Conan retweeted
We found a way to scale humanoid control that reduces GPU-hours by more than 1000×.
Across 129,514 motions, SuperMimic surpasses the state of the art (128 GPUs, 7 days) with 1 GPU in 3 hours.
Project: supermimic.github.io
Demo: supermimic.github.io/demo/
Conan retweeted
The @Tesla Roadster stunts, simulated in MuJoCo! Thrusters are site actuators, the ramp suction uses adhesion actuators, and the builtin box QP decides how hard to fire each jet.
Conan retweeted
A retired NASA engineer who had worked on the Space Shuttle program for 25 years was hired by SpaceX at age 62. He described the first week as the most disorienting experience of his professional life.
At NASA, a design change required 3 meetings, 2 committees, a review board, and 6 to 18 months of documentation. At SpaceX, his new supervisor said "if it works, build it. If it doesn't, change it by Friday."
He sat at his desk after the first day and called his wife. He told her he felt like a man who had been swimming with ankle weights for 25 years and someone just cut them off. He could move. He could decide. He could build something in the morning and test it in the afternoon. The constraint that he had accepted as the nature of aerospace engineering turned out to be the nature of bureaucracy.
Within 6 months he had contributed to more design iterations than he had completed in his last 5 years at NASA. Not because he was working harder. Because the time between having an idea and testing the idea collapsed from months to hours. The same brain. The same knowledge. A different operating system.
He described a specific moment where he proposed a modification to a Raptor engine component. At NASA, the proposal would have entered a queue. At SpaceX, the modification was machined by the end of the day and tested by the following morning. He watched his idea become a physical object in 18 hours. At NASA, 18 hours would not have been enough to schedule the first meeting to discuss whether to schedule a meeting.
He told an interviewer: "I didn't get smarter when I went to SpaceX. I got faster. The intelligence was always there. The system was in the way."
The difference between SpaceX and NASA is not talent. NASA has extraordinary talent. The difference is the time between a thought and a test. SpaceX compresses it. NASA elongates it. The same engineer produces 10x more at SpaceX than at NASA because the bottleneck was never the engineer. It was the meetings about the meetings about the work.
.@physical_int is building one of the smartest flywheels in robotics.
19 partners across factories, farms, kitchens, warehouses, hospitals and homes. Each deployment creates failures and edge cases that become eval + RL data.
The model is the product. The deployment network is the moat.
The Pi API is officially in beta! 🤖
Many people have already been using it to fine-tune π models for their robots -- below are a few
We’re hoping to expand testing significantly. Let us know if you are interested!
Blog: pi.website/blog/api-beta
Signup: pi.website/waitlist
Dear Prime Minister Ambani,
Please accept my humble apologies for not realizing that you are the real boss of India.
Naturally, you would prefer to maintain your monopolistic exploitation of the great people of India, but would you nonetheless consider allowing Starlink to compete?
There are many areas of India, as there are in all countries, with no Internet access, which denies children the opportunity for education and for small businesses the opportunity to sell their goods to a global market. Starlink would change their lives for the better.
Thank you 🙏
A deeper existential crisis in the post-AI era
it feels like everything is becoming inflationary at a greater & greater pace.
there’s simply more of everything, which means each individual unit of it means less & less. so everyone has to do more, signal more, achieve more, post more, & communicate more just to maintain the same relative position.
what a crazy time.
Selecting and setting up the right initial state is an art
As robots become more reliable, DAgger becomes far less efficient.
eg 90% success means the person spends >90% of the time passively watching the robot
Key idea: failures come from similar initial states & we can focus learning on these states!
Paper: arxiv.org/abs/2610.05882
Data companies' revenue machine
but it seems run-rate's PE ratio's not that high?
A company I work with went from $0 -> $60M in 3 months - will hit $100M run-rate this month
Fastest growing company I’ve ever seen. I don’t think people have fully grasped data as a first class citizen alongside compute and algorithms
They are selling frontier data to AI labs and model developers. DMs open if you would like to chat with them
He has actually already given the answer, which is Siri
Man, I wish Steve Jobs were still alive.
He would’ve built the best personal AI agent.
The biggest limitation of Instint and Muse is that they can’t control most apps on my phone.
Apple controls the entire iOS ecosystem. No company is better positioned to build an AI agent that can do literally everything on your phone.
