I dogfooded my code review tool on 67 of my own files. Now I need harsher feedback.
Hey IH,
I’m building a small code review tool for the “before I ask a human to review this” moment.
The honest story: I tried recruiting a few friendly testers first and got basically no signal, so I turned the tool on my own codebase instead. That was useful in a different way. It scanned 67 real files from the product and caught several false-positive patterns I had to fix:
- env-backed secrets being mistaken for hardcoded credentials
- normal Python
.join(...)calls being treated like JS/Python confusion - safe
with open(...)usage being flagged as unclosed files JSON.parseinside try/catch needing better context awareness
That dogfood pass got the benchmark to 64/64 and made the product quieter, which matters because noisy review tools lose trust fast.
What I built:
Check AI Code is a quick first-pass scanner for Python, JavaScript, TypeScript, Java, and Go. It combines static rules with optional AI-enhanced explanations. It is not a replacement for CodeQL/Sonar/Snyk; it is meant as a practical sanity check before human review.
What I need now:
If you write or review AI-generated code, I’d love one real-file test and blunt feedback:
- Did it catch anything you would actually fix?
- Did it produce any false positives?
- Was the explanation useful or too generic?
- Where did the flow feel confusing?
Link: https://checkaicode.com
There’s a free tier and a short Pro trial, no card required. If you test it, the most useful feedback is not “nice tool” but a concrete false positive, missed issue, or confusing step.
Happy to share more about the stack too: Next.js, Prisma, static rules, Semgrep-style checks, and DeepSeek for Pro explanations.
One thing I'd pressure-test before spending too much time on benchmark scores.
The product description makes it sound like the buyer is the developer uploading code.
The pain in the post sounds closer to the reviewer who keeps getting pulled into low-quality AI-generated code that should have been cleaned up earlier.
Those are very different buyers.
If that positioning line is blurry, you can improve detection quality and still make the product harder to sell than it needs to be.
I'd be careful not to let the conversation stay entirely around false positives and model quality. The buyer decision may matter more than the next few points of accuracy.
Happy to put the tighter positioning angle in writing if useful.