Clayton Rabideau

Syntensor

[Avalon Institute 🐦‍⬛ @AvalonInst]

Avalon's magazine is now accepting submissions. The Avalon Quarterly will publish the best in philosophy and short-form literature. The theme of our first issue is Man. Read our submission guidelines below.

[Eric Nguyen @exnx]

Together with my co-founders Michael @MichaelPoli6, Stefano @Massastrello and Armin @athmsx, I am excited to announce @RadicalNumerics is emerging from stealth with a $50M seed round to build general biological intelligence.

We’re also sharing an early preview of our new model Omnii, the most powerful genome language model to date.

Omnii preview link:
https://t.co/ouikMtRVwf

At Radical Numerics, our mission is to master the code of life, and to drive the frontier of biological AI for both design and defense.

This is our dual mandate, which comes from something our own team helped make possible.

Our founding team trained Evo and Evo 2, the largest biological AI models (40B params) trained on DNA sequences. Trillions of tokens across all of life, from microbes to mammals. It’s fully open source, and created the field now known as generative genomics.

Last year, scientists used Evo to generate the world’s first complete genome from scratch using AI. Turns out it was a bacteriophage—a type of virus. It functioned in the real world, and in this case it was harmless. But for us, it was a clear turning point.

It showed that AI is no longer just analyzing biology. It is on the cusp of generating functional lifeforms. Eventually, AI will have the power to design and control life itself.

That should make all of us incredibly excited, and incredibly uneasy. (Anyone can design DNA with a new function, and have it synthesized and delivered, like something from Amazon Prime).

The same technology that will help us cure cancer is the very technology that might create the next global pandemic, or worse, allow the creation of bioweapons that can wipe out populations.

We believe these forces are inseparable. If you work on the frontier of biology, you have to build technology to safeguard it from its misuse. Existing biosecurity tools are sorely losing the arms race, relying on outdated “have I seen this exact thing before?” style algorithms.

We founded Radical Numerics to turn the tide.

And we can’t do that by training on textbooks and natural language. We must understand the language of biology from the raw physical data itself, to reason across every molecule and modality, from DNA to proteins.

The next frontier for AI goes far beyond chatbots or video generators to models that can understand and engineer life.

Today, we’re previewing Omnii, which is already far surpassing Evo 2, and will continue improving as we scale and add new modalities (training now).

1. For human health, Omnii can read and write whole genomes (more on writing later). It’s state of the art (SOTA) on detecting causal variants for disease, and can rank Alzheimer's mutations zero-shot. We’re partnering with a diagnostics company to use Omnii for early cancer detection (pancreatic and multi-cancer).

2. For defense, Omnii is SOTA at detecting AI-generated pathogens. We benchmarked existing detection tools, and they simply can’t detect the AI-generated ones (“deepfake viruses”). We’re partnering with a US national lab to pilot Omnii for detecting the next pandemic, both natural and AI-generated.

We have a data center full of Blackwells in construction now to build the most powerful biological AI models ever. This mission takes a new kind of AI lab that can actually scale on physical, biological data: new alignment research (mid/post training), scaling long context, building out mech interp teams to dissect what these models learn, new architectures and systems designs, all from the ground up.

Our team is made up of AI researchers and scientists from top labs and institutions (e.g. Stanford, MIT, Google DeepMind), but more importantly, we all share the belief that this is the most important challenge of our lifetime. If you feel similarly, we are hiring. We aim to bring the brightest minds in AI and science together to save lives.

Thanks to our partners on this journey, led by Emergence Capital @emergencecap, with Obvious Ventures @obviousvc, Triatomic @TriatomicCap
, and Patrick Collison @patrickc. Our advisors include Eric Horvitz @erichorvitz, CSO of Microsoft, Chris Re @HazyResearch of Stanford, George Church @geochurch of Harvard, and Andrew Weber @AndyWeberNCB, former Assistant Secretary of Defense for Nuclear, Chemical and Biological Defense Programs.

Fortune article: https://t.co/L3f3f1329T

Jobs: https://t.co/EzsHSMcGJ1

[𝖋𝖑𝖔𝖗𝖎𝖆𝖓 𝖙𝖍𝖆𝖒𝖊𝖗 ☀️🤖 @der_flow_]

We've recently gotten into doing lots of audio-visualization RnD at https://t.co/9zgpslma8n . So I've built a @sidefx Houdini HDA that extracts stems from music and outputs clean midi for the instruments. I've then piped it into a little POP solver advecting particles with the filamentsample() function only, because it looks cool!

