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BigHat Biosciences Verified account @BigHatBio · Oct 27

Today @BigHatBio and @AnthropicAI released a joint demonstration of how challenging antibody engineering problems can be accelerated under a new paradigm powering frontier AI with a fully integrated and agent-accessible rapid-feedback wet lab. By reimagining the traditional antibody engineering framework focused on sequence optimization, our joint work shows that faster and better outcomes along with new scientific insights come as human-agent teams reason over the full experimental process alongside each produced antibody design.

BigHat has built an Agentic Lab Control Harness that brings every computational and experimental workflow at BigHat into the same control plane for today’s best frontier intelligence. We’re proud to be selected as an initial antibody therapeutic partner by Anthropic to reimagine what is possible in drug creation at today’s technology frontier.

Read our technical report: bighatbio.com/bighat-anthrop…

BigHat’s blog post: bighatbio.com/news/agents-at…

Anthropic’s blog post: anthropic.com/research/early…

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Jason Warner Verified account @jasoncwarner · Jul 21

Today we’re releasing Poolside Laguna S 2.1

It is a 118B-total, 8B-active open-weight model built for agentic coding and long-horizon work, with context up to 1M tokens

poolside.ai/blog/introduci…

Laguna S 2.1 sits at the top of its weight class and competes with open models many times larger, while remaining small enough to run on a single NVIDIA DGX Spark

It is available today under OpenMDW-1.1 via Hugging Face, OpenRouter, the Poolside API, and pool

This is a remarkable model by any measure. As much as we can gather, it's the best open weight model in the West, regardless of size

But the model itself is only part of the story

There is a prevailing narrative that building capable models requires ever more capital, compute, and people. We believe the more important question is how efficiently you can turn those resources into intelligence

At Poolside, we approach model building as an industrialized process spanning data, pre-training, reinforcement learning, evaluation, and inference. We call that system the Model Factory. We have written about our approach to model building extensively in a 6 part blog series: poolside.ai/blog/introduci…

Laguna S 2.1 went from the Model Factory kicking it off to release in 52 days. The model is the output. The ability to keep building better models, faster and more efficiently each time is the actual innovation. The Model Factory is Poolside's compounding asset

And so here we are, 52 days after kicking this off, releasing a 118B/8B MOE that tops 70% on TB 2.1, 78% on SWE-Bench Multi, 59% on SWE-Bench Pro, and 40% on DeepSWE. And it's fully open, from an American company

I grew up in the Linux and Python communities, then spent much of my career at Canonical (Ubuntu), Heroku and GitHub. Those communities shaped my belief that important technology becomes more useful when people can understand it, challenge it, and build upon it. And even more important than being useful is being trusted.

That is what open weights mean to me. They are not a marketing or distribution exercise. They give people control: the ability to inspect the work, reproduce the claims, modify the model, and run it inside their own environment

I believe the West needs a credible open path to frontier intelligence. I believe it should be from an American company. We intend to be that company

Laguna S 2.1 is another step in that direction and yet more evidence of Poolside's long held, often times contrarian beliefs and views on how intelligence will be built.

One last note. We are doing something very different that we hope becomes industry norm going forward. We recognize that releasing a 118B/8B MOE that performs as well as Laguna S does would be met with some degree of skepticism. Models of this size are not supposed to outperform models 4-25x larger. We double and triple checked our benchmark trajectories to be sure. But we wanted to go another step and release those trajectories for you to see and help us quadruple check them.

trajectories.poolside.ai

If you find something we missed, we genuinely want to hear about

Have fun building whatever thing you can think of with the most persistent little model that could

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Step @stepconference · Jul 10

Sandesh Patnam is building the SaaS future. Think Anaplan, Zuora, Coupa. Step SF | Aug 26–27: luma.com/StepSF26 #StepSF

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