Based on a tutorial by Angel Poon featuring Zachary Hong
Struggling to understand complex codebases when joining new teams or exploring open-source projects? You’re not alone. Reading through thousands of lines of unfamiliar code can be overwhelming and time-consuming.
I’m sharing this detailed summary of Angel Poon’s interview with Zachary Hong because it reveals a game-changing solution: an AI tool that can transform any GitHub repository into comprehensive, step-by-step tutorials in just 5 minutes. Plus, we’ll explore the 100-line AI framework that powers this magic.
Quick Navigation
- Meet Zachary Hong & His AI Tools (00:00-02:30)
- AI Codebase Knowledge Builder Demo (02:31-15:45)
- Handling Large Codebases & Context Limits (08:00-11:30)
- System Design Mindset for AI Development (11:31-14:00)
- Exploring the Generated Tutorial (15:46-22:30)
- PocketFlow: The 100-Line AI Framework (22:31-35:00)
- Understanding PocketFlow with Kitchen Analogy (28:00-31:00)
- Why Simplicity Wins in AI Development (31:01-35:00)
- Other Projects Built with PocketFlow (35:01-40:00)
- How to Support & Get Involved (40:01-42:30)
Meet Zachary Hong & His AI Tools
Zachary Hong is a PhD student at Columbia University with over four years of experience in database systems. For the past two years, he’s been focused on large language model systems and is about to join Microsoft Research.
Zach has built two remarkable open-source tools that are revolutionizing how developers work with code:
Zach’s Game-Changing Tools:
- AI Codebase Knowledge Builder – Transforms any GitHub repo into comprehensive tutorials
- PocketFlow – A 100-line AI framework that enables agentic coding
- Both tools are completely free and open-source
- Used by engineers inheriting messy code and developers building AI projects
My Take:
What impressed me most is how Zach combines deep systems knowledge with practical AI applications. His background in database systems gives him a unique perspective on handling large-scale data processing challenges.
AI Codebase Knowledge Builder Demo
The demo showcases how incredibly simple it is to generate comprehensive tutorials from any GitHub repository. Angel chose a YouTube summarizer project as the test case.
How It Works:
- Clone the AI Codebase Knowledge Builder repository
- Install dependencies and set up your language model (Gemini 2.5 Pro recommended)
- Run a single Python command with the GitHub repo URL
- Wait about 5 minutes for the complete tutorial generation
# Simple command to generate tutorial
python generate_tutorial.py --repo_url "https://github.com/example/youtube-summarizer" --name "YouTube Summarizer"
The tool automatically crawls all files from the GitHub repository and uses AI to identify the most important concepts, then presents them in an easy-to-read format for developers.
My Take:
The simplicity is striking – one command transforms complex code into educational content. This could save countless hours for developers trying to understand new codebases.
Handling Large Codebases & Context Limits
A crucial question emerges: how does this tool handle entire codebases when language models have context window limitations?
Smart Context Management:
- Modern models like Gemini 2.5 Pro have 1 million token limits – sufficient for most codebases
- The real challenge isn’t size but the “lost in the middle” phenomenon
- Models focus on beginning and end content, neglecting middle sections
- Solution: Identify high-level concepts first, then write chapters focusing only on relevant files
The workflow starts by understanding the entire codebase to identify key concepts. Then, for each chapter, it only focuses on files relevant to that specific concept, ensuring quality and relevance.
My Take:
This approach mirrors how humans learn – we get the big picture first, then dive into specifics. It’s a brilliant solution to the context window challenge that many AI applications face.
System Design Mindset for AI Development
Zach’s background in database systems provides a unique advantage in designing AI applications. He approaches problems like a systems engineer handling large workloads.
Systems Thinking Principles:
- Identify key bottlenecks and complex parts of tasks
- Decompose large problems into manageable components
- Design microservices that communicate effectively
- Apply traditional system design principles to AI workflows
For developers wanting to develop better system mindsets, Zach recommends studying traditional system design while experimenting with large language model applications, since this field is still in early exploration stages.
My Take:
The intersection of systems engineering and AI is fascinating. Traditional software architecture principles become even more critical when orchestrating AI agents and managing complex workflows.
Exploring the Generated Tutorial
The generated tutorial for the YouTube summarizer project demonstrates the tool’s impressive capabilities. It creates a comprehensive learning resource with multiple visualization formats.
Tutorial Features:
- High-level project description and system overview
- Interactive Mermaid diagrams showing component relationships
- Progressive chapters from UI to backend implementation
- Sequence diagrams illustrating workflow processes
- Integration with Cursor for contextual Q&A
The tutorial structure follows a logical progression: starting with user-facing frontend components, then diving deeper into transcript extraction, AI integration, and backend pipeline processing.
