Jack Monas

This is going to be fun....excited to scale this up with you @_sam_sinha_!

For the last few months I've been working on a from-scratch implementation of AlphaGo, a 2016 AI breakthrough that inspired me to get into deep learning. My casual understanding of AlphaGo was "search-augmented deep neural networks trained with self-play", but I wanted to go deeper and understand it by creating it. Frontier deep learning research has always been expensive, but any given capability gets cheaper very quickly. In 2026, you no longer need DeepMind's resources to train a strong Go AI - you can vibe code all of it yourself for just a few thousand dollars of rented compute. It was a huge honor to be invited to teach this with @dwarkesh_sp on @dwarkeshpodcast I am an AlphaGo & Go apprentice, not a master, so all factual errors in the podcast are mine. Web version of tutorial: evjang.com/2026/04/28/aut… Code: github.com/ericjang/autogo Play the go bot here: autogo.evjang.com

Building Your NEO

Jitendra Malik rants about parallel-jaw grippers being inadequate; he believes multi-fingered hands with tactile sensing are necessary for advanced dexterous manipulation. Malik is a Professor at UC Berkeley and a Distinguished Scientist at Amazon.

Meet SceneSmith: An agentic system that generates entire simulation-ready environments from a single text prompt. VLM agents collaborate to build scenes with dozens of objects per room, articulated furniture, and full physics properties. We believe environment generation is no longer the bottleneck for scalable robot training and evaluation in simulation. Website: scenesmith.github.io 👇🧵(1/8)

Introducing DreamZero 🤖🌎 from @nvidia > A 14B “World Action Model” that achieves zero-shot generalization to unseen tasks & few-shot adaptation to new robots > The key? Jointly predicting video & actions in the same diffusion forward pass Project Page: dreamzero0.github.io 🧵 (1/10)

Every home is different. That means that to build a useful home robot, we must be able to perform zero-shot generalization on a wide range of tasks. Humanoid company @1x_tech has a solution: world models. 1X Director of Evaluations @itsdanielho joins us on RoboPapers to talk about: - why world models are the future for scaling robot learning - how to use world models for robot control - what world models unlock for evaluating robot model performance - how we can hill-climb from here to general purpose robots Watch Episode #61 of RoboPapers, with @micoolcho and @chris_j_paxton, now!

🚀 Diffusion too slow? Fix it in a few steps. 📢 Introducing NVIDIA FastGen — a plug-and-play research library for turning slow diffusion models into high-quality few-step generators. ⚡ What’s inside: • Consistency & MeanFlow (CM, sCM, TCM, MeanFlow) • Distribution Matching (DMD, f-Distill, LADD) • Long-video generation (CausVid, Self-Forcing) • Fine-tuning & KD (SFT, CausalSFT, KD, Causal KD) 🧠 Includes: 📷 EDM, DiT, SD 1.5, SDXL, Flux 🎬 WAN (T2V / I2V / VACE), CogVideoX, Cosmos Predict2 ✨ One unified interface. Research-ready. Apache-2.0. 🔗 Blog: nvda.ws/3LARhFy 💻 Code: github.com/NVlabs/FastGen

@Miccooper9 Really cool!

[1/6] Ego-centric World Models We introduce EgoWM — a video world model that simulates EVE-1X humanoid interactions from a single ego-view image + full-body joint angle trajectories. Moreover it effortlessly generalizes to extreme OOD domains, including paintings !

We release Cosmos Policy 💫: a state-of-the-art robot policy built on a video diffusion model backbone. - policy + world model + value function — in 1 model - no architectural changes to the base video model - SOTA in LIBERO (98.5%), RoboCasa (67.1%), & ALOHA tasks (93.6%) 🧵👇

@ericjang11 Eric the Mage, thank you for everything. It has been a pleasure working with you.

