A comprehensive cognitive computing ecosystem for AI/ML development
The O9NN organization is a collection of 58 repositories focused on building a modular, high-performance cognitive computing platform. The ecosystem spans multiple programming languages and domains, from low-level neural network implementations to high-level APIs and deployment tools.
Organization Stats:
- Total Repositories: 58
- Members: 3
- Primary Language: Python (30 repos)
- Created: December 3, 2024
The organization follows a systematic naming convention with the cog* prefix, organized into the following categories:
High-performance implementations of neural network primitives and cognitive computing foundations across multiple languages:
- cogpy - Python core library
- cogplan9 - Plan9-inspired cognitive architecture
- cogpilot.jl - Julia implementation for scientific computing
- cognu-mach - C-based Mach kernel integration
- coglux - Zig implementation for systems programming
- coglow - Go implementation for concurrent systems
- coggml - C-based GGML integration
- cogmetal - Rust implementation for memory safety
- cogwhisper - C++ speech recognition integration
- cogllama - C++ LLaMA model integration
- cogllm - Large language model framework (private)
- cogtorch - PyTorch integration layer
- nnpu - Neural network processing unit (C++)
DevOps, monitoring, and configuration management:
- coginfra - Terraform infrastructure as code
- cogci - Continuous integration pipelines
- cogmonitor - System monitoring and observability
- cogconfig - Configuration management (TOML)
- cogdeploy - Deployment automation
Command-line interfaces and developer tools:
- cogcli - Main CLI tool (Python)
- cogtools - Shell utilities
- cogscripts - Automation scripts
Web interfaces and API services:
- cogweb - TypeScript web frontend
- cogapi - Python REST API
- cogserve - Model serving infrastructure
Data processing, model training, and evaluation:
- cogdata - Data processing pipelines
- cogmodels - Pre-trained model repository
- coglearn - Learning algorithms
- cogtrain - Training infrastructure
- cogeval - Model evaluation framework
Documentation, research papers, and assets:
- cogdocs - Main documentation (Markdown)
- cogpapers - Research papers
- cogresearch - Research projects (LaTeX)
- cognotebooks - Jupyter notebooks
- cogexamples - Code examples
- cogassets - Binary assets
- cogmedia - Media files
- cogbrand - Branding materials (SVG)
- cogarchive - Archived content
- coglegacy - Legacy code
- cogviz - Visualization tools (JavaScript)
Quality assurance and performance testing:
- cogbench - Performance benchmarks
- cogtests - Test suites
Cloud deployment and orchestration:
- cogcloud - Cloud infrastructure management
Prototypes and experimental features:
- cogexp - Experimental features
- cogproto - Prototypes
- cogsandbox - Development sandbox
- cogplayground - Interactive playground
Third-party integrations and connectors:
- cogintegrations - Integration framework
- cogconnectors - External service connectors
- cogadapters - Protocol adapters
- cogbridge - Bridge services
- cogsdk - Software development kit
- cogclient - Client libraries
- cogplugins - Plugin system
- cogextensions - Browser/IDE extensions
Native applications:
- cogmobile - Mobile app (Dart/Flutter)
- cogdesktop - Desktop app (Electron)
PygmalionAI ecosystem integrations:
- pyg-galatea-frontend - Frontend interface
- pyg-galatea-ui - Official UI
- pyg-aphrodite-engine - LLM inference engine
- pyg-aphrodite-loadbalancer - Load balancing
- pyg-cli-generator - CLI generator
- pyg-gradio-ui - Gradio prototype
- pyg-paphos-backend - Backend service (Crystal)
- pyg-colossalai-training-code - Training code
- pyg-data-toolbox - Data processing
The organization leverages a polyglot approach for optimal performance and developer experience:
| Language | Repositories | Use Case |
|---|---|---|
| Python | 30 | ML/AI, APIs, tooling |
| C | 4 | Low-level kernels |
| C++ | 4 | Performance-critical code |
| TypeScript | 3 | Web frontends |
| JavaScript | 3 | Visualization, extensions |
| Markdown | 3 | Documentation |
| Shell | 2 | Automation scripts |
| YAML | 2 | CI/CD, deployment |
| Jupyter Notebook | 2 | Research, examples |
| Binary | 2 | Assets, media |
| Julia | 1 | Scientific computing |
| Zig | 1 | Systems programming |
| Go | 1 | Concurrent systems |
| Rust | 1 | Memory-safe implementations |
| Terraform | 1 | Infrastructure as code |
| TOML | 1 | Configuration |
| LaTeX | 1 | Research papers |
| SVG | 1 | Branding |
| Dart | 1 | Mobile development |
| Electron | 1 | Desktop apps |
| Crystal | 1 | Backend services |
- Python 3.11+
- Node.js 18+
- Docker (for containerized deployments)
- Git
# Clone the main CLI tool
git clone https://github.com/o9nn/cogcli.git
cd cogcli
# Install dependencies
pip install -r requirements.txt
# Run the CLI
python cogcli.py --help# Clone core libraries
git clone https://github.com/o9nn/cogpy.git
git clone https://github.com/o9nn/coggml.git
git clone https://github.com/o9nn/cogtorch.git
# Set up development environment
cd cogpy
python -m venv venv
source venv/bin/activate
pip install -e ".[dev]"The O9NN ecosystem follows a modular architecture with clear separation of concerns:
┌─────────────────────────────────────────────────────────────┐
│ Applications Layer │
│ (cogweb, cogmobile, cogdesktop, cogcli) │
└─────────────────────────────────────────────────────────────┘
↓
┌─────────────────────────────────────────────────────────────┐
│ API & Services Layer │
│ (cogapi, cogserve, cogbridge) │
└─────────────────────────────────────────────────────────────┘
↓
┌─────────────────────────────────────────────────────────────┐
│ Core Libraries Layer │
│ (cogpy, coggml, cogtorch, cogllama, cogwhisper) │
└─────────────────────────────────────────────────────────────┘
↓
┌─────────────────────────────────────────────────────────────┐
│ Infrastructure Layer │
│ (coginfra, cogdeploy, cogmonitor, cogci) │
└─────────────────────────────────────────────────────────────┘
We welcome contributions from the community! Please follow these guidelines:
- Fork the relevant repository
- Create a feature branch (
git checkout -b feature/amazing-feature) - Commit your changes (
git commit -m 'Add amazing feature') - Push to the branch (
git push origin feature/amazing-feature) - Open a Pull Request
- Follow language-specific style guides (PEP 8 for Python, etc.)
- Write comprehensive tests
- Document public APIs
- Keep commits atomic and descriptive
- Complete repository descriptions for all projects
- Establish CI/CD pipelines across all repositories
- Create comprehensive API documentation
- Launch cogweb v1.0
- Release cogmobile beta
- Integrate advanced LLM capabilities
- Expand integration ecosystem
- Performance optimization sprint
- Cloud deployment automation
- Multi-language SDK releases
- Community plugin marketplace
- Enterprise features
- 1.0 stable release
- Production-ready deployment tools
- Comprehensive benchmarking suite
- Academic research publications
- GitHub Organization: github.com/o9nn
- Discussions: Use GitHub Discussions in relevant repositories
- Issues: Report bugs and request features via GitHub Issues
Each repository may have its own license. Please refer to individual repository LICENSE files for details.
- Built on top of industry-leading open-source projects
- Inspired by cognitive science and neuroscience research
- Community-driven development
Last Updated: December 26, 2025
Organization Created: December 3, 2024
Total Repositories: 58
Active Contributors: 3