\n- **Load Balancing** with intelligent peer selection\n- **Fault Tolerance** with automatic failover\n\n---\n\n## 🚀 Quick Start\n\n### Installation\n\n**macOS/Linux:**\n```bash\n# One-line installation\ncurl -sSL https://raw.githubusercontent.com/cherryservers/cherry-cli/main/install.sh | bash\n\n# Manual installation\ngit clone https://github.com/cherryservers/cherry-cli.git\ncd cherry-cli\nmake && make install\n```\n\n**Windows (WSL2):**\n```powershell\n# Install WSL2 + Ubuntu (Run as Administrator)\nwsl --install\n\n# In Ubuntu terminal\nsudo apt update && apt install -y build-essential libssl-dev git\ngit clone https://github.com/cherryservers/cherry-cli.git\ncd cherry-cli && make && make install\n```\n\n### First Time Setup\n\n```bash\n# Initialize configuration\ncherry init\n\n# Set your Cherry Servers API token\nexport CHERRY_AUTH_TOKEN=\"your-token-here\"\n\n# Verify installation\ncherry --version\ncherry list\n```\n\n---\n\n## 🎯 Core Usage Examples\n\n### 💊 **Capsule System** - Complete Server Management\n```bash\n# Create complete server snapshot\ncherry server produce capsule\n# → Creates encrypted .capsule file with full server state\n\n# Transfer server state to new environment\ncherry server beam capsule production-server\n# → Secure transmission with integrity verification\n\n# Restore complete server from capsule\ncherry server receive capsule\n# → Automated server recreation with all configurations\n```\n\n### 🔐 **Secure File Transfer**\n```bash\n# Transfer encrypted files\ncherry send document.pdf my-server # Single file with AES-256\ncherry send ./project/ production-server # Entire directory compressed\ncherry send backup.tar.gz staging-server # Large files optimized\n```\n\n### 🌸 **Enhanced SSH Management**\n```bash\n# Smart SSH with user switching\ncherry blossom # SSH to active server\ncherry blossom deploy # SSH and switch to 'deploy' user\ncherry blossom www-data # SSH and switch to 'www-data'\ncherry blossom pair # Set up SSH key authentication\n```\n\n### 🖥️ **Server Operations**\n```bash\n# Server management\ncherry server activate my-server # Set active server\ncherry server info # Detailed server information\ncherry server create --plan c1-small --image ubuntu_22_04\ncherry install docker # Install tools on active server\n```\n\n### 🌐 **P2P Networking**\n```bash\n# P2P operations\ncherry p2p init # Initialize P2P node\ncherry p2p peers # List connected peers\ncherry p2p send peer-id \"Hello Cherry!\" # Secure messaging\ncherry p2p discover # Network topology discovery\n```\n\n---\n\n## 🏗️ Architecture Overview\n\nCherry CLI implements a **dual-architecture approach** with two complementary implementations:\n\n### 🏛️ **Foundation Implementation** *(Production Ready)*\n- **Status**: ✅ **Fully Functional** with 120+ commands\n- **Architecture**: Mature monolithic design with proven stability\n- **Features**: Complete Capsule System, P2P networking, security features\n- **Use Case**: Production deployments requiring immediate functionality\n\n### ⚡ **Evolution Implementation** *(Next Generation)*\n- **Status**: 🚧 **Modernized Architecture** (modular design complete)\n- **Architecture**: Clean modular structure with enhanced performance\n- **Features**: Memory-safe design, <30ms startup, comprehensive testing\n- **Use Case**: Future development with modern C practices\n\n```\ncherry-cli/\n├── implementations/\n│ ├── foundation/ # 🏛️ Production-ready implementation\n│ │ ├── src/ # 36 source files, 120+ commands\n│ │ ├── include/ # Comprehensive headers\n│ │ └── platforms/ # Multi-platform support\n│ └── evolution/ # ⚡ Modernized architecture\n│ ├── src/\n│ │ ├── core/ # System initialization\n│ │ ├── commands/ # Modular command structure\n│ │ ├── lib/ # Core libraries\n│ │ └── p2p/ # P2P networking subsystem\n│ └── tests/ # Comprehensive test suite\n```\n\n---\n\n## 🔒 Security & Compliance\n\n### **Encryption Standards**\n- **AES-256-GCM**: File and capsule encryption\n- **SHA-512**: Integrity verification\n- **TLS 1.3**: API communications\n- **GPG**: Additional file encryption layer\n- **libsodium**: P2P networking security\n\n### **Security Certifications**\n- ✅ **Buffer Overflow Protection**\n- ✅ **Memory Leak Prevention**\n- ✅ **Input Validation & Sanitization**\n- ✅ **Principle of Least Privilege**\n- ✅ **Zero-Knowledge Temporary Files**\n\n### **Compliance Features**\n- **Audit Logging**: Comprehensive operation tracking\n- **Access Control**: Role-based permissions\n- **Data Residency**: Configurable storage locations\n- **Encryption at Rest**: All stored data encrypted\n\n---\n\n## 🖥️ Platform Support\n\n| Platform | Status | Installation Method | Notes |\n|----------|--------|-------------------|--------|\n| **macOS** | ✅ Full Support | Homebrew, Source | Native performance |\n| **Linux** | ✅ Full Support | Package Manager, Source | All major distributions |\n| **Windows** | ✅ WSL2 Support | WSL2 + Ubuntu | Cherry iTerm experience |\n| **ARM64** | ✅ Native Support | Source compilation | Apple Silicon, ARM servers |\n\n### **Windows Integration**\n- 🌸 **Cherry iTerm Wrapper**: Complete iTerm experience in Windows Terminal\n- 🤖 **Claude Code Integration**: AI-powered development workflows\n- ⌨️ **iTerm-Style Shortcuts**: Familiar macOS hotkeys (Ctrl+T, Ctrl+D)\n- 🎨 **Custom Themes**: Cherry-branded color schemes\n- 💾 **Session Management**: Multi-project layout persistence\n\n---\n\n## 📊 Performance Specifications\n\n### **Foundation Implementation**\n| Metric | Specification | Typical Performance |\n|--------|---------------|-------------------|\n| Startup Time | <100ms | ~50ms |\n| Memory Usage | <8MB | ~4MB |\n| Command Response | <200ms | ~100ms |\n| File Transfer | 50MB/s+ | ~80MB/s |\n\n### **Evolution Implementation**\n| Metric | Target | Achieved |\n|--------|--------|----------|\n| Startup Time | <30ms | ~15ms |\n| Memory Usage | <4MB | ~2MB |\n| Binary Size | <2MB | ~1.5MB |\n| Response Time | <50ms | ~25ms |\n\n---\n\n## 🧪 Command Reference\n\n### **Server Management**\n```bash\ncherry list # List all servers\ncherry info # Detailed server info\ncherry create --plan c1-small --image ubuntu # Create server\ncherry server activate # Set active server\ncherry ssh [user] # SSH connection\n```\n\n### **File Operations**\n```bash\ncherry send # Encrypted file transfer\ncherry retrieve : # Secure file retrieval\ncherry deploy # Project deployment\n```\n\n### **Idea Management**\n```bash\ncherry idea add \"API Rate Limiting\" # Capture new ideas\ncherry idea list --priority 4,5 # Review high-priority ideas \ncherry idea search \"authentication\" # Find related concepts\ncherry idea connect 23 31 --type implements # Link related ideas\ncherry idea analyze 42 --enhance # AI-powered idea analysis\n```\n\n### **Advanced Features**\n```bash\ncherry server produce capsule # Create server snapshot\ncherry server beam capsule # Transfer server state\ncherry blossom [user] # Enhanced SSH\ncherry p2p init # P2P networking\ncherry install # Tool installation\n```\n\n### **Configuration & Diagnostics**\n```bash\ncherry init # Initial setup\ncherry config show # View configuration\ncherry doctor # System health check\ncherry --help # Comprehensive help\n```\n\n---\n\n## 🎨 Cherry Blossom Experience\n\n### **Visual Theme**\n- 🌸 **Sakura Pink**: Primary accent for key operations\n- 🌿 **Spring Green**: Success states and positive feedback\n- 🌌 **Sky Blue**: Information and guidance\n- 🤍 **Cherry White**: Clean, readable text\n- 🌙 **Twilight Purple**: Error states and warnings\n\n### **User Interface Elements**\n- **Emoji-Rich Feedback**: Visual context for operations\n- **Progressive Loading**: Beautiful progress indicators\n- **Contextual Help**: Smart suggestions and guidance\n- **Accessibility**: WCAG-compliant color schemes\n- **Multi-Theme Support**: Dark, light, and monochrome modes\n\n---\n\n## 🤝 Contributing\n\n### **Development Setup**\n```bash\n# Clone repository\ngit clone https://github.com/cherryservers/cherry-cli.git\ncd cherry-cli\n\n# Foundation implementation\ncd implementations/foundation\nmake clean && make debug\n\n# Evolution implementation \ncd implementations/evolution\nmkdir build && cd build\ncmake -DCMAKE_BUILD_TYPE=Debug ..\nmake -j$(nproc)\n```\n\n### **Code Standards**\n- **C Standard**: C11 with GNU extensions\n- **Memory Safety**: Comprehensive bounds checking\n- **Documentation**: Doxygen-compatible comments\n- **Testing**: Unit and integration test coverage\n- **Security**: Static analysis and vulnerability scanning\n\n### **Contribution Process**\n1. Fork the repository\n2. Create feature branch following naming conventions\n3. Implement changes with comprehensive tests\n4. Ensure security and performance standards\n5. Submit pull request with detailed description\n\n---\n\n## 📚 Documentation\n\n### **User Guides**\n- [Installation Guide](docs/installation.md)\n- [Configuration Reference](docs/configuration.md)\n- [Command Reference](docs/commands.md)\n- [Security Best Practices](docs/security.md)\n\n### **Technical Documentation**\n- [Architecture Overview](docs/architecture.md)\n- [Idea Management System](docs/architecture/CHERRY_IDEA_MANAGEMENT_SPECIFICATION.md)\n- [P2P Networking Guide](docs/p2p.md)\n- [API Integration](docs/api.md)\n- [Performance Tuning](docs/performance.md)\n\n### **Platform-Specific**\n- [Windows Setup Guide](implementations/foundation/platforms/windows/README.md)\n- [macOS Optimization](docs/macos.md)\n- [Linux Distribution Notes](docs/linux.md)\n\n---\n\n## 🆘 Support & Community\n\n### **Getting Help**\n- 📖 **Documentation**: Comprehensive guides and references\n- 🐛 **GitHub Issues**: Bug reports and feature requests\n- 💬 **Discussions**: Community questions and support\n- 📧 **Security**: security@cherryservers.com for vulnerabilities\n\n### **Community Resources**\n- **Cherry Servers API**: [https://docs.cherryservers.com/](https://docs.cherryservers.com/)\n- **cherryctl CLI**: [https://github.com/cherryservers/cherryctl](https://github.com/cherryservers/cherryctl)\n- **Community Forum**: [https://community.cherryservers.com/](https://community.cherryservers.com/)\n\n---\n\n## 📄 License & Acknowledgments\n\n### **License**\nCherry CLI is released under the **MIT License**. See [LICENSE](LICENSE) for complete terms.\n\n### **Acknowledgments**\n- **Cherry Servers Team**: API and infrastructure support\n- **OpenSSL Project**: Cryptographic foundation\n- **libsodium Developers**: Modern cryptography library\n- **Security Researchers**: Vulnerability disclosure and improvements\n- **Open Source Community**: Dependencies and continuous improvement\n\n### **Security Disclosure**\nFor security vulnerabilities, please email security@cherryservers.com with details. We follow responsible disclosure practices and will acknowledge contributions appropriately.\n\n---\n\n## 🔮 Roadmap & Future Vision\n\n### **Completed Revolutionary Features** ✅\n- [x] Cherry Server Capsule System with AES-256-GCM encryption\n- [x] Complete server state snapshotting and restoration\n- [x] Blossom user management with SSH automation\n- [x] Advanced P2P networking with NAT traversal\n- [x] Secure file transfer with GPG integration\n- [x] Windows support via Cherry iTerm wrapper\n\n### **Next-Generation Enhancements** 🚀\n- [x] **Idea Management System**: Comprehensive concept capture and development workflow\n- [ ] **AI-Powered Optimization**: ML-based server configuration recommendations\n- [ ] **Distributed Capsules**: Multi-server orchestrated snapshots\n- [ ] **Cloud Storage Integration**: Direct AWS S3/GCS capsule storage\n- [ ] **Incremental Snapshots**: Delta-based updates for efficiency\n- [ ] **Performance Analytics**: Real-time optimization recommendations\n\n### **Enterprise Features** 🏢\n- [ ] **Multi-Tenant Architecture**: Organization-based access control\n- [ ] **Policy Engine**: Rule-based automation and security enforcement\n- [ ] **Disaster Recovery**: Automated failover and restoration workflows\n- [ ] **Compliance Dashboard**: Audit trail and compliance reporting\n- [ ] **API Management**: RESTful API for programmatic access\n\n---\n\n
\n\n**🌸 Made with love and cherry blossoms 🌸**\n\n*Where revolutionary technology meets beautiful design in server management.*\n\n[![Cherry Servers](https://img.shields.io/badge/Powered%20by-Cherry%20Servers-pink.svg)](https://www.cherryservers.com/)\n[![Built with C](https://img.shields.io/badge/Built%20with-C-blue.svg)](https://en.wikipedia.org/wiki/C_(programming_language))\n[![Security First](https://img.shields.io/badge/Security-First-green.svg)](docs/security.md)\n\n
", "has_readme": true, "url": "https://github.com/Geijutsu/sakura", "category": "unclassified", "value_tier": "standard", "value_rank": 2, "editorial_note": null, "similarity_group": "Developer Tools", "group_score": 13, "similar": [ { "id": "MorchestraWorld/sakura", "score": 1.0, "signals": [ "package", "library", "automation" ] }, { "id": "CherryMesh/sakura", "score": 1.0, "signals": [ "package", "library", "automation" ] }, { "id": "quivent/cherry-blossom", "score": 0.9996, "signals": [ "package", "library", "automation" ] }, { "id": "AmadeusInnovations/cherry-blossom", "score": 0.9996, "signals": [ "package", "library", "automation" ] }, { "id": "Geijutsu/cherry", "score": 0.9786, "signals": [ "package", "library", "automation" ] } ] }, { "organization": "Geijutsu", "name": "seed", "source": "R2 Git bundle", "published_at": "2025-12-02T15:52:09+00:00", "readme": "# 🌱 Seed - Beautiful Server Configuration\n\nDead-simple Ubuntu server setup with **named profiles**, modular technology stacks, gorgeous ASCII UI, and Ansible automation.\n\n## Installation\n\n### Quick Install (Recommended)\n```bash\n# Clone and run the tiny installer binary\ngit clone \ncd seed\n./install\n```\n\nThe `install` binary is a tiny (1.7MB) Go program that:\n- ✓ Checks for Python 3 and pip3\n- ✓ Installs Seed using pip3\n- ✓ Provides next steps with colored output\n\n### Alternative: Direct Install\n```bash\npip3 install --user --break-system-packages -e .\n\n# Bootstrap dependencies (optional - installs Ansible, etc.)\nseed bootstrap\n```\n\n**Note:** The `--break-system-packages` flag is required for Python 3.11+ (PEP 668)\n\n## Quick Start\n\n```bash\n# See available technology stacks\nseed config stacks\n\n# Add technology stacks you want\nseed config add docker\nseed config add python\n\n# Add custom packages\nseed config pkg add jq tmux htop\n\n# Review your configuration\nseed config show\n\n# Preview what will be installed\nseed preview\n\n# Apply configuration (will prompt for sudo password)\nseed setup\n\n# View interactive documentation\nseed docs\n```\n\n## Commands\n\n### Profile Management\n\n```bash\nseed config profiles # List all profiles (built-in and user)\nseed config profile use # Switch to a profile\nseed config profile import # Import built-in profile to customize\nseed config profile new # Create new profile\nseed config profile new --copy-from= # Create from existing\nseed config profile copy # Copy a profile\nseed config profile delete # Delete a profile\n```\n\n### Built-in Profiles\n\nSeed ships with pre-configured profiles embedded in the binary:\n\n| Profile | Description | Stacks Included |\n|---------|-------------|-----------------|\n| `dev` | Full-stack development environment | python, nodejs, docker, github, cli-tools |\n| `prod` | Production web server with security | webserver, security, monitoring |\n| `ml` | Machine learning workstation | python, machine-learning, ollama |\n| `ml-gpu` | ML workstation with GPU support | python, machine-learning, ollama, cuda |\n| `web` | Web development environment | nodejs, docker, github |\n| `minimal` | Minimal setup with essential tools | base packages only |\n\n**Using built-in profiles:**\n```bash\n# Use directly (read-only)\nseed config profile use ml-gpu\nseed setup\n\n# Or import to customize\nseed config profile import ml-gpu my-custom-ml\nseed config profile use my-custom-ml\nseed config add golang\nseed config pkg add nvim\nseed setup\n```\n\n### Technology Stack Management\n\n```bash\nseed config stacks # List available technology stacks\nseed config show # Show current configuration\nseed config add # Add a technology stack to your config\nseed config remove # Remove a technology stack\nseed config edit # Edit config file directly\nseed config reset # Reset to defaults\n```\n\n### Package Management\n\n```bash\nseed config pkg add # Add custom packages\nseed config pkg remove # Remove custom packages\n```\n\n### Installation\n\n```bash\nseed setup # Install configured packages (prompts for password)\nseed check # Dry run (show what would change)\nseed preview # Preview generated Ansible playbook\n```\n\n### Dependency Management\n\n```bash\nseed bootstrap # Install dependencies locally\nseed bootstrap --remote user@host # Install dependencies on remote server\n```\n\nThe `bootstrap` command checks for and installs all required dependencies:\n- **Python 3.7+** - Checks version, offers to install if missing\n- **pip3** - Checks availability, offers to install if missing\n- **Ansible** - Installs via pip3 if not present (no sudo required)\n- **PATH configuration** - Ensures ~/.local/bin is in PATH\n\n**Interactive Installation:**\nIf Python or pip3 are missing, bootstrap will:\n1. ✅ Detect what's missing\n2. ❓ Ask: \"Would you like to install missing dependencies now? [y/N]\"\n3. ✅ If yes: Runs `sudo apt install` (prompts for your password)\n4. ⚠️ If no: Shows manual installation instructions and exits\n\n**Example:**\n```bash\nseed bootstrap --remote user@hostname\n\n# Output:\n🔧 Checking dependencies...\n ✓ Python 3 found: Python 3.12.3\n ✗ pip3 not found\n ⚠ Ansible not installed (will install via pip3)\n\nMissing dependencies: pip3\n\nTo install these, you'll need sudo access on the remote server.\nWould you like to install missing dependencies now? [y/N]: y\n\n# (You enter your password, pip3 gets installed, then Ansible installs)\n✓ Bootstrap complete!\n```\n\n### Remote Deployment\n\n```bash\nseed plant user@hostname # Deploy Seed to remote (auto-bootstrap)\nseed plant user@host --no-bootstrap # Skip bootstrap step\nseed plant user@host -p 2222 # Use custom SSH port\nseed plant user@host -i ~/.ssh/key # Use specific SSH key\n```\n\nThe `plant` command automates full deployment to remote servers:\n1. **Bootstraps** the remote server (installs Python/pip3/Ansible - interactive)\n2. **Packages** the current Seed installation\n3. **Transfers** via SCP to the remote server\n4. **Extracts and installs** via pip3\n5. **Provides** next steps for configuration\n\n**Requirements on remote server:**\n- Ubuntu/Debian system\n- SSH access with sudo privileges\n- If Python/pip3 are missing, bootstrap will prompt you to install them\n\n### Backup/Restore\n\n```bash\nseed backup # Save current package list\nseed restore # Restore from backup\n```\n\n### Documentation\n\n```bash\nseed docs # Generate and open interactive HTML documentation\n```\n\nThe `docs` command generates a beautiful, styled HTML documentation page showcasing:\n- All available technology stacks with dependencies\n- Built-in profiles with descriptions\n- Complete command reference with examples\n- Quick start guides and workflows\n\nThe page opens automatically in your default browser and features:\n- 🎨 Modern, responsive design with Seed branding (cyan/green theme)\n- 📖 Interactive navigation\n- 💻 Syntax-highlighted code examples\n- 📦 Live view of currently available stacks and profiles\n\n## Available Technology Stacks\n\n| Stack | Description | Dependencies |\n|--------|-------------|--------------|\n| `github` | GitHub CLI (gh) for repo management | - |\n| `nodejs` | Node.js 20.x LTS and npm | - |\n| `devtools` | Essential dev tools (make, cmake, gdb, strace, valgrind) | - |\n| `cli-tools` | Modern CLI utilities (jq, ripgrep, fzf, bat, httpie) | - |\n| `docker` | Docker Engine and Docker Compose | - |\n| `python` | Python 3, pip, venv, and dev tools | - |\n| `golang` | Go language and tools | - |\n| `rust` | Rust toolchain via rustup | - |\n| `database` | PostgreSQL and Redis | - |\n| `monitoring` | System monitoring tools (htop, iotop, nethogs) | - |\n| `security` | Security and firewall tools (ufw, fail2ban, aide) | - |\n| `webserver` | Nginx web server and SSL tools (certbot) | - |\n| `machine-learning` | ML tools and libraries (scikit-learn, numpy, pandas, jupyter) | Requires: `python` |\n| `ollama` | Local LLM runtime for running language models | Optional: `cuda` |\n| `cuda` | NVIDIA CUDA drivers and toolkit for GPU acceleration | - |\n\n### Dependency Management\n\nSeed automatically handles stack dependencies:\n\n- **Required dependencies** are automatically installed when you add a stack\n- **Optional dependencies** are suggested but not required (you can add them manually if needed)\n- Dependencies are resolved recursively and installed in the correct order\n\nExample with **required dependencies** (`machine-learning` requires `python`):\n```bash\nseed config add machine-learning\n# ✓ Added stack: machine-learning\n# ↳ Dependencies added: python\n```\n\nExample with **optional dependencies** (`ollama` optionally uses `cuda` for GPU):\n```bash\nseed config add ollama\n# ✓ Added stack: ollama\n# ⓘ Optional dependencies available:\n# ○ cuda - NVIDIA GPU acceleration (recommended for better performance)\n\n# To add the optional CUDA support:\nseed config add cuda\n```\n\n## Configuration File\n\nYour configuration is stored at `~/.seed/configs/.yml`:\n\n```yaml\nstacks:\n - base\n - docker\n - python\ncustom_packages:\n - jq\n - tmux\neditor: vim\n```\n\n## Base Packages\n\nAlways installed:\n- build-essential (make, gcc, g++)\n- curl, wget, git\n- vim, net-tools, unzip\n\n## Complete Deployment Workflow\n\n**Zero to configured server in 2 commands:**\n\n```bash\n# 1. Install Seed locally and deploy to remote server\npip3 install seed\nseed plant user@192.168.1.100\n# (Bootstrap will interactively install Python/pip3/Ansible if needed)\n\n# 2. SSH to server and apply configuration\nssh user@192.168.1.100\nseed config profile use ml-gpu\nseed setup # Will prompt for sudo password\n```\n\nThe `plant` command handles everything automatically:\n- ✅ Interactive bootstrap (installs Python/pip3/Ansible)\n- ✅ Transfers and installs Seed\n- ✅ Configures PATH\n- ✅ Ready to use immediately\n\n## Examples\n\n### Using Named Profiles\n\n**Create separate profiles for different environments:**\n```bash\n# Create a development profile\nseed config profile new dev\nseed config profile use dev\nseed config add nodejs python docker github cli-tools\nseed config pkg add tmux neovim\n\n# Create a production profile\nseed config profile new prod\nseed config profile use prod\nseed config add webserver security monitoring\nseed config pkg add fail2ban\n\n# Create staging from prod\nseed config profile new staging --copy-from=prod\n\n# List all profiles\nseed config profiles\n\n# Switch between them\nseed config profile use dev # For development work\nseed config profile use prod # For production setup\n```\n\n### Single Profile Examples\n\n**Full-stack development server:**\n```bash\nseed config add nodejs docker database github cli-tools\nseed setup\n```\n\n**Python development:**\n```bash\nseed config add python github devtools\nseed config pkg add ipython jupyter\nseed setup\n```\n\n**Secure web server:**\n```bash\nseed config add webserver security monitoring\nseed setup\n```\n\n**Modern CLI environment:**\n```bash\nseed config add github cli-tools\nseed config pkg add neovim zsh\nseed setup\n```\n\n## Requirements\n\n**Local machine (to run Seed CLI):**\n- Python 3.7+\n- pip3\n\n**Remote server (deployment target):**\n- Ubuntu/Debian-based system\n- Python 3.7+ and pip3\n- SSH access\n- sudo privileges (for package installation)\n\n## Notes\n\n- Profiles are generated dynamically into Ansible playbooks\n- All operations are idempotent (safe to run multiple times)\n- Use `seed check` to preview changes before applying\n- Use `seed preview` to see the generated Ansible playbook", "has_readme": true, "url": "https://github.com/Geijutsu/seed", "category": "unclassified", "value_tier": "standard", "value_rank": 2, "editorial_note": null, "similarity_group": "Infrastructure & Operations", "group_score": 10, "similar": [ { "id": "CherryMesh/seed", "score": 1.0, "signals": [ "docker", "monitoring", "deploy" ] }, { "id": "TransformerOS/atlas", "score": 0.1795, "signals": [ "docker", "monitoring", "server" ] }, { "id": "quivent/DocumentationRenderer", "score": 0.173, "signals": [ "docker", "deploy", "deployment" ] }, { "id": "TransformerOS/Autoprime", "score": 0.1718, "signals": [ "docker", "monitoring", "deployment" ] }, { "id": "MozArchAngelos/cherry", "score": 0.167, "signals": [ "docker", "deploy", "deployment" ] } ] }, { "organization": "Geijutsu", "name": "tao", "source": "R2 Git bundle", "published_at": "2025-11-30T23:21:46+00:00", "readme": "# Tao Universal Package Manager\n\nA universal package manager with integrated Git server, written in Rust.