there is no moat

So bullish western monkey supply. $CRL

$630M says the best protein design model belongs behind a paywall. Baker's lab and NVIDIA just shipped one under BSD 3-Clause. Free for commercial use. Since 2024, that paywall has been the bet Chai Discovery's investors made. It just got a lot harder to defend. Protein design means asking software for a molecule that grips a disease target. Antibodies against the TNF receptor alone pull in billions a year, per Nature. RFD4-Proteina covers seven design tasks, antibodies and enzymes included. That's a toolkit. Chai already open-sourced Chai-1 for structure prediction under Apache 2.0. So the moat was always the design layer. Now that layer has a free competitor with a Nobel name on it. Open models get wired into pipelines. Pipelines become defaults. Defaults win. The hard part was never picking the "best model." It was the plumbing. Licensing friction is plumbing too. Being first isn't just about features. It's about becoming the thing people reach for first. Lately, I find myself using Claude over OpenAI. Paid models still have a shot, but only if they sell what open weights can't: wet-lab validation, proprietary data, faster paths to a real candidate. Would you build on open or pay for the edge?

@Railway it was that bad huh?

🧬 Design proteins faster and send fewer, better candidates to the lab. Introducing RFD4-Proteina, a new NVIDIA BioNeMo open model co-developed with @UWproteindesign. ▫️Fast: 13× faster generation than RFdiffusion3 at 200 residues ▫️ Smart: state-of-the-art across 12+ protein design tasks ▫️ Programmable: set your design inputs and use test-time compute on harder problems Start with our binder-design tutorial nvda.ws/3VK9Uvr

We are bringing agents to the wet-lab bench at Google Research to navigate complex biological landscapes. Learn how we are grounding agents in physical lab robotics to run fast closed-loop molecular design cycles, ultimately discovering single-digit nanomolar protein binders for cancer targets →goo.gle/4B5mMMB

Today we’re releasing Muse Spark Open, the most capable US open-weight model to date. Built for coding and agentic work, it excels at long-horizon tasks, complex software engineering, and multi-step reasoning. Key Model Specs: → 816B total / 42B active parameters → 1M token context window → Natively multimodal across text, image, video, audio

Four cancer cells displaying continuous membrane blebbing videoed through a DIC microscope. No, these cells are not dying. #CellBiology

JUST IN: 🇺🇸 CFTC permits developers to build passive derivatives software without registering as brokers, including for crypto markets.

Today we announced the final design of the Expedited Investigational New Drug (IND) Pilot, a key Trump Administration priority to accelerate American medical innovation and keep early clinical research in the U.S. The program seeks to accelerate the time from a drug being identified to a first-in-human clinical trial. FDA will be partnering drug companies with qualified research institutions that have scientific and regulatory expertise to support efficient IND applications. The agency will accept applications to participate in the pilot until October 30, 2026. fda.gov/news-events/press-an…

Breaking News: Anthropic said it halted potential plots by scientists who used its A.I. models to do research that could have helped develop biological weapons. nyti.ms/3V3epAE

We raised a $40M Series A to build the automated lab for agentic biology. Last week, we shared the work we did with @Anthropic: Claude designed proteins, sent them to our automated wet lab, and got real experimental data back. We believe this is where biology is going: AI agents designing experiments, running them in automated wet labs, learning from the results and iterating. But AI can only move as fast as the experiments behind it. To use the potential of AI to cure all diseases, we need to build high-throughput wet lab infrastructure: a “biological gigafactory”. That’s exactly what we’re doing at Adaptyv. Over the past year we’ve grown our lab throughput by over 5x and onboarded more than 100 customers, ranging from bio AI labs like @chaidiscovery and @boltz_bio to pharmas like @Roche and @novonordisk to dozens and dozens of new startups that use AI to radically speed up drug discovery.

Many drugs work by binding to a specific target in the body and blocking or changing what it does. An important first step in the drug development process is designing a molecule that can bind tightly to its target. Traditionally, that's meant weeks or months of expert work per target, sifting through a large number of candidates to identify the few that work. We wanted to test if Claude could successfully design novel protein binders from scratch (also called de novo design). With a protein design prompt written by a human expert, Claude autonomously designed protein binders against 14 out of 15 targets. We then worked with Adaptyv Bio and Twist Bioscience, who independently built and tested the proteins Claude designed.