[petsounds]

When I read about potential optimizations like this, I can't believe that people trust LLMs enough to do things with minimal oversight. Do people really believe that "AI" products that use LLMs are capable enough to do things like control a computer, or write accurate code? By design, isn't _everything_ a "hallucination" or a guess? Is it really possible to overcome that?

[Workaccount2]

I have written (oversaw?) a few programs that we use in our production test systems using chatgpt and python. A program that sends actions to machines, queries them for results/errors/outputs, and then stores all that in a .csv which it later translates into a nicely formatted excel file. It also provides a start-up guide to show the technician how to hook-up things for a given test.

I am not a programmer. No one at my company is a programmer. It writes code that works and does exactly what we asked it to do. When the code choked while I was "developing" it, I just fed it back into chatgpt to figure out. And it eventually solved everything. Took a day or so, whereas it would probably take me a month or a contractor $10,000 and a week.

LLM's might be bad for high level salary grade programming projects. But for those of us who use computers to do stuff, but can't get past the language barrier preventing us from telling the computer what to do, it's a godsend.

[lll-o-lll]

Really interesting. We programmers live in a bit of a bubble, so it’s good to get this perspective. Perhaps with LLM’s we’ve finally reached the early dreams of the “programmable computer for everyone”, that seemed to slip out of reach after the 80’s.

[starbugs]

In other words: Your problem was simple enough and well enough represented in the training corpus and you were a bit lucky. Also, the problem is not important enough for there to be a requirement for the code to be updatable/fixable at short notice, because effectively now nobody in your org knows how the solution actually works.

For this very constrained subset of a problem domain LLMs are indeed very suitable but this doesn't scale at all.

[danielmarkbruce]

How do you overcome it as a human? If you think through it... you'll come to the conclusion that LLMs can be used to do all kinds of things. Humans don't write down code and then shove it into production, for example.

[Kiro]

> Do people really believe that "AI" products that use LLMs are capable enough to do things like control a computer, or write accurate code?

Of course. It's not a hypothetical question. Almost all of my code is written by Claude 3.5 Sonnet. It's much more robust and accurate than my regular code and I've been programming for 20 years.

[OtomotO]

No it's not, but when humans have invested too much (emotions or money) they do not retreat easily. They rather go all in.

It's just another hype, people. Just like Client/Server, Industry 4.0, Machine Learning, Microservices, Cloud, Crypto ...