Comprehensive Technical Documentation for Full Stack Engineering, AI Development, and Agent Architectures

A complete knowledge base covering CS fundamentals, AI/LLM technologies, engineering best practices, and real-world project case studies.

• CS Core
  • Algorithms & Data Structures
  • System Design
  • Database Internals
  • Network & Operating Systems
• AI & Agents
  • RAG (Retrieval-Augmented Generation)
  • Agent Architectures
  • LLM Fundamentals
  • Prompt Engineering
• Engineering
  • Backend Development
  • Frontend Development
  • DevOps & Cloud
  • Development Tools
• Case Studies
  • Real-world Projects
  • Architecture Refactoring

• Node.js 18.x or higher
• npm or yarn or pnpm

# Clone the repository
git clone https://github.com/YiWang24/AiDIY.git
cd AiDIY

# Install dependencies
npm install

# Start the development server
npm start

# Build the site
npm run build

# Serve the built site
npm run serve

The documentation site will be available at http://localhost:3001

Foundational computer science knowledge essential for software engineering:

• Algorithms & Data Structures

  • Time and space complexity analysis
  • Common algorithms (sorting, searching, graph algorithms)
  • Data structures (arrays, trees, graphs, hash tables)

• System Design

  • Scalability patterns
  • Load balancing
  • Caching strategies
  • Database design principles

• Database Internals

  • Storage engines (B-tree, LSM, etc.)
  • Transaction management (ACID)
  • Query optimization
  • Distributed databases

• Network & Operating Systems

  • TCP/IP networking
  • HTTP/HTTPS protocols
  • Process management
  • Memory management

Modern AI development with focus on practical applications:

• RAG (Retrieval-Augmented Generation)

  • Data processing and chunking strategies
  • Vector indexing and storage
  • Retrieval strategies and optimization
  • Generation and post-processing
  • Evaluation metrics

• Agent Architectures

  • Multi-agent systems
  • Tool use and function calling
  • Context engineering
  • State management
  • Production deployment

• LLM Fundamentals

  • Transformer architecture
  • Fine-tuning strategies
  • Prompt engineering techniques
  • Token optimization
  • Cost management

Practical software engineering knowledge:

• Backend Development

  • API design (REST, GraphQL, gRPC)
  • Database integration
  • Authentication & authorization
  • Performance optimization

• Frontend Development

  • React best practices
  • State management
  • Component architecture
  • Performance tuning

• DevOps & Cloud

  • Docker containers
  • CI/CD pipelines
  • Cloud deployment (AWS, GCP, Azure)
  • Infrastructure as Code

Real-world project examples and refactoring:

• Complete project architectures
• Performance optimization stories
• Debugging complex issues
• Migration strategies

• Documentation Framework: Docusaurus
• Language: MDX (Markdown + JSX)
• Diagrams: Mermaid
• Code Highlighting: Prism.js
• Deployment: Vercel

Contributions are welcome! Please feel free to:

1. Fork the repository
2. Create a feature branch (git checkout -b feature/amazing-feature)
3. Commit your changes (git commit -m 'Add some amazing feature')
4. Push to the branch (git push origin feature/amazing-feature)
5. Open a Pull Request

This project is licensed under the MIT License - see the LICENSE file for details.

Yi Wang

• Website: www.yiw.me
• GitHub: @YiWang24

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Note: This documentation serves as a personal knowledge base and reference guide for full stack engineering and AI development. Feel free to explore, learn, and adapt the content to your needs!