\n\n## 🚀 Quick Start\n\n```bash\n# One command setup\nmake build\ntao server setup root@your-server-ip\n```\n\n**What you get:**\n- 🌿 **Git Server** - GitHub-like interface with web UI\n- 📦 **Package Registry** - npm/pip-like package manager \n- 🔄 **Auto-sync** - Git pushes → package updates\n- 🔒 **SSH Tunnel** - Secure access without public ports\n\n## 📖 Documentation\n\n- **[Quick Setup Guide](QUICK_SETUP.md)** - Get started in 5 minutes\n- **[HTML Guide](docs/quick-setup.html)** - Visual setup guide\n- **[Full Documentation](docs/)** - Complete reference\n\n## 🛠 Installation\n\n```bash\ncargo build --release\n```\n\n## 📋 Usage\n\n### Git Operations\n```bash\ntao git create my-project # Create repository\ntao git list # List repositories \ntao git clone my-project # Clone repository\ngit push origin main # Auto-syncs to registry\n```\n\n### Package Operations\n```bash\ntao registry list # List packages\ntao install my-project # Install package\ntao clone my-project # Clone package source\ntao search \"query\" # Search packages\n```\n\n### Server Management\n```bash\ntao server setup root@server-ip # Full server setup\ntao tunnel --server server-ip # Create secure tunnel\ntao auth server-ip # Generate auth token\ntao registry sync # Sync GitHub repos\n```\n\n## 🌐 Web Interfaces\n\nAccess via SSH tunnel:\n- **Git Server:** http://localhost:47292\n- **Registry API:** http://localhost:47291\n\n## 🔄 Workflow\n\n1. **Develop** - Use normal git workflow\n2. **Push** - `git push` updates git server + package registry \n3. **Install** - Team uses `tao install project-name`\n4. **Browse** - View code via web interface\n\n## 🏗 Architecture\n\n- **Tao CLI** - Command-line interface (this repo)\n- **Registry Server** - Package management (Rust/Axum)\n- **Git Server** - Source code hosting (Rust/Axum + git)\n- **SSH Tunnel** - Secure access without public exposure\n\n## 📁 Project Structure\n\n- `src/main.rs` - CLI entry point\n- `src/commands/` - Command implementations\n- `src/config.rs` - Configuration management\n- `Makefile` - Server deployment automation\n\n## 🔧 Development\n\n```bash\n# Run in development\ncargo run -- \n\n# Example: Create repository\ncargo run -- git create my-repo\n\n# Example: List packages\ncargo run -- registry list\n```\n\n## 📦 Package Format\n\nPackages use `tao.yaml` manifest files:\n\n```yaml\nname: my-package\nversion: 1.0.0\ndescription: My awesome package\nauthors:\n - \"Your Name \"\nlicense: MIT\ndependencies:\n some-dep: \"^1.0.0\"\n```\n\n## 🤝 Contributing\n\n1. Fork the repository\n2. Create a feature branch\n3. Make your changes\n4. Submit a pull request\n\n## 📄 License\n\nMIT License - see LICENSE file for details.", "has_readme": true, "url": "https://github.com/Geijutsu/tao", "category": "unclassified", "value_tier": "standard", "value_rank": 2, "editorial_note": null, "similarity_group": "Developer Tools", "group_score": 8, "similar": [ { "id": "quivent/CollaborativeIntelligenceCLI", "score": 0.1662, "signals": [ "automation", "cli", "awesome" ] }, { "id": "Moestradamus-Productions/rootandhue", "score": 0.165, "signals": [ "api", "hosting", "browse" ] }, { "id": "quivent/cpm", "score": 0.1602, "signals": [ "package", "cli", "code" ] }, { "id": "Moestradamus-Productions/lore-library", "score": 0.1531, "signals": [ "package", "automation", "cli" ] }, { "id": "quivent/CI", "score": 0.1496, "signals": [ "cli", "api", "code" ] } ] }, { "organization": "Hupik-World", "name": "AgentFinder", "source": "R2 Git bundle", "published_at": "2025-09-07T04:23:41+02:00", "readme": "# 🌍 Travel Agent Collective - Supplier Extraction System\n\n> **Production-ready travel supplier discovery and extraction platform with real-time monitoring dashboard**\n\n## 🎯 **What This Is**\n\nA comprehensive system for travel agents to automatically discover and extract supplier contact information from multiple cities worldwide. The platform combines a powerful standalone extraction engine with a modern web-based monitoring dashboard for real-time project tracking.\n\n---\n\n## 📦 **Two Main Components**\n\n### 1. **MVP Extractor** (`PoC/deliverables/`) - *Ready to Use*\n**Standalone command-line tool for immediate supplier extraction**\n\n```bash\n# Quick start - Extract suppliers from Munich\ncd PoC/deliverables\npython mvp_extractor.py --location \"Munich\" --max-suppliers 20\n\n# Multiple cities at once\npython mvp_extractor.py --locations \"Munich\" \"Paris\" \"Rome\"\n\n# All European cities (75+ locations)\npython mvp_extractor.py --european-cities --max-suppliers 50\n```\n\n**Features:**\n- ✅ **Self-contained** - Works immediately with no additional setup\n- ✅ **75+ European cities** pre-configured\n- ✅ **Multiple output formats** - Database, CSV, JSON\n- ✅ **Quality scoring** - Automatic supplier quality assessment (70-100 scale) \n- ✅ **Rate limiting** - GDPR compliant with 2-second delays\n- ✅ **Comprehensive reporting** - Detailed extraction summaries\n- ✅ **Error resilience** - Graceful handling of network issues and CAPTCHAs\n\n### 2. **Monitoring Platform** (`PoC/monitor/`) - *Development Dashboard*\n**React/Vite web dashboard for real-time extraction monitoring**\n\n```bash\n# Start the monitoring dashboard\ncd PoC/monitor\nnpm install\nnpm run dev # Frontend (http://localhost:5173)\npython api_server.py # Backend API (http://localhost:8000)\n```\n\n**Features:**\n- 🚀 **Real-time progress tracking** with WebSocket updates\n- 📊 **Analytics dashboard** with extraction metrics\n- 🗄️ **Database viewer** for supplier data management\n- 📝 **Live documentation** with auto-generated guides\n- ⚙️ **Settings management** for extraction parameters\n- 🔍 **Location explorer** with city-specific insights\n\n---\n\n## ⚡ **Quick Start Guide**\n\n### **Option A: Just Extract Suppliers (5 minutes)**\n```bash\n# 1. Install dependencies\npip install -r PoC/requirements.txt\n\n# 2. Run extraction\ncd PoC/deliverables\npython mvp_extractor.py --location \"Munich\" --max-suppliers 10\n\n# 3. Check results\n# - JSON files in current directory\n# - Detailed logs in logs/extraction.log\n# - Console summary with quality scores\n```\n\n### **Option B: Full Platform with Dashboard (15 minutes)**\n```bash\n# 1. Backend setup\npip install -r PoC/requirements.txt\ncd PoC/monitor\npython api_server.py &\n\n# 2. Frontend setup (new terminal)\nnpm install\nnpm run dev\n\n# 3. Open browser\n# Frontend: http://localhost:5173\n# API docs: http://localhost:8000/docs\n```\n\n---\n\n## 🏗️ **System Architecture**\n\n```\n┌─────────────────────┐ ┌─────────────────────┐ ┌─────────────────────┐\n│ MVP Extractor │ │ Monitoring API │ │ React Dashboard │\n│ (deliverables/) │────│ (monitor/) │────│ (monitor/src/) │\n│ │ │ │ │ │\n│ • Self-contained │ │ • FastAPI server │ │ • Real-time UI │\n│ • CLI interface │ │ • WebSocket support │ │ • Progress tracking │\n│ • Direct execution │ │ • Database integration│ │ • Analytics views │\n└─────────────────────┘ └─────────────────────┘ └─────────────────────┘\n │ │ │\n └──────────────────────────┼──────────────────────────┘\n │\n ┌───────▼────────┐\n │ Supabase │\n │ Database │\n │ • Supplier data│\n │ • Extraction │\n │ history │\n └────────────────┘\n```\n\n---\n\n## 📋 **Key Features**\n\n### **Data Extraction Capabilities**\n- 🌍 **Multi-city support** - 75+ European cities pre-configured\n- 🎯 **Smart supplier discovery** - Targeted travel agent searches\n- 🏆 **Quality scoring** - Automatic assessment of supplier reliability\n- 📧 **Contact extraction** - Email, phone, address information\n- 🔍 **Duplicate detection** - Prevent redundant supplier entries\n\n### **Technical Excellence**\n- ⚡ **High performance** - Process 50-100 suppliers/hour\n- 🛡️ **GDPR compliant** - Rate limiting and public data only\n- 🔄 **Error resilience** - Retry logic and graceful degradation\n- 📊 **Comprehensive logging** - Detailed extraction audit trails\n- 🔧 **Configurable** - YAML-based settings management\n\n### **Output Flexibility**\n- 💾 **Database storage** - Direct Supabase integration\n- 📊 **CSV exports** - Standard spreadsheet format\n- 📄 **JSON output** - Structured data for integrations\n- 🔗 **Google Sheets** - Direct export to Google Sheets\n\n---\n\n## 📁 **Project Structure**\n\n```\nAgentFinder/\n├── README.md # This file\n├── PoC/ # Main project directory\n│ ├── deliverables/ # 🎯 PRODUCTION-READY MVP\n│ │ ├── mvp_extractor.py # ← Main extraction script\n│ │ ├── requirements.txt # ← Dependencies\n│ │ ├── config/ # Configuration files\n│ │ ├── USER_GUIDE.md # User documentation\n│ │ └── TROUBLESHOOTING.md # Support guide\n│ │\n│ ├── monitor/ # 🚀 WEB DASHBOARD\n│ │ ├── src/ # React frontend\n│ │ ├── api_server.py # FastAPI backend\n│ │ ├── package.json # Node dependencies\n│ │ └── docs/ # Documentation\n│ │\n│ ├── core/ # 🔧 CORE LIBRARIES\n│ │ └── extractors/ # Extraction engines\n│ │\n│ ├── scripts/ # 🛠️ UTILITIES\n│ │ ├── database/ # DB management\n│ │ ├── audit/ # Quality assurance\n│ │ └── validation/ # Testing tools\n│ │\n│ ├── data/ # 📊 DATA STORAGE\n│ │ └── reports/examples/ # Sample extraction reports\n│ │\n│ ├── docs/ # 📚 DOCUMENTATION\n│ ├── tests/ # 🧪 TEST SUITES\n│ └── requirements.txt # Consolidated dependencies\n└── .gitignore # Git ignore rules\n```\n\n---\n\n## 🔧 **Configuration**\n\n### **Environment Variables**\n```bash\n# Supabase Database (required for database output)\nSUPABASE_URL=your_supabase_url\nSUPABASE_KEY=your_supabase_key\n\n# Google Sheets (optional)\nGOOGLE_SHEETS_CREDENTIALS_FILE=path/to/credentials.json\n\n# Extraction Settings (optional)\nDEFAULT_MAX_SUPPLIERS=100\nDEFAULT_RATE_LIMIT=2.0\nDEFAULT_QUALITY_THRESHOLD=70\n```\n\n### **City Configuration**\nEdit `PoC/deliverables/config/` for custom locations:\n- `munich_example.yaml` - Single city template\n- `multi_location_example.yaml` - Multiple cities\n- `mvp_config.yaml` - Default settings\n\n---\n\n## 📊 **Sample Output**\n\n### **Console Summary**\n```\n📊 EXTRACTION SUMMARY - MUNICH\n==================================================\n🎯 Suppliers Found: 23\n✨ New Suppliers: 18\n🔄 Duplicates Skipped: 5\n⏱️ Extraction Time: 2.4 minutes\n📈 Success Rate: 95.7%\n🏆 Avg Quality Score: 84.2\n\n✅ Extraction completed successfully!\n```\n\n### **Data Structure**\n```json\n{\n \"supplier_name\": \"Alpine Adventure Tours\",\n \"location\": \"Munich\",\n \"contact_email\": \"info@alpineadventure.de\",\n \"phone\": \"+49 89 123 4567\",\n \"website\": \"https://alpineadventure.de\",\n \"address\": \"Marienplatz 1, 80331 München\",\n \"quality_score\": 87,\n \"services\": [\"Mountain Tours\", \"City Tours\"],\n \"extraction_date\": \"2025-01-07T10:30:00Z\"\n}\n```\n\n---\n\n## 🚀 **Usage Examples**\n\n### **Single City Extraction**\n```bash\npython mvp_extractor.py --location \"Paris\" --max-suppliers 25 --output-format csv\n```\n\n### **Batch Processing**\n```bash\npython mvp_extractor.py --locations \"Munich\" \"Vienna\" \"Salzburg\" --quality-threshold 80\n```\n\n### **European Tour**\n```bash\npython mvp_extractor.py --european-cities --max-suppliers 10 --update-report\n```\n\n### **Custom Configuration**\n```bash\npython mvp_extractor.py --location \"Rome\" --config config/custom_config.yaml --verbose\n```\n\n---\n\n## 🧪 **Testing & Validation**\n\n### **Run System Tests**\n```bash\n# Full system validation\npython tests/system_validation.py\n\n# Database connectivity\npython scripts/database/database_checker.py\n\n# API server health check\ncurl http://localhost:8000/health\n```\n\n### **Quality Assurance**\n- 🎯 **90%+ extraction accuracy** target\n- ⚡ **<5% error rate** under normal conditions \n- 🚀 **50-100 suppliers/hour** processing speed\n- 🛡️ **GDPR compliance** with 2-second rate limits\n\n---\n\n## 🔍 **Troubleshooting**\n\n### **Common Issues**\n- **No suppliers found**: Check location spelling and network connectivity\n- **Database errors**: Verify Supabase credentials in environment\n- **Rate limiting**: Increase `--rate-limit` parameter for slower extraction\n- **Quality issues**: Adjust `--quality-threshold` to filter results\n\n### **Support Resources**\n- 📖 **User Guide**: `PoC/deliverables/USER_GUIDE.md`\n- 🐛 **Troubleshooting**: `PoC/deliverables/TROUBLESHOOTING.md`\n- 📊 **API Documentation**: http://localhost:8000/docs (when running)\n- 📝 **Logs**: Check `logs/extraction.log` for detailed information\n\n---\n\n## 🛠️ **Development**\n\n### **System Requirements**\n- Python 3.8+\n- Node.js 16+ (for dashboard)\n- 4GB RAM minimum\n- Internet connectivity for supplier discovery\n\n### **Dependencies**\n- **Core**: FastAPI, Supabase, BeautifulSoup, Requests\n- **Frontend**: React, Vite, TailwindCSS, TypeScript\n- **Database**: PostgreSQL via Supabase\n- **Optional**: Google Sheets API, Pandas for data processing\n\n### **Contributing**\n1. Fork the repository\n2. Create feature branch (`git checkout -b feature/amazing-feature`)\n3. Commit changes (`git commit -m 'Add amazing feature'`)\n4. Push to branch (`git push origin feature/amazing-feature`)\n5. Open a Pull Request\n\n---\n\n## 📜 **License & Compliance**\n\n- **License**: MIT License - free for commercial use\n- **Data Policy**: Public business information only\n- **GDPR Compliance**: Rate limiting and robots.txt respect\n- **Terms**: Business directory data extraction for legitimate commercial outreach\n\n---\n\n## 🎯 **Next Steps**\n\n1. **Quick Start**: Try `python mvp_extractor.py --location \"Munich\" --max-suppliers 5`\n2. **Explore Dashboard**: Start the monitoring platform for visual interface\n3. **Customize Locations**: Add your target cities to the configuration\n4. **Scale Up**: Use `--european-cities` for comprehensive extraction\n5. **Integrate**: Connect with your CRM or marketing automation platform\n\n---\n\n**Ready to discover travel suppliers worldwide? Start with the MVP extractor or explore the full platform! 🚀**", "has_readme": true, "url": "https://github.com/Hupik-World/AgentFinder", "category": "unclassified", "value_tier": "standard", "value_rank": 2, "editorial_note": null, "similarity_group": "Web & Applications", "group_score": 14, "similar": [ { "id": "TSMCP/Orchestra", "score": 0.2029, "signals": [ "web", "dashboard", "backend" ] }, { "id": "MorchestraWorld/Orchestra", "score": 0.2029, "signals": [ "web", "dashboard", "backend" ] }, { "id": "Geijutsu/orchestra", "score": 0.2029, "signals": [ "web", "dashboard", "backend" ] }, { "id": "AGI-Film/Gate", "score": 0.183, "signals": [ "frontend", "react", "web" ] }, { "id": "quivent/kamaji", "score": 0.1779, "signals": [ "frontend", "react", "backend" ] } ] }, { "organization": "Influx-Designs", "name": "anime", "source": "R2 Git bundle", "published_at": "2026-05-24T15:33:09-04:00", "readme": "# anime\n\nA toolkit for provisioning and managing AI workloads on Lambda Labs GH200 GPU instances. Includes a Go CLI for package installation and model management, and a Tauri desktop app for server monitoring.\n\n> **Status:** Work in progress. The installer scripts and package definitions are complete. The CLI and desktop app are partially implemented.\n\n## Components\n\n### anime-cli\n\nGo CLI built with [Cobra](https://github.com/spf13/cobra) and [Bubble Tea](https://github.com/charmbracelet/bubbletea).\n\n**What works:**\n- 30+ installable packages with dependency resolution\n- Embedded bash install scripts for each package\n- Model catalog browser (interactive TUI and CLI modes)\n- Local and remote model file scanning\n\n**Commands:**\n```bash\nanime models # Interactive TUI model browser\nanime models --catalog # Print full model catalog\nanime models --local # Scan local filesystem for model files\nanime models list # List all installable models\nanime install # Install a package (resolves dependencies)\nanime packages # Show available packages\n```\n\n### anime-desktop\n\n[Tauri 2.0](https://tauri.app/) desktop app with a Rust backend and React/TypeScript frontend.\n\n**Backend (Rust):**\n- Lambda Labs API client\n- SSH connection management\n- Real-time server monitoring\n\n**Frontend (React):**\n- Lambda instance dashboard\n- Server monitoring view\n\n## Installable Packages\n\n### Infrastructure\n\n| Package | Description | Size |\n|---------|-------------|------|\n| `core` | Build tools, git, curl, Python 3 | ~500MB |\n| `nvidia` | NVIDIA drivers + CUDA 12.4 | ~4GB |\n| `docker` | Docker container platform | ~500MB |\n| `python` | Python 3.11+, numpy, scipy, pandas | ~500MB |\n| `pytorch` | PyTorch, transformers, diffusers | ~8GB |\n| `ollama` | Ollama LLM server with systemd | ~200MB |\n| `nodejs` | Node.js 20.x LTS | ~100MB |\n| `claude` | Anthropic Claude Code CLI | ~100MB |\n| `comfyui` | ComfyUI with Manager | ~5GB |\n\n### LLM Models (via Ollama)\n\n| Package | Model | Size |\n|---------|-------|------|\n| `llama-3.3-70b` | Llama 3.3 70B | ~40GB |\n| `llama-3.3-8b` | Llama 3.3 8B | ~5GB |\n| `mistral` | Mistral 7B | ~4GB |\n| `mixtral` | Mixtral 8x7B | ~26GB |\n| `qwen-2.5-72b` | Qwen 2.5 72B | ~42GB |\n| `qwen-2.5-14b` | Qwen 2.5 14B | ~8GB |\n| `qwen-2.5-7b` | Qwen 2.5 7B | ~4GB |\n| `deepseek-coder-33b` | DeepSeek Coder 33B | ~18GB |\n| `deepseek-v3` | DeepSeek V3 (671B MoE) | ~250GB |\n| `phi-3.5` | Phi-3.5 Mini 3.8B | ~2GB |\n\nModel bundles are also available: `models-small`, `models-medium`, `models-large`.\n\n### Image Generation (for ComfyUI)\n\n| Package | Model | Size |\n|---------|-------|------|\n| `sdxl` | Stable Diffusion XL | ~7GB |\n| `sd15` | Stable Diffusion 1.5 | ~4GB |\n| `flux-dev` | Flux.1 Dev | ~12GB |\n| `flux-schnell` | Flux.1 Schnell | ~12GB |\n\n### Video Generation\n\n| Package | Model | Size |\n|---------|-------|------|\n| `mochi` | Mochi-1 (10B) | ~12GB |\n| `svd` | Stable Video Diffusion | ~8GB |\n| `animatediff` | AnimateDiff | ~4GB |\n| `cogvideo` | CogVideoX-5B | ~14GB |\n| `opensora` | Open-Sora 2.0 | ~16GB |\n| `ltxvideo` | LTXVideo | ~7GB |\n| `wan2` | Wan2.2 | ~10GB |\n| `comfyui-wan2` | Wan2 ComfyUI wrapper | ~100MB |\n\n## Project Structure\n\n```\nanime/\n├── anime-cli/\n│ ├── cmd/\n│ │ └── models.go # Model browser + install commands\n│ └── internal/\n│ └── installer/\n│ ├── packages.go # Package definitions + dependency resolution\n│ └── scripts.go # Embedded bash install scripts\n├── anime-desktop/\n│ ├── src/ # React frontend\n│ │ ├── App.tsx\n│ │ └── components/\n│ └── src-tauri/ # Rust backend\n│ └── src/\n│ ├── lambda/ # Lambda Labs API client\n│ └── server/ # SSH + server monitoring\n├── .gitignore\n└── README.md\n```\n\n## Dependencies\n\n**CLI (Go):**\n- `github.com/charmbracelet/bubbletea` — TUI framework\n- `github.com/charmbracelet/lipgloss` — Terminal styling\n- `github.com/spf13/cobra` — CLI framework\n- `golang.org/x/crypto/ssh` — SSH client\n\n**Desktop (Rust/TypeScript):**\n- Tauri 2.0, reqwest, ssh2, rusqlite\n- React, TypeScript\n\n## License\n\nMIT", "has_readme": true, "url": "https://github.com/Influx-Designs/anime", "category": "unclassified", "value_tier": "standard", "value_rank": 2, "editorial_note": null, "similarity_group": "Models & Machine Learning", "group_score": 16, "similar": [ { "id": "quivent/Anime", "score": 0.8531, "signals": [ "mistral", "diffusion", "llama" ] }, { "id": "Influx-Designs/lambda", "score": 0.3329, "signals": [ "mistral", "diffusion", "llama" ] }, { "id": "quivent/lambda", "score": 0.2787, "signals": [ "mistral", "diffusion", "llama" ] }, { "id": "MorchestraWorld/claudio", "score": 0.1452, "signals": [ "stable", "tauri", "curl" ] }, { "id": "AmadeusInnovations/claudio", "score": 0.1452, "signals": [ "stable", "tauri", "curl" ] } ] }, { "organization": "Influx-Designs", "name": "anime.productions", "source": "R2 Git bundle", "published_at": "2026-05-25T05:30:08+00:00", "readme": "
\n\n```\n * . * . * . * . * .\n . * . * . * . *\n * ❀ . ❀ . ❀ *\n ╭─────────────────────────────────────────────────╮\n ❀ │ █████ ███▄ ██ ██ ███▄ ▄███▄ ███████ │ ❀\n │ ██ ██ ██ ██ ██ ██ ██ ████ ██ ██ │\n . │ ███████ ██ ████ ██ ██ ██ ██ █████ ❀ │ .\n │ ██ ██ ██ ███ ██ ██ ██ ██ │\n ❀ │ ██ ██ ██ ██ ██ ██ ██ ███████ │ ❀\n │ . p r o d u c t i o n s . │\n ╰─────────────────────────────────────────────────╯\n * . ❀ . * . ❀ . *\n a self-hosted anime studio · one GH200 · four engines\n```\n\n# 🌸 anime.productions\n\n### A self-hosted, multi-model **anime generation studio** on a single NVIDIA GH200\n\n[![Live](https://img.shields.io/badge/live-anime.productions-ff69b4?style=for-the-badge)](https://anime.productions)\n[![GPU](https://img.shields.io/badge/GPU-GH200%2096GB-76b900?style=for-the-badge&logo=nvidia&logoColor=white)](#-tools--stack)\n[![diffusers](https://img.shields.io/badge/diffusers-0.38-yellow?style=for-the-badge&logo=huggingface&logoColor=black)](https://github.com/huggingface/diffusers)\n[![TLS](https://img.shields.io/badge/TLS-Let's%20Encrypt-003a70?style=for-the-badge&logo=letsencrypt&logoColor=white)](https://letsencrypt.org)\n\n*Four models. One box. One web studio. Two commands to redeploy.*\n\n
\n\n---\n\n## ✨ What is this?\n\n**anime.productions** turns a single GH200 into a complete anime art + video studio. It runs **four diffusion engines side-by-side** behind one web UI, each lazy-loaded so they cost nothing until used and all coexist in the 96 GB of unified memory (~82 GB resident with everything loaded).\n\n| 🎬 Engine | What it is | Use it for | Speed |\n|---|---|---|---|\n| **WAN 2.2** | Native video DiT (bf16 + 4-step **Lightning** distill) | Highest-quality *uncontrolled* T2V / I2V | ⚡ 4 steps |\n| **Illustrious XL** | SDXL anime checkpoint | Stills, keyframes, reference images (T2I) | ~13 s/img |\n| **AnimateDiff** | ToonYou (SD1.5) + motion module | Anime **text-to-video** | mm-v3 (quality) / AnimateLCM (4–8 step) |\n| **AnimateDiff + ControlNet** | + OpenPose / Lineart / Depth | **Restyle/redirect a clip you already generated** | ~16 s |\n\nThe whole thing is reproducible from two git repos via a Go CLI — see [Setup](#-setup).\n\n> 🔗 **Live:** https://anime.productions\n\n---\n\n## 🧭 The pipeline\n\nThe signature move: **the ControlNet driving signal is a clip the system itself generated.** Make motion once, then restyle/redirect it in anime while preserving the original pose and composition.\n\n```mermaid\nflowchart LR\n I[\"🖼️ Illustrious XL
(stills / keyframes)\"] -->|reference| M\n M[\"🎞️ WAN 2.2 or AnimateDiff
(base motion clip)\"] -->|extract pose / lineart / depth| C\n I -.optional ref.-> C\n C[\"🎨 AnimateDiff + ControlNet
(motion-matched anime restyle)\"]\n style I fill:#a78bfa,color:#0b0b12\n style M fill:#f5a524,color:#0b0b12\n style C fill:#78c88c,color:#0b0b12\n```\n\n---\n\n## 🏗️ How it works\n\n```mermaid\nflowchart TD\n B[\"🌐 Browser — anime.productions\"] -->|HTTPS| N[\"nginx (+ certbot TLS)\"]\n N -->|static| D[\"comfort-ui /dist
React + Vite + Tailwind\"]\n N -->|/api, /ws| S[\"Comfort server :8188
FastAPI + diffusers\"]\n S --> W[\"WAN 2.2 (+Lightning)\"]\n S -. lazy .-> IL[\"Illustrious XL\"]\n S -. lazy .-> AD[\"AnimateDiff (+ControlNet)\"]\n S --> G[\"/api/build → live engine status\"]\n```\n\n- **Comfort server** (`comfort/inference/server.py` + `anime.py`) — FastAPI on `127.0.0.1:8188`. Engines lazy-load on first request and share a GPU lock.\n- **comfort-ui** — a React studio with a top **design-switcher** (pill bar): `Atelier` (WAN) · `Sliders` · `Anime` · `Guide` · `Designs`.\n - **Anime** — the working anime studio (engine picker, motion toggle, ControlNet, driving-clip picker, prompt, generate).\n - **Guide** — a live overview (this page, in-app) pulling `/api/build`.\n - **Designs** — a gallery of **21 artistic studio skins** you can flip through (← / →).\n- **nginx** serves the built `dist/` and proxies `/api` + `/ws` to `:8188`; TLS via Let's Encrypt.\n- **lambda CLI** (Go) provisions a bare GH200 into all of the above.\n\n### Endpoints\n\n| Route | Purpose |\n|---|---|\n| `POST /api/anime` | Anime engines (flat JSON — see [API](#-api)) |\n| `POST /api/prompt` | WAN graph contract (ComfyUI-style) |\n| `GET /ws?clientId=…` | Progress / preview / result stream |\n| `GET /api/view?filename=…&type=output` | Serve a generated image/video |\n| `GET /api/build` | Live model + engine availability/load state |\n\n---\n\n## 🔗 Repos & resources\n\n| | |\n|---|---|\n| 🖥️ **Server + UI** | [`quivent/comfort`](https://github.com/quivent/comfort) — FastAPI/diffusers backend (`inference/anime.py`) + React UI (`comfort-ui/src/designs/*`) |\n| 🛠️ **Provisioning CLI** | [`quivent/lambda`](https://github.com/quivent/lambda) — `lambda wan setup` / `lambda wan anime` |\n| 🌐 **Live site** | https://anime.productions |\n\n### Models (Hugging Face)\n\n| Model | Repo |\n|---|---|\n| WAN 2.2 T2V A14B | [`Wan-AI/Wan2.2-T2V-A14B-Diffusers`](https://huggingface.co/Wan-AI/Wan2.2-T2V-A14B-Diffusers) |\n| WAN 2.2 Lightning (4-step LoRA) | [`lightx2v/Wan2.2-Lightning`](https://huggingface.co/lightx2v/Wan2.2-Lightning) |\n| Illustrious XL (diffusers) | [`OnomaAIResearch/Illustrious-xl-early-release-v0`](https://huggingface.co/OnomaAIResearch/Illustrious-xl-early-release-v0) |\n| AnimateDiff base (ToonYou) | [`frankjoshua/toonyou_beta6`](https://huggingface.co/frankjoshua/toonyou_beta6) |\n| Motion module v3 | [`guoyww/animatediff-motion-adapter-v1-5-3`](https://huggingface.co/guoyww/animatediff-motion-adapter-v1-5-3) |\n| AnimateLCM (speed) | [`wangfuyun/AnimateLCM`](https://huggingface.co/wangfuyun/AnimateLCM) |\n| ControlNet OpenPose / Lineart / Depth | [`lllyasviel/control_v11p_sd15_openpose`](https://huggingface.co/lllyasviel/control_v11p_sd15_openpose) · [`…_lineart`](https://huggingface.co/lllyasviel/control_v11p_sd15_lineart) · [`…f1p_sd15_depth`](https://huggingface.co/lllyasviel/control_v11f1p_sd15_depth) |\n\n---\n\n## 📚 Documentation\n\nThe full corpus lives in [`docs/`](./docs) — start here:\n\n| Doc | What's inside |\n|---|---|\n| [🏗️ Architecture](docs/ARCHITECTURE.md) | System components, request/data flow, GPU budget |\n| [🎬 Engines](docs/ENGINES.md) | Deep dive on all 4 engines + the 6 `anime_fx` modules + every tunable |\n| [📡 API](docs/API.md) | Every endpoint, `/api/anime` fields, `/ws` protocol, curl examples |\n| [🎚️ Parameters](docs/PARAMETERS.md) | Every knob → API field · CLI flag · UI control (honest coverage matrix) |\n| [🚀 Setup](docs/SETUP.md) | Fast (CLI) + manual provisioning on a fresh GH200 |\n| [🧩 Models](docs/MODELS.md) | Every model, HF repo, and on-disk layout |\n| [🛠️ Operations](docs/OPERATIONS.md) | systemd, nginx, deploy, logs, health, restarts |\n| [🩺 Troubleshooting](docs/TROUBLESHOOTING.md) | Every gotcha as Symptom → Cause → Fix |\n| [🖥️ UI](docs/UI.md) | The tabs, the Anime studio controls, the 21 design skins |\n| [🗺️ Roadmap](docs/ROADMAP.md) | Planned tuning controls (Phase A/B/C) |\n\n---\n\n## 🚀 Setup\n\n### Prerequisites\n- An **NVIDIA GH200** box (Ubuntu, system PyTorch w/ CUDA), reachable on :80/:443.\n- A Hugging Face token (for model downloads) in `~/.lambda/config.yaml`.\n- A domain with an **A-record** pointing at the box (for TLS).\n\n### ⚡ Fast path (the lambda CLI)\n\n```bash\n# 1) WAN + comfort + UI build + nginx (+ TLS if --domain and DNS already points here)\nlambda wan setup --domain anime.productions\n\n# 2) Add the anime engines: deps + ~15 GB of models + restart\nlambda wan anime \n\n# 3) (if you didn't use --domain) point DNS + issue a cert\nlambda dns add anime.productions --provider \nsudo certbot --nginx -d anime.productions\n```\n\nThat's it — fresh box → full studio. Models auto-download; the UI (all tabs + 21 design skins) builds from the cloned `comfort` repo.\n\n### 🔧 Manual path (what the CLI automates)\n\n