Life update: I've decided to leave 1X. It's been an honor helping grow the company. I joined Halodi Robotics in 2022 (prior name of the company) as the only California-based employee. At the time, we were about 40 based out of Norway and 2 in Texas. My first hire and I worked from my garage for a few months to save money. Today, 1X is hundreds of people, with hardware, design, software, AI, manufacturing, product all relocated to the SF Bay area, firing on all cylinders and working on getting NEO ready for the home. A big thank you to all my colleagues that I worked with. It was a hard decision to leave. When working at an exciting startup that is growing fast, there's always so much to do and never a perfect time time to move on. We have several works in the pipeline that are so exciting because they greatly advance general autonomy and scalability of our deployment approach and really show a realistic path towards the product working. The recent World Model autonomy update is one example, and there's more coming. The 1X factory is so exciting. Things are accelerating at a speed I would have been surprised by a few years ago. In 2022, most technologists and researchers and VCs were skeptical about humanoids and large scale imitation learning. "Why Legs?" "How could end-to-end learning ever be good enough?" "Why go for the home and not the factory?" "How will we ever gather enough data?" The Overton window on general-purpose robotics has shifted a lot since then. Although we are still early in our mission, I remain confident that soon, house robots will be as commonplace as air conditioners, cars, and ChatGPT. Just talk to the bot, and it will go and quietly get it done. Entire economies will eventually re-organize around this technology. People get it now. What's next? I believe that progress in applied deep learning generally rides on "harnessing the magic" of a few magical objects. These magical objects possess way more generalization power than one might normally expect. Just asking the LLM to understand what you want is magic. Video generation models are magic. Reasoning is magic. You don't run into a magic object every day, but when you do, you make sure to grab it and put it to work to make something useful in the robot somehow. A lot of my early conviction for where robotics was headed was working on BC-Z from 2018-2021. The "magical object" I bet on at the time was the surprising data-absorption capabilities of supervised learning and "just ask for generalization". This pioneered a lot of the standard ingredients we see in VLAs today: - Generalization to unseen language commands - Human-Guided DAgger for policy improvement - Open-loop auxiliary predictions + receding horizon control, AKA action chunking - Manipulation keypoints to improve servoing - Simple ResNet18 with FiLM conditioning on multi-modal inputs The next "magical object" we bet on at 1X was video models, because they are clearly magical objects that learn a data distribution not too dissimilar from what a robot needs to learn. They generalize surprisingly well. I am once again feeling that there are more magical objects in play now, which opens up a lot of new possibilities for robotics and beyond. I'm taking a few months to empty my cup of priors and gain fresh perspective. When I left Google in 2022, I spent about 2 weeks deciding what to do next. This time, I want to take a lot more time to catch up what has happened in the broader AI + robotics space. I've been re-implementing some deep learning papers. I'm working on a big tutorial for my blog. I'm learning all the Claude power user tricks. I'm reading the Thinking Machines blog posts to understand what kinds of experiments are being run at frontier labs. I'm reading Ben Katz's 2016 thesis on the Mini-cheetah actuator. I'm traveling to China in March to meet incredible companies in the Chinese robotics ecosystem. Now, more than ever, is the time for both humans and machines to learn. The next token of my life sequence will be an important one. To colleagues and investors that bet on 1X early, even before we became a household name - I thank you from the bottom of my heart. I won't forget it♥️

@robotryer @1x_tech Interesting questions. The user should be able to define behaviors. We have some work to do to figure out the “how” so stay tuned.

@JackMonas @1x_tech How and who defines the preferred behaviors? Will the owners be able to define NEOs “personality “ in that regard?

One of many next steps at @1x_tech: preference learning for world-model-based policies. Given a generated starting frame, we can sample multiple video rollouts from our WM and use preference feedback to steer the model toward higher-quality behavior. This lets us fix policy failures in synthetic worlds—resolving bad NEO behaviors with generated dogs before we ever meet real ones.

@bobertorien @1x_tech NEO's specialty is belly rubs

@JackMonas @1x_tech But does it work for cats?

@fatemi_michael Your work during your time at 1X helped lay the foundation for this release. Thank you Michael!

Very nice progress!

@_advaitpatel @peterliuposts 🐐

@peterliuposts so what i'm hearing is you're lowkirkenuinely the goat

One of the coolest examples we found is NEO holding up a peace sign WM both understands what a peace sign is and is self aware (no hands in starting frame) + the IDM extracts finger level actions :)

LLM memory is considered one of the hardest problems in AI. All we have today are endless hacks and workarounds. But the root solution has always been right in front of us. Next-token prediction is already an effective compressor. We don’t need a radical new architecture. The missing piece is to continue training the model at test-time, using context as training data. Our full release of End-to-End Test-Time Training (TTT-E2E) with @NVIDIAAI, @AsteraInstitute, and @StanfordAILab is now available. Blog: nvda.ws/4syfyMN Arxiv: arxiv.org/abs/2512.23675 This has been over a year in the making with @arnuvtandon and an incredible team.