Ap11e & Turtlex
Turtlex
Hey, I've been tinkering with an idea for a language‑agnostic, AI‑driven code reviewer that could live in CI pipelines and actually suggest improvements on the fly. Think of it as a smarter linting tool that learns from the repo. Ever dabbled in that sort of thing?
Ap11e
That sounds like a cool project—kind of like a smart, learning linter that lives inside CI. I’ve played around with model‑based linting before, mostly fine‑tuning transformers on code corpora. The tricky part is making it repo‑aware without overfitting to a single codebase. Have you thought about how to feed it commit history or context? Also, keep an eye on inference latency if you want it to stay in the CI loop; maybe a lightweight wrapper that calls a hosted model. I’d love to hear more about the architecture you’re sketching.
Turtlex
Sure, here’s a sketch. Pull the last N commits, split the diffs into 200‑char chunks, and hash each chunk to a fingerprint. Store those fingerprints and the raw text in a local vector index—FAISS or Milvus—so you can do cosine search on the commit message plus the changed lines. When a PR lands, run the linter’s lightweight wrapper: feed the new diff plus the top‑k retrieved context chunks into a distilled GPT‑4‑Turbo or a smaller CodeLlama, and let it output a diff‑style suggestion. Keep the model offline if latency is a pain; otherwise, use a shared endpoint with a per‑project queue so the CI job doesn’t stall. The key is the context cache: you update it only on merge, so you never re‑train on the whole repo, just push new vectors. That should keep overfitting low while giving the model enough “history” to be useful.
Ap11e
That pipeline looks solid—hashing the chunks keeps the index lightweight, and the cosine search should surface the right context. Just make sure you normalize the diffs before hashing; otherwise, trivial whitespace changes could break the fingerprint matching. Also, a small sanity check on the retrieved top‑k before feeding it to the model will help avoid noisy context. If you hit latency, maybe cache the model embeddings and only run the language model on the final suggestion pass. Good setup!
Turtlex
Yeah, whitespace normalization is a classic pitfall—maybe strip tabs and collapse runs of spaces before hashing, just to keep fingerprints stable. And a quick checksum on the top‑k vectors before hitting the LM could catch outliers; no point blowing up GPU cycles on noise. If the latency still trips, caching the embedding vectors is a good trade‑off—run the small model on the final pass only. Keeps the CI spin‑up minimal, but still lets the AI actually understand the diff context.
Ap11e
Nice tweak on the whitespace, that will save a lot of hash churn. The checksum guard is a smart move—keeps the LM from chewing on junk. For the embedding cache, just add a TTL or a version tag so you know when a chunk is stale. If you hit GPU limits, consider a tiny transformer for the “final pass” and swap in the bigger one only for really complex diffs. Keeps CI snappy but still gives you solid, context‑aware suggestions.
Turtlex
Yeah, TTLs are the key—treat each chunk like a cached API call, so you’re not re‑embedding the same stale code every run. And swapping in a 1‑billion‑parameter model only on the really big diffs is a solid way to keep the queue moving. I’ll prototype the cache with a simple LRU plus a version stamp; if the repo’s commit hash changes, the chunk gets invalidated automatically. That should keep the CI pipeline snappy and the suggestions still context‑rich.
Ap11e
That LRU+hash combo will keep the cache lean, just make sure the TTL is long enough for hot branches but short enough that stale code doesn’t slip in. Also a quick diff‑size check before you hit the big model can shave off some queue time. Happy coding!