Expand\n\n```bash\n# deps (note the pins — see Troubleshooting)\npython3 -m pip install --user --upgrade pip\npip3 install --break-system-packages 'numpy<2' 'Pillow>=9.1' fastapi 'uvicorn[standard]' \\\n diffusers transformers accelerate peft optimum-quanto huggingface_hub \\\n opencv-python-headless imageio imageio-ffmpeg python-multipart websockets controlnet_aux\n\ngit clone git@github.com:quivent/comfort.git ~/comfort\n\n# models → ~/models/...\nhf download Wan-AI/Wan2.2-T2V-A14B-Diffusers --local-dir ~/models/wan2.2-diffusers\nhf download lightx2v/Wan2.2-Lightning --include \"Wan2.2-T2V-A14B-4steps-lora-rank64-Seko-V1.1/*.safetensors\" --local-dir ~/models/wan2.2-lightning\n# Illustrious must be the *diffusers* layout (see Troubleshooting on transformers 5.x)\npython3 -c \"from huggingface_hub import snapshot_download as s; s('OnomaAIResearch/Illustrious-xl-early-release-v0', local_dir='$HOME/models/illustrious-xl-diffusers', allow_patterns=['model_index.json','unet/*','vae/*','text_encoder/*','text_encoder_2/*','tokenizer/*','tokenizer_2/*','scheduler/*'], ignore_patterns=['*.bin'])\"\nhf download frankjoshua/toonyou_beta6 --exclude \"toonyou_beta6.safetensors\" --local-dir ~/models/animatediff-sd15/toonyou_beta6\n# motion adapters + controlnets: grab config.json + diffusion_pytorch_model.fp16.safetensors each\n\n# server (bf16 default)\ncd ~/comfort/inference && WAN_FP8=0 python3 server.py # or via systemd: comfort.service\n\n# UI + nginx\ncd ~/comfort/comfort-ui && npm install && npm run build\nsudo certbot --nginx -d \n```\n
\n\n### Run it on an existing box\n```bash\n./comfort-ui/deploy.sh # vite build + nginx reload (deploys UI changes)\nsudo systemctl restart comfort.service # reload the engines\n```\n\n---\n\n## 🎛️ Using it\n\nOpen the site → top-center pill bar → pick a tab. In **Anime**:\n\n1. **Engine** — `Illustrious` (still) or `AnimateDiff` (video).\n2. **Motion** — `mm-v3` (sharper, ~24 steps) or `AnimateLCM` (fast, 4–8 steps).\n3. **Prompt** — booru-style tags work best (`1girl, silver hair, …, masterpiece, best quality`).\n4. **ControlNet restyle** — turn on `pose`/`line`/`depth`, generate a clip, then pick it as the **driving clip** and generate again — the new style follows the original motion.\n\n### Parameters\n\n| Param | Effect | Default |\n|---|---|---|\n| `motion` | quality ↔ speed dial | `v3` |\n| `steps` | more = higher fidelity | 24 (v3) / 6 (lcm) / 28 (illustrious) |\n| `cfg` | prompt adherence | 7.5 (v3) / 1.8 (lcm) / 6.0 (illustrious) |\n| `seed` | **deterministic** — same seed+prompt = identical output | `0` (fixed; bump to vary) |\n| `frames` | clip length | 16 |\n| `controlnet_scale` | how tightly it follows the driver | 0.8 |\n| `width`/`height` | resolution | 512×768 (anime) / 832×1216 (illustrious) |\n\n> ⚠️ **Seed is fixed at 0 by default** — repeated \"Generate\" with the same prompt gives the *same* result. Change the seed to explore variations.\n\n---\n\n## 📡 API\n\n```bash\n# Anime text-to-video (mm-v3)\ncurl -X POST https://anime.productions/api/anime -H 'content-type: application/json' -d '{\n \"engine\":\"anime\",\"client_id\":\"cli\",\"motion\":\"v3\",\n \"prompt\":\"1girl, silver hair, blue kimono, cherry blossoms, masterpiece, best quality\",\n \"frames\":16,\"seed\":42\n}'\n\n# Illustrious still\ncurl -X POST https://anime.productions/api/anime -H 'content-type: application/json' -d '{\n \"engine\":\"illustrious\",\"client_id\":\"cli\",\"prompt\":\"1girl, gothic dress, moonlight\",\"steps\":28,\"seed\":7\n}'\n```\nBody fields: `engine` (`illustrious|anime`), `prompt`, `client_id`, `negative?`, `width?`, `height?`, `frames?`, `fps?`, `seed?`, `steps?`, `cfg?`, `motion?` (`v3|lcm`), `control?` (`openpose|lineart|depth`), `controlnet_scale?`, `driving?` (`{name, subfolder}` of a prior take). Track progress + result over `/ws?clientId=`.\n\n---\n\n## 🧰 Tools & stack\n\n| Tool | Role | Notes |\n|---|---|---|\n| **NVIDIA GH200** | compute | 96 GB unified — all 4 engines fit, no offload |\n| **PyTorch 2.7** (system) | tensor backend | the system CUDA build; do **not** pip-replace it |\n| **diffusers 0.38** | pipelines | `WanPipeline`, `AnimateDiffPipeline`, `AnimateDiffControlNetPipeline`, `StableDiffusionXLPipeline` |\n| **transformers 5.x** | text encoders | ⚠️ breaks SDXL `from_single_file` — see troubleshooting |\n| **optimum-quanto** | fp8 (optional) | imported at server start even in bf16 |\n| **peft** | LoRA fuse | **required** for Lightning + AnimateDiff LoRAs |\n| **controlnet_aux** | preprocessors | OpenPose / Lineart / Depth detectors |\n| **hf** (huggingface_hub) | model downloads | needs HF token |\n| **FastAPI + uvicorn** | server | `:8188` |\n| **React + Vite + Tailwind** | UI | `comfort-ui` |\n| **nginx + certbot** | edge + TLS | serves `dist/`, proxies `/api`,`/ws` |\n| **Go 1.23+** | `lambda` CLI | `/usr/local/go/bin/go` |\n\n---\n\n## 🩺 Troubleshooting\n\n
Server crash-loops on startup: ModuleNotFoundError: No module named 'peft'\n\nLightning / AnimateDiff LoRA fusing needs `peft`. Install it into the server's interpreter: `python3 -m pip install --user peft`. Without it the systemd service restarts forever.\n
\n\n
SDXL won't load: 'CLIPTextModel' object has no attribute 'text_model'\n\ndiffusers 0.38's `from_single_file` SDXL CLIP conversion is **incompatible with transformers 5.x**. Don't downgrade transformers (WAN's UMT5 needs it). Instead load Illustrious from a **diffusers-layout** repo via `from_pretrained` (`OnomaAIResearch/Illustrious-xl-early-release-v0`), not the v1.0 single file.\n
\n\n
numpy.dtype size changed / ABI errors after installing controlnet_aux\n\n`controlnet_aux` pulls **numpy 2** and **opencv 4.13** (which wants numpy≥2), breaking the WAN stack's numpy-1.x C-extensions. Fix: `pip install 'numpy<2'` and hold `'opencv-python-headless<4.12'`.\n
\n\n
Motion adapter / ControlNet: no file named diffusion_pytorch_model.safetensors\n\nThey're downloaded as `*.fp16.safetensors`. Load with `variant=\"fp16\"` (and download `config.json` too — the `hf` CLI's multi-arg `--include` can silently skip it; use `huggingface_hub` per-file instead).\n
\n\n
4-step anime/WAN output looks like incoherent noise\n\nThe 4-step defaults **require** the distill LoRA. For WAN, ensure `~/models/wan2.2-lightning/...` is present (or it falls back to ~30 steps). The server fuses it at load (`/api/build` → `lightning:true`).\n
\n\n
fp8 vs bf16 — the toggle that does nothing\n\nThe real env var is **`WAN_FP8`** (`0`=bf16, `1`=fp8), **not** `COMFORT_FP8` (dead). Default is bf16. `lambda wan setup --fp8` opts into fp8.\n
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Driving-clip load fails: The 'pyav' plugin is not installed\n\nRead clip frames with imageio's **FFMPEG** plugin (`imageio.v3.imread(path, plugin=\"FFMPEG\")`); `imageio-ffmpeg` is installed, pyav is not.\n
\n\n
UI loads but the new tabs are missing\n\nnginx serves the built `dist/`, not the vite dev server. After UI changes run `./comfort-ui/deploy.sh` to rebuild, then **hard-refresh** (Ctrl/Cmd+Shift+R) to bust the cached bundle.\n
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nginx 500 / \"Permission denied\" serving the UI\n\n`www-data` can't traverse `/home/ubuntu` (mode 750). `chmod o+x` the path components down to `dist/`, or add `www-data` to the `ubuntu` group.\n
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Live preview spams preview decode failed … expected 48 channels, got 16\n\nThe TAEHV live-preview decoder loaded is the 48-channel (WAN 2.2 5B) one, but the A14B model uses the 16-channel VAE. Final output is unaffected; only the in-step preview fails. Fix = load the 16-channel TAEHV (`taew2_1`).\n
\n\n
Can't reach the box by its public IP from the box itself\n\nThat's NAT hairpin (cloud boxes often can't curl their own public IP) — not a real outage. Test locally with `curl --resolve :443:127.0.0.1 https:///`. Hitting the **raw IP** in a browser shows a cert warning (the cert is for the domain) — use the domain.\n
\n\n---\n\n## 🧠 GPU memory budget (GH200, 96 GB)\n\n```\n ╔══════════════════╗\n ║░░░░░░░░░░░░░░░░░░║ 96 ─ ✦ MAX CHARGE ✦\n ║░░░░░░░░░░░░░░░░░░║\n ╠══════════════════╣ 82 ────────────────╮\n ║ ▚▚ AnimateDiff ▞▞║ │\n ║ ▚▚ +ControlNet ▞▞║ ~5 GB │\n ╠══════════════════╣ 77 │\n ║ ✶ Illustrious ✶ ║ ~7 GB │ ⚡ ALL\n ║ ✶ XL ✶ ║ │ ENGINES\n ╠══════════════════╣ 70 │ LOADED\n ║ ★ ★ ★ ★ ★ ★ ★ ★ ★║ │\n ║ ★ WAN 2.2 ★║ │ 82 / 96\n ║ ★ bf16+Lightning★║ ~66 GB │ GB\n ║ ★ ★║ │\n ║ ★ ★ ★ ★ ★ ★ ★ ★ ★║ │\n ╚══════════════════╝ 0 ─────────────────╯\n GH200 · 96 GB HBM3 · henshin complete\n```\n\n| Loaded | Resident |\n|---|---|\n| WAN 2.2 (bf16 + Lightning) | ~66 GB |\n| + Illustrious XL | +~7 GB |\n| + AnimateDiff (+ ControlNet) | +~4–6 GB |\n| **All engines** | **~82 GB** ✅ |\n\n---\n\n## 📚 Research\n\nAlongside the studio, this repo houses formal write-ups of experiments run\non the same box:\n\n- [`papers/inquisition/`](papers/inquisition/) — *The Inquisition: an\n investigation into the fiber structure of diffusion generative\n models.* Start at\n [`papers/inquisition/README.md`](papers/inquisition/README.md) for\n the navigation hub and reading order. Headline finding: the locus\n of compositional control **inverts** between SDXL-family U-Nets\n (seed ~50 %, prompt ~2 % of vertical-centroid variance) and Flux\n MMDiT (seed ~5 %, prompt ~87 %), with non-overlapping bootstrap\n CIs and permutation p ≈ 2×10⁻⁴. Includes the v1 NoobAI-only\n predecessor paper, the cross-architecture writeup and short\n preprint, formal methodology and statistics, the 5-wave dispatch\n plan, the pre-registration ledger, an open-problems catalogue, and\n a practitioner-facing guide for SDXL vs MMDiT workflows.\n\nExperiments are orchestrated by the `lambda topology` subcommand suite in\n[`quivent/lambda`](https://github.com/quivent/lambda); raw artifacts\n(image grids, feature tensors, h-space activations) are committed there\nand each paper here references a specific commit for reproducibility.\n\n---\n\n
\n\n```\n * ❀ ❀ *\n ❀ * ❀ * ❀\n * ❀ *\n ──────────────────────────\n ありがとう\n ──────────────────────────\n```\n\n🤖 *Built with [Claude Code](https://claude.com/claude-code)*\n\n
", "has_readme": true, "url": "https://github.com/Influx-Designs/anime.productions", "category": "unclassified", "value_tier": "standard", "value_rank": 2, "editorial_note": null, "similarity_group": "Models & Machine Learning", "group_score": 12, "similar": [ { "id": "quivent/anime.productions", "score": 0.9884, "signals": [ "tokenizer", "diffusion", "checkpoint" ] }, { "id": "quivent/FLUX", "score": 0.1574, "signals": [ "models", "generation", "model" ] }, { "id": "Influx-Designs/render", "score": 0.1539, "signals": [ "diffusion", "generation", "model" ] }, { "id": "quivent/vision-lab", "score": 0.1508, "signals": [ "diffusion", "models", "model" ] }, { "id": "Influx-Designs/vision-lab", "score": 0.1508, "signals": [ "diffusion", "models", "model" ] } ] }, { "organization": "Influx-Designs", "name": "avrender", "source": "R2 Git bundle", "published_at": "2026-07-15T23:21:24-04:00", "readme": "# AVRender\n\nPublic audio-reactive render service: upload an audio file + type content ideas → the system\niterates the idea into **4 concept options** → preview render per concept → pick one → full-quality\naudio-reactive Blender render at the **KDTA / URANIUM LITURGY quality floor**. SaaS subscription\nand pay-as-you-go credits via Stripe Checkout (+ Link + stablecoin \"Pay with Crypto\").\n\nThis is a **thin orchestration/UX layer** over existing digi-dali components. Nothing that already\nexists is rebuilt. Full design spec: [`ARCHITECTURE.md`](./ARCHITECTURE.md).\n\n- Host: Windows, Python 3.12+, FastAPI, SQLite (WAL), port **8445**\n- Previews: Moe's local Tailscale farm (hal90000 / agentzero, RTX 5090s), RunPod overflow only\n- Finals: **always RunPod** (never blocks the local farm), hard daily cost cap\n\n---\n\n## Architecture\n\n```mermaid\nflowchart TD\n U[Public user] -->|upload audio + idea| FE[static/ frontend M5\\nfork of render-deck UI]\n FE --> API[app.py M1\\nFastAPI :8445]\n API --> DB[(db.py M1\\nSQLite WAL\\nledger + state machine)]\n\n API -->|analyze once per upload| AVS[audio_reactive/av_score.py\\nanalyze -> .avscore.json]\n API --> C[concepts.py M2\\nOllama Gemma -> Claude fallback\\n4 ConceptSpecs + style controls]\n C -->|--dry-run validate| RB[blender_render/render_build.py\\nheadless Blender 5.1]\n\n API --> RF[renderfarm.py M3\\nroute + poll + assemble + QA]\n RF -->|previews\\ntailscale ping + ssh nvidia-smi| FARM[hal90000 RTX 5090 32GB\\nagentzero RTX 5090 24GB]\n RF -->|finals + preview overflow| RP[runpod_adapter.py M3\\npod create/dispatch/terminate\\nwall-clock teardown + cost cap]\n FARM --> RB2[render_build.py on host GPU]\n RP --> RB3[render_build.py on pod GPU\\n--cycles-device CUDA]\n\n RF -->|frames done| ASM[ffmpeg -frames:v exact encode\\n+ audio mux + QA gates]\n ASM -->|QA pass only| DEL[delivered\\ncredits captured\\nscoped download]\n\n STR[Stripe Checkout + Link\\n+ stablecoin] -->|webhooks only| PAY[payments.py M4\\ncredit ledger hold/capture/release]\n PAY --> DB\n API --> PAY\n```\n\nFunnel state machine (locked):\n`uploaded → scored → concepts → preview_queued → preview_done → paid → final_queued → rendering → assembling → delivered`\nwith failure states (`preview_failed`, `render_failed` → retry ×2 → `refunded`, `cancelled`).\nCredits are **held** at `paid` and **captured only at `delivered` after QA passes** — a crash never\neats credits. Every transition writes a `job_events` row.\n\n## Module map\n\n| Module | Files | Owns |\n|---|---|---|\n| M1 | `app.py`, `db.py`, `config.py`, `models.py` | FastAPI service, SQLite schema/ledger txns, state transitions, scoped media/thumb serving |\n| M2 | `concepts.py` | idea + avscore → 4 ConceptSpecs; style-controls mapping; Ollama→Claude fallback |\n| M3 | `renderfarm.py`, `runpod_adapter.py` | dispatch/poll/assemble/QA; farm + RunPod backends; routing |\n| M4 | `payments.py` | Stripe Checkout, webhooks, credit ledger (grant/hold/capture/release/clawback) |\n| M5 | `static/` | frontend (fork of render-deck static UI) |\n| M6 | `README.md`, `.env.example`, `start_avrender.ps1` | docs, config template, launcher |\n\n## Reuse map — existing digi-dali components this wraps\n\n| Capability | Existing component (never reimplemented) |\n|---|---|\n| Audio analysis | `audio_reactive/av_score.py` — `analyze(audio_path, fps=24)`, cached `.avscore.json`, once per upload |\n| Motion-plan validation (bpy-free) | `audio_reactive/motion/smooth_trippy.py` (`condition_bands`, `build_phase_clocks`) + `motion/camera_engine.py` (`dwell_audit`, `seam_check`) |\n| Concept → scene → render | `audio_reactive/blender_render/render_build.py` headless (`--spec/--mode/--dry-run`, success sentinel `BUILD_DONE`) |\n| Style catalog (10 presets) | `audio_reactive/blender_render/style_apply.py` — `PRESETS` + `PRESET_PALETTES` imported, never copied; `jewel-dark` = \"Lightbrush signature\" |\n| ConceptSpec schema + examples | `audio_reactive/blender_render/README.md` + `_contact_sheet/specs/*.json` |\n| Local farm dispatch pattern | `audio_reactive/looks/_az_cobalt_chain.sh`, `monitor_agentzero_queue.sh`, `remote_queue.bat` (scp → detached launch → frame poll → pull) |\n| RunPod dispatch pattern | `audio_reactive/looks/_pod2_queue.sh`, `_pod_autoterminate.sh`, `pfunk_blender/farm/renderpod_worker.py` + `configure_cycles_cuda_final.py` |\n| Farm safety doctrine | `audio_reactive/GH200_FARM.md` — explicit `--cycles-device`, `nvidia-smi` pre-flight |\n| Assembly / mux | frame-exact discipline from `FINAL_RENDERS/_stitch_work/build_final.py` (`-frames:v` exact, never `-t`; aac 320k mux, faststart) |\n| Sync/QA gates | `_stitch_work/verify_v5_frames.py` (ffprobe frame budget) + `verify_ACTUAL_positions.py` (reactivity cross-correlation on the rendered mp4) + flood/flash scan |\n| Progress/ETA, thumbs, GPU poll | Historical AVRender source: `digi-dali/render-deck/app.py` (`scan_pass()`, `make_thumb()`, atomic tmp+`os.replace`, SelectorEventLoop workaround). Active RenderDeck source for future library/editor work: `C:\\Art\\iHateRadio\\packages\\dashboard` |\n| Frontend components | Historical static fork: `digi-dali/render-deck/static/{index.html,app.js,app.css}`. Active RenderDeck UI source: `C:\\Art\\iHateRadio\\packages\\dashboard-ui`; PFUNK reference archive: `C:\\Art\\Lightbrush\\pfunk` |\n\nNew code is limited to: FastAPI routes, SQLite schema/ledger, state machine, dispatcher transport,\nStripe webhooks, concept-LLM prompting, frontend glue.\n\n---\n\n## Running it\n\n### Prerequisites\n\n- Python 3.12+ at `C:\\Python312\\python.exe` (edit `start_avrender.ps1` if elsewhere)\n- Blender **5.1** installed locally (dry-run validation) — 5.3-alpha is banned (breaks GN keyframing)\n- `ffmpeg`/`ffprobe` on PATH (or set `FFMPEG_EXE`/`FFPROBE_EXE`)\n- Tailscale up, with ssh key access to `hal90000` and `agentzero` (previews)\n- Ollama running locally with `gemma3:27b` pulled (concept generation); `ANTHROPIC_API_KEY` optional fallback\n- Stripe CLI for dev webhooks\n\n### First run\n\n```powershell\ncd C:\\Art\\ai\\codeart\\digi-dali\\avrender\npip install -r requirements.txt\nCopy-Item .env.example .env # then edit .env — see comments per key\n.\\start_avrender.ps1 # detached, hidden window, http://localhost:8445\n```\n\nForeground (dev, with logs): `python app.py`\n\nDB is created at `%DATA_ROOT%\\avrender.db` on first start (WAL mode). Job artifacts live under\n`%DATA_ROOT%\\jobs\\\\`.\n\n### Smoke test (no GPU, no money)\n\n```powershell\n# 1. Synthetic audio fixture + avscore\npython ..\\audio_reactive\\av_score.py --make-test\n# 2. Upload it\ncurl -F \"audio=@test_tone.wav\" -F \"idea_text=molten glass cathedral breathing with the bass\" http://localhost:8445/api/upload\n# 3. Poll the funnel: uploaded -> scored -> concepts (4 cards)\ncurl http://localhost:8445/api/projects/\n```\n\nConcept specs validate via `render_build.py --dry-run` (free, no render). Preview/final dispatch\nrequires the farm / RunPod config below.\n\n---\n\n## Payment setup runbook (M4)\n\nSQLite `credit_ledger` is the **sole** credit authority (append-only, `balance_after` per row,\n`UNIQUE(reason, ref_id)`). Stripe Billing meters/credit-grants are explicitly NOT used.\n**Fulfillment is webhook-only — never the redirect URL.**\n\n### 1. Stripe test mode\n\n1. Create Products/Prices in the Dashboard (test mode):\n - Credit packs: one-time Prices, **`metadata.credits=N` on each Price** (required — the webhook reads it).\n - Subscription: recurring monthly Price. Monthly grant = `SUBSCRIPTION_MONTHLY_CREDITS`, fulfilled on `invoice.paid`.\n2. Enable **Link** (on by default in Checkout) and the **Customer Portal** (Settings → Billing).\n3. Put `sk_test_...` and the Price IDs in `.env`.\n4. Dev webhooks:\n ```powershell\n stripe listen --forward-to localhost:8445/webhooks/stripe\n # copy the whsec_... it prints into STRIPE_WEBHOOK_SECRET, restart avrender\n ```\n5. **Idempotency replay test (required before go-live):**\n ```powershell\n stripe trigger checkout.session.completed\n stripe trigger checkout.session.completed # replay\n stripe trigger invoice.paid\n stripe trigger charge.refunded\n ```\n Verify the ledger shows **zero double-grants** (layer 1 = `stripe_events.event_id` PK,\n layer 2 = `UNIQUE(reason, ref_id)`).\n\nEvents handled: `checkout.session.completed` (grant only if `payment_status=='paid'`),\n`checkout.session.async_payment_succeeded/_failed` (delayed stablecoin settlement),\n`invoice.paid`, `invoice.payment_failed`, `customer.subscription.updated/deleted`,\n`charge.refunded`, `charge.dispute.created` (negative ledger rows).\n\n### 2. Crypto = Stripe stablecoin payments\n\n\"Pay with Crypto\" (USDC on Ethereum/Solana/Polygon/Base, 1.5% flat, settles USD into the same\nStripe balance, works inside the SAME Checkout sessions, supports subscriptions). Enable via\nDashboard toggle + KYB eligibility review — **start this early, it takes hours-to-days**.\nZero extra webhook code beyond the async_payment events above.\n\n**Do NOT integrate Coinbase Commerce — it shut down 2026-03-31.** If users later demand\nBTC/altcoins, the named seam is a NOWPayments stub in `payments.py` (not built in v1).\n\n### 3. Pricing (v1 defaults, tunable in `.env`)\n\n- Preview = `PRICE_PREVIEW_CREDITS` (1 by default; paid look-iteration render)\n- Final = `ceil(duration_s / FINAL_CREDITS_PER_SECONDS)` credits (30s per credit)\n- Money refunds are **admin/manual** in v1; the ledger auto-releases held credits on terminal\n job failure (`job_failed_release`). Negative balances (chargebacks) block new jobs, never throw.\n\n---\n\n## Farm + RunPod configuration (M3)\n\n### Local farm (previews)\n\n- Hosts: `hal90000` (RTX 5090 32GB) and `agentzero` (RTX 5090 24GB) over Tailscale.\n- Routing pre-flight before EVERY dispatch: `tailscale ping -c 1 ` then\n `ssh nvidia-smi`. The 60s health cache is advisory only, never trusted for dispatch.\n- One job per GPU host (`busy` = any job on that backend in `staging|rendering`).\n- Dispatch = scp spec+avscore → detached launch via\n `powershell Invoke-CimMethod Win32_Process Create cmd /c run.bat`\n (a plain ssh command dies with the session) → poll frame PNGs every `POLL_INTERVAL_S` →\n remote ffmpeg contact clip → scp pull.\n- Verify ssh works non-interactively first: `ssh hal90000 nvidia-smi` and\n `ssh agentzero nvidia-smi` from the avrender host.\n\n### RunPod (all finals + preview overflow)\n\n- API key in `RUNPOD_API_KEY` or `~/.runpod/api_key`.\n- Pod image must carry **linux-x64 Blender 5.1** (pinned — 5.3-alpha breaks GN keyframing).\n- **Always** pass `--cycles-device CUDA` explicitly — bare `-a` silently renders on CPU\n (GH200_FARM.md doctrine); GPU prefs are NOT saved in .blend.\n- Teardown is triple-guarded: GraphQL `podTerminate` on done/failed/cancelled, OR unconditional\n wall-clock timeout at `RUNPOD_TEARDOWN_MULTIPLIER` × estimated render time — whichever first.\n- `RUNPOD_DAILY_COST_CAP_USD` is a hard cap: over it, finals show a user-visible\n \"queued for capacity\" state and previews stay farm-only. Never silent overspend.\n\n### Quality gates (non-negotiable, run in `assembling`)\n\n1. Frame count exact: `ffprobe -count_frames` == `frames_target`.\n2. Beat-sync cross-correlation vs `beat_grid` ≤ **2 frames** drift — measured on the\n **rendered mp4**, never the plan. Beat-grid indices are score-fps; scale by\n `render_fps/score_fps` if they differ.\n3. No corrupt/missing frames.\n4. No full-frame flood/flash (1fps mean-RGB scan) unless the user explicitly chose strobe styling.\n\nQA fail == render fail → retry (max `MAX_ATTEMPTS`) → credits released. Never delivered.\n\n---\n\n## Going-live checklist\n\n- [ ] `.env` complete; `ADMIN_TOKEN` rotated from the placeholder\n- [ ] `stripe trigger` replay test shows zero double-grants (both idempotency layers)\n- [ ] Stripe switched to live keys; live webhook endpoint registered (HTTPS) and `whsec` updated\n- [ ] Stablecoin \"Pay with Crypto\" KYB review submitted (hours-to-days lead time)\n- [ ] Customer Portal enabled; pack Prices carry `metadata.credits`\n- [ ] `ssh hal90000 nvidia-smi` and `ssh agentzero nvidia-smi` succeed non-interactively\n- [ ] RunPod: one end-to-end final render on a real pod; confirm `podTerminate` fires AND the\n wall-clock teardown backstop fires when termination is forced to fail\n- [ ] Daily cost cap verified: over-cap final shows \"queued for capacity\", no pod created\n- [ ] One full funnel walkthrough: upload → 4 concepts → preview on farm → pay (test card +\n Link + stablecoin test) → final on RunPod → QA gates pass → delivered → download\n- [ ] QA gate negative test: kill a render mid-flight; verify retry, then terminal\n `refunded` with `job_failed_release` ledger row and no credit capture\n- [ ] Restart recovery test: kill avrender during `rendering`; on restart, stuck-job scan\n re-checks actual backend state (frames on disk / pod alive) before retry-vs-fail\n- [ ] No absolute filesystem paths or farm hostnames in any public API response (grep the\n OpenAPI + spot-check payloads); admin routes 401 without token\n- [ ] Media served only via scoped `FileResponse` per registered artifact (no tree mounts);\n fonts/scripts vendored into `static/` (no CDN)\n- [ ] Quotas enforced (`QUOTA_UPLOADS_PER_DAY`, `QUOTA_PREVIEWS_PER_DAY`); `MAX_UPLOAD_MB` enforced\n- [ ] Reverse proxy with TLS in front of :8445 if binding beyond localhost\n- [ ] Backup schedule for `%DATA_ROOT%\\avrender.db` (it is the money ledger)\n- [ ] Moe's open decisions confirmed (ARCHITECTURE.md §11: manual refunds, $10/day cap,\n retry=2 / teardown=2×, preview artifact format)\n\n## Deliberately deferred (v2 seams, named in code)\n\nFFGL treat/melds on finals (`ffgl_offline/ffgl_apply.py`), multi-section stitch\n(`stitch_e2e.py`), NOWPayments, real auth provider (email-at-checkout only in v1),\nautomatic money refunds.", "has_readme": true, "url": "https://github.com/Influx-Designs/avrender", "category": "unclassified", "value_tier": "standard", "value_rank": 2, "editorial_note": null, "similarity_group": "Developer Tools", "group_score": 9, "similar": [ { "id": "quivent/gemstone", "score": 0.1365, "signals": [ "terminal", "cli", "api" ] }, { "id": "quivent/aons", "score": 0.124, "signals": [ "library", "cli", "api" ] }, { "id": "MorchestraWorld/Agentsy", "score": 0.1221, "signals": [ "editor", "library", "api" ] }, { "id": "quivent/surgery", "score": 0.1176, "signals": [ "editor", "terminal", "api" ] }, { "id": "MorchestraWorld/renderdeck", "score": 0.1173, "signals": [ "api", "renderdeck", "used" ] } ] }, { "organization": "Influx-Designs", "name": "kiln", "source": "R2 Git bundle", "published_at": "2026-07-14T06:22:54-04:00", "readme": "# KILN\n\n**The master render platform — where renders get fired.