A deterministic alternative to embedding-based repo understanding

Hey everyone, I'm Avi a CS student at FHNW in Switzerland. I’ve been a bit frustrated with how AI coding tools handle larger codebases. Most of them rely on embeddings + prompting, which is cool for fuzzy stuff, but sometimes feels inconsistent, hard to reason about, and probably token-heavy. So I wanted to try something more “boring” and predictable. I built a small prototype called ai-context-map. It uses static analysis to build a structural graph of a repo: files imports / dependencies some basic symbols (mostly Python for now) The idea is to precompute a map of the repo so an AI (or even a human) doesn’t have to rediscover structure every time. No ML, no embeddings, no API calls. Just parsing + graph stuff. It outputs something like a .ai/context.yaml file. Very simplified example: entry_points : - path : src/main.py core_modules : - src/services/auth.py task_routes : api_change : - src/api/routes.py - src/services/auth.py anchors : - symbol : login_user file : src/services/auth.p