using C was 100 times as productive as assembly. what happened was not that we finished software 100 times faster, but that we did projects 100 times bigger in the same time

same thing with smol local LLMs versus the big ones in the sky. your smol local LLM will only be able to tackle projects which are not comercially valuable anymore, because people expect 100x scope and features. which is fine as a hobby/art project

yes, we'll do amazing things with local LLMs in 2 years, but the big LLMs will do things beyond imagination (assembly vs C)

▲

2ndorderthought an hour ago | parent [-]

I disagree. I think people can make very good software by balancing their use of AI and their market knowledge. I still believe for the foreseeable future people can make wildly loved or mission critical software with 0 ai and have it be met with market interest.

I think we are going to see a surge in software claiming to do everything and becoming bloated and unsustainable.

I already see 1gpu local models 1 shotting games via vibe coding. I see people doing agentic programming, granted more slowly and cheaply than 12 Claude sessions.

The difference isn't as big as it was 2 months ago. In the past 45 days so many model releases have happened. Meanwhile frontier performance has stagnated and degraded. If it's a taste of what is to come I welcome it.

▲

hparadiz an hour ago | parent [-]

I'm like two months into a vibe coded C project. My issues are the same as ever. How to pack memory. What syscalls to run and when. Is the program stable after running for 24 hours? When I want to make a change it's usually a trade off with something else. There's no accounting for taste among humans. Let alone among an LM. It's great at implementing my ideas but terrible at coming up with those ideas. Architecture is always going to be king.

	▲	sheepscreek 8 minutes ago \| parent [-]
		Models are heavily fine tuned and trained to follow instructions. They are trained to be subservient. I am sure that cuts into their ability to think creatively. The other risk with a lot of creative thinking is risking hallucinations (creative thinking = perhaps trying what’s not in its training set = hallucination basically). So I will rephrase creative thinking as desired or useful hallucination that is still firmly within the constraints of the prompt. If that sounds complicated, that’s because it is! It’s a tricky balance to get right. I think the current architecture for most GPT models isn’t sufficient to solve this problem for good. I suppose we need to do more research into what constitutes desirable vs undesirable hallucination and how to shift the balance towards the latter.