[Clayton Rabideau @ClaytonRab]

@PrinceVogel @DeneckeChase also we can't evolve as quickly as bacteria so many mutations will be silent mutations

[Clayton Rabideau @ClaytonRab]

@PrinceVogel @DeneckeChase I would guess that if humans still have ancestors that at least a few sub-components of a few genes might be literally identical, but not many

[𝖒𝖎𝖈𝖍𝖆𝖊𝖑𝖈𝖚𝖗𝖟𝖎 @michaelcurzi]

3d printed titanium crown (1/1) photographed by ya boi

[Avalon Institute 🐦‍⬛ @AvalonInst]

The Victor's crown is here. The submission deadline has been extended, so you still have till tomorrow at 11:59pm to join, compete and win the crown

[Radical Numerics @RadicalNumerics]

Introducing RND1, the most powerful base diffusion language model (DLM) to date.

RND1 (Radical Numerics Diffusion) is an experimental DLM with 30B params (3B active) with a sparse MoE architecture.

We are making it open source, releasing weights, training details, and code to catalyze further research on DLM inference and post-training.

We are researchers and engineers (DeepMind, Meta, Liquid, Stanford) building the engine for recursive self-improvement (RSI) — and using it to accelerate our own work. Our goal is to let AI design AI.

We are hiring.

[Clayton Rabideau @ClaytonRab]

@AdamLocklin @eriktorenberg @VCBrags or perhaps you aren't sufficiently online

[Michael Poli @MichaelPoli6]

Life update: I started Radical Numerics with Stefano Massaroli, Armin Thomas, Eric Nguyen, and a fantastic team of engineers and researchers. We are building the engine for recursive self‑improvement (RSI): AI that designs and refines AI, accelerating discovery across science and industry.

Three core beliefs:
- We need orders of magnitude more AI systems in the world: models and interfaces built with purpose, to augment specific domains and workflows
- The complexity and resources required to develop AI are growing rapidly. Human development speed is the bottleneck.
- The process of developing frontier AI in new domains is ripe for disruption.

https://t.co/I1HNkCF1Ko

[Clayton Rabideau @ClaytonRab]

@MichaelPoli6 Congrats Michael!

[Avalon Institute 🐦‍⬛ @AvalonInst]

Please join the Avalon Institute for our inaugural public event! There will be poetry, music, philosophy, drinking, dialogues, and a panel on education in post-academic America. Looking forward to seeing you there

https://t.co/lGtSlFpUVq

[Clayton Rabideau @ClaytonRab]

@PrinceVogel @pinkddle not only that, they mostly don't seem to outperform just taking the average!

[Clayton Rabideau @ClaytonRab]

@Sauers_ "linear baselines" = just take the average expression

[Kyle Tretina, Ph.D. @AllThingsApx]

👀#DayhoffAtlas dropped for #SynBio:👀

3.34B natural🧬 + 46M structure‑guided synthetic protein sequences (from 240k novel backbones), all open‑source

Hybrid Mamba‑Transformer learns single seqs & MSAs → 51.7 % of unfiltered designs express in E. coli🦠✨ #SyntheticBiology #ProteinDesign

[Stefano Massaroli @Massastrello]

LFM2 is live. We keep state coefficients time-invariant because Hyena gated short convs already supply the adaptive dynamics—and the data agree. Same accuracy, leaner compute budget. Efficiency in practice, not on paper. #LiquidAI

[Michael Poli @MichaelPoli6]

It's easy (and fun!) to get nerdsniped by complex architecture designs. But over the years, I've seen hybrid gated convolutions always come out on top in the right head-to-head comparisons.

The team brings a new suite of StripedHyena-style decoder models, in the form of SLMs designed for edge devices. For these architectures, we opted for a short explicit (SE) convolution parametrization that brings speedups across a range of practical prompt lengths.

Amazing work from the pretraining, post-training, and infrastructure teams for bringing these models to life!

[Clayton Rabideau @ClaytonRab]

@posedscaredcity @rohanpaul_ai you're missing that the unit economics are better when you aren't using an entire wafer.

[Clayton Rabideau @ClaytonRab]

@Sauers_ Usually not without some tradeoff