Your AI Reviewer Needs a Baseline: A Zero-Cost Patch Audit Loop
Reviewing is the new bottleneck. Generated code passes through more reviews than ever, and the reviewer's own accuracy stays unmeasured.
A model that generates plausible code is only useful when its review catches the defects that matter. That property is measurable. It is rarely measured. The current round of AI discussion has plenty of opinions about the reviewer's new role and almost no numbers attached to it.
This article walks through a reproducible patch audit loop. It runs on a free server, spends tokens from a free allowance, and produces a score that tells a solo founder when to trust the AI review and when to read the diff alone.
The Review Debt Problem