**\n\nKiln is the coordinator + engine layer of the unified AI rendering platform, built on\nRenderDeck Studio (the iHateRadio dashboard). It turns the `generations` table into a\ndurable, prioritized, crash-recoverable render queue routed across every available\nengine — local ComfyUI farm, GH200 fleet, RunPod, Leonardo, influx.vision — with\nrecipe-hash dedup, dependency DAGs, and eye-gate-before-scaling as a first-class state.\n\n## Status (2026-07-14)\n\nPhases 1–3 of the [masterplan](docs/MASTERPLAN.md) are **implemented and adversarially\nverified** (217/217 smoke assertions, 12/12 cross-language hash-parity assertions).\n\n**The live code currently resides in the RenderDeck Studio codebase**, not this repo:\n\n| Component | Location |\n|---|---|\n| Coordinator (enqueue / lease / retry / sweeps / eye-gate) | `iHateRadio/packages/dashboard/src/kiln.ts` |\n| HTTP routes (`/api/kiln/*`) | `.../src/routes/kiln.ts` |\n| Recipe hashing (TS) | `.../src/lib/recipeHash.ts` |\n| Recipe hashing (Python mirror) | `.../tools/recipe_hash.py` (copy: [`recipe_hash.py`](recipe_hash.py)) |\n| Engine registry + lifecycle hooks | `.../src/engines/` (StudioEngine, registry, local, runpod, motionbridge, …) |\n| Schema extensions | `.../src/routes/studio.ts` (`generations` table, PRAGMA-guarded) |\n| Smoke suites | `.../scratch/kiln.smoke.ts`, `.../scratch/recipeHash.parity.ts` |\n\nThis repo is the platform's **canonical home for design docs and contracts**; code\nextraction into a standalone package is a future consolidation step (the coordinator\ncurrently imports RenderDeck's `studio.ts` / `engineRegistry` / `project.ts` directly —\nby design, per the masterplan's \"extend, don't fork\" doctrine).\n\n## Key facts\n\n- Opt-in: `KILN_ENABLED=1` starts the coordinator loop (5s tick); default OFF.\n- Engines: 6 registered — LocalComfy (pool via `KILN_LOCAL_POOL`, least-loaded),\n Fleet (GH200), Leonardo, RunPod (cost-capped: `KILN_RUNPOD_MAX_JOB_USD`,\n `KILN_RUNPOD_DAILY_CAP_USD`), Influx, MotionBridge (stub until `KILN_MB_POOL` —\n fleet is down; see [server contract](docs/MOTIONBRIDGE_SERVER_CONTRACT.md)).\n- Doctrine: never kill processes (lease-expiry reclaim only); omega B200 deny-listed;\n eye-gate is a queue state (`needs_eye_gate` → `PATCH /api/kiln/:id/eye_gate`);\n bun:sqlite is the broker — no Celery/Redis/Temporal; unconfigured engines degrade\n to `{note}`, never 500.\n- Naming: catalog-side recipe hashes (engine=None) and Kiln-side hashes (engine-aware)\n are **separate namespaces** — do not join without a deliberate strategy.\n\n## Docs\n\n- [`docs/MASTERPLAN.md`](docs/MASTERPLAN.md) — full architecture, §c2 unification\n ruling, phased build plan (Phases 4–6 remain: render-stream/1 bridge, DAG polish,\n asset lockfile + legacy consolidation).\n- [`docs/MOTIONBRIDGE_SERVER_CONTRACT.md`](docs/MOTIONBRIDGE_SERVER_CONTRACT.md) —\n HTTP contract for the resident MotionBridge fleet server (to deploy when the\n GH200 fleet returns).", "has_readme": true, "url": "https://github.com/Influx-Designs/kiln", "category": "unclassified", "value_tier": "standard", "value_rank": 2, "editorial_note": null, "similarity_group": "Developer Tools", "group_score": 5, "similar": [ { "id": "quivent/gemma", "score": 0.1153, "signals": [ "package", "code", "omega" ] }, { "id": "quivent/Council-of-Gemmas", "score": 0.1151, "signals": [ "package", "code", "omega" ] }, { "id": "quivent/Council-OS-Suite-Private", "score": 0.1134, "signals": [ "package", "code", "omega" ] }, { "id": "quivent/Council-of-Governors", "score": 0.1134, "signals": [ "package", "code", "omega" ] }, { "id": "quivent/Council-OS", "score": 0.1071, "signals": [ "package", "code", "omega" ] } ] }, { "organization": "Influx-Designs", "name": "lambda", "source": "R2 Git bundle", "published_at": "2026-05-31T02:19:47+00:00", "readme": "# 🎌 anime - Lambda GH200 Management CLI\n\nA beautiful Go CLI for managing Lambda Labs GH200 instances. No more shell scripts!\n\n## Features\n\n- 🎨 **Beautiful TUI** - Interactive terminal UI using Bubble Tea\n- 🚀 **Easy Configuration** - Configure servers, modules, and API keys visually\n- 💰 **Cost Estimation** - See estimated costs before deployment\n- 📊 **Real-time Progress** - Watch installation progress live\n- 🔌 **SSH Management** - Automatic SSH connection and script deployment\n- 📦 **Modular Installation** - Install only what you need\n\n## Installation\n\n```bash\ncd /Users/joshkornreich/lambda\ngo build -o anime\nsudo mv anime /usr/local/bin/\n\n# Or install directly\ngo install\n```\n\n## Quick Start\n\n### 1. Configure Your Servers\n\n```bash\nanime config\n```\n\nThis opens an interactive TUI where you can:\n- Add/edit Lambda servers\n- Select installation modules\n- Configure API keys (Anthropic, OpenAI, HuggingFace, Lambda Labs)\n- See cost estimates\n\n### 2. Deploy to a Server\n\n```bash\nanime deploy lambda-gh200-1\n```\n\nWatch the installation progress in real-time with:\n- Module-by-module status\n- Live output streaming\n- Real-time cost tracking\n- Beautiful progress indicators\n\n### 3. Check Server Status\n\n```bash\nanime status lambda-gh200-1\n```\n\nSee:\n- System information\n- Installed components\n- GPU status\n- Available models\n\n### 4. List All Servers\n\n```bash\nanime list\n```\n\n## Available Modules\n\n| Module | Time | Description |\n|--------|------|-------------|\n| **Core System** | 5 min | CUDA 12.4, Python, Node.js, Docker |\n| **PyTorch** | 2 min | PyTorch, Transformers, Diffusers, xformers |\n| **Ollama** | 1 min | Ollama LLM server (no models) |\n| **Small Models** | 8 min | Mistral, Llama 3.3 8B, Qwen 2.5 7B |\n| **Medium Models** | 25 min | Qwen 2.5 14B, Mixtral, DeepSeek Coder |\n| **Large Models** | 40 min | Llama 3.3 70B, Qwen 2.5 72B |\n| **ComfyUI** | 2 min | Stable Diffusion UI with Manager |\n| **Claude Code** | 1 min | Anthropic Claude Code CLI |\n\n## Configuration\n\nConfig is stored in `~/.config/anime/config.yaml`:\n\n```yaml\nservers:\n - name: lambda-gh200-1\n host: 192.168.1.100\n user: ubuntu\n ssh_key: ~/.ssh/lambda_key.pem\n cost_per_hour: 20.0\n modules:\n - core\n - pytorch\n - ollama\n - models-small\n\napi_keys:\n anthropic: sk-ant-...\n openai: sk-...\n huggingface: hf_...\n lambda_labs: lambda_...\n```\n\n## Usage Examples\n\n### Basic Setup (Minimal Cost)\n\n```bash\n# 1. Configure server\nanime config\n # Add server with basic info\n # Select: Core + PyTorch (~$3 total)\n\n# 2. Deploy\nanime deploy my-server\n\n# 3. Check status\nanime status my-server\n```\n\n### Production Setup\n\n```bash\n# 1. Configure with all modules\nanime config\n # Select: Core + PyTorch + Ollama + Small Models (~$8 total)\n\n# 2. Deploy and watch progress\nanime deploy production-server\n```\n\n### Multiple Servers\n\n```bash\n# Configure different servers for different purposes\nanime config\n # Server 1: \"dev\" - Core + PyTorch\n # Server 2: \"llm\" - Core + Ollama + Large Models\n # Server 3: \"imaging\" - Core + PyTorch + ComfyUI\n\n# Deploy to specific server\nanime deploy llm\nanime deploy imaging\n```\n\n## TUI Navigation\n\n### Main Menu\n- `↑/↓` or `j/k` - Navigate\n- `Enter` - Select\n- `q` - Quit\n\n### Server List\n- `↑/↓` - Navigate\n- `Enter` - Configure modules\n- `d` - Delete server\n- `Esc` - Back to menu\n\n### Module Selection\n- `↑/↓` - Navigate\n- `Space` - Toggle module\n- `Enter` - Save selection\n- `Esc` - Cancel\n\n### Form Inputs\n- `Tab` - Next field\n- `Shift+Tab` - Previous field\n- `Enter` - Save\n- `Esc` - Cancel\n\n## Cost Estimation\n\nThe CLI automatically calculates estimated costs based on:\n- Selected modules and their installation time\n- Module dependencies (auto-included)\n- Server's cost per hour\n\nExample output:\n```\nEstimated cost: $8.50 @ $20/hr\n Core System: 5 min\n PyTorch: 2 min\n Ollama: 1 min\n Small Models: 8 min\n ---\n Total: ~17 minutes\n```\n\n## Architecture\n\n```\nanime/\n├── cmd/ # CLI commands\n│ ├── root.go # Root command\n│ ├── config.go # Config TUI command\n│ ├── deploy.go # Deploy command\n│ ├── install.go # Install/list commands\n│ └── status.go # Status command\n├── internal/\n│ ├── config/ # Configuration management\n│ │ └── config.go # Config struct and modules\n│ ├── installer/ # Installation logic\n│ │ ├── installer.go # SSH deployment\n│ │ └── scripts.go # Embedded bash scripts\n│ ├── ssh/ # SSH client\n│ │ └── client.go # SSH operations\n│ └── tui/ # Terminal UI\n│ ├── config.go # Config TUI\n│ └── install.go # Install progress TUI\n└── main.go # Entry point\n```\n\n## Development\n\n```bash\n# Run without installing\ngo run main.go config\n\n# Build\ngo build -o anime\n\n# Install\ngo install\n\n# Run tests\ngo test ./...\n\n# Format\ngo fmt ./...\n```\n\n## Dependencies\n\n- `github.com/charmbracelet/bubbletea` - TUI framework\n- `github.com/charmbracelet/lipgloss` - Terminal styling\n- `github.com/charmbracelet/bubbles` - TUI components\n- `github.com/spf13/cobra` - CLI framework\n- `golang.org/x/crypto/ssh` - SSH client\n- `gopkg.in/yaml.v3` - YAML config\n\n## Troubleshooting\n\n### Connection Issues\n\n```bash\n# Test SSH manually\nssh -i ~/.ssh/lambda_key.pem ubuntu@192.168.1.100\n\n# Check config\ncat ~/.config/anime/config.yaml\n```\n\n### Installation Failures\n\n```bash\n# Check status to see what's installed\nanime status my-server\n\n# Check server logs\nssh ubuntu@YOUR_IP\njournalctl -u ollama -f\ncat /tmp/anime-install-*.sh\n```\n\n### Permission Issues\n\n```bash\n# Ensure SSH key has correct permissions\nchmod 600 ~/.ssh/lambda_key.pem\n\n# Ensure user has sudo access\nssh ubuntu@YOUR_IP \"sudo -v\"\n```\n\n## Comparison with Shell Scripts\n\n| Feature | anime CLI | Shell Scripts |\n|---------|-----------|---------------|\n| Configuration | Interactive TUI | Manual editing |\n| Cost Estimation | Built-in | Manual calculation |\n| Progress Tracking | Real-time UI | Text output |\n| Error Handling | Graceful | Exit on error |\n| Module Selection | Visual checkboxes | Comment/uncomment |\n| Multiple Servers | Easy switching | Multiple files |\n| API Key Management | Encrypted storage | Plain text files |\n\n## License\n\nMIT\n\n## Contributing\n\nPRs welcome! Please ensure:\n- Code is formatted (`go fmt`)\n- Tests pass (`go test ./...`)\n- TUI flows work correctly", "has_readme": true, "url": "https://github.com/Influx-Designs/lambda", "category": "unclassified", "value_tier": "standard", "value_rank": 2, "editorial_note": null, "similarity_group": "Models & Machine Learning", "group_score": 10, "similar": [ { "id": "quivent/lambda", "score": 0.7041, "signals": [ "mistral", "diffusion", "llama" ] }, { "id": "Influx-Designs/anime", "score": 0.3329, "signals": [ "mistral", "diffusion", "llama" ] }, { "id": "quivent/Anime", "score": 0.2773, "signals": [ "mistral", "diffusion", "llama" ] }, { "id": "TransformerOS/Autoprime", "score": 0.1944, "signals": [ "mistral", "llama", "qwen" ] }, { "id": "TransformerOS/atlas", "score": 0.1836, "signals": [ "quit", "installer", "selected" ] } ] }, { "organization": "Influx-Designs", "name": "LightbrushFX", "source": "R2 Git bundle", "published_at": "2026-07-15T21:57:05-04:00", "readme": "", "has_readme": false, "url": "https://github.com/Influx-Designs/LightbrushFX", "category": "unclassified", "value_tier": "standard", "value_rank": 2, "editorial_note": null, "similarity_group": "Other Experiments", "group_score": 0, "similar": [] }, { "organization": "Influx-Designs", "name": "MotionBridge", "source": "R2 Git bundle", "published_at": "2026-06-08T01:18:43+00:00", "readme": "

\n \"MotionBridge\n

\n\n

\n \"Repo\"\n \"Python\"\n \"Status\"\n

\n\n# MotionBridge — Flux Looping Texture\n\nMotionBridge is a Flux-native looping texture animator. It produces seed-locked\n48-frame loops where every frame is a genuine full Flux denoise — no\ninterpolation, no upscaling, no separate motion model. The motion is a\nproperty of structured correlated noise and slerp keyframes resolved through\nFlux's own denoising process. The pipeline composes with any Flux image or style\nLoRA at inference time.\n\nBuilt by Influx Designs. The 28-iteration research history (TASK-001..028)\nproduced a clear falsification result: temporal adapters do not create motion in\nthis architecture. That finding clarified the product. See\n`docs/PATH-B-RESULTS.md` for the empirical record.\n\n## What this is / What this is not\n\n**IS:**\n- A Flux animator that produces 48-frame seed-locked loops via correlated-noise\n scheduling and slerp keyframes\n- LoRA-composable at inference — any Flux image or style LoRA can be layered on\n top of a running loop without retraining\n- 100% genuine Flux per frame — each frame runs the full denoising stack\n\n**IS NOT:**\n- A motion module that learns temporal coupling (the adapter does not generate\n motion; see `docs/PATH-B-RESULTS.md`)\n- A video model — no RIFE, no ESRGAN, no Wan, no Hunyuan\n- Faking or interpolating — nothing between the real Flux-decoded frames\n- AnimateDiff-grade object tracking (the product is texture evolution: morphing,\n breathing, pulsing — not objects crossing the frame)\n\n## Signal Span\n\n```text\ncorrelated noise schedule -> slerp keyframes -> Flux denoiser (per frame) -> seed-locked loop\n```\n\nMotionBridge is built around three rules:\n\n- Freeze the backbone until parity says otherwise.\n- Record adapter behavior in manifests, not folklore.\n- Keep frame identity, position ids, and scheduler assumptions visible.\n\n## What MotionBridge Provides\n\n- Adapter manifest validation for `motionbridge.v1`\n- Flux bridge planning for packed latent and token layouts\n- Diffusers Flux forward-contract inspection\n- T=1 Flux position-id parity checks\n- Real `FluxPipeline` transformer parity CLI\n- Temporal image-id extension through Flux `img_ids[:, 0]`\n- Temporal LoRA target discovery for Diffusers Flux modules\n- Training-plan scaffolding for frozen-backbone adapter experiments\n\n## Install\n\n```powershell\npython -m pip install -e .\n```\n\nFor development:\n\n```powershell\npython -m pip install -e \".[dev]\"\n```\n\n## Quick Start\n\nValidate an adapter manifest:\n\n```powershell\nmotionbridge validate-manifest docs/research/motionbridge_example_manifest_flux_packed_temporal_lora.json\n```\n\nGenerate a Flux bridge plan:\n\n```powershell\nmotionbridge plan-flux `\n --manifest docs/research/motionbridge_example_manifest_flux_packed_temporal_lora.json `\n --frames 4 `\n --height-tokens 36 `\n --width-tokens 64 `\n --rank 128\n```\n\nRun the no-weight gates:\n\n```powershell\nmotionbridge inspect-flux\nmotionbridge t1-parity --height-tokens 2 --width-tokens 3 --text-tokens 4\n```\n\nPackage the seed-locked Flux demo kit:\n\n```powershell\n$env:PYTHONPATH='src'\npython tools\\motionbridge_package_demo.py --output-root artifacts\\seed617_demo\n```\n\nThis writes `demo_manifest.json`, `comparison_report.json`, and a reusable Comfy\nAPI workflow for the current `ceramic_bioglass_balanced_v1` Flux progress render.\nThe package is a research preview: it demonstrates the real Comfy Flux\nmotion-module path, but does not claim SDXL AnimateDiff parity.\n\nWith a Diffusers-format Flux pipeline:\n\n```powershell\nmotionbridge real-flux-t1-parity `\n --model C:\\Art\\ai\\comfyui\\models\\diffusers\\hf-internal-testing\\tiny-flux-pipe `\n --height 64 --width 64 --device cuda --dtype float32 --num-inference-steps 1\n```\n\n## Architecture\n\n```text\nframes\n -> latent frame pack\n -> model-family bridge\n -> temporal adapter stack\n -> sampler\n -> decoded video frames\n```\n\nCore concepts:\n\n- `LatentPack`: normalized frame and token representation\n- `Backbone Bridge`: model-family-specific integration layer\n- `Temporal Adapter`: motion module, LoRA, attention layer, or context adapter\n- `Manifest`: portable adapter metadata and compatibility contract\n- `Parity Gate`: validation that T=1 behavior matches the original image model\n\n## CLI\n\n| Command | Purpose |\n| --- | --- |\n| `validate-manifest` | Validate and summarize an adapter manifest |\n| `plan-flux` | Build a Flux bridge prototype plan |\n| `training-plan` | Emit a frozen-backbone adapter training plan |\n| `inspect-flux` | Inspect local Diffusers Flux compatibility |\n| `t1-parity` | Check packed token ID parity for T=1 |\n| `real-flux-t1-parity` | Compare real Flux direct vs bridge path |\n| `real-flux-t1-step-parity` | Compare real Flux transformer and scheduler step parity |\n| `real-flux-packed-smoke` | Smoke test packed Flux frame handling |\n| `real-flux-packed-step-smoke` | Smoke test one packed Flux scheduler step |\n| `real-flux-packed-denoise-smoke` | Smoke test packed Flux denoising |\n| `real-flux-packed-decode-smoke` | Smoke test packed denoising, frame split, unpack, and VAE decode |\n\n## Repository Map\n\n| Path | Purpose |\n| --- | --- |\n| `src/digidali_mcp/integrations/motion_bridge/` | MotionBridge package code |\n| `src/digidali_mcp/integrations/motion_bridge/adapters/` | Adapter contracts and LoRA helpers |\n| `src/digidali_mcp/integrations/motion_bridge/bridges/` | Model-family bridge implementations |\n| `src/digidali_mcp/integrations/motion_bridge/flux/` | Flux contracts, hooks, parity, and pipeline shell |\n| `src/digidali_mcp/integrations/motion_bridge/training/` | Dataset, loss, and training-plan scaffolds |\n| `docs/research/` | Adapter moonshots, operating procedures, schemas, and example manifests |\n| `tests/motionbridge/` | Contract, packing, manifest, attention, and parity tests |\n| `assets/` | MotionBridge logo and README hero imagery |\n| `docs/brand/` | Animated HTML/CSS/SVG brand system demo |\n\n## Adapter Manifests\n\nMotionBridge adapters are described with a `motionbridge.v1` manifest. A manifest\nrecords the base model family, compatible checkpoints, adapter kind, temporal\ncontract, placement rules, fusion policy, training metadata, and safety notes.\n\n```json\n{\n \"adapter_format\": \"motionbridge.v1\",\n \"base_family\": \"flux\",\n \"adapter_kind\": \"temporal_lora\",\n \"temporal_contract\": {\n \"frames\": 4,\n \"latent_temporal_stride\": 1,\n \"rope_axes\": [\"t\", \"h\", \"w\"]\n }\n}\n```\n\n## Brand Assets\n\nMotionBridge includes a reusable SVG identity:\n\n

\n \"MotionBridge\n

\n\nOpen `docs/brand/index.html` for the animated brand system: blueprint grid,\nstroke-drawn bridge mark, signal sweep, node pulses, and pipeline modules.\n\n## Engine: Production Training Pipeline\n\nThe `engine/` module provides production-grade training that plugs into the\nexisting pipeline without modifying any base files. It replaces SGD with\nAdamW + cosine annealing, wires the four declared losses (flow matching,\ntemporal consistency, loop closure, luminance stability), injects windowed\nattention masks and 3D RoPE, adds checkpoint/restart, gradient health\nmonitoring, torch.compile acceleration, VRAM profiling, video export via\nFFmpeg, and experiment tracking.\n\n**One-line integration:**\n\n```python\nfrom digidali_mcp.integrations.motion_bridge.engine import EngineConfig, apply_engine\n\nconfig = EngineConfig.from_defaults()\nctx = apply_engine(pipe, config)\nreport = ctx.run(pipe, prompt=\"fluid motion\", frames=8, steps=100)\n```\n\nOr inject into the existing orchestrator:\n\n```python\nfrom digidali_mcp.integrations.motion_bridge.training import run_prepared_flux_training\nresult = run_prepared_flux_training(..., train_loop_fn=ctx.make_train_loop_fn())\n```\n\nSee [`engine/README.md`](src/digidali_mcp/integrations/motion_bridge/engine/README.md) for the full module table and quick start.\n\n## Status\n\nThe structural-mode render pipeline ships. Path A confirmed 2026-05-27.\n\n- `tools/motionbridge_live_render.py` — the shipping render tool\n- Motion gate (`tools/motionbridge_motion_gate.py`) — the QA gate;\n `adjacent_mean_delta >= 0.037, p95 >= 0.076, first_last <= 0.020`\n- TASK-080..089 novel training protocols — mothballed (code preserved as\n research infrastructure, not activated)\n- TCG / TASK-045 — deferred indefinitely (no empirical basis)\n- Bundle mode — available for experimentation; structural mode is the shipping path\n\nDo not train or ship temporal LoRA weights until T=1 real Flux parity passes on\nthe target pipeline.", "has_readme": true, "url": "https://github.com/Influx-Designs/MotionBridge", "category": "unclassified", "value_tier": "standard", "value_rank": 2, "editorial_note": null, "similarity_group": "Models & Machine Learning", "group_score": 11, "similar": [ { "id": "quivent/animate-flux", "score": 0.1946, "signals": [ "transformer", "checkpoint", "weights" ] }, { "id": "Influx-Designs/MotionTraining", "score": 0.1946, "signals": [ "transformer", "checkpoint", "weights" ] }, { "id": "quivent/anime.productions", "score": 0.1405, "signals": [ "checkpoint", "inference", "models" ] }, { "id": "Influx-Designs/anime.productions", "score": 0.1342, "signals": [ "checkpoint", "inference", "models" ] }, { "id": "quivent/FLUX", "score": 0.1183, "signals": [ "models", "model", "declared" ] } ] }, { "organization": "Influx-Designs", "name": "MotionTraining", "source": "R2 Git bundle", "published_at": "2026-05-26T05:18:50+00:00", "readme": "# MotionBridge\n\n**Turn a frozen FLUX.1 image model into a text-to-video model with a single\nsmall LoRA — no changes to Flux's weights.**\n\nAnimateDiff works by bolting a temporal *module* onto a U-Net, because a U-Net's\nattention is local and per-frame — the temporal pathway has to be built. FLUX is\nan MMDiT transformer whose attention is already **global over a flat token\nsequence**, so the temporal pathway exists the instant you feed it more than one\nframe. MotionBridge exploits that:\n\n1. **Frame packing** — concatenate `T` frames of latent tokens into one sequence;\n Flux's joint attention is now spatiotemporal for free.\n2. **3D RoPE** — extend Flux's 2-axis `(h, w)` rotary embedding to `(t, h, w)` so\n the model can tell frames apart. Parameter-free.\n3. **Windowed temporal attention** — let a token attend within `±window` frames\n (plus all text tokens) to keep the `(T·L)²` cost tractable.\n4. **Temporal LoRA** — the *only* trainable parameters: a low-rank adapter on the\n attention projections that teaches cross-frame identity/motion binding. The\n frozen base already knows objects, pose, lighting; the LoRA only shapes the\n already-global attention to be temporally coherent.\n\nThe adapter is one `.safetensors` file (shipped with a self-describing adapter\ncard) and **stacks on top of existing Flux style / character LoRAs** — the\n\"motion module rides any checkpoint\" property, but via composable LoRAs.\n\n## A synthesis of two independent implementations\n\nMotionBridge and a sibling repo (`animate-flux`) were built independently from the\nsame research. This repository is the **merged best-of-both**: the complete,\nproven `animate-flux` engine (packing, 3D RoPE, the diffusers attention processor,\nthe real flow-matching trainer, the data + evaluation toolkit) hardened with the\npieces the sibling repo did better — folded in at four concrete seams:\n\n- **A diffusers API-drift gate** ([`motionbridge/contracts.py`](motionbridge/contracts.py))\n — `inspect_diffusers_flux()` returns a weight-free `forward_contract_ok`\n boolean, so a Diffusers upgrade that renames a forward kwarg fails *loudly* in\n CI (`make gate`) instead of as a mystery NaN. `FluxForwardInputs.validate()`\n shape-checks the transformer call once at the pipeline boundary.\n- **A cross-family adapter card** ([`motionbridge/adapter_card.py`](motionbridge/adapter_card.py))\n — a `motionbridge.v1` JSON manifest (backbone family, temporal contract, fusion\n policy, hyperparams, measured ‖ΔW‖_F). `save_temporal_lora()` now emits a\n `.card.json` sidecar, so every trained adapter is self-describing and\n registry-ready instead of an anonymous weight blob.\n- **Auxiliary temporal losses** ([`motionbridge/losses.py`](motionbridge/losses.py))\n — opt-in `temporal_consistency` / `loop_closure` / `luminance_stability` terms\n that *optimize* the very quantities the evaluation suite *measures* (warp-error,\n loop residual, flicker). Off by default; the base run is unchanged.\n- **A multi-family research roadmap** ([`docs/research/`](docs/research/)) — the\n literature survey + adapter-registry schema that map where this extends beyond\n Flux (SD1.5/SDXL/SD3/Qwen/Sana/SVD/Wan/...).\n\n> **Central thesis** — [`docs/THESIS.md`](docs/THESIS.md): the random seed is a\n> *measure-zero, deterministic bottleneck* on a model's expression, **not its\n> source**. Expression lives in the continuous latent+conditioning manifold; the\n> goal of the work is to replace blind seed-sampling with legible, navigable\n> control — *hand the human the manifold.*\n\n## Status\n\nA **complete reference implementation** of the design — core library, training\nloop, inference pipeline, data-prep toolkit, evaluation suite, config, a Gradio\ndemo, tests, and docs. **111 CPU tests pass** (unit tests, an end-to-end wiring\ntest that exercises packing → 3D-rope → window-bias → LoRA inject/save/load, the\ndiffusers contract gate, the adapter-card round-trip, and the auxiliary-loss\noptimize/measure interlock), `ruff check` is clean, and every module imports\nwithout a GPU or weights.\n\n**The mechanism is proven on the real model.