AI

AI coding systems frequently lack persistent structural understanding of repositories. They can generate code. They struggle to reason about a codebase they didn’t write. This project built a system that gives a local LLM a structural map of any Python repository — before it touches a single line of code. The Problem Large codebases are opaque. You can read files sequentially, but you can’t reason about dependency structure, identify which modules are most central, or understand what a change to one function propagates to — not without analysis tooling. ...

Modern repositories are increasingly too large and interconnected for shallow debugging workflows. Static analysis tells you about structure. Dynamic analysis tells you what happens at runtime. Neither alone tells you why a structurally important module is failing. This project combined both. The Core Idea Traditional debugging tools answer: “What failed?” This system attempts to answer: “What structurally important subsystem is most likely responsible for failure propagation?” ...

Most AI coding tools are generation engines. You describe what you want, they produce code. This works for isolated functions, boilerplate, and anything where the context fits in a prompt. It breaks down when the problem is understanding an existing system — navigating a codebase you didn’t write, debugging a failure with no obvious error message, figuring out why something that worked in isolation fails in the larger system. These are comprehension problems, not generation problems. And comprehension requires structure, not just pattern matching. ...