
aspiring space cowboy @PrimeIntellect
Joined December 2024
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new blog post: why taking inference scaling seriously leads to swarms.
contrary to popular belief, I don't think multi-agent systems are a new scaling axis. they're just structured inference scaling.
hallerite.com/posts/on-the-n…
.@mikasenghaas just got one-shot by the Barbour section in KaDeWe
many such cases
You go to a party in SF and thr first thing people ask you is "What are you working on?"
You go to a party in Berlin and the first thing people ask you is "What's your background?"
I thought it was a meme, then it happened to me..
Konstantin Dunas retweeted
It's insane that apparently the only non zero-sum thinking people in the world were already software engineers
"Reading" is one of the core staples of "self-improvement" slopfluencers, but it's truly incredible how few books are actually worth your time.
unavailable
Konstantin Dunas retweeted
Together with NVIDIA we post-trained Nemotron 3.5 Super VL on thousands of tasks
Reflecting enterprise workflows in sales, operations, marketing, finance
We improved the AutomationBench-AA score by ~70% on the public split
Konstantin Dunas retweeted
We are releasing Ultramarine 1 Flash, a frontier-class model built for knowledge work. It is open-source and achieves similar performance to Fable 5, while being 60x cheaper.
It was post-trained by a team of 5 for ~$300k all-in. Available on API now, with weights releasing in 2 weeks.
Konstantin Dunas retweeted
Prime Intellect has acquired Moondream
We’re bringing Moondream’s open vision models and inference optimizations into our stack
Train and deploy multimodal models across vision, video, voice, decision models and computer use
Very interesting how the infra he talks about here is not too disimilar of what swarms will need.
How do always-on, proactive agents like @Bot work?
Watch my lecture at Stanford CS146S:
2:35 How we got here
6:13 What changed in the models
10:06 Inside an always-on agent
18:07 The harness
27:26 Context engineering
34:26 Where this is going
Konstantin Dunas retweeted
Over two weeks, Prime Agent orchestrated a swarm of over 2,000 agents to rewrite itself in Rust.
With 10,000+ sandboxes, 200B+ GLM-5.3 tokens, and 16,000 agent-to-agent messages, this was our largest test yet of Prime Agent’s multi-agent capabilities and our infrastructure powering these agent swarms.
Prime Agent now reaches usable input ~13× faster and uses 83% less startup memory than before.
Konstantin Dunas retweeted
Inference scaling works, but every agent eventually faces the same problem: its context window fills up.
In this essay, we argue the only scalable fix leads straight to swarms.
conversely, he who deeply understands Hayek & Coase should consider working on multi-agent systems
No idea if this is correct, but I have been saying..
verification is trivially scalable these days, validation much less so.
BREAKING: OpenAI’s solution to Navier–Stokes does not match its Lean verification.
The most important article to read today is not one of OpenAI’s 700 AI-generated math papers.
It is this other paper, making a deep and worrying point:
A Lean-verified proof does not automatically validate the proof written in natural language, nor does it mean that the formal statement captures the intended theorem.
During translation, an AI can change an assumption, weaken a statement, or replace the argument entirely.
It can hallucinate another theorem.
Lean correctly verifies the result.
But the proved result may no longer be what the paper claims.
This is a general problem. Things get spicy when the authors examine OpenAI’s proposed Navier–Stokes solution.
They identify at least two mismatches between the written intermediate results and their Lean counterparts:
One estimate claims that four additional input derivatives suffice. The Lean version requires five: a weaker result.
A pressure-flux estimate is obtained through a different bound, and proved through a different argument.
It is not clear whether these mismatches invalidate the entire proof.
But they raise an important issue.
OpenAI is flooding us with claimed revolutionary breakthroughs. Yet nobody knows whether the proofs are correct or whether they prove what they claim to be proving.
Epistemia at scale.
*
Paper in the first reply
Konstantin Dunas retweeted
Open superintelligence accelerates open scientific research.
The Alzheimer’s Translation Challenge will share a 150M cell atlas on the Alzehimer's Disease workbench and Prima Mente's modeling platform.
Prime Intellect is lending infrastructure support to researchers to build models based on this dataset. The best hypotheses will be tested in Prima Mente's wet lab.
Today we're launching the Alzheimer's Translation Challenge, a global AI competition to discover new therapeutic hypotheses for Alzheimer's disease.
At its core is a new 150M cell atlas of neurons, astrocytes, and microglia across different genetic backgrounds under combinatorial perturbations, with multi-modal readouts.
The data will be made available through the AD workbench, Hugging Face and Prima Mente's modeling platform.
The challenge is led by @PrimaMente and @AlzData in collaboration with @nvidia, @huggingface, @nebiusai, Talisman Therapeutics, @UltimaGenomics, @cellanome, @PrimeIntellect, @GoodfireAI, and @boltz_bio.
In 1906, Alois Alzheimer described a new disease of the brain. After 120 years of progress, we still don't fully understand what drives this disease. We want to change that.
Participants will use the dataset to build AI models, assessed through a series of evals from general benchmarks to more complex tasks.
Top teams will be shortlisted, and their experimental hypotheses tested in Prima Mente's wet lab. Teams win when their hypotheses demonstrate the predicted molecular and functional effect.
As a follow-up on my previous multi-agent work for the RL Residency @PrimeIntellect, I've been exploring cooperative agents with communication (aka swarms). I wrote-up my findings and thoughts in this post: nphard.io/2026/10/07/eleusin…