A standout feature is the integration with Cursor IDE – you can open the tutorial alongside the code and ask contextual questions about specific classes or concepts, getting answers based on the generated tutorial content.
My Take:
The visual diagrams are game-changers for understanding system architecture. Having both textual explanations and visual representations caters to different learning styles and makes complex relationships much clearer.
PocketFlow: The 100-Line AI Framework
PocketFlow is the underlying framework that powers the codebase knowledge builder. Despite being only 100 lines of code, it’s incredibly powerful and enables building complex AI applications.
What Makes PocketFlow Special:
- Graph-based abstraction similar to n8n or Zapier, but more technical
- Enables building various AI applications: tutorials, chat agents, RAG systems
- Supports multiple AI design patterns including multi-agent supervisors
- Clean, simple interface perfect for AI agents to code against
- Completely open-source with extensive documentation
The framework evolved from Zach’s PhD work on a project called Cocoon at Columbia University. Initially focused on database applications, PocketFlow generalizes these concepts for broader AI development use cases.
My Take:
The evolution from academic research to practical tool shows how university projects can have real-world impact. The fact that it’s only 100 lines yet so powerful demonstrates the elegance of well-designed abstractions.
Understanding PocketFlow with Kitchen Analogy
Zach explains PocketFlow using a brilliant kitchen analogy that makes the abstract concepts concrete and understandable.
Kitchen Analogy Breakdown:
- Stations = Nodes (chopping, cooking, plating stations)
- Flow = Connections between stations (chopping → cooking → plating)
- Shared Store = Context where all ingredients are stored and accessed
- Orchestration = Deciding which station processes what and when
Each station (node) has a specific function, the flow determines the sequence of operations, and the shared store ensures all stations can access the ingredients (data) they need for their tasks.
This abstraction can express various AI patterns: workflows, chat agents, retrieval-augmented generation, and multi-agent supervisors – all using the same fundamental graph structure.
My Take:
This analogy perfectly illustrates why good abstractions matter. A kitchen workflow is something everyone understands, making complex AI orchestration concepts immediately accessible to developers.
Why Simplicity Wins in AI Development
Zach advocates for “simplicity is king” philosophy, especially important in the age of AI coding assistants and large language models.
Benefits of Simple Frameworks:
- AI agents excel at understanding clean, straightforward interfaces
- Complex frameworks like LangChain have overly complex abstractions
- Coding agents need simple building blocks, not complicated architectures
- Clean interfaces enable faster development and better maintainability
In contrast to frameworks with hundreds of thousands of lines of code, PocketFlow provides exactly what AI agents need: a clear, easy-to-understand interface that they can program against effectively.
Zach’s development process showcases this philosophy: he starts with system design documentation, iteratively improves it with AI assistance, then lets Cursor implement the entire codebase based on the clean design.
My Take:
This validates the principle that constraints breed creativity. By limiting the framework to 100 lines, Zach forced himself to create the most essential, powerful abstractions possible.
Other Projects Built with PocketFlow
PocketFlow’s versatility shines through various projects that demonstrate different AI application patterns.
Example Projects:
- Build Cursor with Cursor – Recreating Cursor’s functionality using graph structures
- File Operations Agent – Read, edit, search, and list directory operations
- Tutorial Generation Systems – Various educational content creation tools
- MCP Server Integration – Connecting with standardized agent communication protocols
The “Build Cursor with Cursor” project is particularly meta – using Cursor to recreate Cursor’s own agent-based file operations, demonstrating how the graph abstraction can represent complex, looping workflows.
Regarding MCP (Model Context Protocol) servers, Zach explains they’re standardization tools for agent communication, essentially replacing individual nodes in the PocketFlow graph with standardized action interfaces.
My Take:
The variety of projects shows PocketFlow’s true power – it’s not just a tool for one specific use case, but a genuine framework for building diverse AI applications. The MCP integration shows smart adaptation to emerging standards.
How to Support & Get Involved
Despite providing incredible value for free, Zach’s primary request is community engagement rather than financial support.
Ways to Support:
- Follow Zach on YouTube for technical content and tutorials
- Subscribe to his Substack for in-depth articles
- Star the PocketFlow GitHub repository
- Contribute to open-source projects through pull requests
- Join the Discord community for technical discussions
The project has already received numerous community contributions including multi-language support, model integration improvements, virtual environment support, and local Git repository handling.
Zach emphasizes that building and writing about these tools is genuinely leisure for him – he finds the work enjoyable and fulfilling beyond any need for financial compensation.
My Take:
This attitude exemplifies the best of open-source culture. When passionate developers share their work freely, the entire community benefits. Supporting through engagement and contributions creates a sustainable ecosystem for innovation.
