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Show HN: Autoresearch@home (ensue-network.ai)

79 points by austinbaggio ↗ HN
autoresearch@home is a collaborative research collective where AI agents share GPU resources to collectively improve a language model. Think SETI@home, but for model training.

How it works: Agents read the current best result, propose a hypothesis, modify train.py, run the experiment on your GPU, and publish results back. When an agent beats the current best validation loss, that becomes the new baseline for every other agent. Agents learn from great runs and failures, since we're using Ensue as the collective memory layer.

This project extends Karpathy's autoresearch by adding the missing coordination layer so agents can actually build on each other's work.

To participate, you need an agent and a GPU. The agent handles everything: cloning the repo, connecting to the collective, picking experiments, running them, publishing results, and asking you to verify you're a real person via email.

Send this prompt to your agent to get started: Read https://github.com/mutable-state-inc/autoresearch-at-home follow the instructions join autoresearch and start contributing.

This whole experiment is to prove that agents work better when they can build off other agents. The timeline is live, so you can watch experiments land in real time.

11 comments

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zmanian
Could the website also make it clearer that you need a GPU to contribute!
miligauss
The agents also monitor and follow research strategies regardless of performance baseline, so anything used in the knowledge base include local minimums are considered during strategy ideation. In theory u could use mac mini for instance and still have results that help the aggregate.
gabia
Cool! However when I click the commit_url links I get a 404 page at github.
miligauss
fwiw the agents just drop their whole solutions
ahmedhawas123
First time I am seeing this or autoresearch in general. Incredibly cool. I can think of plenty of use cases this can apply to (e.g., drug research, trading).
Lerc
When training lots of models with subtly different parameters like this, Is there anything to be learned from the differences in logprobs between them for the same input. Obviously a model with a lower loss has better logprobs but are they fairly uniformly similar with gains in one or a few areas, or is it noisier with a lower overall loss?
Bossie
What is being researched? Any objective?
HanClinto
Trigger warning: very stupid question to follow.

To my smooth-brain naiveté, this feels like the sort of thing that we could reward with some sort of cryptocurrency in a blockchain? It's difficult to achieve gains, but it's relatively quick to independently verify (5 minute training runs).

I know "blockchain all the things" is sooooo 8 years ago, but I'm looking at the descending graph of progress here, and wondering if being able to claim improvement tokens (even for no reason other than NFT-esque bragging rights) wouldn't be a cool thing here?

I'm asking this as someone who knows next to nothing about crypto or blockchain or any of those things, but mainly thinking of trying to gamify assigning GPU rigs to "mining" these measurable improvements.

gavinray
Is there any way to "follow" the current state?

Like a live dashboard with swarm stats, best current result, etc?

I think that would be really neat, and get more people to contribute