The vibe that led me astray
Fresh off optimizing our cache system with Claude Code, I spotted another performance issue. A series of .replace() calls using regular expressions that looked perfect for improvement. Same pattern as before: inefficient code, clear bottleneck, easy win.
I made assumptions about the text processing volume and didn’t dig deeper. Claude and I wrote tests. All green. Claude rewrote the code to be faster and less memory intensive.
I told the team we were good to deploy.
Immediate failures in production.
The Cycle
Add the failing case as a test. Iterate. Deploy. Fail again. Add another test. Iterate. Deploy. Fail again.
Each time the old code passed and the new code didn’t.
After the third round and probably eight hours of my team’s time wasted, something clicked.
The Realization
When AI rewrites code to be “better,” it optimizes for passing the tests you gave it. The test suite represents your understanding of the problem, not the problem itself.
The original developer had achieved 100% test coverage. This gave me confidence that all cases were handled. But 100% coverage means you’ve executed all code paths, not that you’ve tested all scenarios. Those green checkmarks just confirm every line ran during testing, not that every real-world case got validated.
The old regex code wasn’t elegant. But it was working. All those seemingly unnecessary parts were handling edge cases the test suite never captured.
What I Missed
The cache optimization worked because I understood the system deeply first. With the regex code, I saw green tests and assumed that I understood. I didn’t ask why the code was structured that way. I treated it like the cache problem because the surface looked similar.
My team trusted my judgment. I wasted their time because I confused a repeat pattern with repeat circumstances.
The Lesson
Before asking AI to improve code, understand why it works in its current form. Not just what it does, but what invisible constraints it’s solving for. Test coverage measures code execution, not correctness. And one successful optimization doesn’t guarantee the next will follow the same path.