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AI WROTE THE CODE. WHO CHECKS THE BUGS?
AI can generate a working-looking feature in seconds but “it runs” doesn't automatically mean “it's correct.”
One of the biggest lessons I’m learning with AI-assisted development is that the hardest bugs aren't always obvious. They can hide in:
🐛 Edge cases the original prompt never mentioned
🧠 Incorrect assumptions about how the application should behave
🔐 Security issues that don't appear during normal testing
🔄 Regression bugs that break something that previously worked
⚠️ Poor error handling when real users do unexpected things
🗄️ Data/API problems that only appear under different conditions
That’s why I don't treat AI-generated code as the final answer.
My workflow is:
GENERATE → REVIEW → REPRODUCE → TRACE → TEST → VERIFY → SHIP
First, I understand what the code is actually doing.
Then I test the happy path and the paths the AI may not have considered.
I check the stack trace, inspect the data flow, test edge cases, review dependencies and security-sensitive areas, and verify that the fix doesn't introduce another problem.
This is becoming even more important as coding agents take on larger development tasks. GitHub's current tooling, for example, combines code review, testing, dependency checks, secret scanning, and security analysis around agent-generated changes.
The lesson isn't “don't use AI to code.
“It runs” vs “it’s correct” is the gap — AI-generated features need the same edge-case passes you’d do on junior dev output, just faster to produce.
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🟢 Design 02: Minimal, refined and sophisticated.
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futuristic