And yet somehow, Siri still sucks…
Conan retweeted
Can we build intelligent robots to assemble complex AI hardware? 🦾
At NVIDIA’s Seattle Robotics Lab, we’re tackling that question through GB300 tester-tray assembly, from fastening busbars to inserting delicate connectors. 👇
I’ve had 3 diff people tell me they are 100% sure this “fastest to $1B annualized revenue” is 3 diff companies.
Very likely more than 1 data co has recently hit that mark, many of which are on Pavlov’s List.
Many more will get there in the next 6mos.
Total data spend is >$15B.
Fastest company ever to $1b annualized revenue
Nope it’s not Higgsfield or Cursor or Cognition.
Met a data company yesterday that did 0 revenue in November 2025 and 80m “in month revenue” in September 2026. They will hit $1b annualized from 11 months of being started. They are still under the radar so I can’t share who they are. Hopefully soon, with their permission.
I don’t make the rules.
why can't we just have confidential cloud with private inference
Is that bc local inference adds more trust?
Introducing Underdog, your Private Personal AI on devices you already own
Today we're announcing our backing from @a16z @khoslaventures @HummingbirdVC Anthology (@AnthropicAI @MenloVentures) @patrickc @naval @rauchg @polynoamial @Thom_Wolf @Mascobot @tszzl @OfficialLoganK and other top AI leaders
Our mission is to provide free, capable, reliable and private AI to billions of people.
Underdog’s Law: today’s frontier intelligence reaches your devices in six months.
We’re starting with fast, capable AI that runs on consumer hardware. By co-designing models and inference engines, we’re pushing the frontier of capability, speed, power and data efficiency.
Our research also spans agentic commerce, confidential inference, and how AI will reshape the internet economy.
Privacy and capability no longer needs to be a tradeoff. If you believe in this future, join us. Time to build.
This is an exciting frontier. I just remembered watching a sample of egocentric surgical footage last week; it was so graphic that I had a hard time keeping my composure
Replying to @dylanmcguir3
@dylanmcguir3 and I just raised a $7.5M pre-seed led by @a16z to build a robotic surgeon @alephsurgery
Much of the early investment in Physical AI has focused on industries where labor is relatively abundant and low-cost. We believe the biggest opportunities to change the world lie at the opposite end of the spectrum.
Surgery is among the most valuable forms of labor on the planet, but it's far too scarce and expensive - due to an exceptionally long training pipeline and highly concentrated expertise.
We’re building the first surgeon that scales: one system that can acquire, refine, and transfer surgical skills across millions of procedures. v1 is already learning to perform surgical tasks autonomously from demonstrations, and improve from its own experience.
Lots of great folks behind us! @BoxGroup, Reveille VC, Constellation, @forward_deploy, @Valkyrie_vc, @DiscipulusVent, @maniacvc + more
Another sign of AI’s growing appetite for data. Like @ilyasut said in 2024, “Data is the fossil fuel of AI.” Will a few big providers dominate, we see @mercor @scale_AI @handshake @AfterQuery @micro1_ai or will growing demand create room for more players? Will demand stay concentrated in frontier labs, or will downstream companies become major buyers of task-specific training and evaluation data?
Proximal builds infrastructure that enables AI to improve from real-world experience.
In the past year, we have grown to more than $200M in annualized revenue helping frontier labs and enterprises improve coding agents. Now, we are expanding beyond software engineering.
Recent progress in mathematics demonstrates what happens when models are trained in domains with massive amounts of public data and easy verifiability, making it easy to find weaknesses, generate training tasks, and iterate quickly.
We build infrastructure that enables this feedback loop in other domains: we are excited about a world in which AI systems design targeted drugs, accelerate chip design, and rewrite legacy software that still runs hospitals, power grids, governments, and other critical infrastructure.
Since starting, we have assembled a small team of researchers and engineers and built world-class infrastructure for post-training and synthetic data research to overcome the challenges of scaling manual data curation.
Our team comes from Cursor, Google DeepMind, Meta Superintelligence, Prime Intellect, Citadel, and Jane Street. More than half are former founders.
We’re fortunate to be backed by investors who share that vision, including @generalcatalyst who led our $15M Seed Round at a $300M valuation, as well as @chemistry, @svangel, @dvlamoen and individuals like @LiamFedus, @kevinweil, and @bernhardsson























