Stop Clinging to the Craft: Modern LLMs Can Review Code Better Than You Can!
The era of trivial single-file autocomplete is over. With the release of flagship reasoning models like Gemini 4 Argon , AI has crossed a major capability boundary: the ability to build and verify global execution graphs better—and far more tirelessly—than humanly possible. I made a post on X/Twitter about programmers still thinking that LLMs are only good for generation, but can't review. I pointed out that was true until the flagship models like Argon incorporate internal call tree evaluation before the Chain of Thought step. I recently ran Argon on a 9-file complex source code file suite that comprised a very tricky object-passing and relay race baton-passing scheme to accomplish web page scraping. I asked it to do a deep examination of every inconsistent parameter use, every swallowed exception that would not bubble up, and every logic flow error where during the call tree a piece of code is not...