Then when I have a bunch of wrong answers, I can give those as context as well to the model and make it avoid those pitfalls. At that point my constraints for the problem are so rigorous that the LLMs lands at the correct solution and frankly writes out the code 100x faster than I would. And I’m an advanced vim user who types at 155 wpm.

See, it's comments like this that make me suspect that I'm working on a completely different class of problem than the people who find value in interacting with LLMs.

I'm a very fast typer, but I've never bothered to find out how fast because the speed of my typing has never been the bottleneck for my work. The bottleneck is invariably thinking through the problem that I'm facing, trying to understand API docs, and figuring out how best to organize my work to communicate to future developers what's going on.

Copilot is great at saving me large amounts of keystrokes here and there, which is nice for avoiding RSI and occasionally (with very repetitive code like unit tests) actually a legit time saver. But try as I might I can't get useful output out of the chat models that actually speeds up my workflow.

If you're an experienced dev, having a peer that enthusiastically suggests a bunch of plausible but subtly wrong things probably net-net slows you down and annoys you. If you're more junior, it's more like being shown a world of possibilities that opens your mind and seems much more useful.

Anyway, I think the reason we see so much enthusiasm for LLM coding assistants right now is the overall skew of developers to being more junior. I'm sure these tools will eventually get better, at least I hope they do because there's going to be a whole lot of enthusiastically written but questionable code out there soon that will need to be fixed and there probably won't be enough human capacity to fix it all.