zlacker

>>tablet+(OP)
I’d love to see the authors of effusive praise of generative AI like this provide the proof of the unlimited powers of their tools in code. If GAI (or agents, or whatever comes next …) is so effective it should be quite simple to prove that by creating an AI only company and in short order producing huge amounts of serviceable code to do useful things. So far I’ve seen no sign of this, and the best use case seems to be generating text or artwork which fools humans into thinking it has coherent meaning as our minds love to fill gaps and spot patterns even where there are none. It’s also pretty good at reproducing things it has seen with variations - that can be useful.

So far in my experience watching small to medium sized companies try to use it for real work, it has been occasionally useful for exploring apis, odd bits of knowledge etc, but overall wasted more time than it has saved. I see very few signs of progress.

The time has come for llm users to put up or shut up - if it’s so great, stop telling us and show and use the code it generated on its own.

>>grey-a+ba
Many, many people are in fact “using the code it generated on its own”. I’ve been putting LLM-assisted PRs into production for months.

With no disrespect meant, if you’re unable to find utility in these tools, then you aren’t using them correctly.

>>ryboso+Ee
Okay, how am I supposed to use them "correctly"? Because me explaining step by step, more so than a junior developer, how to do a small task in an existing codebase for it to get it wrong not once, not twice, not three times, but more is not a productivity boost.

And here's the difference between someone like me and an LLM: I can learn and retain information. If you don't understand this, you don't have a correct understanding of LLMs.

>>dougla+Wj
It is entirely true that current LLMs do not learn from their mistakes, and that is a difference between eg an LLM and a human intern.

It is us, the users of the LLMs, that need to learn from those mistakes.

If you prompt an LLM and it makes a mistake, you have to learn not to prompt it in the same way in the future.

It takes a lot of time and experimentation to find the prompting patterns that work.

My current favorite tactic is to dump sizable amounts of example code into the models every time I use them. I find this works extremely well. I will take code that I wrote previously that accomplishes a similar task, drop that in and describe what I want it to build next.