Triatomic Capital

‘AppliedAI’ in Deep tech, Health tech, & New economy // Helping great entrepreneurs build 'century-defining' technologies & businesses.

[Eric Nguyen @exnx]

Incredibly excited to highlight Omnii Genome, one of the eight applications of Omnii-1

Omnii Genome fuses 28 multiomic modalities natively across biology

Against comparable benchmarks vs. AlphaGenome, it outperforms on all

Omnii Genome is also *generative*, which means it can also be prompted any modality or cell state, and infer missing modalities

e.g., prompt on desired gene expression, methylation pattern, and then generate the required DNA, or transcription factor pattern

We believe this is going to accelerate so many new possibilities in life science, from multiomic target discovery, diagnostics, and gene therapy

[Radical Numerics @RadicalNumerics]

1/10 Introducing Omnii Genome, one of eight Advances built on Omnii-1.

A single generalist model now outperforms leading specialized models (AlphaGenome) in linking genetic changes to gene activity and mapping regulatory interactions.

These capabilities help make sense of the 98% of the human genome that doesn’t code for proteins

How Omnii-1 works:

[Rima Alameddine @ralamedd]

I'm thrilled to join @ChemifyX 's Board of Directors as an independent non-executive director!

Chemify is pioneering programmable chemistry, combining AI, advanced robotics, and chemical synthesis.

Excited to work with @LeeCronin and his outstanding team to hyperscale chemistry!

https://t.co/U5kyUIs7Ct

[@dMatrix @dMatrix_AI]

From specialized processors to complete AI inference systems. d-Matrix is scaling up with Squadron™, a new Silicon Valley lab and a growing team.

Silicon Valley Business Journal explores what’s next: https://t.co/3yOFSKf9gK

#AIInference

[Zain Asgar @zainasgar]

https://t.co/aTkDk6Cyot

[Gimlet Labs @gimletlabs]

Why hardware specialization is back.

AI inference needs more tokens/sec per user and more tokens per watt.

Power limits. Slowing transistor scaling. The memory wall. Different chips are optimized for different constraints.

Our co-founder @zainasgar on what comes next.

[Peter Zhou @xtzhou]

When we backed @RadicalNumerics, the bet was that one general model could learn the language of biology, the same way LLMs did for text. Today Omnii-1 beats AlphaGenome, designs antibodies and cancer vaccine candidates, and designed a phage covered by @carlzimmer in @NYTimes. All from one model. 🧬

[Stefano Massaroli @Massastrello]

Omnii-1 is a huge milestone for @RadicalNumerics.

Building it required advancing architectures, pre-training, post-training and inference to make new biological applications possible.

Pre-training meant tokenizing and blending continuous and discrete biological modalities. Post-training adapted that foundation to different applications.

At inference, we’re exploring algorithms for reasoning across biological modalities and allocating test-time compute across selective generation and conditioning steps.

Making these models train and run at scale meant pushing the underlying numerics and systems alongside the modeling. The frontier of biological AI runs all the way down the stack.

We have a cracked team. Really proud of what we’ve built together.

[CoreWeave @CoreWeave]

Every math teacher has written "show your work" in red pen. @CarinaLHong turned it into an output format.

The @axiommathai team built an AI for really hard math that writes its proofs as code a machine can check. Watch Carina's story 👇

[tae kim @firstadopter]

An Interview with d-Matrix CEO Sid Sheth on Why 3D-Stacked Memory Compute Is a Game Changer https://t.co/TTZNQIyV5X

[Jeff Huber 🇺🇸 @jhuber]

Big day for @RadicalNumerics: Omnii-1 is out – one multimodal model across DNA, RNA, proteins and more.

And in a world first, Omnii-1-designed phages were given to a patient with an antibiotic-resistant E. coli infection.

Excited to back @exnx & team! https://t.co/VShvDqOruC
https://t.co/NkFWOrIzRD

[Jeff Huber 🇺🇸 @jhuber]

Omnii-1: a leap toward General Biological Intelligence...

[Eric Nguyen @exnx]

I'm excited to share that @tri_dao has joined @RadicalNumerics as a scientific advisor!

Tri and I were labmates at Stanford's Hazy Research, where he pioneered advances in efficient ML systems (FlashAttention).

Scaling AI across multimodal biological data demands entirely new approaches to ML systems, and Tri will help guide our research at this frontier.

Welcome aboard, Tri!

Read more: https://t.co/cbObj5iFYu

[Tapa Ghosh @semiDL]

Choosing whether to allocate scarce watts between low-latency and high-throughput chips is a scary decision

It's hard to predict over the next 3 years how much demand will be low-latency vs high-throughput

Luckily with Volantis, you can have both modes in one chip :)

[@dMatrix @dMatrix_AI]

Breaking through the memory wall. Rethinking AI inference.

d-Matrix CEO @sidsheth joins Tae Kim (@firstadopter) to discuss memory-centric compute, 3D-stacked DRAM, and the future of low-latency AI inference.

Read the interview: https://t.co/b44G64VxOS

#AIInference #AIInfrastructure

[Eric Nguyen @exnx]

I'm thrilled to welcome Jill Hruby @jill_hruby and Paul Friedrichs as biodefense advisors to @RadicalNumerics!

Jill formerly led the National Nuclear Security Administration, and Paul served as the inaugural White House Director of Pandemic Preparedness and Response.

As AI opens new frontiers in biology, strengthening our ability to defend against biological threats is more important than ever.

We're incredibly fortunate to learn from their experience and leadership as we advance this mission.

Welcome aboard, Jill and Paul!

Read the announcement:
https://t.co/jOqHE1sHOK

[Jeff Huber 🇺🇸 @jhuber]

I don't always want to compute efficiently, but when I do ... I use Efficient Computer.

Keep your computers fed and your robots moving, my friend.

More: @efficient_hq #100xbetter

[Sid Sheth @sidsheth]

A big week for d-Matrix at #OCP.

Corsair rackscale is now generally available — air-cooled, industry-standard, ready for existing data centers. Grateful to @Supermicro for building alongside us.

We introduced Squadron, our multi-rack clusters scaling to 1,000+ Corsair XPUs.

And we ran the industry’s first public demo of Attention-FFN Disaggregation: one NVIDIA H100 + 8 Corsair XPUs hitting 845 tokens/sec/user.

All credit to the d-Matrix team and our partners.

The future is memory-centric inference compute — here today.

🔗 https://t.co/vt4s5bu5PZ

[Radical Numerics @RadicalNumerics]

1/8 Does text-to-image generation need language pretraining?

Today, we share Halcyon, a family of early-fusion, base models trained from scratch. Halcyon outperforms strong late-fusion baselines with up to 275× less pretraining compute. Models and recipes are open.

Halcyon is part of a research program at Radical Numerics on the science of multimodal pretraining and scaling.