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It shouldn't be prohibitively expensive because the more you break the work into smaller parts, theoretically the smaller a model can be that completes it.

Therefore your agent swarms don't have to be cutting edge API calls, they can be hosted on clusters of tiny cheap computers with small CPU GPU. For orgs you could basically invest in a local server that has 100s of small computers.

You can use engineers and frontier models to plan and do task subdivision. And then shit that out to the local cluster that's basically handling each small task without knowing what it's supposed to do.

The real issue is the middle. Testing, verifying interconnectivity of mid level abstractions, building that amount of tech debt at such a high rate and actually being aware in any way what it is that you've built.

I think for most MBAs, considering the US and a significant amount of global markets only care about short term market upside, and AI is still in the middle of the hype cycle, that's not a serious issue for anyone who is profit oriented.

It's only a real issue for the losers like you and me who care about sustainability and infrastructure that survives longer than one market hurricane season.

Making a high-throughput revision control system is doable, there have been experiments here on HN with git-in-postgres etc. The question is, what is the actual bottleneck here?

I made a VCS for LLM assisted work, bootstrapping it on its own code as it was developed. In fact, if it did not really work, it would not even exist! In my experience, keeping track of things and evaluating them was the bottleneck and I focused on that https://replicated.live/blog/worktree Effectively, the question is "how to leverage human evaluation and feedback".

The Cursor people concluded their last post with "taste, judgement, and direction came from humans". Sounded surprisingly sane after "don't compile it, commit it faster" some paragraphs earlier.

Seems very reasonable to me tbh, the VCS was built to facilitate human work structures, rebuilding it to facilitate autonomous work seems appropriate.

It's hard not read such quotes and immediately think of the Infinite Monkey Theorem.

https://en.wikipedia.org/wiki/Infinite_monkey_theorem

I mean why even use VCS at that point?

Of course it is

not in Rust. might be different enough ...

> What am I missing?

They’re testing the same models on the same task but different ways of organising the swarms and the new approach works better.

This is more about automated long horizon work.

Wouldn't that be in the authors' interest to disclose, if true?

I don't think there is a lot test harness, given there is a golden implementation (sqlite3), with clear interface (SQL, and sqlite3 db files). Once you slop crappy implementation, then you can create one group of agents finding gap where slop doing wrong vs golden, and another group of agents filling the gap meeting the conformance.

I assume they did similar, as long as test case(or spec) can be parallelized. Having parallel execution / testing environment is crucial if we need 1000 commits/second throughput.

I would not surprised they have 1+ millions of specs.

I would bet they won't release generated specs - actually this will be a valuable asset like https://sqlite.org/th3.html

I call this junk "meta-agentic engineering", it reminds me of people who have the coolest nvim configs, spend hundreds of hours customizing it but ultimately get less work done than the guy with minimal workflows, if any at all.

I look on twitter and it's just people building tools for agents to use agents, some weird customization loop going on in the LLM space right now. Ultimately these are trends pushed on us from model providers because they 10x token consumption. Its literally just BS trends to increase revenue at these companies, most of it is largely useless.

I don't think Gas Town was actually all that much ahead of the curve - see Cursor's swarm post from January this year, mentioned at the very top of this blog post.

I also don't know that beads is a very strong example that Yegge knows what he's doing - it's famously been described as (pseudo)malware.

Everything Steve yegge has done has been trash. That's why nobody is talking about beads or gas town. It was clear even in the beginning that it was a borderline AI-psychosis-fueled trash fire.

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They received $60 billion to use grok and make it better. I think they going to be okay.

Good news, the new manual will be 83500 pages.

Because it's mostly a load of crap.

Most of these reusable, automated workflows sound really cool in theory, but in practice either they require a lot of customization (not really reusable) or a lot of babysitting (not really automated).

AI has improved my productivity significantly, I'm not a hater, but we also have to be honest about its (current) limitations. The whole "software factory" thing is just a pipe dream people sell to execs / investors / online courses.

Yes, that's true, but in the post, they even mention that building the spec is the scarce resource.

  For that to work, the swarm has to actually follow the spec, which is what much of this post is about. We gave the swarm 835 pages of prose and it came back with a database. What was scarce in this experiment, and what we expect to be scarce in software engineering going forward, is the right description of intent.

Well, the tool works well. I am grateful that Nature has seen fit that the same kind of person who can make it doesn’t also have the ability to identify what needs exist in the market because then all software would flow out of OpenAI and Anthropic and the rest of us would run about their legs and peep about. Whether product development will survive the next generation of models is still unknown.

it feels kind of like a concept car a large manufacturer would show off at a motor show. Everyone knows there is no intention to actually ship that thing, but they're exploring concepts and technology and trying to make inroads in what comes next.

The bottleneck has always been on the product side for 99% of cases. For the 1% where it's engineering, LLMs don't help as much.

The fact that the swarm needs high quality docs to work from will keep most of the tech industry safe.

Cursor is saying we need to get improve the harness and tooling, but the models are getting better. One would have to assume that will continue and that cheaper/smaller models will continue to become even more useful.

If there's a human who could write SQlite in an hour we should be paying them at least that much.