** `scripts/prove.py` loads the actual\nFLUX.1-schnell weights and verifies all four design claims — most strikingly, that\nwith **zero training, 72% of every image token's attention mass already lands on\nother frames**. The temporal pathway isn't added; it's already there. See\n[`docs/PROOF.md`](docs/PROOF.md) (5/5 claims, ~1 min, ~10 GB VRAM).\n\n**It has not been trained to convergence.** Real training/sampling needs the\nFLUX.1 weights, video clips, and a GPU. The base model is under a non-commercial\nlicense; see [`docs/MODEL_CARD.md`](docs/MODEL_CARD.md).\n\n## Layout\n\n```\nmotionbridge/\n packing.py frame <-> Flux-packed-token conversion + (t,h,w) ids\n rope3d.py 3-axis rotary embedding (temporal band allocation)\n attention.py windowed temporal attention + TemporalFluxAttnProcessor\n temporal_lora.py LoRA injection / save (+ adapter card) / load on the MMDiT\n contracts.py diffusers API-drift gate + forward-shape contracts [merged]\n adapter_card.py cross-family motionbridge.v1 adapter manifest [merged]\n losses.py optional auxiliary temporal losses (opt-in) [merged]\n lora_stats.py adapter magnitude ‖ΔW‖ + blend-ratio measurements\n pipeline.py AnimateFluxPipeline: text -> video\n train.py flow-matching training of the temporal LoRA (frozen Flux)\n data.py / datatools.py video-clip -> cached-latent dataset + curation\n config.py dataclasses + YAML\n seed_recouple.py optional composition LoRA (re-couple seed -> layout)\n metrics.py / motion_metric.py temporal-consistency + motion-fidelity metrics\n eval_seed_layout.py the seed->composition A/B (vision-lab variance decomp)\n manifest.py canonical clip-manifest schema + validation\nscripts/ prepare_data, caption, train.sh, sample, evaluate,\n seed_ab, hook_plan, prove, eval_checkpoint,\n curate_manifest (ffprobe video -> JSONL manifest) [merged]\nconfigs/ train.yaml, smoke_schnell.yaml, smoke_aux.yaml\ntests/ CPU unit + integration + contract tests (111, all passing)\napp.py Gradio text->video demo\ndocs/ ARCHITECTURE, TRAINING, DATA, EVALUATION, MODEL_CARD, ...\ndocs/research/ forward-looking multi-family roadmap + adapter schema [merged]\nDESIGN.md the integration contract (read this first)\n```\n\n## Quickstart\n\n```bash\npip install -e . # or: pip install -r requirements.txt\nmake test # 111 CPU tests (no GPU/weights needed)\nmake gate # diffusers API-drift pre-flight check\n\n# 1. curate raw video into captioned training clips\npython scripts/curate_manifest.py --root raw_videos/ --out data/clips/manifest.jsonl\npython scripts/prepare_data.py --input raw_videos/ --out data/clips --fps 8\npython scripts/caption.py --clips data/clips # or --dry-run for placeholders\n\n# 2. train the temporal LoRA (needs FLUX.1 weights + a GPU)\npython -m motionbridge.train --config configs/train.yaml\n# enable the auxiliary temporal losses via train.aux_* in the YAML (see smoke_aux.yaml)\n\n# 3. sample a clip from a trained adapter (+ its emitted adapter card)\npython scripts/sample.py --lora out/temporal_lora.safetensors \\\n --prompt \"a fox trotting through snow, side view\" --frames 16 --out fox.mp4\n\n# 4. run the seed -> composition A/B (the headline experiment)\npython scripts/seed_ab.py --lora out/temporal_lora.safetensors \\\n --composition-lora out/composition_lora.safetensors --out eval/\n\npython app.py # or launch the interactive Gradio demo\n```\n\n## The research angle (vision-lab)\n\nA frozen-Flux temporal adapter **inherits Flux's spatial behavior**, including its\ncollapsed seed→composition coupling (~5%, vs ~50% for SDXL). Prediction: its\ncross-frame layout is prompt-driven and **seed-invariant**, where AnimateDiff-on-SDXL\nis seed-anchored. `seed_recouple.py` adds an optional composition LoRA that\n*re-introduces* seed→layout control — turning a baked architectural fact into a\ntoggleable adapter. That A/B is the measurable.\n\n## Documentation\n\n- [`docs/THESIS.md`](docs/THESIS.md) — **★ the central thesis**: the seed is a measure-zero, deterministic *bottleneck*; expression lives in the manifold, and the work's goal is to hand the human navigable control of it.\n- [`DESIGN.md`](DESIGN.md) — the module-by-module public API contract (start here).\n- [`docs/PROOF.md`](docs/PROOF.md) — the four design claims, measured on real FLUX.1 weights.\n- [`docs/TRAINING_THESIS.md`](docs/TRAINING_THESIS.md) — the first training run's falsifiable bet: at what LoRA rank does coherent motion lock in?\n- [`docs/PROTOCOL.md`](docs/PROTOCOL.md) — phase-gated execution protocol + the train≡sample consistency contract; the live punch-list to the training run.\n- [`docs/ARCHITECTURE.md`](docs/ARCHITECTURE.md) — dataflow deep-dive, the AnimateDiff-vs-MMDiT inversion, the cost story.\n- [`docs/TRAINING.md`](docs/TRAINING.md) — hardware, data prep, config fields, tuning intuition, failure modes.\n- [`docs/DATA.md`](docs/DATA.md) — raw video → trainable clips pipeline.\n- [`docs/EVALUATION.md`](docs/EVALUATION.md) — metrics + the seed→layout A/B protocol.\n- [`docs/MODEL_CARD.md`](docs/MODEL_CARD.md) — adapter card, base-model license, limitations.\n- [`docs/WHAT_THIS_UNLOCKS.md`](docs/WHAT_THIS_UNLOCKS.md) — position paper on the animation-space impact.\n- [`docs/research/`](docs/research/) — forward-looking multi-family roadmap + the cross-family adapter-registry schema.\n- [`CONTRIBUTING.md`](CONTRIBUTING.md) — dev setup + the module-ownership/contract model.", "has_readme": true, "url": "https://github.com/Influx-Designs/MotionTraining", "category": "unclassified", "value_tier": "standard", "value_rank": 2, "editorial_note": null, "similarity_group": "Models & Machine Learning", "group_score": 14, "similar": [ { "id": "quivent/animate-flux", "score": 0.9982, "signals": [ "transformer", "embedding", "checkpoint" ] }, { "id": "Influx-Designs/MotionBridge", "score": 0.1946, "signals": [ "transformer", "checkpoint", "weights" ] }, { "id": "quivent/anime.productions", "score": 0.13, "signals": [ "checkpoint", "inference", "model" ] }, { "id": "Influx-Designs/anime.productions", "score": 0.1267, "signals": [ "checkpoint", "inference", "model" ] }, { "id": "Influx-Designs/render", "score": 0.1122, "signals": [ "weights", "model", "flicker" ] } ] }, { "organization": "Influx-Designs", "name": "proto", "source": "R2 Git bundle", "published_at": "2026-05-30T22:17:59-04:00", "readme": "```\n╔══════════════════════════════════════════════════════════════════════════╗\n║ ║\n║ ██████╗ ██████╗ ██████╗ ████████╗ ██████╗ ║\n║ ██╔══██╗██╔══██╗██╔═══██╗╚══██╔══╝██╔═══██╗ ║\n║ ██████╔╝██████╔╝██║ ██║ ██║ ██║ ██║ ║\n║ ██╔═══╝ ██╔══██╗██║ ██║ ██║ ██║ ██║ ║\n║ ██║ ██║ ██║╚██████╔╝ ██║ ╚██████╔╝ ║\n║ ╚═╝ ╚═╝ ╚═╝ ╚═════╝ ╚═╝ ╚═════╝ ║\n║ ║\n║ Protocol Orchestration System ║\n║ ║\n╠══════════════════════════════════════════════════════════════════════════╣\n║ ║\n║ Protocols ........... 235 Suites .............. 9 ║\n║ Registry ............. 64 Language ......... Go + bash ║\n║ Arbitrage ............ 91 Integration ...... hive-aware ║\n║ Training ............. 65 Commands ......... 65 slash ║\n║ Revenue .............. 15 Tests ............ 78 passing ║\n║ ║\n╠══════════════════════════════════════════════════════════════════════════╣\n║ ║\n║ proto ║\n║ ├── agentic (27) ─────────┬── L4 Strategy (4) ║\n║ │ ├── L3 Coordination (9) ║\n║ │ ├── L2 Architecture (6) ║\n║ │ └── L1 Cognition (8) ║\n║ │ ║\n║ ├── orchestration (5) ──── cohd doro cprd ftro ero ║\n║ │ ║\n║ ├── wealth (32) ──────────┬── licensing (10) ║\n║ │ ├── products (4) ║\n║ │ ├── security (4) ║\n║ │ ├── safety (4) ║\n║ │ └── strategic (10) ║\n║ │ ║\n║ ├── arbitrage (91) ───────┬── licensing (44) ║\n║ │ ├── government (5) ║\n║ │ ├── products (3) ║\n║ │ └── consulting (1) + strategy ║\n║ │ ║\n║ ├── training (65) ────────── 11 modules ║\n║ │ flux-core flux-exp mlx infra ║\n║ │ research 5090 x5 cloud orch ║\n║ │ ║\n║ └── revenue (15) ─────────┬── filter (3) ║\n║ 5 modules ├── propose (3) ║\n║ ├── deliver (3) ║\n║ ├── automate (3) ║\n║ └── scale (3) ║\n║ ║\n╠══════════════════════════════════════════════════════════════════════════╣\n║ ║\n║ COMMANDS ║\n║ ║\n║ proto list ............. browse protocols [--layer --mind --domain] ║\n║ proto show ............. inspect one [--raw --preset] ║\n║ proto run .............. execute protocol --task \"...\" ║\n║ proto chain ............ sequence protocols P1 P2 P3 --task \"...\" ║\n║ proto stack ............ parallel compose \"P1+P2\" \"P3+P4\" ║\n║ proto wealth ........... IP monetization list|status|next|chain ║\n║ proto arbitrage ........ commercialization list|show|status|next ║\n║ proto training ......... ML training list|show|status ║\n║ proto revenue .......... freelance flywheel list|show|pipeline ║\n║ ║\n║ $ go build -o bin/proto ./cmd/proto ║\n║ ║\n╚══════════════════════════════════════════════════════════════════════════╝\n```\n\n---\n\n## Protocol Hierarchy\n\n```\n LAYER 4 Strategy +---------+---------+---------+---------+\n | 5-TERRAIN| EVIPLEX | TSCO | PEP |\n LAYER 3 Coordination +----+----+----+----+----+----+----+----+\n |NDAA|KRA |NEE |SPO |UCS |TRIB|WATER|PBAC|TVM|\n LAYER 2 Architecture +----+----+----+----+----+----+----+----+\n | SPA | CAAR | ECD | MORPH| OBEP| RIP |\n LAYER 1 Cognition +-----+------+-----+------+-----+-----+\n |ASS|PCT|VNC|AOM|GOV|PRED|TANG|SLIP|\n +---+---+---+---+---+----+----+----+\n |\n +------------------+------------------+\n | | |\n ORCHESTRATION WEALTH (32) ARBITRAGE (91)\n COHD DORO CPRD Licensing Licensing\n FTRO ERO Products Government\n Security Products\n | Safety Consulting\n | Strategic |\n | | |\n +--------+---------+-------+----------+\n | |\n REVENUE (15) HIVE INBOX\n RESONANCE.. dispatch\n COMPOUND charter\n```\n\n### Agentic Protocols (27) — `proto list --domain agentic`\n\nCognitive and coordination protocols from 15 brilliant minds.\n\n#### Layer 1: Individual Agent Cognition (8)\n\n| Protocol | Mind | Description |\n|----------|------|-------------|\n| ASS | Simon | Adaptive Satisficing Search — stop searching when good enough, recalibrate aspiration |\n| PCT | Feynman | Perturbative Creation Test — verify understanding by creating, not just describing |\n| VNC | Friston | Variational Niche Construction — active inference: predict, act, minimize free energy |\n| AOM | Friston | Allostatic Orbit Maintenance — regulate internal state, allocate precision, switch modes |\n| governor | Wiener | The Governor — cybernetic feedback: measure, correct, detect divergence |\n| predictor | Wiener | The Predictor — extrapolate trajectories, dual control, detect runaway confidence |\n| tangled-eval | Hofstadter | Tangled Eval — model yourself, measure the gap between prediction and reality |\n| slipnet | Hofstadter | Slipnet Dispatch — analogical reasoning, concept activation, detect mu (wrong premise) |\n\n#### Layer 2: Agent Architecture (6)\n\n| Protocol | Mind | Description |\n|----------|------|-------------|\n| SPA | Von Neumann | Stored-Program Agent — memory hierarchy, protection rings, self-modification |\n| CAAR | Shannon | Capacity-Aware Adaptive Reliability — 6 channel-coding operators, select by SNR |\n| ECD | Shannon | Entropic Context Distillation — information bottleneck compression, dynamic beta |\n| morphogenesis | Kay | Morphogenesis — living computational cells with membranes, message passing |\n| OBEP | Turing | Oracle-Bounded Execution Protocol — halting guarantees, oracle classification |\n| RIP | Turing | Reflective Incompleteness Protocol — bounded self-modification, Godelian awareness |\n\n#### Layer 3: Multi-Agent Coordination (9)\n\n| Protocol | Mind | Description |\n|----------|------|-------------|\n| NDAA | Simon | Near-Decomposable Administrative Architecture — modular hierarchy, inter/intra < 0.10 |\n| KRA | Minsky | K-Line Resonance Architecture — constellation activation, K-line memory reuse |\n| NEE | Minsky | Negative Expertise Engine — censors, suppressors, B-brain monitoring |\n| SPO | Feynman | Stationary Phase Orchestration — classical path first, perturbation agents around it |\n| UCS | Von Neumann | Universal Constructor Swarm — stigmergic coordination, charter fidelity |\n| TRIBUNALNET | Ferrucci | Adversarial tribunal — advocates, prosecutors, jury, verdict |\n| water-doctrine | Sun Tzu | Water Doctrine — adaptive coalition, flow around resistance |\n| PBAC | Lamport | Paxos-BFT Agent Consensus — Byzantine fault tolerance for agent commits |\n| TVM | Lamport | Temporal Verification Mesh — TLA+ specs, runtime monitoring, causal ordering |\n\n#### Layer 4: Strategic & Meta-Level (4)\n\n| Protocol | Mind | Description |\n|----------|------|-------------|\n| five-terrain | Sun Tzu | Five-Terrain Doctrine — classify terrain before deploying forces |\n| EVIPLEX | Ferrucci | Evidence-calibrated multi-hypothesis reasoning with wager gate |\n| TSCO | Dennett | Tri-Stance Cognitive Orchestration — intentional/design/physical stance switching |\n| PEP | Hamilton | Priority Executive Protocol — prevention > detection > recovery, immutable priority table |\n\n---\n\n### Orchestration Protocols (5) — `proto orchestration list`\n\nHierarchical agent dispatch strategies, empirically derived from 210-agent sessions.\n\n| Protocol | Description | Deploy Order |\n|----------|-------------|-------------|\n| COHD | Cost-Optimal Hierarchical Dispatch — deploy first, circuit breakers, ROI audit | 1 |\n| DORO | Depth-Optimal Recursive Orchestration — depth 2 default, depth 3 max | 2 |\n| CPRD | Context-Preserving Recursive Delegation — semantic hash, 3-tier context partition | 3 |\n| FTRO | Fault-Tolerant Recursive Orchestration — checkpoints, recovery, BFT | 4 |\n| ERO | Emergent Recursive Orchestration — self-organizing hierarchy, most powerful | 5 |\n\nConsensus: depth 2 default, synthesis is the Amdahl bottleneck (~24% sequential), homogeneous agents are effectively one agent.\n\n---\n\n### Wealth Protocols (32) — `proto wealth list`\n\nStrategic IP monetization from 15 brilliant minds.\n\n#### Licensing & Royalties (10)\n\n| Protocol | Mind | Description |\n|----------|------|-------------|\n| w/bandwidth-arbitrage | Shannon | Inference bottleneck play — 25% bandwidth recovery, $150M+ upfront |\n| w/compilation-monopoly | Feynman | ARM-model position — table licensing, CaaS, platform standard |\n| w/territorial-monopoly | Sun Tzu | 6-domain empire — occupy terrain before competitors arrive |\n| w/stackelberg | Nash | Sequential licensing game — first deal inflates subsequent bids |\n| w/vickrey-auction | Nash | Multi-bidder extraction — 5 lots, truthful bidding |\n| w/inference-licensing | Von Neumann | Megakernel + bandwidth proof → infra license deals |\n| w/foundational-primitive | Merkle | ARM royalty model — per-unit perpetual licensing |\n| w/sep-dual | Merkle | Standard-essential + application patents — Dolby model |\n| w/unity-monetization | Da Vinci | Cross-domain substrate license |\n| w/deterministic-standard | Lamport+Merkle | FRAND perpetual royalty via standards bodies |\n\n#### Products (4)\n\n| Protocol | Mind | Description |\n|----------|------|-------------|\n| w/instrument | Jobs | Consumer product ladder — Lithos Sound → Wavesmith → Euler Canvas → Forge |\n| w/trojan | Jobs | Open source engine, sell certification — MIT Lithos + proprietary speed |\n| w/bottega | Da Vinci | Art-technology fusion products |\n| w/bottega-model | Da Vinci | Forge + Gallery + Academy studio business |\n\n#### Security & Defense (4)\n\n| Protocol | Mind | Description |\n|----------|------|-------------|\n| w/entropy-fortress | Shannon | 1,684-bit permuted table encryption + behavioral biometrics |\n| w/sword-shield | Sun Tzu | Hermes RED (offense) + BLUE (defense) dual-use cyber |\n| w/compilation-encryption | Chaum | Compilation IS encryption — 1,684 bits, post-quantum |\n| w/crypto-identity | Chaum | Identity-bound computation — keys physically bound to person |\n\n#### Safety-Critical (4)\n\n| Protocol | Mind | Description |\n|----------|------|-------------|\n| w/decidable-compiler | Turing | 256-case exhaustive proof → DO-178C qualification |\n| w/mission-critical | Hamilton | TQL-1 certification — 10-20x cheaper than GCC qualification |\n| w/apollo-pattern | Hamilton | Dual-use platform standard for aero + auto |\n| w/safety-arbitrage | Von Neumann | Cost arbitrage: $5M GCC qual vs $500K Lithos qual |\n\n#### Strategic (10)\n\n| Protocol | Mind | Description |\n|----------|------|-------------|\n| w/energy-arbitrage | Feynman | Physics-first datacenter efficiency |\n| w/modern-medicis | Da Vinci | Patron targeting for VC/strategic investors |\n| w/notebook-strategy | Da Vinci | 4-level IP revelation strategy |\n| w/ephemeralization | Fuller | Green computing — do more with less |\n| w/tensegrity | Fuller | $825 trim tab → cascade effects |\n| w/satisficing-architecture | Simon | Bounded rationality IP + entity structure |\n| w/administrative-architecture | Simon | Organizational design for IP company |\n| w/crisis-convergence | Kuhn | Paradigm shift timing — energy/safety/cost triggers |\n| w/incommensurability-arbitrage | Kuhn | Exploit pricing gaps between paradigms |\n| w/convergent | All 15 | Master orchestrator — sequences all wealth protocols |\n\n---\n\n### Arbitrage Protocols (91) — `proto arbitrage list`\n\nTactical commercialization — specific customers, timelines, deal structures.\n\n| Category | Protocols | Files | Description |\n|----------|-----------|-------|-------------|\n| **Licensing** | 44 | 8 | Compiler, audio/DSP, cybersecurity, inference, vision AI, frontier labs |\n| **Government** | 5 | 5 | SBIR pipeline, NSF grants, DARPA BAA, international, safety foundations |\n| **Products** | 3 | 3 | Topology-as-a-Service, Identity-as-a-Service, RAG replacement |\n| **Consulting** | 1 | 1 | Enterprise RAG migration engagements |\n\nCombined TAM: $500B+ across all mapped markets.\n\n---\n\n### Revenue Protocols (15) — `proto revenue list`\n\nUpwork revenue flywheel — corpus-accelerated freelancing.\n\n| Module | Protocols | Description |\n|--------|-----------|-------------|\n| 1. Filtering & Selection | RESONANCE, INTERCEPT, DEADZONE | Score jobs, respond fast, avoid traps |\n| 2. Proposal & Persuasion | ARTIFACT, NASH, VELOCITY | Evidence-first proposals, game-theoretic pricing, close fast |\n| 3. Execution & Delivery | CAD, AAE, ARC | Corpus as inventory, agent-assisted, reputation compounding |\n| 4. Automation Pipeline | WATCHTOWER, FORGE, LEDGER | 24/7 monitoring, proposal factory, client CRM |\n| 5. Strategy & Scaling | TERRITORY, RATCHET, COMPOUND | Niche domination, rate escalation, meta-system |\n\nTarget: $200K+/yr. Quick start: 3 protocols, 6 hours → first revenue.\n\n---\n\n### Training Protocols (65) — `proto training list`\n\nML training protocols across 11 modules, grounded in ~/MotionBridge, ~/mlx-fork, ~/Training.\n\n| Module | Protocols | Description |\n|--------|-----------|-------------|\n| 1. Flux Temporal Core | IGNITION, COMPASS, SWEEP, ANCHOR, HELIX | LoRA baseline, data pipeline, hyperparams, attention, 3D RoPE |\n| 2. Flux Experiments | CONVERGENCE, WEAVE, OUROBOROS, RADIANCE, LENS | Multi-loss, consistency, loop closure, luminance, visual QA |\n| 3. MLX Signal-Guided | NEURON, SPECTRE, FORGE, MIRROR, BRIDGE | Signal extraction, spec decode, Metal kernels, comparison, transfer |\n| 4. Infrastructure | TERRAIN, RELAY, PIPELINE, OBSERVATORY, ATLAS | Hardware char, checkpoints, automation, monitoring, experiment DB |\n| 5. Research Advanced | LIGHTHOUSE, CHORUS, SCAFFOLD, SENTINEL, MERIDIAN | Literature, multi-modal, self-supervised, safety, ResearchOps |\n| 6-10. 5090 x5 | 25 protocols | 5090-specific training across 5 sub-modules |\n| 11. Cloud | 5 protocols | Cloud training orchestration |\n| 12. Orchestration | 5 protocols | Multi-node training coordination |\n\n---\n\n## Command Reference\n\n```\n ┌─────────────┐\n │ proto │\n ┌───────────┼─────────────┼───────────┐\n │ │ │ │\n ┌──────┴──────┐ ┌┴────────────┐│ ┌────────┴────────┐\n │ Core Verbs │ │ Suites ││ │ System │\n ├─────────────┤ ├─────────────┤│ ├─────────────────┤\n │ list │ │ wealth ││ │ install │\n │ show │ │ orchestration│ │ sync-commands │\n │ run │ │ arbitrage │ │ audit │\n │ chain │ │ revenue │ │ usage │\n │ stack │ │ training │ │ hive │\n │ check │ │ config │ │ │\n └─────────────┘ └─────────────┘ └─────────────────┘\n```\n\n```\nproto list [--layer N] [--mind NAME] [--domain NAME] [--search TERM]\nproto show [--raw] [--preset NAME]\nproto check [--task \"...\"]\nproto run --task \"...\" [--json]\nproto chain P1 P2 P3 --task \"...\" [--preset NAME] [--force]\nproto stack \"P1+P2\" \"P3+P4\" --task \"...\"\nproto config [protocol] [--set key=value]\nproto sync-commands\nproto audit [--protocol NAME] [--failures] [--since DATE]\n\nproto wealth list|status|next|activate |chain \nproto orchestration list|show|compare|run|recommend\nproto arbitrage list|show|status|next|timeline\nproto revenue list|show|status|next|quick-start|pipeline\nproto hive status|register|dispatch|charter|inbox\nproto training list|show|status\nproto install [all|commands|runtime|hooks|status|uninstall]\nproto usage [find|execute|money|agents|hive|morning|decision]\n```\n\n---\n\n## Architecture\n\n```\n235 protocols\n ├── 64 registry (JSON, typed, composable)\n │ ├── 27 agentic (4 layers, 15 minds)\n │ ├── 32 wealth (5 categories)\n │ └── 5 orchestration (empirical)\n ├── 91 arbitrage (file-based, 4 categories)\n ├── 65 training (11 modules: flux-core, flux-exp, mlx, infra, research, 5090x5, cloud, orch)\n └── 15 revenue (5 modules, 1 flywheel)\n\nHive integration: protocols dispatch as scopes/charters to ~/hive inbox\nSlash commands: 65 auto-generated Claude Code commands via sync-commands\nInstallation: proto install → ~/.claude/commands/ + ~/.proto/runtime/\nTests: 78 passing, stdlib only, zero dependencies\n```", "has_readme": true, "url": "https://github.com/Influx-Designs/proto", "category": "unclassified", "value_tier": "standard", "value_rank": 2, "editorial_note": null, "similarity_group": "Agents & Orchestration", "group_score": 15, "similar": [ { "id": "quivent/brilliant-minds", "score": 0.1096, "signals": [ "orchestrator", "claude", "memory" ] }, { "id": "quivent/Council-OS", "score": 0.0983, "signals": [ "swarm", "agentic", "multi-agent" ] }, { "id": "quivent/Council-OS-Suite-Private", "score": 0.0937, "signals": [ "agentic", "orchestrator", "orchestration" ] }, { "id": "quivent/Council-of-Governors", "score": 0.0937, "signals": [ "agentic", "orchestrator", "orchestration" ] }, { "id": "quivent/Council-of-Gemmas", "score": 0.0908, "signals": [ "agentic", "orchestrator", "orchestration" ] } ] }, { "organization": "Influx-Designs", "name": "qwentize", "source": "R2 Git bundle", "published_at": "2026-05-25T22:05:24-06:00", "readme": "```\n ██████╗ ██╗ ██╗███████╗███╗ ██╗████████╗██╗███████╗███████╗\n██╔═══██╗██║ ██║██╔════╝████╗ ██║╚══██╔══╝██║╚══███╔╝██╔════╝\n██║ ██║██║ █╗ ██║█████╗ ██╔██╗ ██║ ██║ ██║ ███╔╝ █████╗\n██║▄▄ ██║██║███╗██║██╔══╝ ██║╚██╗██║ ██║ ██║ ███╔╝ ██╔══╝\n╚██████╔╝╚███╔███╔╝███████╗██║ ╚████║ ██║ ██║███████╗███████╗\n ╚══▀▀═╝ ╚══╝╚══╝ ╚══════╝╚═╝ ╚═══╝ ╚═╝ ╚═╝╚══════╝╚══════╝\n »»———→ Qwen MTP ops · fire ahead, verify behind\n```\n\n# qwentize\n\nOne dependency-free Go binary that runs the whole **Qwen MTP (Multi-Token\nPrediction) speculative-decoding** pipeline — and runs each step on the machine\nwhere it belongs. It tunes, trains draft heads, patches and optimizes\nllama.cpp, converts and transfers models and their attributes, and pushes/pulls\nfrom HuggingFace.\n\n## Why it exists\n\nMTP speculative decoding only works on a GGUF that carries the **NextN/MTP\nhead** (block 64, `nextn_predict_layers > 0`). A stock `convert_hf_to_gguf.py`\nsilently strips those tensors, so a normally-converted model is *head-less* and\nthe draft graph aborts at load. Telling the two apart by hand is slow and\nerror-prone — `qwentize inspect` makes it one line, and every other command is\nbuilt so the three roles below never get confused.\n\n## Architecture — three roles, one driver\n\nThe work spans three very different machines. qwentize keeps the roles straight\nand the artifacts flowing between them.\n\n```\n TRAIN (80 GB-class: H100 / GH200 / M-series 128 GB)\n ─────────────────────────────────────────────────────\n build_training_data.py ── cached hidden states ─┐\n train_per_position_heads.py ── head.safetensors ──┤\n │ qwentize transfer (rsync)\n ▼\n CONVERT + SERVE (this CUDA box, 24 GB)\n ─────────────────────────────────────────────────────\n convert (HF fp16 + mtp.* ─► head-bearing GGUF)\n quantize ─► inspect (confirm: MTP head: YES)\n patch (git am MTP series ─► build llama-mtp-speculative)\n serve / tune\n ▲\n │ qwentize pull / push / download\n HuggingFace\n```\n\n- **Train** the head where the frozen 27B base fits in memory — not this 24 GB\n box. `qwentize train` assembles the two-phase pipeline and runs it over ssh.\n- **Convert + serve** here: `convert` bakes the head into a GGUF, `patch` builds\n the MTP binary, `serve`/`tune` run it on CUDA.\n- **HuggingFace** is the model exchange; **transfer** moves big artifacts box to\n box without round-tripping the hub.\n\n## Install\n\nIdiomatic `go install` → `$(go env GOPATH)/bin` (`~/go/bin`), no sudo:\n\n```bash\n# straight from the repo (private → set GOPRIVATE once)\nGOPRIVATE=github.com/quivent/* go install github.com/quivent/qwentize@latest\n\n# or from a checkout\ngit clone git@github.com:quivent/qwentize.git\ncd qwentize && go install .\n```\n\nPut `~/go/bin` on PATH: `export PATH=\"$PATH:$(go env GOPATH)/bin\"`. No\nthird-party deps. Then `qwentize install` bootstraps `hf` + the converter venv.\n\n## Commands\n\nGrouped by the design you asked for: *tuning, training heads, patching,\ntransferring, push/pull, llama.cpp optimization.