AI Experiments & Research Lab

This repository serves as my personal laboratory for exploring and experimenting with various cutting-edge AI technologies, focusing on areas like Generative AI, Agentic AI, and Model Context Protocol (MCP).

🗂 Project Catalog

1. Machine Learning Basics

• ML Hello World
  • Simple linear regression example using TensorFlow
  • Demonstrates basic neural network concepts
  • Example: Predicting values using y = 5x + 5 relationship

2. Generative AI

• Stable Diffusion Implementation
  • Text-to-image generation optimized for Apple Silicon
  • Uses Metal Performance Shaders (MPS) for hardware acceleration
  • Example: Generating creative images from text prompts

3. Agentic AI (Planned)

• Autonomous AI agents that can:
  • Perform complex tasks through decomposition
  • Interact with their environment
  • Make decisions and learn from outcomes
• Implementation examples:
  • Task planning and execution
  • Multi-agent collaboration
  • Environment interaction

4. Model Context Protocol (MCP)

• Weather MCP Server

  • Integrates with Open-Meteo API for weather data
  • Demonstrates async API handling in MCP
  • Example: Getting current weather conditions by coordinates

• GitHub Models Helper

  • Interacts with GitHub's AI models
  • Compare responses from different models
  • Features model listing, metadata, and response comparison
  • Example: Comparing GPT-4, Claude, and other models' responses

🎯 Goals

1. Experimentation: Test and validate different AI approaches and technologies
2. Learning: Document insights and learnings from each implementation
3. Innovation: Combine different technologies to create novel solutions
4. Sharing: Provide clear documentation and examples for others to learn from

📚 Project Structure

.
├── README.md                 # Main documentation
├── inference-example/       # ML model inference example
│   ├── inference.py        # Inference script
│   ├── training.py         # Model training script
│   └── README.md          # Documentation
├── ml-hello-world/         # Basic ML experiments
│   ├── hello-model.py      # Linear regression implementation
│   └── README.md          # ML-specific documentation
├── stable-diffusion/       # Stable Diffusion experiments
│   ├── image.py           # Image generation implementation
│   └── README.md          # SD-specific documentation
├── mcp-server-weather/     # Weather MCP server
│   ├── server.py          # Weather API integration
│   └── README.md          # Documentation
├── gh-models-helper/       # GitHub Models MCP server
│   ├── src/               # TypeScript source files
│   └── README.md          # Documentation
└── agentic-ai/            # (Planned) Agentic AI experiments

🛠 Technologies Used

• Python 3.9+
• TensorFlow
• PyTorch
• Hugging Face Transformers
• Diffusers
• More to be added as experiments grow

🚀 Getting Started

Each project directory contains its own README with specific setup instructions and requirements. Navigate to the specific project you're interested in to get started.

📝 Documentation Standards

Each experiment/project includes:

1. Clear explanation of concepts
2. Implementation details
3. Setup instructions
4. Usage examples
5. Results and observations

🤝 Contributing

Feel free to:

• Open issues for discussion
• Submit pull requests with improvements
• Share your own experiments
• Suggest new areas of exploration

📄 License

This repository is MIT licensed unless otherwise specified in individual project directories.

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🔬 This is an active research repository, and new experiments are added regularly. Check back often for updates!