Overviews aren’t code though. In code, for me, they don’t pass 80/20 tests well enough, sometimes even on simple cases. (You get 50-80% of an existing function/block with some important context prepended and a comment, let it write the rest and check if it succeeds). It doesn’t mean that LLMs are useless. Or that I am antillamist or a denier - I’m actually an enthusiast. But this specific claim I hear often and don’t find true. Maybe true for repetitive code in boring environments where typing and remembering formats/params over and over is the main issue. Not in actual code.

If I paste the actual non-trivial code, it starts deviating fast. And it isn’t too complex, it’s just less like “parallel sort two arrays” and more like “wait for an image on a screenshot by execing scrot (with no sound) repeatedly and passing the result to this detect-cv2.py script and use all matching options described in this ts type, get stdout json as in this ts type, and if there’s a match, wait for the specified anim timeout and test again to get the settled match coords after an animation finishes; throw after a total timeout”. Not a rocket science, pretty dumb shit, but right there they fall flat and start imagining things, heavily.

I guess it shines if you ask it to make an html form, but I couldn’t call that life-changing unless I had to make these damn forms all day.

If 20 mins of informations can legitimately be condensed into 20 seconds, it sounds like the original wasn't worth reading in the first place. Could have skipped the llm entirely.

My experience with overviews is that they are often subtly or not so subtly inaccurate. LLMs not understanding meaning or intent carries risk of misrepresentation.