Self Learning Code Reviewer

The problem

Every code reviewer you've ever used has told you the same wrong thing more than once. You correct it, it forgets, next PR it's back — and eventually you stop reading the findings at all, which is exactly when the real bug slips through.

What it does

Runs a full baseline review — correctness, security, performance, maintainability — the first time, no setup required. From there, every correction you give it becomes part of a permanent, visible record: what was wrong, why, and what your team actually wants instead. Tell it something's a false positive twice and it stops raising it — once isn't enough, so a single bad call from you can't accidentally blind it to a real issue. Every adjustment is logged as clean, structured data you can export and use to fine-tune your own model later.

Why this beats prompting it yourself

A pasted style guide is a snapshot — it doesn't get smarter, and it doesn't remember last week's correction. This does both. You're not re-teaching it the same lesson every session; you're building an actual record of how your team reviews code, one real correction at a time.

Use cases

A team tired of explaining the same architectural exception in every PR. A legacy codebase where the "known issues" list is longer than the changelog. An engineering org where nobody agrees on what counts as High severity. A team quietly building toward their own fine-tuned review model, one correction at a time.

Known limitations

It adapts in context, not by retraining itself — no weights change on their own. It has a long memory, not a new brain.