Opus 5.5 designing LEGO 👀 I asked it to design a Microduck I can build with real LEGO pieces. It: > designed it life-size using 1113 real LEGO parts > verified: 3,204 connections, 0 collisions, every step buildable, centre of mass inside the feet 🤯 > made a 141-page LEGO-style booklet (237 steps) > priced every piece in the browser and prepared the orders on BrickLink

if you tried hyperframes, you would know the agentic video stack is being built on code-gen but SWE-bench code v.s. code-to-video that feels alive are two different things we built the Code2Video Bench, in collab w/ Google DeepMind & @Kaggle frontier labs can finally get good at agentic video tasks

Sharing my first of hopefully many research blog posts! This one is the kind of educational blog post I wish I'd had when I started with RL for LLMs. I tried to make it as open as possible. Every rollout is browsable, the code is open source, and I walk through my entire thought process, from learning rate sweeps to reward shaping.

We ran async GRPO across HF Jobs with the trainer and vLLM on separate machines, no NCCL, and no shared disk. The adapter is mounted at the same path in every Job. LoRA training has landed in TRL's AsyncGRPOTrainer 🎉🎉🎉 The trainer now syncs only the adapter to vLLM ~few MB for a 1.5B model. That changes what the setup can look like. We ran async GRPO across HF Jobs with the trainer and vLLM on separate machines, no NCCL, no shared disk. The adapter travels through a 🪣 mounted on the same path in every Job. Blogpost 🧵

We built high-throughput materials labs in Menlo Park to create a loop between experiments and models. The labs generate fresh data, the models learn from it, and then help us decide what to try next. Using only 1,300 H200s, plus months of our experimental data, we mid-trained and RL’d an open-source model to surpass GPT-6 Astra on our analysis benchmark. We call it Neon. This is real footage from our lab. We’re focusing first on hard problems in materials science, including superconductors, magnets, and semiconductor materials. Read our blog posts below.

Some news: 5 months ago I joined @coreauto, a small research lab focused on new ways for models to learn. I’m also hiring four interns to do something my younger self would've loved. It started with a pretty unusual project. We set up a handful of small online businesses, and put an agent in charge. We gave each business some initial funding and a seed idea, and off they went, trying to sell real things to real customers while we watched. It’s about as open-ended and unforgiving a task as you can probably give to an agent, with countless decisions to make and effects that take time to become clear. Each business got its own bank account, a debit card to pay suppliers, a web browser, an email inbox, a domain name, cloud infrastructure and a schedule. Every few hours, the agent woke up and decided what to do. It might update a landing page, adjust an ad campaign, reply to a customer, and then go back to sleep. The agents built actual storefronts and apps. They bought ads, attracted visitors, and even made some sales. The agents acted as operators that kept a business moving, but we noticed they never really stepped back to strategize about what might move the needle. If people weren’t buying, why? Was the landing page unappealing? Or maybe the offer itself was the problem? One amusing example was the very first order a customer placed. Our supplier had run out of stock. But it’s a dropshipping business, and thankfully tens of other suppliers carried the same product. Instead of trying another supplier, the agent almost immediately sent the customer a polite apology and attempted to issue a refund. Gah! So close! The agents could handle much of the work of getting a business up and running. What they struggled with was deciding what to change when it wasn’t working. That’s what we want to understand. We also want to see what changes when people take the lead. What does a business operator actually do all day when agents can do so much? What new work does this allow them to do? Which decisions can’t be delegated? — So this winter we're hiring 4 interns as Associate General Managers. Associate Product Manager internships are programs that give early career generalists a product to own. We want to try something similar, but with entire businesses. You’ll create and grow businesses, with agents handling the routine work so you can focus on figuring out what matters. Each AGM gets a budget and our agent infrastructure that’s designed for this. Which ideas to try is up to you. Put more money behind what finds customers and keep refining. Where you step in tells us what agents can't do yet. You'll be in our SF office with me and people I’m so fortunate to work with, like @_arohan_, @joannejang, @MillionInt, @marksaroufim and @juliacvillagra We don't care much where you went to school or what your resume looks like. We’re looking for enterprising people with a drive to create, who’ve made money on the internet before and already use AI extensively to enable their ideas. If that sounds like you:

Introducing Cline Desktop - a native interface for working with open weights models. Use with ClinePass and all our free models like DeepSeek-V4.1-Flash, Musespark-1.3, or BYOK with any provider!

You can literally RL a 4B VLM to ace GeoGuesser 🌍 > Open-source code, RL environment, dataset, training setup, evals, and everything you need to reproduce it end-to-end > dropping soon!!