🏆 Neo Scholar applications are open! Are you a college student who excels at CS? Follow in the footsteps of Neo Scholars who founded Cursor, Chai Discovery, Applied Compute, Flint, Cognition, & more. Apply to join one of tech’s strongest communities. neo.com/scholars

Are robots ready to accelerate science labs? We created the WetLabs Benchmark to test 9 critical tasks, from easy to hard, and let 3 leading models take 20 attempts per task Astra leads but it comes close...

our office was peak tech bro for months: desks and nothing else. then we got a couch and every engineer migrated to it. then a rug and everyone started sitting on the floor. that was our cue to actually make the place nice. one of our investors even forward-deployed their office manager to help us find furniture (thank you @neo team!) now we do our weekly bug bashes on the couch

Read this incredible profile of @LuanaLopesLara, cofounder of @Kalshi. Luana is among the boldest founders we've backed at @Neo, and this piece brings her to life wonderfully. (I was pleasantly surprised to see myself mentioned in the first paragraph.) sidedoors.co/p/luana-lopes-l…

Sapien is the engine behind the world’s most important companies. Today, we’re announcing our latest fundraise at a $180M valuation. We build systems that learn how complex businesses work, enabling financial & operational optimization for the people who build everything else.

Today, I’m excited to publish the book Evolution by Genetic Optimization, a 35,000 word, 100+ page piece on the new mechanism for directed human evolution. This book synthesizes 120 years of genomics science, 10 years of my life, and the astonishing insight that man’s manifestation is now programmable. I believe, alongside AI, Genetic Optimization will be one of the central technologies of the 21st century and perhaps beyond. If the computer was once described as a “bicycle for the mind,” GO is something different: an amplifier for the mind and body itself. It is available online in the link below. Those close to Nucleus will also receive a physical copy. I hope you enjoy. mynucleus.com/evolution

@KianSadeghi5 (CEO, @nucleusgenomics) reflected on The Economist’s case for “designer-ish babies” and the growing market and debate around embryo screening. economist.com/leaders/2026/0…

@WisprFlow raised a $280M Series B at a $2B valuation and introduced Notetaker.

@transfyrai came out of stealth announcing $25M in seed funding to capture tacit laboratory knowledge and make scientific work more reproducible and transferable.

@Etched raised $700M at a $21B valuation and delivered its first inference rack to Jane Street, which led the round and became its first customer.

This week, @ActinideInc became the first startup in history to enrich uranium, producing HALEU, the fuel for next-gen and small modular reactors. We did it using Endurance, a particle accelerator that we designed and built in Texas. Endurance usually produces enriched Yb-176, a precursor for cancer therapies worth roughly $30,000 per gram. After a bulk Yb-176 delivery on July 30, we reconfigured Endurance for uranium.

One misconception about Applied Compute is that we are primarily a forward-deployed post-training team. That is a big part of what we do, but much of the team builds the underlying training stack and platform, AC2. All of our customer work runs on AC2, which means the platform is constantly being pushed by training runs for real production workloads. We use AC2 to build evals, train models, and then serve those models for leading AI products. The abstractions and functionality in AC2 have been shaped by everything we’ve had to solve to ship custom models to production. We’re excited to share more about AC2. It is the best way to train and serve a custom open-source model for your use case

our cloud agent usage at decagon is up 6x over the past 3 months. cloud agents are quickly becoming the default way our team works because: - they scale beyond a single laptop: each agent gets its own isolated sandbox (no worktrees) so there’s no local resource contention - you can spawn swarms from anywhere (web, slack, linear): designers and PMs are shipping - they can run for hours, spin up all services, and put up PRs with screenshots/videos but if verification isn't trustworthy, you just multiply slop. the bottleneck isn’t model intelligence, it’s the environment setup and verification. to verify effectively, you need to: - give it the same tools an engineer has locally (same stack, MCPs, context) - create a framework (like @poteto's pstack) that encodes how the agent should create/test its changes and ship artifacts you can review without reconstructing the session the horizon of work you can give agents is much longer - cloud agents can pretty much ship while you sleep. probably the highest leverage thing an eng team can invest in right now.