indiv0

Still exploring different options but at the moment I run a daemon on each node that connects via iroh to link the nodes (and NAT traversal). Once the link is established the nodes share data via a Raft-based datastore. I can launch a TUI client on any machine. This presents a chat interface like a typical LLM harness. From here I can configure which model I want to use, how the model is executed and on which node (for example I can use Ollama on machine A to serve a model that’s running on machine B, but executing commands via this system on machine C). Each daemon then acts like a typical LLM harness, but headless. So the commands and tool calls are streamed into the shared store and finally end up in my TUI. Since all the state is in the distributed store I can pick up the progress wherever I want, as long as there’s a node running there.

Still a lot of big problems like latency so I’m not too happy with the current design. It’s a LOT of state to replicate, and obviously things get problematic when an agent was working on a project on machine A but then ends up on machine B which doesn’t have that project directory around at all. So the agent needs to re-orient itself to figure out what happened. Still pretty fun to play with.

Funny. I started working on a similar product myself just a few days ago. I have multiple machines for multiple different purposes. Each one runs different agents (some local, some cloud), for different tasks. Different ones store different data (a NAS on one, my VMs on another, etc.). I wanted a durable layer over all of them to be able to start work on any one of them and resume on any other, and for all the disparate agents to share memory/configurations where possible, and for each node in the network to make all the others aware of its purpose (so when the NAS is online, the agents know how to connect to it and what it contains). Sadly no VC funding for me, building it on my lonesome :)

Hard to avoid spoilers here but Vernor Vinge hits on pretty much exactly this in A Fire Upon the Deep. Though he's interested less in the hard sci-fi aspects of how/why and more on the consequences of it (story-wise).

That’s a fair point. I wouldn’t want to take machine god’s proclamations on faith either. I’d prefer it if the knowledge was always within our grasp. There is also a possible middle ground where we don’t understand the questions or the answers but we still benefit from the effects of the application of that knowledge. “Sufficiently advanced technology is indistinguishable from magic” and all that. Hopefully we can still judge the results based on the effects. Rejecting a call to genocide should be easy enough, but on the other hand the Native Americans couldn’t foresee what the smallpox blankets would do to them. Working from a position where you are the weaker side of a knowledge gap is a scary thought. It’s a rational fear and I think a lot of people will end up on that side of the divide if AI continues to advance.

My instinct is to agree with you. I believe that the drive to a deeper understanding of the problems is what helps us unlock new areas of study, and find opportunities to transfer techniques or bridge otherwise unconnected domains.

But let’s consider a hypothetical: what if an intuitive understanding of the true “boundaries” of mathematics (if such things exist) is beyond the capabilities of a human mind? If there truly is no way to simplify some proofs down from 200,000 line incomprehensible gibberish to something you could teach to a high schooler or undergraduate or even a PhD. Is the proof still worthless? Sure, at the moment, it might be. Finding such a proof and understanding the implications of it are different skills, the latter of which AI almost certainly does not possess at the moment. But there may come a time where the AI can view the bigger picture and make the leaps you described (say, an eka-Calculus from an eka-unit-circle). These leaps may be as unintelligible to us as the proof in OP is.

I guess the question is: assuming that we can’t make the proof beautiful enough to spark deeper human understanding, do we still want it if it sparks deeper AI understanding?

Personally I would hate to live in a universe where the boundaries of science are beyond intuitive human understanding, but I think it’s almost certainly the case. The idea that the rules are all within our grasp reeks of anthropocentrism to me. I would love for the universe to prove me wrong though. It’d be a pleasant, hilarious coincidence if they do fit within the boundaries of our understanding.

You're not wrong, but if I'm talking to a Chinese Room, I'm still going to use pronouns and all sorts of meatbag-specific language. It doesn't matter if there really is a real person on the other end or not -- it's easier for me to just default to the assumption that there is. Monkey brain gonna anthropomorphize.

On the other hand if I try to talk to Facebook, all he says in response is "200 OK".

> music is basically a greenfield for AI wherever you look

AIN'T THAT THE TRUTH.

My girlfriend is studying musicology and she has some physical disabilities that make it difficult for her to write things down sometimes. So I try to help her by writing some AI-powered TTS/OCR/etc. apps here and there. It becomes painfully obvious that music was never considered an important part of any AI training dataset, anywhere.

These days, I'm pleasantly surprised by how well Opus 4.8 understands/explains music theory (as you said). But ask him to transcribe/OCR/OMR some sheet music and he'll confidently give you the MusicXML/Lilypond equivalent of "2 + 2 = horse".

I really hope this ignored area will be swept up with the rest of the rising AI wave, but it's still criminally undervalued.

This thread will become a typical "haha slop company made slop" but I've been bitten by a bug exactly like this before in a (pre-AI, artisan) OSS project. The maintainer there didn't properly account for DST when calculating last backup time, so the app started and never stopped writing/re-writing backups continuously.

Perhaps the framing shouldn't be "haha slop" but rather why doesn't the AI write better quality software than we do? To which the answer is obvious IMO -- even emergent properties can't elevate AI intelligence too far above the training dataset. So how do we get to superintelligent (or at least "not-wreck-your-NVMe-endurance-telligent") AI, if we, as a whole, are not smart enough ourselves?

Judge not the slop-bot, lest ye be judged yourself, engineer.

Can I get his decipher-forgotten-ancient-text skill? I want to try my hand at the Voynich Manuscript

GP wasn’t asking how you gauge their tone. They were asking how would you classify the tone of the comment they were replying to. The implication being “I wasn’t the first with a condescending tone”.

Eventually even a system like that can be gamed, similarly to how Leetcode-maxxing and the like sprung up in response to typical SV interview questions. Studying for the job becomes studying for the test becomes studying for the pre-test test.

We just set all the lints to `warn` by default then `RUSTFLAGS="--deny warnings"` when building for release (or in CI).