Semantic Codebase Search

Buildable now. The repo, the spec and the deployment steps are live — the written walkthrough for this one is still being drafted.

Indexes a Python codebase at function granularity using AST chunking and FAISS, skips unchanged files via file hashing on re-index, and compares two embedding models side by side on the same query. grep finds text; this finds intent — and the re-index/hashing logic is the same pattern real production search tools use to stay fast on large repos.

Demonstrates embeddings + vector search applied to a problem every engineer already has — a concrete talking point for Developer Productivity or Platform Engineer roles.

Clone this project, run it locally, and compare your output against the description above.

Semantic Codebase Search on GitHub ↗

The quiz isn't written yet

The project itself is ready to build — the repo, the spec and the deployment steps are all live. What's missing is the written quiz that goes with it, and the order those get written in is decided by which ones people actually ask for.

The assignment isn't written yet

The project itself is ready to build — the repo, the spec and the deployment steps are all live. What's missing is the written assignment that goes with it, and the order those get written in is decided by which ones people actually ask for.