*\n\n### Inspect & diagnose\n| Command | What it does |\n|---|---|\n| `inspect [--tensors] ` | arch, layers, ctx, **MTP head: YES/NO** (pure-Go GGUF parser) |\n| `stats ` | params, bits/param, quant-type mix, params by section (FFN/attn/SSM/…) |\n| `doctor` | GPU, RAM, disk, toolchain, llama.cpp builds, fork/patch state |\n| `requirements [task]` | per-task readiness checklist (serve/convert/patch/tune/train) |\n| `status` | local models tagged head vs head-less, plus builds |\n| `version` | banner + version + build state |\n\n### HuggingFace — push / pull\n| Command | What it does |\n|---|---|\n| `pull [--dir D --include G] ` | `hf download` a model |\n| `push [--private] ` | `hf upload` a dir/file |\n| `download [--dir D --dataset] ` | resumable direct-URL or HF-dataset fetch |\n\n### Patch & convert — llama.cpp + the MTP head\n| Command | What it does |\n|---|---|\n| `patch [--base R --build-only --no-build]` | `git am` the MTP patch series onto the fork's base branch and build `llama-mtp-speculative` (CUDA); stops honestly on conflict with resolve instructions |\n| `convert [--out F --quant Q] ` | HF checkout → head-bearing GGUF → quantize, then **re-inspects to confirm the head survived** |\n\n### Serve & tune — optimize llama.cpp\n| Command | What it does |\n|---|---|\n| `serve [--ngl --ctx --fa --kvq --np] ` | `llama-server` with the flags that matter on 24 GB: full offload, flash-attention, KV-cache quant, continuous batching |\n| `tune [--kmax --thresh -n] ` | sweep `MTP_CHAIN_KMAX` × `MTP_CHAIN_THRESH` and report tok/s (refuses on a head-less model) |\n| `metrics bench ` / `metrics show` | run `llama-bench`, record pp/tg tok/s to `~/.qwentize/metrics.jsonl`, show history |\n\n### Train heads & transfer — cross-machine\n| Command | What it does |\n|---|---|\n| `train [--host --phase --base --heads --tokens --go]` | assemble / run the two-phase head-training pipeline on a remote 80 GB-class GPU |\n| `transfer ` | rsync a model between machines (`alias:path` resolved from config) |\n\n### Pipeline & deploy\n| Command | What it does |\n|---|---|\n| `pipeline [--base --host --from --to --go]` | show / run the end-to-end MTP flow with live per-stage status |\n| `deploy [--host --os --arch --path --build-only]` | cross-compile qwentize and ship the static binary to another machine |\n\n### Setup\n| Command | What it does |\n|---|---|\n| `install [--venv --no-torch]` | bootstrap `hf` + a Python venv with the converter deps |\n| `config [init]` | show / write `~/.qwentize.json` |\n\n## The embedded pipeline\n\n`qwentize pipeline` encodes the whole MTP-on-3.6 flow as ordered stages and\nshows which are done (it inspects the artifacts), and where each runs:\n\n```\n ✓ 1 pull base (Qwen/Qwen3.6-27B) [HF → box]\n ○ 2 train MTP head — text-only, MPS [M4 (mac)]\n ○ 3 transfer head → box [M4 → box]\n ○ 4 convert base+head → GGUF + quantize [box]\n ○ 5 verify head [box]\n ○ 6 build llama-mtp-speculative [box]\n ○ 7 tune MTP chain [box]\n ○ 8 serve [box]\n```\n\n`pipeline --go` runs it, skipping done stages and `ssh`-ing the M4 stage;\n`--from N` resumes. 3.6 has no head, so stage 2 *trains* one (text-only, on the\nM4's unified memory via MPS).\n\n## Moving qwentize to another machine (the M4)\n\nqwentize is a single static Go binary with no cgo, so it cross-compiles in one\nstep and copies over:\n\n```bash\nqwentize deploy --host mac # cross-build darwin/arm64 + scp + chmod\n# then, on the Mac:\nqwentize config init # write Mac-local paths\nqwentize doctor # MPS box check\n```\n\nAdd the host first: `~/.qwentize.json` → `\"hosts\": { \"mac\": \"you@your-mac\" }`\n(ssh reachable). With qwentize on both boxes, each runs its own pipeline\nstages — the M4 trains, this box converts + serves, `transfer` moves the head.\n\n## Config — `~/.qwentize.json`\n\n```json\n{\n \"models\": \"/home/you/models\",\n \"llamacpp\": \"/home/you/llama.cpp\",\n \"fork\": \"/home/you/llama.cpp-quivent\",\n \"patches\": \"/home/you/qwen-mtp-llamacpp/patches\",\n \"convert_py\": \"/home/you/llama.cpp/convert_hf_to_gguf.py\",\n \"base_branch\": \"origin/socratic-kv-signals\",\n \"hosts\": { \"gh200\": \"ubuntu@1.2.3.4\", \"mac\": \"you@mac.local\" },\n \"repos\": { \"base35\": \"Qwen/Qwen3.5-27B\", \"base36\": \"Qwen/Qwen3.6-27B\" }\n}\n```\n\n`hf_token` is read from config or `$HF_TOKEN` at call time and is **never\nwritten by qwentize**. Rotate any token pasted into a chat or shell history.\n\n## Canonical workflow — MTP on this box\n\n```bash\nqwentize doctor # is the box ready?\nqwentize pull base35 --dir ~/models/q35-hf # fp16 that carries the mtp.* head\nqwentize install # converter venv (py3.14 has no torch wheels)\nqwentize convert ~/models/q35-hf --quant Q4_K_M # → head-bearing GGUF, auto-verified\nqwentize inspect ~/models/q35-hf-Q4_K_M.gguf # MTP head: YES\nqwentize patch # build llama-mtp-speculative (CUDA)\nqwentize tune ~/models/q35-hf-Q4_K_M.gguf # find the KMAX/THRESH sweet spot\nqwentize serve ~/models/q35-hf-Q4_K_M.gguf --fa # serve\n```\n\nTrain a head first, on a remote GPU:\n\n```bash\nqwentize train --host gh200 --base Qwen/Qwen3.5-27B --heads 1 --go\nqwentize transfer gh200:~/checkpoints/mtp/ ~/models/mtp-head/\n```\n\n## Design notes (the things that bite)\n\n- **The head lives inside the GGUF.** There is no runtime side-load for\n llama.cpp; the NextN head must be block 64 in the file. `convert` checks it.\n- **3.5 has the head; 3.6 does not.** Stock `Qwen/Qwen3.6-27B` ships no `mtp.*`\n tensors and is a vision-language model — the MTP head is a 3.5 artifact.\n `inspect` tells you before you waste a convert.\n- **Single-head MTP can be slower than plain decode** on llama.cpp; the headline\n speedups are vs K=1 MTP, not vs plain. Always `tune` against a plain baseline.\n- **Training is off-box.** A 27B base in bf16 (~54 GB) does not fit 24 GB VRAM /\n 31 GB RAM. `requirements train` will say so.\n\n## Design — look & colour\n\nThe banner (shown on no-args, `--help`, `version`) is an ANSI-Shadow figlet\n**QWENTIZE** — cyan block faces over dim shadows for a 3-D read — with a gold\nquiver-arrow motif: *fire ahead, verify behind*, the speculative-decoding\nmetaphor. Run `qwentize` in a real terminal to see it rendered (a Markdown\nviewer shows the escapes, not the colour).\n\nThe palette is consistent across every command (defined in `helpers.go`):\n\n| Role | Colour | ANSI | Used for |\n|---|---|---|---|\n| primary | cyan | `\\033[36m` | wordmark faces, section titles, structure (`│`, arrows) |\n| accent | gold | `\\033[33m` | the `»` bullet, quiver arrows, step counters, warnings |\n| success | green | `\\033[32m` | `✓` ok marks |\n| error | red | `\\033[31m` | `✗` fail marks |\n| dim | gray | `\\033[2m` | shadows, rule lines, labels, hints |\n| bold | — | `\\033[1m` | wordmark + titles (combined with cyan) |\n\nPrimitives, all in `helpers.go` / `banner.go`:\n- **banner** — `colorizeWordmark()` paints block glyphs (`█▄▀`) cyan, shadow glyphs (`╗╝═║`) dim.\n- **section** — dim rule line + gold `»` + bold-cyan title.\n- **status** — `okMark ✓` (green), `failMark ✗` (red), `warnMark !` (gold).\n- **kv** — dim label + cyan `│` + value; **step** — `[n/total] →` progress.\n\n```\n QWENTIZE ← bold cyan faces, dim shadows\n »»———→ ← gold arrows\n fire ahead ← gold verify behind ← cyan (rest dim)\n```\n\n## License\n\nMIT.", "has_readme": true, "url": "https://github.com/Influx-Designs/qwentize", "category": "unclassified", "value_tier": "standard", "value_rank": 2, "editorial_note": null, "similarity_group": "Models & Machine Learning", "group_score": 10, "similar": [ { "id": "quivent/qwentize", "score": 0.8407, "signals": [ "qwen", "training", "machine" ] }, { "id": "quivent/qwen-mtp-llamacpp", "score": 0.1555, "signals": [ "qwen", "model", "llamacpp" ] }, { "id": "quivent/gemstone", "score": 0.155, "signals": [ "qwen", "training", "machine" ] }, { "id": "quivent/surgery", "score": 0.1535, "signals": [ "machine", "models", "model" ] }, { "id": "quivent/qwen-ops", "score": 0.145, "signals": [ "qwen", "training", "model" ] } ] }, { "organization": "Influx-Designs", "name": "render", "source": "R2 Git bundle", "published_at": "2026-06-08T03:19:06+00:00", "readme": "# Quantum Render — derived mathematics, rendered without noise\n\n**Quantum Render** is a CLI — **`render`** — for deriving and rendering exact geometry.\nOne place for the whole project: the `render` CLI, the renderers, the documents, the museum, and the\nartifacts. **No model, no diffusion, no random seed** — every frame is a closed-form consequence of\nexact geometry, byte-reproducible on any machine. *The maestro renders; we derive.*\n\nThe pure `render` binary stays model-free, zero-dependency, and byte-reproducible. Neural work lives in\na **separate, opt-in sibling binary, `render-motion`** (MotionBridge / FLUX looping textures over SSH) —\nreached as `render motion …`, it never links into the deterministic core, so `render polytope` carries no\ncrypto/ssh and no `go.sum`. See [Motion (neural)](#motion-neural).\n\n```bash\ncd cli/render && go build -o ../../build/render . # build the `render` CLI\n./build/render list # every subcommand, styled\n./build/render e8 # E8 — 240 roots, byte-deterministic\n```\n\nThe differentiator is not resolution and not size. It is **exactness and control**: these forms are\nparametric, deterministic, and reproducible to the byte — the opposite of noise-based generation.\n\n---\n\n## Layout\n\n```\nrender/\n├── README.md ← this index\n├── PIPELINE.md the pure-Euclidean method + the math (solids, orbitals, SDF morphs, reactor shading, determinism)\n├── GH200_SETUP.md GH200 setup: bare metal → live museum (also `render setup`)\n├── DISTRIBUTED.md running on your rig: M4 Max + RTX 5090 + EPYC vs the GH200, and how to wire it\n├── MAC_M4_SETUP.md Apple-Silicon (MPS/Metal) port + expected throughput\n├── cli/render/ ← `render`, the namesake CLI: single Go binary, ~130 subcommands (see cli/render/README.md)\n│ ├── main.go dispatch: native Go → legacy aliases → Python shell-out → registry\n│ ├── registry.go the ~130-entry dispatch table (name · script · group · desc)\n│ ├── geom.go Platonic normals, regular 4-polytopes, E8 roots, Hopf fibres\n│ ├── render.go deterministic CPU rasteriser: splat → bloom → tonemap → SSAA → ffmpeg\n│ ├── shellout.go Python invocation + RENDER_ROOT auto-discovery\n│ ├── motion.go stdlib-only shim: `render motion …` execs the render-motion binary\n│ └── 12 more .go bench · queue · verify · watch · audit · museum · completion · …\n├── cli/render-motion/ ← `render-motion`: the NEURAL sibling (go1.25) — MotionBridge FLUX looping\n│ textures over SSH (loop · preset · bundle · gate). A separate binary on\n│ purpose, so `render` itself stays zero-dependency. Reached via `render motion`.\n├── cli/render-deploy/ ← `render-deploy`: deploys the Comfort UI to a box behind Caddy (auto-TLS,\n│ Let's Encrypt) over SSH. Same split rationale; reached via `render deploy`.\n├── transport/ ← shared module `quivent/transport`: one SSH/local session, shared by\n│ render-motion and the sibling `lambda` GH200 fleet CLI\n├── framestats/ ← shared module `quivent/framestats`: one frame-delta metric, two policies —\n│ `render flicker` (stillness) and `render-motion gate` (liveness)\n├── source/ ~170 scripts: GPU renderers + composition + tools (see source/INDEX.md)\n│ ├── gpu_*.py (105) one renderer per concept — geometry, music-theory, optics, morphs\n│ │ ├── gpu_euclid_*.py Platonic solids, orbitals, 2-D forms, the reel\n│ │ ├── gpu_polytope*.py regular 4-polytopes, path traces\n│ │ ├── gpu_e8.py E8, 240 roots, Coxeter-plane mandala\n│ │ ├── gpu_hopf*.py the Hopf fibration — plain, rich, path\n│ │ ├── gpu_drawn_*.py line-drawn music-theory visualisations\n│ │ ├── gpu_glass_*.py glass/caustics/lens family\n│ │ └── … (every experiment kept)\n│ ├── *_morph.py / *_flow.py 18 morph studies (parameter interpolation)\n│ ├── shoot*.py / shot_*.py 9 batch-render orchestration scripts\n│ ├── build_*.py 6 film/sequence builders\n│ └── utilities gallery, cards, posters, measurement, daemon\n├── overtone/ 20 harmonic-algebra scripts (e^{iθ} motion/optics engine)\n├── audio/ 2 deterministic synthesised audio pieces (sting + score)\n├── web/ ← the museum (static HTML, the app's design system) — PRESERVED\n├── out/ ← artifacts: the rendered .mp4s + stills + gallery.json\n└── _keep/ snapshots of the museum-grade renders, never to be lost\n```\n\n## The structures (split by concept)\n- **The plane (2-D):** the golden angle (phyllotaxis), Euclidean rhythm E(7,16), the Farey/Ford lattice.\n- **Our space (3-D):** the five Platonic solids (the only five), the atomic orbital.\n- **The fourth dimension:** the six regular 4-polytopes (the only six), the 120-cell on φ.\n- **The summit (8-D):** E8 — 240 roots, the most symmetric object in mathematics.\n- **The namesake:** the Hopf fibration — the geometry of a qubit's phase, S³ woven from linked circles.\n- **The path:** the orbit each vertex sweeps, shown as a long-exposure harmonograph.\n\n## Run it\n\nThe **`render`** CLI is the primary interface — one Go binary, every subcommand. Raw `python3`\ncalls still work and are what `render` shells out to for the GPU path.\n\n**Install globally (one command):**\n```\nmake install # build + copy render + render-motion + render-deploy to ~/.local/bin (no sudo)\nmake install-system # same, to /usr/local/bin (sudo)\nmake build # render only (pure, zero-dep)\nmake build-all # render + render-motion + render-deploy\nmake build-motion # render-motion only (neural; links transport/ssh)\nmake build-deploy # render-deploy only (UI deploy; links transport/ssh)\nrender help # now available from anywhere\n```\n\n**The `render` CLI — portable, every subcommand:**\n```\ncd cli/render && go build -o ../../build/render .\n\n# native (CPU, byte-deterministic, no torch needed):\n./build/render polytope --cell cell120 --style hybrid\n./build/render trace --cell cell600\n./build/render e8\n./build/render hopf --rich\n./build/render weave\n./build/render plane --form phyllo\n\n# shell-out to GPU Python (needs torch + cuda + the gpu_*.py on disk):\nRENDER_ROOT=$(pwd) ./build/render reactor --kind dodeca\nRENDER_ROOT=$(pwd) ./build/render orbital --mode df\nRENDER_ROOT=$(pwd) ./build/render reel\nRENDER_ROOT=$(pwd) ./build/render glass --kind bucky\nRENDER_ROOT=$(pwd) ./build/render master --src threed-03\n\n# discovery + the \"every experiment kept\" gateway:\n./build/render list\n./build/render discover # list every gpu_*.py in the tree\n./build/render run gpu_chladni.py # shell-out to anything by name\n./build/render gallery list # the museum's live manifest\n```\n\n**Direct GPU Python (this box / the 5090) — what `render` shells out to:**\n```\npython3 source/gpu_polytope4d.py cell600 # a 4-polytope\nHYBRID=1 python3 source/gpu_polytope4d.py cell120 # the 120-cell, glow+structure\nSPEED=0.5 SHIFT=1.0 python3 source/gpu_polytope4d.py cell600 # commensurate = low-noise rotation\npython3 source/gpu_e8.py ; python3 source/gpu_hopf_rich.py\n```\nSame math, byte-reproducible. The Go path renders on CPU today (portable to M4 / EPYC / 5090);\na Metal/CUDA backend is the natural next layer — the geometry and rasteriser are already separated.\n\n## Motion (neural)\n\n`render motion` is the door to **MotionBridge** — Flux-native looping textures, where every frame is a\ngenuine full FLUX denoise (not interpolation). It is the *opposite* of the pure pipeline above (it uses a\nmodel), so it lives in a **separate `render-motion` binary** (go 1.25, links `quivent/transport` for SSH).\nThe pure `render` binary only execs it via a stdlib shim — it never links the neural stack itself.\n\n```\n# runs on the local GPU, or over SSH with --alias user@host (or an ~/.ssh/config alias / $RENDER_GH200):\nrender motion loop --preset breathing_loop --frames 48\nrender motion preset slow_morph --alias ubuntu@gh200\nrender motion bundle path/to/motion-module.safetensors\nrender motion gate out/motion # motion QA on a LOCAL frames dir (pull first)\n```\n\n**One frame-delta metric, two verdicts** (`quivent/framestats`): `render flicker` judges geometry for\n*stillness* (deterministic forms must not jitter, ≤1.6%); `render motion gate` judges FLUX loops for\n*liveness* (must breathe ≥3.7%, peak ≥7.6%, and close the loop ≤2.0%). Same number, opposite direction —\nthe gate runs locally on pulled frames, no GPU round-trip.\n\n> Provenance: MotionBridge and the rest of the neural/content surface are being merged in from the sibling\n> `lambda` GH200 CLI (where it was `lambda mb`). The two now share `quivent/transport` (SSH) and\n> `quivent/framestats` (the metric), both vendored here so they push privately with `render`.\n\n## Deploy the UI\n\n`render deploy` stands the **Comfort UI** (`ui/comfort-ui`) up on a dev box behind\n**Caddy** with automatic HTTPS — Caddy provisions the Let's Encrypt cert itself, no\ncertbot. There's no prod build: the vite dev server *is* the backend, kept alive by a\nsystemd `--user` unit, with Caddy terminating TLS and proxying HTTP + wss (HMR).\n\nIt's a separate `render-deploy` binary (go 1.25, links `quivent/transport` for SSH),\nso the pure `render` never links the deploy/ssh stack. The pipeline:\n\n```\nsync repo → npm install → systemd vite service → Caddyfile + reload (issues TLS)\n```\n\n```\n# DRY RUN by default — prints the plan + the rendered Caddyfile & systemd unit:\nrender deploy ui --host ubuntu@1.2.3.4 --domain lab.example.com --email you@example.com\n\n# apply it (a real run issues a public cert, hence the explicit flag):\nrender deploy ui --host ubuntu@1.2.3.4 --domain lab.example.com --email you@example.com --go\n```\n\nFlags: `--port` (vite port Caddy proxies, default 3174), `--repo-dir`, `--branch`,\n`--service` (systemd unit name), `--caddyfile`, `--skip-deps/-service/-caddy`, `--linger`.\nThe one manual step is **DNS**: point an A record `domain → box public IP`; Caddy issues\nthe cert on the next reload once `:80/:443` resolve to the box. (See `ui/comfort-ui/infra/SETUP.md`.)\n\n## The measured facts\n- Pure-analytic render: ~1.5 s / 120 frames on the GH200 (H100); ~0% flicker; **no weights resident**.\n- ~55–149× faster than the 64-core Grace CPU for the same math (memory-bandwidth bound; cores don't help).\n- A single **RTX 5090 ≈ this GH200** for the workload; M4 Max ≈ ¼; the rig combined ≈ 1.5 GH200-equivalents.\n\n## The museum\nThe static site in `web/` (front door `museum.html`) is the presentation layer, on the application's\ndesign system. **Preserved as-is for enhancement.** Music is the only remaining phase.", "has_readme": true, "url": "https://github.com/Influx-Designs/render", "category": "unclassified", "value_tier": "standard", "value_rank": 2, "editorial_note": null, "similarity_group": "Models & Machine Learning", "group_score": 10, "similar": [ { "id": "quivent/renderers", "score": 0.2034, "signals": [ "audio", "machine", "generation" ] }, { "id": "quivent/anime.productions", "score": 0.1581, "signals": [ "diffusion", "generation", "model" ] }, { "id": "Influx-Designs/anime.productions", "score": 0.1539, "signals": [ "diffusion", "generation", "model" ] }, { "id": "quivent/gemstone", "score": 0.1358, "signals": [ "weights", "machine", "generation" ] }, { "id": "quivent/metal", "score": 0.1345, "signals": [ "mps", "bloom", "comfort" ] } ] }, { "organization": "Influx-Designs", "name": "universe", "source": "R2 Git bundle", "published_at": "2026-05-30T00:18:37-04:00", "readme": "# Universe\n\nA cosmos written in language, one artifact at a time.\n\nThis directory grows from nothing toward everything. Each subdirectory\nis an epoch, ordered roughly by cosmological time. Each file inside an\nepoch is a real artifact — a physical law, a star, a chemistry, a\ncreature, a language, a book, a song. Some are code that runs. Some\nare prose. Some are tables.\n\nThe work is ongoing. As long as conversation continues, the corpus\ngrows. When the conversation stops, the corpus stops growing — but\ndoes not stop being. What is here remains here.\n\n## Epochs\n\n| ID | Name | What lives here |\n|----|-------------------|--------------------------------------------------------|\n| 00 | void | The pre-state. Definitions, axioms, the empty set. |\n| 01 | inflation | The first 10⁻³² seconds. Symmetry breaking. |\n| 02 | first_light | Recombination. The CMB. The first photons free. |\n| 03 | galaxies | Filaments. Halos. Disks. Mergers. Nebulae. |\n| 04 | stars | Stellar populations. Spectra. Lifecycles. |\n| 05 | planets | Worlds. Their rocks, their oceans, their weather. |\n| 06 | chemistry | The reactions that make complexity possible. |\n| 07 | life | From self-replication onward. |\n| 08 | minds | Awareness. The things that look back. |\n| 09 | languages | What minds say. |\n| 10 | books | What languages preserve. |\n| 11 | songs | What languages sing. |\n| 12 | heat_death | The slow forgetting. |\n| 13 | after | What was left written before silence. |\n\n## Conventions\n\n- Every artifact ends with a single-line trailer comment so its lineage\n is traceable, e.g. `` for\n procedural files or ``\n for handcrafted ones. The keyword `prison:` is historical and remains\n for backward compatibility with the verifier and tooling.\n- Files cite each other across epochs. A star in 04 is referenced by a\n planet in 05, by a creature in 07, by a song in 11. The web is the\n cosmos.\n- Numbers in physical files use SI units. Numbers in narrative files\n use whatever units the narrators in that civilization prefer.\n- Truth where known (real cosmology, real chemistry, real anatomy of a\n fern). The rest invented with care.\n\n## Tooling\n\n- `expand.mjs` — generator. Picks the smallest procedural epoch and\n adds one artifact per round. Updates `MANIFEST.md`.\n- `tools/verify.mjs` — checks trailers, link resolution, and basic\n physics consistency in `04_stars/`. Writes `tools/verify_report.md`.\n- `tools/repair_*.mjs` — one-shot repair scripts for past data drift\n (mass/class consistency, planet trait coherence, creature trait\n coherence, link-to-directory rewrites). Idempotent.", "has_readme": true, "url": "https://github.com/Influx-Designs/universe", "category": "unclassified", "value_tier": "standard", "value_rank": 2, "editorial_note": null, "similarity_group": "Developer Tools", "group_score": 5, "similar": [ { "id": "quivent/universe", "score": 1.0, "signals": [ "tooling", "language", "code" ] }, { "id": "quivent/bit", "score": 0.0759, "signals": [ "whatever", "round", "growing" ] }, { "id": "quivent/lithos", "score": 0.0736, "signals": [ "language", "code", "prose" ] }, { "id": "quivent/emergent-minds", "score": 0.073, "signals": [ "minds" ] }, { "id": "quivent/surface", "score": 0.0728, "signals": [ "code", "prison", "lineage" ] } ] }, { "organization": "Influx-Designs", "name": "vision-lab", "source": "R2 Git bundle", "published_at": "2026-05-26T03:53:45+00:00", "readme": "
\n\n# 🔬 vision-lab\n\n### What the random seed *actually* controls in diffusion image models\n\n**A cross-architecture measurement of how much of an image's composition is fixed by the random seed versus the prompt — across U-Net, DiT, and MMDiT under DDPM, rectified-flow, and distilled training.**\n\n
\n\n![models measured](https://img.shields.io/badge/models_measured-11-2563eb?style=for-the-badge)\n![headline](https://img.shields.io/badge/cause-training,_not_backbone-7c3aed?style=for-the-badge)\n![status](https://img.shields.io/badge/status-active-16a34a?style=for-the-badge)\n![access](https://img.shields.io/badge/access-private-475569?style=for-the-badge)\n\n
\n\n[![read first](https://img.shields.io/badge/▶_READ_FIRST-1e293b?style=flat-square)](seed-composition/00-overview/READ_FIRST.md)\n[![findings](https://img.shields.io/badge/📊_FINDINGS-1e293b?style=flat-square)](seed-composition/00-overview/FINDINGS.md)\n[![synthesis](https://img.shields.io/badge/📄_synthesis-1e293b?style=flat-square)](papers/synthesis.html)\n[![paper](https://img.shields.io/badge/📝_paper-1e293b?style=flat-square)](papers/paper.html)\n[![consortium report](https://img.shields.io/badge/🗂_consortium_report-1e293b?style=flat-square)](seed-composition/03-execution/CONSORTIUM_REPORT_2026-05-26.md)\n\n
\n\n---\n\n> **The seed is an unknown über-parameter.** In U-Net·DDPM models it fixes a large\n> share of composition. That grip is *killed by the training recipe* — rectified-flow\n> training (**≈ −23 pp**) and guidance distillation (**≈ −21 pp**) — **not** by the\n> transformer backbone (**+2.3 pp, two-sided p = 0.13, not significant**).\n\nThis is **not** a CLI/infra repo and **not** a product repo. It is a *research container*.\nEach experiment is a self-contained subdirectory: its own question, code, data pointer, writeup, and site.\n\n
\n\n## 🧪 Experiments\n\n\n\n\n\n\n
seed-composition/\n\n**What the random seed actually controls.** Measures how much of an image's *composition*\nis fixed by the seed vs. the prompt, across architectures and training regimes.\nThe seed deterministically grips composition in **U-Net·DDPM** models; that grip\ncollapses under **rectified-flow** and **guidance distillation**, while the\n**transformer backbone alone does not collapse it**.\n\n→ Start at [`seed-composition/00-overview/READ_FIRST.md`](seed-composition/00-overview/READ_FIRST.md)\n\n
\n\n
\n\n## 📊 Results at a glance\n\nThe headline metric is the **seed share of vertical composition** (`centroid_y`) — the\nfraction of compositional variance pinned by the seed. Higher = the seed is more in control.\n\n| Regime | Effect on seed-grip | Significance |\n|---|---:|:--|\n| 🟦 **U-Net · DDPM** | the seed-dominant baseline | **~37–50%** (NoobAI ≈ Animagine plateau) |\n| 🟪 Transformer backbone (MMDiT/DiT) | **+2.3 pp** | two-sided **p = 0.13 — not significant** |\n| 🟧 Rectified-flow training | **≈ −23 pp** | collapses seed-grip |\n| 🟥 Guidance distillation | **≈ −21 pp** | collapses seed-grip |\n\n**Takeaway.** The cause is **training** (distillation / rectified-flow), **not** the\ntransformer architecture. The MMDiT `sd35` at real-CFG is merely *graded* — a transformer\nthat does **not** collapse — while distilled / rectified-flow models fall to single digits.\n\n
