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Whether you’re dealing with a legacy codebase or an AI-generated project, working with “code you didn’t write yourself” has become the default for most software developers. Understanding an unfamiliar codebase can be a tedious and time consuming process when you’re using standard profilers, debuggers, and static analysis techniques. This book shows you how to use an efficient AI-driven process to quickly and accurately make sense of any software project in just a few hours, complete with prompts, workflow pipelines, and mental models.

Author and Microsoft AI researcher Zachary Huang argues that in an AI-first environment, the developers who thrive will be those who build architectural thinking rather than passively accepting AI’s output. The book transforms this thesis into a hands-on training program, reframing AI code’s flaws as workout opportunities for your skills as a developer.

The ten mental models you’ll master in this book are timeless, and the book’s transferable methodology works on any codebase, in any language, at any scale. Every chapter teaches you to ask sharper questions and demand concrete outputs. You’ll build the muscle to interrogate AI, catch its confident nonsense, and ship real changes in hours.

Every comprehension technique is demonstrated on production open source codebases—from Next.js to pytest, Rails, and React—showing you real prompts and real output. You’ll create a collection of useful analysis tools, from a simple one-page codebase map, to your own 200 line Codebase Knowledge Builder workflow, to a reusable agent skill file for your codebase. Plus, all chapters are visual-first, teaching you to generate ERDs, sequence diagrams, component trees, DAGs, and architecture maps with AI.

As with all Manning books, you’ll appreciate the attention to detail. This book has a carefully crafted learning progression, along with dozens of browsable illustrations and annotated code listings. The crystal-clear text respects your time by making learning efficient and easy.

What's inside

• Map any unfamiliar codebase on one page in under an hour
• Reverse-engineer a product’s intent from its schema and git history
• Decode backend, frontend, ML, and infrastructure code with universal patterns
• Generate ERDs, sequence diagrams, and architecture maps with one prompt
• Ship a real PR to an unfamiliar open source repo, with tests that prove it’s safe

About the reader

For working developers drowning in unfamiliar codebases. Whether you’re onboarding to a new team, debugging legacy systems, or shipping AI-generated code you don’t fully understand, this book is for you.

About the author

Zachary Huang is an AI researcher at Microsoft Research AI Frontiers where he works on large language model agents and systems. He holds a PhD in Computer Science from Columbia University and was a Google PhD Fellow. Before Microsoft, he held research positions at Databricks and the Microsoft Gray Systems Lab. Crack Any Codebase with AI grows directly out of his open source work on Codebase Knowledge Builder, an AI agent that turns any GitHub repo into a beginner-friendly tutorial. The project reached 12K+ GitHub stars and the front page of Hacker News.