I think this is right on, and the things that LLM excels at (react components was your example) are really the things that there's just such a ridiculous amount of training data for. This is why LLMs are not likely to get much better at code. They're still useful, don't get me wrong, but they 5x expectations needs to get reined in.

A breadth and depth of training data is important, but modern models are excellent at in-context learning. Throw them documentation and outline the context for what they're supposed to do and they will be able to handle some out-of-distribution things just fine.

I would love to see some detailed failure cases of people who used agentic LLMs and didn't make it work. Everyone is asking for positive examples, but I want to see the other side.

Also the variation is the focus of each person.

Based on my own personal experience:

- on some topics, I get the x100 productivity that is pushed by some devs; for instance this Saturday I was able to make two features that I was reschudeling for years because, for lack of knowledge, it would have taken me many days to make them, but a few back and forth with an LLM and everything was working as expected; amazing!

- on other topics, no matter how I expose the issue to an LLM, at best it tells me that it's not solvable, at worst they try to push an answer that doesn't make any sense and push an even worst one when I point it out...

And when people ask me what I think about LLM, I say : "that's nice and quite impressive, but still it can't be blindly trusted and needs a lot of overhead, so I suggest caution".

I guess it's the classic half empty or half full glass.