Overview

“Algorithm Analysis and Design in the AI Era” is a forward-thinking algorithm textbook designed for the age of AI assistants. When coding agents can generate implementations, human engineers need to master the art of judgment rather than just implementation.

Core Philosophy

  • Judgment-First Approach: In an era where AI can write code, human value lies in judgment rather than implementation
  • Six-Question Diagnostic Method: Understand problem → Establish baseline → Retrieve knowledge → Leverage structure → Choose paradigm → Acknowledge boundaries
  • From Concrete to Abstract: Each concept starts from real-world scenarios, gradually extracting general principles

Key Features

  1. AI-Augmented Learning: Every chapter includes examples of how to work with AI assistants on algorithm problems
  2. Decision Trees: Flowcharts for choosing algorithmic approaches based on problem characteristics
  3. Complexity Intuition: Building intuition for time/space complexity without heavy mathematics
  4. Real-World Applications: Case studies from modern software development and data science
  5. Interactive Content: Built with VitePress for responsive reading experience

Technology Stack

  • VitePress: Modern static site generator with fast hot reload
  • KaTeX: High-quality math typesetting
  • Markmap: Interactive mind maps for visualizing algorithmic concepts
  • GitHub Pages: Hosting and automated deployment

Teaching Approach

Each chapter follows a consistent structure:

  1. Real Problem: Start with a concrete software development challenge
  2. AI Interaction: Show how to collaborate with AI to solve it
  3. Algorithm Selection: Guide through choosing the right algorithm
  4. Complexity Analysis: Understand trade-offs and constraints
  5. Pattern Recognition: Extract general principles for future problems

Target Audience

  • Computer Science Students: Learning algorithms in the age of AI
  • Software Engineers: Wanting to deepen algorithmic thinking
  • Data Scientists: Needing to understand algorithmic foundations
  • Educators: Looking for modern teaching materials

Online Access

📚 Read the textbook online

Repository Structure

algorithm_in_llm_era/
├── docs/
│   ├── chapters/           # Textbook chapters
│   │   ├── ch01/          # Chapter 1: When AI Writes Your Code
│   │   ├── ch02/          # Chapter 2: Data Structures: From Interface to Cache
│   │   └── ...
│   └── .vitepress/        # VitePress configuration
├── dev-notes/             # Four-layer knowledge base
│   ├── raw/              # Raw materials (papers, blog posts)
│   ├── refined/          # Structured notes after digestion
│   ├── zettel/           # Atomic knowledge cards
│   └── methodology/      # Writing methodology
└── .github/workflows/     # GitHub Actions for deployment

Local Development

# Install dependencies
npm install

# Start development server
npm run docs:dev

# Build for production
npm run docs:build

Project Vision

This project aims to bridge the gap between traditional algorithm education and the realities of modern AI-assisted software development. By focusing on judgment, decision-making, and problem-solving heuristics, it prepares learners for a future where AI is a collaborator rather than just a tool.

License

MIT License