Lately, I've been asking ChatGPT for answers to problems that I've gotten stuck on. I have yet to receive a correct answer from it that actually increases my productivity.
I've been able to get code working in libraries that I'm wholly unfamiliar with pretty rapidly by asking the LLM what to do.
As an example, this weekend I got a new mechanical keyboard. I like to use caps+hjkl as arrows and don't want to remap in software because I'll connect to multiple computers. Turns out there's a whole open source system for this called QMK that requires one to write C to configure the keyboard.
It's been over a decade since I touched a Makefile and I never really knew C anyway but I was able get the keyboard configured and also have some custom RGB lighting on it pretty easily by going back and forth with the LLM.
It is just very random. LLMs help me write a synthesizer using an odd synth technique in an obscure musical programming language with no problem, help me fix my broken linux system no problem but then can't do anything right with the python library pyro.
I think it is why people have such different experiences. It all depends randomly on how what you want to solve lines up with what the models are good at.
At least for the type of coding I do, if someone gave me the choice between continuing to work in a modern high-level language (such as C#) without LLM assistance, or switching to a low-level language like C with LLM assistance, I know which one I would choose.
Likewise, under no circumstances would I trade C for LLM-aided assembly programming. That sounds hellish. Of course it could (probably will?) be the case that this may change at some point. Innovations in higher-level languages aren't returning productivity improvements at anywhere close to the same rate as LLMs are, and in any case LLMs probably benefit from improvements to higher-level languages as well.
