It’s odd how we have such different experiences. As an example yesterday I had GPT4 take an existing python script, clean it up, add timing statistics and progress bars. 10 mins works. Would have taken me probably 1 hour.
I do think you have a point about it being most useful for trivial or boilerplate problems but it’s still very useful as there is always much of that.
It's like they said, it depends a lot on what kind of work you're doing and whether you are working with public frameworks or internal APIs. If you're just banging out some code that is very similar to what other people write using very well-known APIs, then it's fantastic. If you need to debug a large complex code base, then it doesn't currently have enough capacity to understand and retain all the information required. It can't do many other things you would need as well.
I'm currently writing a demo that I'll present next week using Jetpack Compose, and it's a UI toolkit I'm not really familiar with, so it's been really helpful for that. In fact, I have a tool that is almost like a build system that compiles English language spec files down to the code, and then allows me to edit them and to continue to work with the AI by just changing the spec and the code simultaneously. That's been really tremendously effective, especially with GPT-4.
On the other hand, for working on my main product, it's pretty useless because all of that work is debugging and making a lot of small changes all over the code base, which is too advanced for it currently. And I think that will get solved, but it isn't solved yet.
BTW the above paragraphs were dictated using the Whisper API. I didn't change a single thing about it. Whisper is just as impressive and useful as the LLMs, in my view.
I do think you have a point about it being most useful for trivial or boilerplate problems but it’s still very useful as there is always much of that.