Mine have been glowing, I feel it's still underhyped, I've been able to achieve things preciously impossible, in part thanks to the huge context length - granted I haven't tested and don't have access to GPT-4-128k, but even in the small it often find I prefer Claude.
Unfortunately the message limit is still too small, and inference can get very slow, but if the trend continues and somehow we have a model of this level that's as fast as Groq, and with practically unlimited use, it seems pretty revolutionary. At this point I think that anyone working with code that doesn't see how this will change the craft is willfully blind, LLMs as they are today are already amazing mental prosthetics, and I couldn't see myself going without. Even if I worked with 100% novel code that somehow couldn't benefit from LLM assistance, I still remain a computer user and I use LLMs to write scripts, to figure out how-tos constantly.
But I see this as moment where programmers got their first Photoshop. Sure, you still need to understand design, but the friction of production is greatly, greatly reduced.
I’ve been using GPT4 a bit and it’s pretty good at explaining stuff that I would otherwise need to look up myself but not groundbreaking.
What should I be doing with LLMs to vastly improve my productivity?
I definitely agree anyone not using LLMs for coding is missing out.
For me, it's a stack overflow ++, but is also hit or miss.
Common experience: I want to make a simple change to this bash script. Seems simple enough to ask GPT-4. It produces some piece of code, seems to do the job. Code review -> you don't handle this corner case. Shit. GPT-4, what's wrong? answer is suspicious... A couple of "man" later, I got it. Basically, it made me lose 30 minutes. I could have lost 30mn more if had insisted to get the answer from it.
I'm not saying it's not useful, but it's not the graal that will save us from learning our craft.