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I felt motivated enough to write a follow-up/reply post http://replicated.live/blog/follow-up
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.
>To facilitate this rate of activity, we built a new version control system (VCS) from scratch. Throughput was not the only reason to own this layer. Every change in the system passes through the VCS, so it is where collisions first become visible, and several of the coordination mechanisms in the next section are implemented directly inside of it.
Talk about inventing the universe to make a button.
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.
The issue with AI is it has no real memory, context, long term reasoning, etc. These are a lot of what we use to determine how well we're doing something as well as why.
The "bottleneck" so to speak with ai is that it will just circularly build junk forever, expand scope infinitely, and just continue building.
At its best it will autobuild your unicorn saas. But on average it's a cancer cluster of ever expanding code with no plan.
The issue with unsupervised AI is that when you increase the throughput you decrease the amount of human supervision. So you have to have some way to deal with that.
If I'm going to give AI a gigantic task that's really complicated and it will probably mess it up 100s of times, how will I compensate for that without constantly looking over it's shoulder?
Some critical parts of agentic development are literally months old while most mature ones are merely a couple years old.
Critical thinking was the most critical checkpoint for models to reach.
Now its only a matter of time for the right tooling and orchestration to come along. Once agent memory and recall works well, all of those bottlenecks will go away one by one.
https://en.wikipedia.org/wiki/Infinite_monkey_theorem
If you already know what you want in an infinite library, it is there. If you don't know exactly what you're looking for to the letter, you might not be able to find it.
But I’m glad someone else is running all these experiments so we can see who is right in their guess about the outcome.
Monkeys have been reading Shakespeare all their lives (training). Effectively, this is LLM uncompression. Hence, "in Rust, from nothing but its documentation" -- not entirely true.
Douglas Adams said this beautifully in the Campaign for Real Time & the poet Lallalfa.
And I just found out h2g2.com is no longer online, so no reference.
Isn't SQLite's source code in its training data?
It seems both likely that they were and impossible to remove that code from pretraining. Doesn’t that make this just about LLM memorization of the training set? 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.
I guess that makes sense since the harness is the product in the case of Cursor.
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 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
Kudos
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.
That said, I love the work on figuring out these harness coordination jobs. While there are analogs to human management there is also this enticing feeling that, since the models are broadly deterministic, we might be able to get repeatable science-type lessons about managing them with enough testing.
I like it so much I even recorded a video walking through the process!
https://www.youtube.com/watch?v=efUmcCiRoDU (I do walk through how I do this in our app DevSwarm here)
Is the original part the VCS that works at 1000 commits/second? Or spending a bunch of money comparing models?
And it's a bit of a shame: by virtue of their position (their embedding in the fabric of venture capitalism), it seems like they can only make a subset of things -- what they can make is dictated enormously by capital, as they are engines of capital.
Not sure the point I'm trying to make, I just find it amusing.
Perhaps the point is that it might be more worthwhile to give independent creators a billion dollars to play around with agent swarms if we want to keep diversity in the evolutionary algorithm that is the software industry high.
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.
They say SaaS is dead and you can vibe code any SaaS using Opus and yet despite having Mythos with them they have to use JS terminal to run Claude Code.
Why don't they just vibe code CC? Why doesn't Dario have a 100000 agent swarm to run entirety of Anthropic or at least software department?