around sf 🌉

We're bringing together our friends and community to celebrate our Series C. Join us at Noguchi's Sunken Garden in NYC on June 16th or at the Legion of Honor in SF on June 25th. Invites are limited, apply here: modal.com/c-function

I’m so excited about the launch of ESMFold2, ESMC, and the new ESM Atlas. This was a massive team effort, and I’m grateful to have worked with such an incredible group @biohub. A headline result I’m especially excited about: ESMFold2 can design minibinders and antibodies with nanomolar affinity, target selectivity, and functional activity against therapeutically relevant targets. Today, we’re sharing the full binder design protocol.

@FireworksAI_HQ @cursor_ai highlighted why delta-compressed weight sync matters for RL at frontier scale. slime brings this capability to OSS: lossless delta sync for Megatron ↔ SGLang disaggregation — ship deltas, not full checkpoints. This is another step toward a fully open-source stack where rollout/inference and training are truly decoupled and deployed separately. PR: github.com/THUDM/slime/pull/…

How does Doppel stay ahead of phishing campaigns that spin up millions of domains at once? Machine learning engineers William Gill and Ishana Shastri explain how they use @modal to: - React to sudden cyberattack influxes with highly elastic GPU clusters. - Replace complex Cloud Run and Docker setups with simple decorators in a single code file. - Make experimentation cost-efficient by running parallel experiments in the background. Learn more: doppel.com/blog/how-doppel-e…

Today we're announcing ESMFold2, an open scientific engine to power prediction, design, and discovery across protein biology. The new model delivers state of the art performance on protein interactions, especially antibodies, a critical modality for therapeutics. We have designed and validated miniprotein binders and single chain antibodies across five therapeutic targets that are important in cancer and immunology. We are seeing very high success rates, and affinities at levels consistent with therapeutic activity. We’re also releasing an atlas of 6.8 billion proteins, and 1.1 billion predicted structures. ESMFold2 is built on a state of the art language model that has been trained on billions of protein sequences. A world model of protein biology emerges through language modeling. We’ve used the techniques of mechanistic interpretability developed to understand large language models to understand the concepts ESM uses to represent proteins. The model’s representation space has a compositional organization of features across scales, levels of complexity, and abstraction, that reflects and mirrors the understanding of protein biology developed through a century of empirical science. This understanding emerges without prior knowledge, just from language modeling of protein sequences. Language models are becoming a powerful substrate to understand and program biology. The design of protein interactions is one of the most fundamental problems in biophysics, and has critical implications for the discovery of new medicines. A simple gradient based search with the model was able to discover high-affinity protein binders. I'm excited by the potential this has to accelerate basic science and the understanding of proteins. And especially for the new avenues it opens up for therapeutic design and medicine.