The most dangerous moment in an AI-built app is not when the app is obviously broken. That part is...
Marcus Y. Kim establishes a practical QA checklist for AI-coded apps, emphasizing end-to-end workflow testing with persistence verification (create-save-refresh-edit-delete) and edge cases over visual presence. For CTOs deploying AI-accelerated code, this discipline prevents silent data failures hidden by convincing UIs—a critical risk as AI tools pressure QA cycles before production.
The most dangerous moment in an AI-built app is not when the app is obviously broken.
That part is annoying, but at least it is honest.
The dangerous moment is when the app looks done.
The buttons are there. The screens load. The AI says it fixed the bug. You click around for thirty seconds and nothing immediately catches fire.
Then your brain starts whispering the sweetest lie in software:
The demo worked perfectly. ✅ Production? First real users. 50% failure rate. ❌ The Gap...
AI safety is becoming a software architecture problem. For years, developers learned that good...
The biggest mistake I made with AI app builders was trusting a polished first screen too early. Now...
I hate writing test scripts. Not because testing is unimportant. The opposite. I care about quality....
AI is creating software code faster than ever, and it is increasingly testing that code, too.
Making AI-Generated Code Fail Gracefully If your app generates code with an LLM and executes it, you...