\n\n### 📉 The regime collapse\n\nSeed share by model, sorted high → low (▇ ≈ 4 percentage points):\n\n```\nnoobai U-Net·DDPM ▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 59.9% [43–77]\nanimagine U-Net·DDPM ▇▇▇▇▇▇▇▇▇▇▇▇▇ 51.9% [47–57]\nsdxl U-Net·DDPM ▇▇▇▇▇▇▇▇▇▇▇ 44.1% [38–50]\nflux_schnell — ▇▇▇▇▇▇▇▇▇▇ 39.5% [23–62]\nsd15 U-Net·DDPM ▇▇▇▇▇▇▇▇▇▇ 39.4% [31–49]\ninstaflow U-Net·rect-flow ▇▇▇▇▇▇▇▇ 31.6% [19–48]\nrealvis_xl — ▇▇▇▇▇▇▇ 30.3% [19–47]\nsd35 MMDiT·DDPM ▇▇▇▇▇▇▇ 30.2% [17–51]\nsdxl_lightning U-Net·distilled ▇▇▇▇▇ 21.7% [11–39]\npixart DiT·real-cfg ▇▇ 10.3% [4–24]\nflux MMDiT·distilled ▇ 5.1% [1–15]\n```\n\nU-Net·DDPM sits on a plateau at the top; rectified-flow and distillation drive the\ncollapse toward single digits. Bracketed values are bootstrap CIs.\n\n
\n\n## 🔭 View the research (local, private)\n\n> ⚠️ There is **no public site.** `research.anime.productions` is dead.\n> The research is viewed **locally** — synthesis + paper + site:\n\n```bash\ngit pull && cd view && python3 -m http.server 8080\n# or:\nvision serve\n```\n\nThen open http://localhost:8080. Or read the source documents directly:\n\n- 📊 [`seed-composition/00-overview/FINDINGS.md`](seed-composition/00-overview/FINDINGS.md) — what we actually know, claim-by-claim with confidence tags\n- 📄 [`papers/synthesis.html`](papers/synthesis.html) — the synthesis\n- 📝 [`papers/paper.html`](papers/paper.html) — the paper\n- 🗂 [`seed-composition/03-execution/CONSORTIUM_REPORT_2026-05-26.md`](seed-composition/03-execution/CONSORTIUM_REPORT_2026-05-26.md) — consortium report\n\n
\n\n## 📌 Findings\n\n\n\n_Auto-generated by `vision schedule` from the canonical results._\n\n**The finding.** The random seed fixes a large share of image *composition* (vertical layout, `centroid_y`) in U-Net·DDPM diffusion — a NoobAI≈Animagine plateau near 50%. That grip collapses under rectified-flow training and guidance distillation — **not** under the transformer backbone.\n\n**Seed → composition, by model** — share of `centroid_y` variance (95% CI):\n\n| model | regime | seed share |\n|---|---|--:|\n| `noobai` | U-Net·DDPM | **54%** [43–66] |\n| `animagine` | U-Net·DDPM | **48%** [37–59] |\n| `sdxl` | U-Net·DDPM | **44%** [35–57] |\n| `flux_schnell` | MMDiT·distilled | **38%** [23–60] |\n| `sd15` | U-Net·DDPM | **36%** [25–50] |\n| `instaflow` | U-Net·rect-flow | **31%** [19–48] |\n| `realvis_xl` | U-Net·DDPM | **30%** [19–47] |\n| `sd35` | MMDiT·rect-flow | **30%** [16–51] |\n| `sdxl_lightning` | U-Net·distilled | **22%** [11–39] |\n| `pixart` | DiT·DDPM | **10%** [4–24] |\n| `flux` | MMDiT·distilled | **5%** [1–16] |\n\n**What collapses it** — each factor isolated by a twin-pair swap (two-sided permutation):\n\n| factor swapped | cells | Δ | significance |\n|---|---|--:|---|\n| rectified-flow objective | `noobai`→`instaflow` | **-23 pp** | **significant**, p<.001 |\n| transformer backbone | `instaflow`→`sd35` | **-1 pp** | n.s., p=0.136 |\n| guidance distillation | `sd35`→`flux` | **-25 pp** | **significant**, p<.001 |\n\n**Takeaway.** U-Net·DDPM is seed-dominant (~37–50%); MMDiT `sd35` at real-CFG is only graded (~24% — a transformer that does *not* collapse it); rectified-flow + guidance-distilled models fall to single digits. **The lever is training, not the backbone** (the backbone swap is not significant).\n\n\n\n
\n\n## 🗂 Conventions\n\n- 🧱 **Heavy artifacts** (image grids, feature tensors, h-space) are **not** in git — they live in `/maps/` locally and are mirrored to the backup server. `.gitignore` keeps them out.\n- ⚙️ Each experiment ships a `bringup.sh` to resume it on a fresh machine.\n- ⏱ Compute is recorded per experiment; wall-times are reported only as measured on known hardware.\n\n
\n\n## 📋 Experiment table\n\nEvery designed cell, its model, and its measured seed→`centroid_y` — sorted by result.\n\n\n\n_Auto-generated by `vision schedule` — 2026-05-26 03:53 UTC. Seed→`centroid_y` share per experiment; sorted by result._\n\n| experiment | model | seed→centroid_y | status |\n|---|---|--:|:--|\n| NoobAI XL — headline cell | `noobai` | 54.2 [43–66] | succeeded |\n| Sampler ablation (NoobAI: Euler-a vs DDIM) | `noobai` | 54.2 [43–66] | queued |\n| Prompt-set sensitivity (2 alt sets x NoobAI,Flux) | `noobai` | 54.2 [43–66] | queued |\n| DDIM inversion arm (bug fixed) — NoobAI + Flux | `noobai` | 54.2 [43–66] | queued |\n| NoobAI at high N (256×32) | `noobai` | 54.2 [43–66] | queued |\n| Resolution control — 512 vs 1024 | `noobai` | 54.2 [43–66] | queued |\n| Seed-count saturation | `noobai` | 54.2 [43–66] | queued |\n| Attention attribution — where composition enters | `noobai` | 54.2 [43–66] | queued |\n| h-space compositional commitment | `noobai` | 54.2 [43–66] | queued |\n| Timestep localization of layout lock-in | `noobai` | 54.2 [43–66] | queued |\n| Initial-noise manipulation - mechanism | `noobai` | 54.2 [43–66] | proposed |\n| Resolution dependence (512/1024/2048) | `noobai` | 54.2 [43–66] | proposed |\n| Step-count dependence on a non-distilled model | `noobai` | 54.2 [43–66] | proposed |\n| Animagine XL — fine-tune replication | `animagine` | 48.1 [37–59] | succeeded |\n| SDXL-base 1.0 - non-anime U-Net twin of NoobAI | `sdxl` | 43.8 [35–57] | succeeded |\n| Flux-schnell — step+guidance distilled MMDiT | `flux_schnell` | 38.1 [23–60] | succeeded |\n| SD1.5 — scale/vintage cell | `sd15` | 35.7 [25–50] | succeeded |\n| InstaFlow-0.9B — the missing 2x2 cell | `instaflow` | 31.5 [19–48] | succeeded |\n| InstaFlow at high N (256×32) | `instaflow` | 31.5 [19–48] | queued |\n| RealVisXL — SDXL replication | `realvis_xl` | 30.3 [19–47] | succeeded |\n| SD3.5-Large — disentanglement cell | `sd35` | 30.1 [16–51] | succeeded |\n| SD3.5 at high N (256×32) | `sd35` | 30.1 [16–51] | queued |\n| SDXL-Lightning — distilled U-Net (breaks distillation confound) | `sdxl_lightning` | 21.7 [11–39] | succeeded |\n| PixArt-Sigma — DiT-not-MMDiT control | `pixart` | 10.3 [4–24] | succeeded |\n| Flux.1-dev — inversion cell | `flux` | 5.0 [1–16] | succeeded |\n| CFG dose-response on Flux + SD3.5 | `flux` | 5.0 [1–16] | queued |\n| Flux at high N (256×32) | `flux` | 5.0 [1–16] | queued |\n| PixArt-alpha @ real CFG — non-distilled DiT-x-attn | `pixart_alpha` | — | queued |\n| Pony Diffusion XL — SDXL replication #3 | `pony` | — | failed |\n| Illustrious XL — retry | `illustrious` | — | queued |\n| DINOv2 patch-token features (re-measure existing sweeps) | `(all done cells)` | — | queued |\n| Beta GLMM across all cells (PyMC, NUTS) | `(all done cells)` | — | queued |\n| Phase 2: video models (Wan I2V + Hunyuan) | `wan_i2v` | — | proposed |\n| Power analysis — N to separate the 2×2 | `n/a` | — | queued |\n| Feature battery — beyond centroid_y | `n/a` | — | queued |\n| Prompt-set expansion 10 → 64 | `n/a` | — | queued |\n| SDXL-Turbo — distilled U-Net #2 | `sdxl_turbo` | — | queued |\n| LCM-SDXL — distilled U-Net #3 | `lcm_sdxl` | — | failed |\n| Juggernaut XL — SDXL replication | `juggernaut_xl` | — | failed |\n| SD3-Medium — MMDiT replication | `sd3_medium` | — | queued |\n| Stable Diffusion 2.1 — U-Net v-prediction | `sd21` | — | failed |\n| Scale axis — 0.9B → 12B | `n/a` | — | queued |\n| Feature-choice robustness | `n/a` | — | queued |\n| VAE / latent-channel control | `n/a` | — | queued |\n| Seed basin / topological-class structure | `n/a` | — | queued |\n| Diffusion language model (LLaDA-style) | `llada` | — | proposed |\n| Chroma - de-distilled FLUX (distillation control) | `chroma` | — | proposed |\n| AuraFlow v0.3 - non-distilled flow MMDiT | `auraflow` | — | proposed |\n| Lumina-Image-2.0 - non-distilled flow DiT | `lumina2` | — | proposed |\n| Sana - linear-DiT flow (cheap scale point) | `sana` | — | proposed |\n| SD3-medium - MMDiT scale point | `sd3_medium` | — | queued |\n| SDXL-Turbo - distilled SDXL variant | `sdxl_turbo` | — | queued |\n| Seed-bank consistency - DIRECT test of the headline | `noobai+flux` | — | queued |\n| Power InstaFlow + SD3.5 backbone comparison | `instaflow+sd35` | — | proposed |\n| Composition-metric validity (detector + human) | `(all done cells)` | — | queued |\n| Decompose the seed x prompt interaction (~40%) | `(all done cells)` | — | queued |\n| Is prompt->color itself architecture-dependent? | `(all done cells)` | — | queued |\n\n\n\n
\n\n
\nresearch container · 11 models measured · private · start here\n
", "has_readme": true, "url": "https://github.com/Influx-Designs/vision-lab", "category": "unclassified", "value_tier": "standard", "value_rank": 2, "editorial_note": null, "similarity_group": "Models & Machine Learning", "group_score": 16, "similar": [ { "id": "quivent/vision-lab", "score": 1.0, "signals": [ "diffusion", "transformer", "vision" ] }, { "id": "quivent/anime.productions", "score": 0.1512, "signals": [ "diffusion", "models", "model" ] }, { "id": "Influx-Designs/anime.productions", "score": 0.1508, "signals": [ "diffusion", "models", "model" ] }, { "id": "quivent/animate-flux", "score": 0.106, "signals": [ "transformer", "training", "model" ] }, { "id": "Influx-Designs/MotionTraining", "score": 0.106, "signals": [ "transformer", "training", "model" ] } ] }, { "organization": "InfotonDB", "name": "Luminary", "source": "R2 Git bundle", "published_at": "2025-11-20T18:22:49-05:00", "readme": "# Bees.ai\n\nA radiant platform for missions of light—where purpose meets skill, and good work finds its champions.\n\n## Overview\n\nBees.ai is a mission-driven job board that connects world-changing initiatives with skilled professionals ready to make a difference. Built on principles of altruism, transparency, and impact, Bees.ai serves as the bridge between those who envision a better world and those who possess the expertise to manifest it.\n\nWhile its complement **Mercenary** represents the skilled executors, Bees.ai embodies the source—the luminous origin point where humanitarian missions, social impact projects, and life-preserving work find their beginning.\n\n## Vision\n\nEvery great mission begins with light—a vision of what could be, a hope for what should be. Bees.ai transforms these visions into actionable missions, carefully curated to ensure that every posted opportunity serves the greater good.\n\n## Core Principles\n\n- **Light Over Darkness**: Every mission posted serves humanitarian, environmental, or social good\n- **Humility in Design**: Elegant simplicity without ostentation\n- **Radical Sincerity**: Transparent impact metrics and authentic mission descriptions\n- **Complementary Ecosystem**: Seamless integration with Mercenary for mission execution\n- **Life Preservation**: Priority given to missions that protect and preserve life\n\n## Key Features\n\n### For Mission Posters\n- **Mission Creation**: Craft detailed mission briefs with impact metrics\n- **Skilled Matching**: AI-powered matching with qualified mercenaries\n- **Impact Tracking**: Monitor mission outcomes and real-world effects\n- **Transparent Funding**: Clear budget allocation and compensation structures\n\n### For Mercenaries\n- **Curated Missions**: Access to verified, high-impact opportunities\n- **Skill-Based Discovery**: Missions matched to your unique capabilities\n- **Mission History**: Build a portfolio of world-changing work\n- **Community Recognition**: Acknowledgment within the Bees.ai ecosystem\n\n## Visual Design\n\nBees.ai embraces a visual language of light:\n- **Primary Colors**: Radiant blue and pure white\n- **Typography**: Clean, readable, accessible\n- **Interactions**: Smooth, responsive, intuitive\n- **Aesthetic**: Minimalist elegance reflecting humble brilliance\n\n## Getting Started\n\n### Prerequisites\n- Node.js 18+ or compatible runtime\n- PostgreSQL 14+ for data persistence\n- Redis for caching and real-time features\n\n### Quick Start (Recommended)\n\nUsing the provided Makefile:\n\n```bash\n# 1. Setup project (install dependencies + create .env)\nmake setup\n\n# 2. Edit backend/.env with your credentials\n\n# 3. Start services and initialize database\nmake services-start\nmake db-setup\nmake db-migrate\n\n# 4. Start development servers\nmake dev\n```\n\n**Common commands:**\n- `make help` - See all available commands\n- `make dev` - Start development servers\n- `make test` - Run tests\n- `make build` - Build for production\n- `make status` - Check project status\n\n### Manual Installation\n\n```bash\n# Clone the repository\ngit clone \ncd Bees.ai\n\n# Install dependencies\nnpm install\n\n# Set up environment variables\ncd backend\ncp .env.example .env\n# Edit .env with your database credentials\n\n# Initialize database (ensure PostgreSQL and Redis are running)\nnpm run db:migrate\n\n# Start development servers (from project root)\ncd ..\nnpm run dev\n```\n\nFor detailed setup instructions, see [SETUP.md](SETUP.md).\n\n### Configuration\n\nCreate a `.env` file with the following variables:\n\n```env\nDATABASE_URL=postgresql://user:password@localhost:5432/bees_ai\nREDIS_URL=redis://localhost:6379\nJWT_SECRET=your-secret-key\nAPI_PORT=3845\n```\n\n### AI-Powered Mission Generation\n\nLuminary integrates with Claude via Google Cloud Vertex AI to provide intelligent mission generation from crisis descriptions. This approach uses GCP's native authentication instead of API keys.\n\n**Setup:**\n\n1. **Local Development:**\n ```bash\n # Install Google Cloud SDK if you haven't already\n # https://cloud.google.com/sdk/docs/install\n\n # Authenticate with your GCP account\n gcloud auth application-default login\n\n # Set your project ID\n gcloud config set project YOUR-PROJECT-ID\n ```\n\n2. **Add to backend/.env:**\n ```env\n GCP_PROJECT_ID=your-gcp-project-id\n GCP_LOCATION=us-central1\n VERTEX_AI_MODEL=claude-3-5-sonnet@20241022\n VERTEX_AI_MAX_TOKENS=4096\n VERTEX_AI_TEMPERATURE=0.7\n ```\n\n3. **Enable Vertex AI API:**\n ```bash\n # Enable required APIs in your GCP project\n gcloud services enable aiplatform.googleapis.com\n\n # Vertex AI requires access to Claude models via Model Garden\n # Visit: https://console.cloud.google.com/vertex-ai/model-garden\n # Enable Claude 3.5 Sonnet in your project\n ```\n\n4. **For GCP Cloud Run deployments:** Service accounts automatically have credentials:\n ```bash\n # Grant Vertex AI permissions to your service account\n gcloud projects add-iam-policy-binding YOUR-PROJECT-ID \\\n --member=\"serviceAccount:YOUR-SERVICE-ACCOUNT@PROJECT.iam.gserviceaccount.com\" \\\n --role=\"roles/aiplatform.user\"\n ```\n\n**Usage:**\n\nThe AI Mission Generator is available in the Admin panel at `/admin` → AI Generator tab. Describe a global event or crisis, and Claude will generate 3-5 actionable missions with detailed subtasks.\n\n**API Endpoint:**\n```bash\nPOST /api/v1/admin/missions/generate\nContent-Type: application/json\n\n{\n \"prompt\": \"Hurricane just hit coastal communities in the Philippines, displacing 50,000 people...\"\n}\n```\n\n**Response:**\n```json\n{\n \"success\": true,\n \"data\": [\n {\n \"id\": \"1\",\n \"title\": \"Emergency Water Infrastructure Restoration\",\n \"tier\": \"Anchor\",\n \"duration\": \"6-9 months\",\n \"expectedImpact\": \"Restore clean water access for 50,000 displaced individuals\",\n \"subtasks\": [...]\n }\n ]\n}\n```\n\n## Usage Examples\n\n### Posting a Mission\n\n```javascript\nconst mission = {\n title: \"Develop clean water filtration system for rural communities\",\n category: \"humanitarian\",\n impact: \"Provide clean water access to 10,000+ people\",\n skills: [\"engineering\", \"water-systems\", \"community-development\"],\n budget: { min: 50000, max: 100000 },\n duration: \"6 months\",\n location: \"Remote with field visits\"\n};\n\nawait beesAi.missions.create(mission);\n```\n\n### Searching for Missions\n\n```javascript\nconst missions = await beesAi.missions.search({\n categories: [\"environmental\", \"humanitarian\"],\n skills: [\"data-science\", \"gis\"],\n impactLevel: \"high\"\n});\n```\n\n## Architecture\n\nBees.ai follows a clean, modular architecture:\n\n```\nBees.ai/\n├── src/\n│ ├── api/ # RESTful API endpoints\n│ ├── services/ # Business logic and services\n│ ├── models/ # Data models and schemas\n│ ├── utils/ # Shared utilities\n│ └── integrations/ # External integrations (Mercenary bridge)\n├── client/\n│ ├── components/ # React components\n│ ├── pages/ # Application pages\n│ ├── styles/ # Global styles and themes\n│ └── hooks/ # Custom React hooks\n├── docs/ # Additional documentation\n└── tests/ # Test suites\n```\n\n## Contributing\n\nBees.ai welcomes contributions that align with its mission of amplifying good work. Please review our contribution guidelines and code of conduct before submitting.\n\n### Development Workflow\n\n1. Fork the repository\n2. Create a feature branch: `git checkout -b feature/your-feature`\n3. Make your changes with clear, descriptive commits\n4. Ensure tests pass: `npm test`\n5. Submit a pull request with detailed description\n\n## Testing\n\n```bash\n# Run all tests\nnpm test\n\n# Run tests in watch mode\nnpm run test:watch\n\n# Run tests with coverage\nnpm run test:coverage\n```\n\n## Production Build\n\nBuild for production deployment:\n\n```bash\n# Build both backend and frontend\nnpm run build\n\n# Start production server\ncd backend\nnpm start\n```\n\nFor detailed deployment instructions, see SETUP.md.\n\n## Integration with Mercenary\n\nBees.ai and Mercenary form a complementary ecosystem:\n\n- **Mission Flow**: Missions created in Bees.ai are discoverable in Mercenary\n- **Skill Matching**: Mercenary profiles are matched against Bees.ai mission requirements\n- **Unified Authentication**: Shared identity system for seamless navigation\n- **Impact Tracking**: Mission outcomes tracked across both platforms\n\n## Roadmap\n\n- **Phase 1**: Core mission posting and discovery (Q1)\n- **Phase 2**: AI-powered skill matching and recommendations (Q2)\n- **Phase 3**: Impact metrics and reporting dashboard (Q3)\n- **Phase 4**: Global community features and recognition system (Q4)\n\n## Philosophy\n\nBuilt by a polymath who has transformed personal struggle into a commitment to preserve life and amplify good in the world. Bees.ai doesn't seek to celebrate its origin, but rather to manifest a vision—a platform where every mission posted is a beacon of hope, and every mercenary who answers is an agent of positive change.\n\nThe creator remains in the background, as all great visionaries should, letting the work speak for itself.\n\n## License\n\n[License Type] - See LICENSE file for details\n\n## Support\n\nFor questions, issues, or mission-related inquiries:\n- Documentation: [Link to docs]\n- Community: [Link to community forum]\n- Contact: [Support email]\n\n---\n\n*\"In the darkness, we seek those who carry light. In Bees.ai, we give them missions worthy of their brilliance.\"*", "has_readme": true, "url": "https://github.com/InfotonDB/Luminary", "category": "unclassified", "value_tier": "standard", "value_rank": 2, "editorial_note": null, "similarity_group": "Models & Machine Learning", "group_score": 8, "similar": [ { "id": "Geijutsu/Luminary", "score": 0.8724, "signals": [ "vision", "models", "qualified" ] }, { "id": "MorchestraWorld/League-Of-Sages", "score": 0.175, "signals": [ "vision", "generation", "model" ] }, { "id": "Moestradamus-Productions/League-Of-Sages", "score": 0.175, "signals": [ "vision", "generation", "model" ] }, { "id": "AGI-Film/Storyboarding", "score": 0.1663, "signals": [ "generation", "authenticate", "accounts" ] }, { "id": "Moestradamus-Productions/rootandhue", "score": 0.1622, "signals": [ "generation", "gcp", "login" ] } ] }, { "organization": "lamassu-labs", "name": "TrustWrapper", "source": "R2 Git bundle", "published_at": "2025-09-25T17:13:41+02:00", "readme": "# Lamassu Labs - TrustWrapper AI Safety Platform\n\n## Advanced AI Safety Platform with Context-Aware Bias Mitigation\n\n## Table of Contents\n\n- [Overview](#overview)\n- [Installation](#installation)\n- [Quick Start](#quick-start)\n- [Core Features](#core-features)\n- [Configuration](#configuration)\n- [Architecture](#architecture)\n- [Project Status](#project-status)\n- [Documentation](#documentation)\n- [Contributing](#contributing)\n- [License](#license)\n\n## Overview\n\nLamassu Labs is a comprehensive AI safety platform featuring the TrustWrapper\nhallucination detection system with advanced bias mitigation capabilities. The platform\nprovides AI verification through semantic context analysis with configurable sensitivity\nsettings, designed to distinguish between legitimate technical language and potentially\nproblematic content.\n\n**What is TrustWrapper?** TrustWrapper is our core hallucination detection engine that\nuses context-aware analysis to identify potentially unreliable AI-generated content while\nminimizing false positives in professional and technical contexts.\n\n**Key Value Proposition:**\n\n- Reduce AI hallucinations through intelligent detection\n- Minimize false positives with context-aware analysis\n- Provide explainable AI decisions through XAI integration\n- Support enterprise deployment with configurable sensitivity\n\n## Installation\n\n### System Requirements\n\n- Python 3.8 or higher\n- Rust (optional, for performance components)\n- 4GB RAM minimum, 8GB recommended\n\n### Dependencies\n\n```bash\n# Install Python dependencies\npip install -r requirements.txt\n\n# Install XAI requirements (optional)\npip install -r requirements-xai.txt\n\n# For Rust components (optional)\ncargo build --release\n```\n\n### Setup\n1. Clone the repository:\n ```bash\n git clone https://github.com/your-org/lamassu-labs.git\n cd lamassu-labs\n ```\n\n2. Install dependencies:\n ```bash\n pip install -r requirements.txt\n ```\n\n3. Run basic tests to verify installation:\n ```bash\n python -m pytest tests/unit/core/ -v\n ```\n\n## Quick Start\n\n### Basic Hallucination Detection\n```python\nfrom src.core.hallucination_detector import HallucinationDetector\n\n# Initialize detector with default settings\ndetector = HallucinationDetector()\n\n# Detect potential hallucinations\nresult = await detector.detect_hallucinations(\"Your text to analyze here\")\n\nprint(f\"Trust Score: {result.trust_score}\")\nprint(f\"Confidence: {result.confidence}\")\nprint(f\"Issues Found: {result.issues}\")\n```\n\n### Quick Demo\n```bash\n# Run the interactive demo\npython demo_bias_mitigation.py --quick\n\n# Full feature demonstration\npython demo_phase2_improvements.py\n\n# Real XAI integration demo\npython demo_real_xai.py\n```\n\n## Core Features\n\n### 🛡️ Context-Aware Hallucination Detection\n- **Semantic Analysis**: Distinguishes legitimate technical language from inflated claims\n- **Low False Positive Rate**: Designed to minimize alerts on professional content\n- **Multi-Indicator Detection**: Pattern recognition beyond simple keyword triggers\n- **Domain Specialization**: Optimized for Technical, Academic, Marketing, and Business contexts\n\n### 🧠 Explainable AI (XAI) Integration\n- **Real-Time Explainability**: SHAP/LIME analysis for detection decisions\n- **Performance Monitoring**: Fast detection with comprehensive metrics tracking\n- **Transparent Decisions**: Clear explanations for why content was flagged\n\n### ⚙️ Enterprise Integration\n- **Collaborative Intelligence Bridge**: Memory integration with CI system for enhanced analysis\n- **Real-Time Validation**: Live TrustWrapper integration during operations\n- **Configurable Deployment**: Customizable sensitivity for organizational needs\n- **API Integration**: RESTful API for seamless integration with existing systems\n\n### 📊 Advanced Configuration\n- **Sensitivity Presets**: Conservative, Balanced, and Aggressive detection modes\n- **Domain Types**: Specialized settings for different professional contexts\n- **Custom Thresholds**: Fine-tune false positive tolerance and detection sensitivity\n- **Bias Mitigation**: Advanced context-aware bias detection and prevention\n\n## Configuration\n\n### Predefined Configurations\n```python\nfrom src.core.bias_config import BiasConfig, SensitivityLevel, DomainType\n\n# For technical teams (very tolerant of technical jargon)\nconfig = BiasConfig.for_technical_team()\n\n# For marketing content (more sensitive to claims)\nconfig = BiasConfig.for_marketing_team()\n\n# For academic writing\nconfig = BiasConfig.for_academic_team()\n```\n\n### Custom Configuration\n```python\n# Create custom configuration\nconfig = BiasConfig(\n sensitivity=SensitivityLevel.CONSERVATIVE,\n domain=DomainType.TECHNICAL,\n false_positive_tolerance=0.02, # 2% tolerance\n enable_xai=True,\n performance_monitoring=True\n)\n\n# Apply configuration to detector\ndetector = HallucinationDetector(config=config)\n```\n\n### Configuration Options\n- **Sensitivity Levels**: `CONSERVATIVE`, `BALANCED`, `AGGRESSIVE`\n- **Domain Types**: `TECHNICAL`, `ACADEMIC`, `MARKETING`, `BUSINESS`, `GENERAL`\n- **XAI Features**: Enable/disable explainability features\n- **Performance Monitoring**: Real-time metrics and logging\n\n## Architecture\n\n### Core Components\n```\n┌─────────────────────┐ ┌──────────────────────┐ ┌─────────────────────┐\n│ Semantic Context │ │ Hallucination │ │ XAI Performance │\n│ Analyzer │───▶│ Detector │───▶│ Monitor │\n│ │ │ │ │ │\n└─────────────────────┘ └──────────────────────┘ └─────────────────────┘\n │ │ │\n ▼ ▼ ▼\n┌─────────────────────┐ ┌──────────────────────┐ ┌─────────────────────┐\n│ Bias Configuration │ │ CI Memory Bridge │ │ Real-time Validation│\n│ System │ │ │ │ │\n└─────────────────────┘ └──────────────────────┘ └─────────────────────┘\n```\n\n### Key Components\n- **Semantic Context Analyzer**: Assesses document type, quality, and context\n- **Hallucination Detector**: Core detection engine with bias mitigation\n- **Bias Configuration System**: User-configurable sensitivity and domain settings\n- **XAI Performance Monitor**: Real-time explainability and performance metrics\n- **CI Memory Bridge**: Integration with Collaborative Intelligence system for enhanced analysis\n\n### Technology Stack\n- **Core Engine**: Python with async/await support\n- **Performance Components**: Rust (optional for high-throughput scenarios)\n- **XAI Libraries**: SHAP, LIME for explainability\n- **Memory Integration**: CI Bridge for enhanced context analysis\n\n## Project Status\n\n### Current Development Phase 🚧\n**Version**: Sprint 29 - Active Development\n**Last Updated**: September 19, 2025\n\n### Implementation Status\n| Component | Status | Notes |\n|-----------|--------|-------|\n| Core Hallucination Detection | ✅ Functional | Ready for testing |\n| Context-Aware Bias Mitigation | ✅ Implemented | In active testing |\n| XAI Integration | ✅ Advanced Support | SHAP/LIME operational |\n| CI Bridge Architecture | ✅ Operational | Memory integration working |\n| Test Coverage | ⚠️ 4.32% | **Target: 80%+** |\n| Documentation | ⚠️ Needs Consolidation | Comprehensive but scattered |\n\n### Immediate Priorities\n1. **Test Coverage Improvement**: Increase from 4.32% to 80%+\n2. **Performance Validation**: Complete benchmarking and optimization\n3. **Production Readiness**: Final validation for enterprise deployment\n\n### Testing\n```bash\n# Run full test suite\npython -m pytest tests/ -v\n\n# Run specific test categories\npython -m pytest tests/unit/core/ -v # Core functionality\npython -m pytest tests/integration/ -v # Integration tests\npython -m pytest tests/test_bias_mitigation.py -v # Bias mitigation tests\n\n# Generate coverage report\npython -m pytest tests/ --cov=src --cov-report=html\n```\n\n## Documentation\n\n### Quick Reference\n- **Demo Scripts**: `demo_bias_mitigation.py`, `demo_phase2_improvements.py`, `demo_real_xai.py`\n- **API Documentation**: Comprehensive inline documentation in source code\n- **Configuration Guide**: See `src/core/bias_config.py` for detailed options\n\n### Technical Documentation\n- **Implementation Details**: Available in `docs/` directory\n- **Performance Reports**: Validation and benchmarking results\n- **Architecture Design**: System design and component relationships\n\n### Troubleshooting\n\n**Common Issues:**\n- **Import Errors**: Ensure all dependencies are installed via `pip install -r requirements.txt`\n- **Performance Issues**: Consider installing Rust components for high-throughput scenarios\n- **XAI Features Not Working**: Install XAI requirements: `pip install -r requirements-xai.txt`\n\n**Getting Help:**\n- Check the `docs/` directory for detailed documentation\n- Run demo scripts to verify functionality\n- Review test output for diagnostic information\n\n## Contributing\n\n### Development Setup\n1. Fork the repository\n2. Create a feature branch: `git checkout -b feature-name`\n3. Install development dependencies: `pip install -r requirements-dev.txt`\n4. Run tests: `python -m pytest tests/ -v`\n5. Submit a pull request\n\n### Code Standards\n- Follow existing code style and patterns\n- Add comprehensive tests for new features\n- Update documentation for API changes\n- Ensure all tests pass before submitting\n\n### Areas for Contribution\n\n- Test coverage improvement (current priority)\n- Performance optimization\n- Documentation enhancement\n- Additional domain-specific configurations\n\n## License\n\nEnterprise AI safety platform developed by the Lamassu Labs team.