Midcentury @MidcenturyAI
Real-world superintelligence midcentury.xyz San Francisco Joined December 2025-
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Impressive OSS work from @sorozco0612. Closing sim2real gap means making teleop + data collection actually 3D-grounded. Cool @LeRobotHF integration as well
1/ I’ve been working on a new open-source project: LeRobot 3D 🤖📷 It’s a 3D-grounded teleoperation stack for the SO101 robot, built to make 3D perception and robot geometry easier to use as part of the LeRobot ecosystem. 🧵👇
50+ builders showed up at @fdotinc SF lab this weekend for the Physical AI Hack World Tour. Midcentury was proud to sponsor. 12 teams trained robotic arms to flip bottles, set tables, and move chess pieces, each with 40 to 50 video demonstrations and 2 to 5 hours of training time. This is what the front lines of physical AI actually look like. Shoutout to @fdotinc and @Ryan_Resolution for hosting
decentralization in AI in compute has existed forever. @SemiAnalysis_ has covered at-length why it suffers vs centralized options IMO decentralized/OSS data/benchmarks/evals seem long-term more interesting technically and directly impactful for future of AI deployment
@abra55513382 I think CSPs are the next big disruption target. Distributed compute in a world of AI seems like an obvious outcome. We’re just waiting for its “Hello world” moment so we know it is possible.
One model for all of physical labor. World-scale data collection speed, now finally announced.
Amazing endeavor. The closed loop data flywheel for everyday robotics only happens with mission-driven programs like this.
Introducing Eastworlds We help robots leave the lab faster Discover More eastworlds.io
This post demonstrates one of the most nuanced understandings of the AI space I’ve seen in a minute. This was a great read and really sharpened my mental model on the cyclical nature of the space right now— I highly recommend taking the time to read it. I wonder if the issue she’s describing in this post can be solved with some kind of abstraction in the post-training layer. For example, could we train a separate model to train future open source models on our proprietary data (sharpening synthetic data and evals with each iteration)? Another example that comes to mind are LoRA adapters and how they abstract the trained parameters in a portable way. I wonder if that strategy can be adapted to work cross-model on a larger scale. If we had a way to train some higher-level architecture once, and apply the performance gain to all future OSS releases, that would partially mitigate the part of the cycle where frontier generalist models takeover again. With this kind of technology, vertical AI would be a lot more feasible and stable. Just a thought though!
Our team member @B_S_N_Y had a chance to go to the ARC-AGI-3 Summit recently. Small curated crowd of high-power researchers and AI builders. Learnings in post
ARC-AGI-3 Insights: @arcprize is part of a dying breed of benchmarkers that are actually focused on identifying genuine intelligence, rather than objective capability alone. If you ask most benchmarkers out there about the mission or purpose of their benchmark, they wouldn’t
We attended a talk by @drfeifei at @Southpkcommons and had a great time. Discussion centered on world models, their open-ended definitions, and how they relate to building systems for real-world interaction and robotics. one cool learning: she spent years running a dry cleaning business before founding world labs
Some of the most important intelligence technology in the world was developed by @drfeifei. Her track record speaks for itself. And she's not stopping anytime soon. Join us at SPC on March 18th. Spots to RSVP below.
ego data is starting to see real evidence it helps scale robotic models! 20,000 hours used in total, one of the largest pre-training sets here.... what if I told you that certain players were already scaling to 1 million 👀
We trained a humanoid with 22-DoF dexterous hands to assemble model cars, operate syringes, sort poker cards, fold/roll shirts, all learned primarily from 20,000+ hours of egocentric human video with no robot in the loop. Humans are the most scalable embodiment on the planet. We
This is a cool demo but ultimately sorely limited. An agent without control over a learning mechanism will always make the same mistakes and leak the same info every time. Agents in order to be successful need to self-improve and operate their own learning loop from past experiences. Otherwise this is another twitter demo
I built the first AI that earns its existence, self-improves, and replicates without a human wrote about the technology that finally gives AI write access to the world, The Automaton, and the new web for exponential sovereign AIs WEB 4.0: The birth of superintelligent life
Huge congrats to the winners 🏆 @Poorvi_rh @anishmadan23 @SamuelPfrommer @praveenvnktsh @vib2810_ @shriishwaryaasv @sangam_chapagai @MahimanaB @DhabariaAnjali Gary Lim Benedict Chan These individuals are the future of Physical AI and Robotics
Midcentury contributed experience-based data that helps physical AI systems learn nuance. ▪️World-model gaming data Interactive environments where AI learns cause and effect. If I move, collide, or turn, what happens next? ▪️Ego-centric data First-person perspective data. Vision, motion, and spatial context as the agent experiences it, not from an outside observer. This kind of data is critical for training embodied systems that need to operate in the real world.
Physical AI Hacks was packed with the biggest minds in Robotics Hosted by @Oli_Robotics × @Fdotinc @Midcentury was proud to sponsor and support select teams with real-world training data, including world-model gaming data and ego data for physical AI.
new neo-labs will have to focus on net-new training paradigms to enable recursive self-improvement. May be directionally bearish for RL but importance of data will still remain
Introducing Q Labs, a research lab focused on solving generalization. Alongside others (SSI, Flapping Airplanes), we see data efficiency as the key problem, but we're taking an unconventional approach to solve it: a new learning algorithm approximating Solomonoff induction.
come train with our world model and ego data and try out some of our early benchmarks! Super excited for @MidcenturyAI/@getoro_xyz to be a sponsor for researchers and builders building cool stuff at @fdotinc
SF is officially the capital of Physical AI. 🌉 Thrilled to announce @cline and @virtuals_io are joining us as a sponsors for the Physical AI Hackathon in SF on Jan 31 – Feb 1! 400+ builders have already applied to get their hands on real hardware and multimodal data. If
RL Environments all the way down. playable worlds, playable apps, playable workspaces.
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