81–90 of 159 posts
This is almost a year behind Steve Yegge's first post on beads. Gas Town and Gas City provide orchestration for the swarm. So far I haven't seen a perfect implementation but this idea isn't new.
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Shakespeare-level literature was produced. We are the infinite monkeies, and one was shakespeare.
How much slop you figure Shakespeare produced?
It’s rare to run into prodigious artists who simply produce a few perfect pieces - as a rule those few magnum opuses are built on a hill of cutting room floor scraps.
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Yes and this can be proven because what Anthropic says and does is vastly different 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?
They've said for some time that Claude Code is 90% LLM generated code.
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Even if no Rust code for it was seen during training, an LLM can trivially transpile SQLite's C codebase to Rust on the fly. For example, I just asked ChatGPT to write John Carmack's famous Fast Inverse Square Root algorithm in Erlang, without searching online or thinking, and it transpiled it immediately (while also extracting the knowledge in the same step). SQLite's semantics/code are stored in the middle layers o…
Then you would expect the implementation to be structured the same as SQLite, and having glanced at the result, it looks like at least some things aren't. For example, it seems to use an operator-tree executor rather than SQLite's bytecode interpreter.
Love to see these crazy kinds of experiments going on. Even if this doesn't 100% work or is prohibitively expensive for now, these are glimpses into the future in the same way people were talking about coding agents in 2023 when we just had tab complete.
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.
>The browser swarm from earlier this year peaked at roughly 1,000 commits per hour on Git. The new system peaks at around 1,000 commits per second. >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 coor…
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 focu…
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?
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It's hard not read such quotes and immediately think of the Infinite Monkey Theorem. https://en.wikipedia.org/wiki/Infinite_monkey_theorem
If at the end Shakespeare-level literature is produced, does it matter whether we arrived there by random keystrokes?
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.
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If at the end Shakespeare-level literature is produced, does it matter whether we arrived there by random keystrokes?
2*128 is the UUID namespace, which is astronomical. That is like 21 letters. Making one sentence of Shakespeare by random search is strictly 0% chance. SHA256 is ~43 letters then. "LLM broke all of cryptography and creates random data to fit a hash" -- are we there yet? Random search does not work at this scale. In all their cases, they played with pre-existing well-studied software, so they had an embedded fitness f…
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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 focu…
People often think of tech/business as bottleneck issues ie whats my limiting factor. That's relevant to pre-ai business as humans are relatively self organizing. 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…
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.