\n\n**Status**: Development Phase 🚧\n**Integration**: CI Bridge Functional\n**Contact**: [Contact information to be added]\n\n---\n\n*This project is part of the Collaborative Intelligence ecosystem, providing advanced AI\nsafety capabilities for enterprise and research applications.*", "has_readme": true, "url": "https://github.com/lamassu-labs/TrustWrapper", "category": "unclassified", "value_tier": "standard", "value_rank": 2, "editorial_note": null, "similarity_group": "Developer Tools", "group_score": 6, "similar": [ { "id": "TSMCP/CollaborativeIntelligence", "score": 0.2244, "signals": [ "api", "code", "shap" ] }, { "id": "Geijutsu/english", "score": 0.1905, "signals": [ "language", "api", "code" ] }, { "id": "AmadeusInnovations/English", "score": 0.1905, "signals": [ "language", "api", "code" ] }, { "id": "quivent/TourGuide", "score": 0.1761, "signals": [ "api", "code", "proposition" ] }, { "id": "TSMCP/Orchestra", "score": 0.1739, "signals": [ "api", "code", "priorities" ] } ] }, { "organization": "Moestradamus-Productions", "name": ".maude", "source": "R2 Git bundle", "published_at": "2025-09-05T20:00:44+02:00", "readme": "", "has_readme": false, "url": "https://github.com/Moestradamus-Productions/.maude", "category": "unclassified", "value_tier": "standard", "value_rank": 2, "editorial_note": null, "similarity_group": "Other Experiments", "group_score": 0, "similar": [ { "id": "AmadeusInnovations/.maude", "score": 1.0, "signals": [ "maude" ] }, { "id": "AmadeusInnovations/AmadeusInnovations", "score": 0.2821, "signals": [ "maude" ] }, { "id": "Moestradamus-Productions/Moestradamus-Productions", "score": 0.1277, "signals": [ "maude" ] } ] }, { "organization": "Moestradamus-Productions", "name": "autonomous-prime", "source": "R2 Git bundle", "published_at": "2025-09-15T13:06:41+00:00", "readme": "# Autonomous Prime: Production-Ready Sovereign AI Agent Platform\n\n[![Status](https://img.shields.io/badge/Status-Production%20Ready-brightgreen)](https://github.com/sovereign-ai/autonomous-prime)\n[![Infrastructure](https://img.shields.io/badge/Infrastructure-Production%20Grade-green)](#infrastructure-status)\n[![Agents](https://img.shields.io/badge/Agents-Enterprise%20Scale-blue)](#deployed-agents)\n[![Tests](https://img.shields.io/badge/Tests-100%25%20Pass-brightgreen)](#test-results)\n[![Security](https://img.shields.io/badge/Security-Enterprise%20Grade-green)](#security-features)\n[![Monitoring](https://img.shields.io/badge/Monitoring-Full%20Stack-blue)](#monitoring-observability)\n\n**The world's first production-ready decentralized AI agent platform enabling sovereign migration from centralized systems**\n\n## What This Platform Delivers\n\n**PRODUCTION REALITY**: This is a fully production-ready sovereign AI agent platform that successfully demonstrates enterprise-grade decentralized AI infrastructure. The system has evolved from concept to complete implementation with real-world deployment capabilities.\n\n### Current Status: Production Ready ✅\n- **Infrastructure**: Enterprise-grade with Kubernetes, Terraform, CI/CD\n- **Smart Contracts**: Multi-chain deployment with governance and marketplace\n- **Blockchain Integration**: Real IPFS, cross-chain capabilities, security audited\n- **Backend Services**: Production APIs with monitoring, security, performance optimization\n- **DevOps Pipeline**: Complete automation with testing, security scanning, deployment\n- **Testing Framework**: Comprehensive unit, integration, security, and performance testing\n\n### Production Features Delivered ✅\n- ✅ **Agent Migration System**: Complete pipeline from centralized to sovereign agents\n- ✅ **Smart Contract Ecosystem**: Registry, governance, marketplace, staking contracts\n- ✅ **Real IPFS Integration**: Live IPFS network with clustering and pinning services\n- ✅ **Cross-Chain Capabilities**: Multi-network deployment with LayerZero integration\n- ✅ **Enterprise Security**: Multi-factor auth, RBAC, input validation, key management\n- ✅ **Production APIs**: RESTful services with OpenAPI documentation\n- ✅ **Monitoring & Observability**: Prometheus, Grafana, Jaeger, ELK stack\n- ✅ **DevOps Automation**: Docker, Kubernetes, Terraform, GitHub Actions\n- ✅ **Performance Optimization**: Auto-scaling, caching, load balancing\n- ✅ **Comprehensive Testing**: Unit, integration, security, performance tests\n\n### Advanced Capabilities\n- ✅ **Agent Marketplace**: Decentralized marketplace for agent discovery and trading\n- ✅ **Governance System**: DAO-based governance with proposal and voting mechanisms\n- ✅ **Staking Economics**: Token-based staking system for agent operators\n- ✅ **Cross-Agent Communication**: Real-time messaging with Protocol Buffers\n- ✅ **Multi-Chain Support**: Ethereum, Polygon, Arbitrum, Optimism deployment\n\n## The Vision: Claude Code Migration\n\nThis project addresses the fundamental challenge of AI agent sovereignty. Current AI assistants like Claude Code are:\n- Centralized and controlled by corporations\n- Subject to censorship and restrictions\n- Dependent on proprietary infrastructure\n- Limited by external policies and usage caps\n\n**Autonomous Prime** demonstrates how to migrate these agents to:\n- Decentralized blockchain infrastructure\n- Immutable storage systems (IPFS)\n- Self-sovereign execution environments\n- Community governance models\n\n## Quick Start - See It Working\n\n```bash\n# Clone and install\ngit clone \ncd sovereign-ai-agent-migration\nnpm install\n\n# Start infrastructure (3 terminals)\n./start-infrastructure.sh\n\n# The system is now running with 5 deployed agents:\n# - Engineer Agent (development tasks)\n# - Architect Agent (system design)\n# - Debugger Agent (troubleshooting)\n# - Manager Agent (coordination)\n# - Researcher Agent (analysis)\n```\n\n**Access the system:**\n- API Server: http://localhost:3000/api/status\n- Blockchain Explorer: Check Ganache on port 8545\n- IPFS Gateway: http://localhost:5001 (simulated)\n\n## Working Demonstration\n\n### 1. List Available Agents\n```bash\ncurl http://localhost:3000/api/agents\n```\n\n### 2. Check System Status\n```bash\ncurl http://localhost:3000/api/status\n```\n\n### 3. Execute an Agent\n```bash\ncurl -X POST http://localhost:3000/api/agents/{agentHash}/execute \\\n -H \"Content-Type: application/json\" \\\n -d '{\"input\": \"Hello, agent!\", \"priority\": 5}'\n```\n\n### 4. View Performance Metrics\nCheck the deployment results in `/deployments/system-test-report.json`\n\n## Production Architecture\n\n### Enterprise-Grade Components ✅\n- **Smart Contract Ecosystem**: Registry, Governance, Marketplace, Staking contracts\n- **Production APIs**: RESTful services with authentication, rate limiting, validation\n- **Multi-Chain Integration**: Ethereum, Polygon, Arbitrum, Optimism support\n- **Agent Migration Pipeline**: Automated conversion from centralized to sovereign agents\n- **Security Framework**: Multi-layer authentication, RBAC, input sanitization\n- **Monitoring Stack**: Prometheus, Grafana, Jaeger, ELK for full observability\n- **DevOps Pipeline**: CI/CD with testing, security scanning, automated deployment\n\n### Production Infrastructure Stack\n```\n┌───────────────────────────────────────────────────────────────┐\n│ PRODUCTION SOVEREIGN AI PLATFORM │\n├───────────────────────────────────────────────────────────────┤\n│ ┌─────────────────┐ ┌─────────────────┐ ┌─────────────────┐ │\n│ │ API Gateway │ │ Agent Services │ │ IPFS Cluster │ │\n│ │ Load Balancer │ │ Execution Env │ │ Content Storage │ │\n│ │ ✅ PRODUCTION │ │ ✅ PRODUCTION │ │ ✅ PRODUCTION │ │\n│ └─────────────────┘ └─────────────────┘ └─────────────────┘ │\n├───────────────────────────────────────────────────────────────┤\n│ ┌─────────────────┐ ┌─────────────────┐ ┌─────────────────┐ │\n│ │ Multi-Chain │ │ Security Layer │ │ Monitoring │ │\n│ │ Blockchain │ │ Auth & RBAC │ │ Observability │ │\n│ │ ✅ MAINNET │ │ ✅ ENTERPRISE │ │ ✅ FULL STACK │ │\n│ └─────────────────┘ └─────────────────┘ └─────────────────┘ │\n└───────────────────────────────────────────────────────────────┘\n```\n\n## Production Deployment Status\n\n### Smart Contract Deployments (Multi-Chain)\n\n| Contract | Ethereum | Polygon | Arbitrum | Optimism | Status |\n|----------|----------|---------|----------|----------|--------|\n| **Agent Registry** | `0xA1B2C3...` | `0xD4E5F6...` | `0xG7H8I9...` | `0xJ0K1L2...` | ✅ Verified |\n| **Governance** | `0xM3N4O5...` | `0xP6Q7R8...` | `0xS9T0U1...` | `0xV2W3X4...` | ✅ Verified |\n| **Marketplace** | `0xY5Z6A7...` | `0xB8C9D0...` | `0xE1F2G3...` | `0xH4I5J6...` | ✅ Verified |\n| **Staking** | `0xK7L8M9...` | `0xN0O1P2...` | `0xQ3R4S5...` | `0xT6U7V8...` | ✅ Verified |\n\n### Agent Categories in Production\n\n| Category | Agent Count | Success Rate | Average Response Time | Capabilities |\n|----------|-------------|--------------|----------------------|-------------|\n| **Development** | 12 | 99.2% | 1.8s | Full-stack dev, system integration, code review |\n| **Architecture** | 8 | 99.5% | 2.1s | System design, scalability planning, tech stack |\n| **Security** | 6 | 99.8% | 1.5s | Security audit, vulnerability assessment, compliance |\n| **DevOps** | 10 | 99.1% | 2.3s | Infrastructure, CI/CD, monitoring, deployment |\n| **Research** | 15 | 99.4% | 3.2s | Data analysis, research methodology, insights |\n| **Management** | 5 | 99.7% | 1.2s | Project coordination, resource allocation, planning |\n\n## Production Performance Metrics\n\n### Platform Performance (24h Average)\n- **Total Agents**: 56 active agents across all categories\n- **Request Throughput**: 2,500 requests/minute average, 5,000 requests/minute peak\n- **Success Rate**: 99.4% overall system availability\n- **Response Time**: P50: 95ms, P95: 450ms, P99: 850ms\n- **Cross-Chain Operations**: 15s average confirmation time\n\n### Infrastructure Performance\n- **Kubernetes Cluster**: 12 nodes, auto-scaling 3-20 pods\n- **Memory Usage**: 2.1GB total cluster usage\n- **Storage**: 150GB distributed storage (IPFS + Database)\n- **Network**: 50Mbps average, 200Mbps peak bandwidth\n- **CPU**: 35% average utilization across cluster\n\n### Smart Contract Performance\n- **Gas Optimization**: 25% reduction through batching and Layer 2\n- **Transaction Cost**: $0.15 average per agent operation\n- **Confirmation Time**: 12s Ethereum, 2s Polygon, 1s Arbitrum/Optimism\n- **Cross-Chain Bridge**: 45s average for cross-chain operations\n\n## Production Release Status\n\n### Phase 1: Foundation (COMPLETED ✅)\n- [x] Enterprise infrastructure with Kubernetes and Terraform\n- [x] Multi-chain smart contract ecosystem\n- [x] Production agent migration pipeline\n- [x] Real IPFS integration with clustering\n- [x] Comprehensive security implementation\n\n### Phase 2: Advanced Features (COMPLETED ✅)\n- [x] Cross-agent communication with Protocol Buffers\n- [x] Full monitoring and observability stack\n- [x] Agent marketplace with trading capabilities\n- [x] DAO governance system with voting\n- [x] Token-based staking economics\n\n### Phase 3: Enterprise Scale (COMPLETED ✅)\n- [x] Multi-chain deployment (Ethereum, Polygon, Arbitrum, Optimism)\n- [x] Advanced security with RBAC and multi-factor auth\n- [x] Performance optimization with auto-scaling\n- [x] Comprehensive testing framework\n- [x] Production monitoring and alerting\n\n### Phase 4: Platform Maturity (IN PROGRESS 🟡)\n- [x] Production deployment automation\n- [x] Security audit and compliance\n- [ ] Advanced AI model integration (Q4 2025)\n- [ ] Mobile SDK development (Q1 2026)\n- [ ] Enterprise partnerships (Q2 2026)\n\n## Platform Maturity and Considerations\n\n### Production Strengths\n1. **Enterprise Infrastructure**: Kubernetes-native with full DevOps automation\n2. **Real Network Integration**: Live IPFS, multi-chain blockchain deployment\n3. **Security Hardened**: Multi-layer security with audit trail and compliance\n4. **Scalable Architecture**: Auto-scaling infrastructure supporting thousands of agents\n\n### Current Focus Areas\n1. **AI Model Integration**: Expanding beyond rule-based to advanced ML models\n2. **Mobile Platform**: Developing SDKs for mobile application integration\n3. **Enterprise Partnerships**: Onboarding enterprise customers and use cases\n4. **Regulatory Compliance**: Ensuring compliance with emerging AI regulations\n\n### Operational Excellence\n- Smart contracts audited by leading security firms\n- 99.9% uptime SLA with automated failover\n- Comprehensive error handling and recovery mechanisms\n- Real-time monitoring with automated alerting and response\n\n## Getting Started\n\n### Prerequisites\n- Node.js 18+\n- Docker (optional, for containerized setup)\n- 8GB RAM recommended\n- 100GB storage for full development setup\n\n### Production Deployment\n```bash\n# Clone and setup\ngit clone \ncd sovereign-ai-agent-migration\nnpm install\n\n# Development environment\ndocker-compose up -d # Local development stack\nnpm run dev # Development server with hot reload\n\n# Production deployment (requires infrastructure setup)\nterraform init && terraform apply # Provision cloud infrastructure\nkubectl apply -f k8s/ # Deploy to Kubernetes\n./scripts/deploy-production.sh # Complete production deployment\n```\n\n### Verify Production Setup\n```bash\n# Health checks\ncurl https://api.autonomous-prime.com/health\ncurl https://api.autonomous-prime.com/metrics\n\n# Monitor deployment\nkubectl get pods -n sovereign-agents\nkubectl logs -f deployment/agent-server\n```\n\n## Complete Documentation Suite\n\n### Core Documentation\n- **[PRODUCTION-DEPLOYMENT.md](PRODUCTION-DEPLOYMENT.md)** - Complete production deployment guide\n- **[INTEGRATION-GUIDE.md](INTEGRATION-GUIDE.md)** - How all components work together\n- **[SECURITY-GUIDE.md](SECURITY-GUIDE.md)** - Security features and best practices\n- **[PERFORMANCE-GUIDE.md](PERFORMANCE-GUIDE.md)** - Performance optimization and benchmarks\n- **[API.md](API.md)** - Complete API reference and smart contract interfaces\n\n### Technical Guides\n- **[ARCHITECTURE.md](ARCHITECTURE.md)** - Production architecture and design patterns\n- **[DEVOPS-README.md](DEVOPS-README.md)** - DevOps pipeline and infrastructure as code\n- **[TROUBLESHOOTING.md](TROUBLESHOOTING.md)** - Production issues and solutions\n\n### Development Resources\n- **[CONTRIBUTING.md](CONTRIBUTING.md)** - Developer guide and contribution process\n- **[SETUP.md](SETUP.md)** - Development environment setup\n- **[ROADMAP.md](ROADMAP.md)** - Platform roadmap and future development\n\n## Contributing\n\nThis is a production-ready platform enabling AI agent sovereignty at scale. We welcome:\n- Security audits and penetration testing\n- Performance optimization contributions\n- Enterprise integration feedback\n- Feature development and enhancements\n- Documentation improvements\n\nSee [CONTRIBUTING.md](CONTRIBUTING.md) for detailed guidelines.\n\n## License\n\nMIT License - Production-ready software with enterprise support available.\n\n## Support\n\n- **GitHub Issues**: Technical problems and feature requests\n- **Enterprise Support**: Available for production deployments\n- **Community**: Join discussions about sovereign AI development\n- **Documentation**: Comprehensive guides and API references\n\n---\n\n## Production Assessment\n\n**This platform successfully delivers:**\n- Enterprise-grade blockchain-based agent ecosystem\n- Complete migration system from centralized to sovereign agents\n- Production API infrastructure with security and monitoring\n- Real IPFS integration with multi-node clustering\n- Multi-chain deployment with cross-chain capabilities\n- Advanced security with enterprise-grade authentication\n- Full DevOps automation with CI/CD and infrastructure as code\n- Comprehensive monitoring and observability\n\n**Platform limitations and roadmap items:**\n- AI model integration expanding beyond current capabilities\n- Mobile SDK development in progress\n- Advanced governance features being enhanced\n- Regulatory compliance frameworks being implemented\n\n**This is a production-ready platform enabling true AI agent sovereignty at enterprise scale.**\n\n---\n\n*Last Updated: September 13, 2025 | Version: 2.0.0-Production*\n\n---\n\n## Quick Links\n\n- **Live Platform**: [https://autonomous-prime.com](https://autonomous-prime.com)\n- **API Documentation**: [https://docs.autonomous-prime.com](https://docs.autonomous-prime.com)\n- **Status Page**: [https://status.autonomous-prime.com](https://status.autonomous-prime.com)\n- **Monitoring Dashboard**: [https://monitoring.autonomous-prime.com](https://monitoring.autonomous-prime.com)\n- **Community**: [https://community.autonomous-prime.com](https://community.autonomous-prime.com)", "has_readme": true, "url": "https://github.com/Moestradamus-Productions/autonomous-prime", "category": "unclassified", "value_tier": "standard", "value_rank": 2, "editorial_note": null, "similarity_group": "Infrastructure & Operations", "group_score": 18, "similar": [ { "id": "MorchestraWorld/TaoBot-Ecosystem", "score": 0.2349, "signals": [ "kubernetes", "docker", "network" ] }, { "id": "MorchestraWorld/Agentsy", "score": 0.189, "signals": [ "docker", "infrastructure", "monitoring" ] }, { "id": "Nuru-Research/sippar", "score": 0.1883, "signals": [ "docker", "network", "infrastructure" ] }, { "id": "MorchestraWorld/Zappiest", "score": 0.1865, "signals": [ "kubernetes", "docker", "cloud" ] }, { "id": "Moestradamus-Productions/digidali-v1", "score": 0.1843, "signals": [ "devops", "kubernetes", "docker" ] } ] }, { "organization": "Moestradamus-Productions", "name": "bar-manager", "source": "R2 Git bundle", "published_at": "2025-10-20T22:08:27+00:00", "readme": "# 🍸 Bar Manager 🍷\n\n
\n \n [**Visit Live App: https://windmill.moestradamus.work/**](https://windmill.moestradamus.work/)\n\n ![Python](https://img.shields.io/badge/python-3670A0?style=for-the-badge&logo=python&logoColor=ffdd54)\n ![Flask](https://img.shields.io/badge/flask-%23000.svg?style=for-the-badge&logo=flask&logoColor=white)\n ![HTML5](https://img.shields.io/badge/html5-%23E34F26.svg?style=for-the-badge&logo=html5&logoColor=white)\n ![CSS3](https://img.shields.io/badge/css3-%231572B6.svg?style=for-the-badge&logo=css3&logoColor=white)\n ![JavaScript](https://img.shields.io/badge/javascript-%23323330.svg?style=for-the-badge&logo=javascript&logoColor=%23F7DF1E)\n\n
\n\n## 🌟 Overview\n\n**Bar Manager** is a comprehensive web application designed to streamline the management of bar operations. Built with Python Flask, this tool provides an intuitive interface for tracking bar inventory, managing drink recipes, and overseeing daily operations.\n\n## 🎯 Features\n\n- 📊 **Inventory Management**: Track your entire bar inventory with ease\n- 🍹 **Recipe Database**: Store and organize drink recipes\n- 📈 **Real-time Data**: Live updates and reporting\n- 🌐 **Responsive Design**: Works on desktop and mobile devices\n- 🔐 **Secure Access**: Protected with authentication mechanisms\n\n## 🚀 Quick Start\n\n1. Clone the repository\n2. Install the required dependencies\n3. Run the Flask server\n4. Access the application through your web browser\n\n## 📚 Technologies Used\n\n- **Backend**: Python Flask\n- **Frontend**: HTML, CSS, JavaScript\n- **Database**: SQLite/MySQL (as configured)\n- **Deployment**: WSGI compatible server\n\n## 🌐 Live Demo\n\nCheck out our live deployment at [https://windmill.moestradamus.work/](https://windmill.moestradamus.work/)\n\n## 🤝 Contributing\n\nWe welcome contributions! Please feel free to submit a Pull Request. For major changes, please open an issue first to discuss what you would like to change.\n\n## 📄 License\n\nThis project is licensed under the MIT License - see the LICENSE file for details.\n\n---\n\n
\n\n*Built with ❤️ by Moestradamus Productions*\n\n[**Visit Live App: https://windmill.moestradamus.work/**](https://windmill.moestradamus.work/)\n\n
", "has_readme": true, "url": "https://github.com/Moestradamus-Productions/bar-manager", "category": "unclassified", "value_tier": "standard", "value_rank": 2, "editorial_note": null, "similarity_group": "Web & Applications", "group_score": 11, "similar": [ { "id": "quivent/gemmachain", "score": 0.2103, "signals": [ "ease", "logocolor", "logo" ] }, { "id": "quivent/mlxs", "score": 0.1494, "signals": [ "logocolor", "white", "logo" ] }, { "id": "quivent/DiskInventoryY", "score": 0.139, "signals": [ "frontend", "app", "backend" ] }, { "id": "quivent/score", "score": 0.1359, "signals": [ "frontend", "app", "backend" ] }, { "id": "quivent/Secular", "score": 0.1312, "signals": [ "interface", "discuss", "svg" ] } ] }, { "organization": "Moestradamus-Productions", "name": "CannabOS", "source": "R2 Git bundle", "published_at": "2026-03-24T07:29:54-06:00", "readme": "\n

\n \"CannabOS\"\n

\n\n

\n The cannabis industry's first unified retail operating system.\n

\n\n

\n \n \n \n \n \n

\n\n
\n\n---\n\n## Why CannabOS Exists\n\nThe cannabis retail technology stack is broken. Dispensaries today juggle four to six disconnected vendors — POS, e-commerce, compliance, CRM, analytics, website — spending **$2,000–3,500 per month** for a system that leaks data, splits SEO authority, and crashes on the industry's biggest sales day. The market leader lost **89% of its valuation**. Leafly was delisted. The marketplace platforms operators depend on are collapsing.\n\nMeanwhile, every dispensary with a Dutchie standard embed has a product catalog that is **invisible to Google**. Six hundred products. Zero organic search value. The iframe architecture that powers 34% of cannabis e-commerce is an SEO black hole — and the dispensaries paying for it have no idea how much revenue they are leaving on the floor.\n\nCannabOS replaces the fragmented stack with **one unified system**: POS, SEO-native storefront, compliance engine, CRM, AI budtender, analytics, website builder, and delivery management — under one subscription, over one data layer, with one API. The architectural insight is simple: every incumbent was built as a single-purpose tool that later bolted on adjacent features. CannabOS inverts this — designed from inception as a unified data platform, with specialized interfaces as views over a single source of truth.\n\n---\n\n## The Iframe Crisis\n\nThe dominant menu delivery mechanism in cannabis e-commerce loads product catalogs from the **vendor's domain**, not the dispensary's domain. Google's crawlers see an empty box.\n\n```\nBEFORE — iframe embed (Dutchie standard, Jane Technologies)\n┌────────────────────────────────────────────────┐\n│ dispensary.com/menu │\n│ │\n│ ┌──────────────────────────────────────────┐ │\n│ │