Disclosure: I’m one of the builders of this. Most AI-assisted code review tools operate either inside editors or at PR time. We wanted to explore a different insertion point: during git commit, where diffs are smaller and developer context is still fresh. Approach: We hook into the Git workflow (pre-commit / commit-msg). At commit time: the staged diff is extracted only that diff is analyzed (not the full repo) the diff is passed to an LLM (model configurable) the model evaluates for issues like correctness, security risks, and obvious performance problems Why commit-time? We found two extremes problematic: editor-time feedback → too noisy, often ignored PR-time review → too late, diffs are large Commit-time sits in between: changes are still small fixes are cheaper the developer is already pausing Observations so far Smaller diffs produce more relevant feedback Developers are more likely to act on suggestions immediately Latency is acceptable if feedback quality is high Design choices Git-native instead of editor plugins Avoids fragmentation across IDEs and AI tools Diff-only analysis Reduces token usage and keeps feedback focused Synchronous feedback We debated async, but immediate feedback seemed more actionable git-lrc on GitHub submitted by /u/athreyaaaa
Originally posted by u/athreyaaaa on r/ArtificialInteligence