I kept seeing the same dumb mistakes in AI-generated code, so I built a local tool to catch them

Drift Zef — Thu, 21 May 2026 01:26:44 +0000

I've been using AI coding tools pretty heavily lately — mostly GROK fast code and some sonnet and haiku. The speed is incredible, and It's been fun to learn, but I started noticing the same patterns of mistakes showing up in the code.
Not huge architectural disasters. Just the small, annoying things that are easy to miss when you're moving fast and building with AI:

Bare except: blocks that swallow every error
Extremely generic function names like process(), handle(), or execute()
Code that confidently references imports or error paths that don't actually exist These aren't hard to spot if you're carefully reviewing every line, but when you're accepting a lot of AI suggestions, they slip through more often than I'd like.

Another example

try:

result = db.execute(query)

return result

except:

return None

This was generated code that would have silently hidden database failures in production. The tool caught it immediately.

Why I built something.

I got tired of catching the same classes of issues by hand, so I made a small, local CLI tool to do it for me.
It's called DriftCode Auditor. It runs entirely on your machine (no code is sent anywhere). I used AI to build it and guide the entire process, but behind it all is a human being. DriftCode Auditor respects .gitignore, and is fast enough to run on real projects.
It currently focuses on three main areas:

Privacy risks (hardcoded secrets, PII, etc.)
Maintainability issues (long functions, generic names, missing error handling, etc.)
Some basic architecture smells The goal isn't to replace thoughtful code review. It's to act as a quick safety net for the obvious stuff that AI tends to generate.

How to try it

If you want to kick the tires:

pipx install driftcode-auditor

driftcode-auditor --path . --privacy --maintainability

It works on any codebase and gives you a clear report of what it found.
What I'm doing now
I'm currently testing the tool with real users to figure out what's actually useful. I posted a thread on X earlier today sharing some of the patterns it's been catching.
If you use AI coding tools regularly, I'd genuinely appreciate it if you tried it on something you've been working on and told me what it found (or missed). Even "it didn't catch anything" is useful feedback.

GitHub: https://github.com/DriftApplied/driftcode-auditor

Discussions (where people can share real examples): https://github.com/DriftApplied/driftcode-auditor/discussions

I'm also happy to take suggestions for new rules or improvements.

Drift Zev
DistortionApplied
X @driftapplied