​ Had an experience during a review recently that really made me pause. Someone pushed a ~600-line PR that was generated, scaffolded, and opened in under twenty minutes. Syntactically clean, formatted, passed the basic test suite. But when I asked what happens to a specific edge case in the request handler, the response wasn't an explanation of the logic. It was literally: "Hang on, let me ask the model." That broke my brain a bit. We all use LLMs for dev work (whether it is running local coding weights or hooking up agents like Aider and Continue), but the mainstream narrative around "10x productivity" feels completely backwards. Typing syntax was never the bottleneck in software engineering. System comprehension was. We flipped the standard ratio (80% understanding the problem, 20% writing the implementation) into 5% prompting, zero typing, and 95% staring blankly at synthetic edge cases when production breaks at 2:00 AM. If a dev cannot explain what their code is doing under the hood without feeding it back into a context window, they didn't write a feature. They just smuggled an unvetted black box into the repo and put their name on the commit. Speed to generate tokens is vanity. Speed to debug unmaintainable boilerplate is sanity. Are you seeing genuine, clean architectural gains using LLMs in your workflows, or are teams just automating future outages? How do you keep your own deep comprehension intact when leaning heavily on coding models?   submitted by   /u/Sleepybear2611 [link]   [comments]