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ZDNET's key takeaways
Codex struggles with big-picture debugging in complex codebases.
Deep Research excels at diagnosis when code context is large.
Human testing and oversight still remain critical with AI coding.
"Huh?!?" Sometimes, when I'm coding and something doesn't behave quite right, and I'm not entirely sure what's up, my brain fires off an internal "Huh?!?" I think it's my way of recognizing "there be dragons" but without escalating into a full-tilt panic loop.
A few days into my AI-coding productizing process, after my four-day uber-performance AI-assisted programming sprint, something wasn't quite right. At first, it didn't seem terribly wrong (which was be a misjudgment because it actually was).
Also: I got 4 years of product development done in 4 days for $200, and I'm still stunned
I eventually solved the problem using both OpenAI's Codex and ChatGPT Deep Research. That proved to be a necessary team-up. I'll explain why in short order.
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