Artificial intelligence is losing hype

I just signed up for the free version. Claude Sonnet does properly use malloc/free to manage the buffer where GPT-4o screws up (Yay!) It manages to gets through the whole process of initializing a graphics device and grabbing a queue from the device. It took some questionable shortcuts to get there (and didn't leave any comments explaining those shortcuts and the problems they could cause down the road), but fine, the code works.

After that it goes completely off the rails by trying to issue draw commands before binding a graphics pipeline, which is both illogical and illegal. After a lot of prodding, I did manage to get it to bind a graphics pipeline, but it forgot about the texture.

So Claude Sonnet is definitely better than GPT-4o, but it still feels raw, like a game of whack-a-mole where I can get it to fix a mistake, but it reintroduces an old one. I also have to be the one offering the expertise. I can prompt it to fix the issues because I know exactly what the issues are. If I was using this to try to fill in for a gap in my knowledge, I would be stuck when I ran the code and it crashed - I would have no idea where to go next.

Update: Took about 50 min of experimenting, but I did get Claude to generate code that doesn't have any obvious defects on first inspection, although it cut off about halfway through because of the generation limit. That's the best result that I've seen from an LLM yet. But that's after about a dozen very broken broken programs, and again, I think the domain expertise here is key in order to be able to reprompt and correct.

> I've written a lot of tests, I think this would have taken 3-4x longer to do by hand. Surely an hour?

I guess my point is I'm skeptical.

I don't believe what you had the end would have taken you that long to do by hand. I don't believe it would have taken an hour. It certainly would not have taken me or anyone on my team that long.

I feel like you're projecting that, if you scale this process, so say, having 5 LLMs running in parallel, then what you would get is you spending maybe 20% more time reviewing 5x PRs instead of 1x PR, but getting 5x as much stuff done in the end.

Which may be true.

...but, and this is really my point: It's not true, in this example. It's not true in any examples I've seen.

It feels like it might be true in the near-moderate future, but there are a lot of underlying assumptions that is based on:

- LLMs get faster (probably)

- LLMs get more accurate and less prone to errors (???)

- LLMs get more context size without going crazy (???)

- The marginal cost of doing N x code reviews is These are assumptions that... well, who knows? Maybe? ...but right now? Like, today?

The problem is: If it was actually making people more productive then we would see evidence of it. Like, actual concrete examples of people having 10 LLMs building systems for them.

...but what we do see, is people doing things like this, which seem like (to me at least), either worse or on-par with just doing the same work by hand.

A different workflow, certainly; but not obviously better.

LLMs appear to have an immediate right now disruptive impact on particular domains, like, say, learning, where its extremely clear that having a wise coding assistant to help you gain simple cross domain knowledge is highly impactful (look at stack overflow); but despite all the hand waving and all the people talking about it, the actual concrete evidence of a 'Devin' that actually builds software or even meaningfully improves programmer productivity (not 'is a tool that gives some marginal benefit to existing autocomplete'; actually improves productivity) is ...

...simply absent.

I find that problematic, and it makes me skeptical of grand claims.

Grand claims require concrete tangible evidence.

I've no doubt that you've got a workflow that works for you, and thanks for sharing it. :) ...I just don't think its really compelling, currently, to work that way for most people; I don't think you can reasonably argue it's more productive, or more effective, based on what I've actually seen.

Honestly, the only thing I have found somewhat useful with LLMs is to get smarter tab complete and to occasionally fill out small methods in classes, and finally writing unit tests and adding documentation. it saves me a little time on mostly improving test coverage and readability. But until I give it some examples it usually hallucinates even method names within the class that are very similar but slightly different and some of the time saved is lost by having to fix it's mistakes. I would say it's improving my LoC output by maybe 5-15% max, but the tab complete is nice when writing code.

>I don't think so, I think it's more about openness. I've noticed older software engineers tend to be more anti-LLM and quick to dismiss.

Wow, a highly ageist comment, if there ever was one.

Congrats. Trying for a job and looking for less competition, maybe?

Notice that your statement is as full of assumptions as mine. That was intentional on my part, to bring out my point.

In this case, the manual is rather poor, so a tool that can cobble together an answer from different sections of the documentation plus blog posts and stack overflow is superior to the manual.