Make the decision behind the tool.

Start with a bounded evaluation

Choose a representative set of small changes. Include work that already received human review, but avoid selecting only changes with known bugs. State what counts as a useful finding before inspecting the tool’s output.

For each finding, record whether a reviewer could reproduce the trigger, whether the impact matters, and whether the proposed fix survived relevant tests. Keep unsupported warnings and duplicate findings in the denominator.

Measure decisions, not generated comments

Track validated findings, dismissed findings, review effort, and follow-up work. A high comment count is not evidence of quality. A fast run is not the same as a faster merge.

When comparing workflows, keep repository type, change size, reviewer experience, and time period visible. Report sample size and uncertainty. AntiCode has not published an independent productivity benchmark.

Map the data path before connecting

Document what leaves the developer machine, what is sent by the service to another provider, and what is stored after the review. Ask who can access findings that contain source excerpts.

For the current AntiCode website, GitHub provides identity and selected repository data; the backend stores workspace records in Supabase. An AI review also sends the selected diff to OpenAI. Local native checks and optional native reporting are separate workflows.

Agree on an exit path

Define how repository membership is removed, who can revoke provider access, and what retention and deletion commitments your organization needs. Disconnection should not be confused with complete erasure.

If a required contractual or technical control is not available, do not infer it from a polished dashboard. Ask for written confirmation before expanding the pilot.

References

• Google: what to look for in a code review ↗
• OpenAI API data controls ↗