The Weekly Fix
The code looks clean, reads well, and runs. It is also wrong. Two habits that catch confident-wrong code before it ships.
AI confidently writes code that is completely wrong.
The code looks clean and runs fine, but it does the wrong thing or falls over on one input you did not think to test.
Confident-wrong code almost always has a single edge case the model did not model: an empty array, a null, a very large input, a timezone. The model optimizes for plausible, not correct.
Two habits. First, before accepting any code, ask how would you verify this works? It forces the model to surface its own test cases. Second, when something feels off, paste it back and ask what happens if X is empty, null, or huge? The crack is almost always one unhandled input.
Here is the code you just wrote: [paste]. Before I trust it: how would you verify it works? List the exact inputs that would break it (empty, null, very large, malformed, concurrent) and show me the one-line guard for each.
Full entry in the database: AI confidently writes code that's completely wrong →