pranav

robot learning @uwcse • prev @morph_labs @atlasfellow

[Dwarkesh Patel @dwarkesh_sp]

In medieval times, within the arms race of ever more demonic torture devices, some sadistic genius came up with the idea of the Little Ease.

This was a prison cell built so small in every dimension that a grown man could not stand upright in it nor lie down at full length nor properly sit.

The pain is relentless and without relief and inflicted by one's own body. Prisoners were known to go insane within a few days. A stay at the Little Ease was considered even more cruel than the rack, the thumbscrew, and the other ghoulish machinery of the Tower of London.

A breeding pig will spend her whole life in a version of that box.

These are social, roaming creatures (more intelligent than dogs) who will never leave this corset of steel.

They have been selectively bred to be bigger than their frames can support. Yet we put them in cells so confined that they cannot comfortably sit, and their attempts to do so (for example, by sneaking their limbs into adjacent stalls) reliably lead to fractures and sprains.

They cannot sweat, yet have nothing to roll around in to cool themselves off. Except their own manure, which (contrary to the common misconception) they are so averse to (thanks to their strong sense of smell) that new sows will often suffer from constipation to avoid soiling the space from which they eat and sleep.

Here is how the writer Matthew Scully described what saw at one of Smithfield’s “gestation barn”:

> “Sores, tumors, ulcers, pus pockets, lesions, cysts, bruises, torn ears, swollen legs everywhere. Roaring, groaning, tail biting, fighting, and other “Vices,” as they’re called in the industry. Frenzied chewing on bars and chains, stereotypical “vacuum” chewing on nothing at all, stereotypical rooting and nest building with imaginary straw. And “social defeat,” lots of it, in every third or fourth stall some completely broken being you know is alive only because she blinks and stares up at you … creatures beyond the power of pity to help or indifference to make more miserable, dead to the world except as heaps of flesh into which the [insemination] rod may be stuck once more and more flesh reproduced.”

—

The Save Our Bacon Act is trying to unroll the few state protections we have against this barbaric cruelty - for example California’s Prop 12 - which banned the sale of pork from pigs kept in gestation crates.

It’s incredibly important we don’t end up with this sort of federal preemption.

SOB will not only kill the most important animal welfare related laws in the US of the past decade, but more importantly, it will also restrict ALL future legislative progress (aka how the animal welfare movement has gotten its biggest wins).

The Senate is currently deciding whether to add the SOB Act to the Farm Bill.

With relatively little money now, we can discourage the most pivotal senators in the Ag committee from backing this amendment.

Defeating this bill is even more important given the amount of philanthropic funding I expect to come online in the next year or two.

It will plausibly be over 10x more expensive to repeal SOB than to prevent it from passing in the first place.

All that money that could be spent transforming our society's relationship to mass animal suffering will instead have to be spent just getting us back to where we are right now.

That's why money spent now fighting this bill (and I mean right NOW) is so effective.

If you’re in a position to donate six figures, please DM me.

[pranav @_pranavnt]

@willdepue https://t.co/OHp1F3vFkW is essentially doing spectral autoregression

[Tyler Westenbroek @ty_westenbroek]

Real-world RL is still too brittle and data-hungry for long-horizon, contact-rich tasks.

We introduce Simulation Distillation (SimDist), which turns large-scale simulated experience into reusable world-model priors for rapid real-world adaptation.

By combining online planning with dynamics adaptation, SimDist achieves high success rates on tasks requiring precision, force, and reactivity.

Play with our interactive visualization to see for yourself: https://t.co/qFGNySxdAl

(1/n)

[Andrej Karpathy @karpathy]

@si_pbc @sonyatweetybird @MikowaiA @YasminRazavi @tszzl @_milankovac_ VPT (https://t.co/CSxHcXY6Vh) blew my mind back in 2022 so I was very excited to see SI scale up the idea with FDM1, but for knowledge work / computer use. Excited and looking forward to more!

[Ryan Kaufman @ryankaufman]

Delayed life update — I left @xai to join the amazing crew at @si_pbc.

Loving the small team vibes and fast research cycle. Excited to show you what we’ve been cooking!

[Standard Intelligence @si_pbc]

We’ve raised 75m in new funding from Sequoia and Spark Capital—partnering with @sonyatweetybird, @MikowaiA, and @YasminRazavi, all of whom are deeply supportive of our long-term mission. We’ve also brought on angels & advisors including @karpathy, @tszzl, and @_milankovac_.

-----

Our early results with FDM-1 moved computer use from a data-constrained regime to a compute-constrained one; this latest round of funding unlocks several orders of magnitude of compute scaling for that work. With the FDM model series we have a path to scale agentic capabilities through video pretraining, and we expect to achieve superhuman performance on general computer tasks in the same way that current language models have superhuman performance on coding tasks.

We’re also now able to invest in the blue-sky research necessary to our long term mission of building aligned general learners. To realize the civilizationally transformative impacts of AI, models must generalize far out of their training distributions, actively exploring and building skills in new environments. This capability represents a substantial shift from the current paradigm of model training. We believe that current alignment techniques are insufficient to predictably and safely steer a model with human-level learning capabilities, and so we’re doing work to study small versions of this problem in controlled environments to develop a science of alignment for general learners.

We’re a team of 6 people in San Francisco. We’re hiring world-class researchers and engineers to help us achieve our mission. If that’s you, please get in touch.

[Sonya Huang 🐥 @sonyatweetybird]

https://t.co/9pzUdUwv2t

[Thiel Fellowship @thielfellowship]

Welcome 2026 Thiel Fellows!

WHO ARE THEY?

Victor Boyd: Birmingham, AL - @VictorWBoyd
Cavalla is on a mission to get anything anywhere in under 5 hours. Starting by building autonomous forklifts, through to developing hypersonic highways.

Samuel Carvalho: Recife, Brazil - @samuelclcc
Praso is building the new infrastructure for wholesale commerce — powering procurement, credit, and workflow tools for SMBs across underserved areas in Brazil.

Nick Dobroshinsky: Sammamish, WA - @NDobroshinsky
EveryTicker is democratizing institutional-grade financial research across the entire U.S. stock market, including the thousands of smaller companies Wall Street ignores.

Ishan Gupta: Kanpur, India - @ishangpta
Juicebox is building an AI recruiter that helps companies make better hiring decisions. Agents that understand real skills and move hiring from guesswork to true meritocracy.

Antoni Kiszka: Strzyżowice, Poland - @antoni_kiszka
Derpetual is building the infrastructure to create a market for any asset — with leverage.

Milan Lustig: Cold Spring Harbor, NY - @HighPriestOfSWO
Opt32 is building modern compute infrastructure to put AI onboard objects in the physical world — from robots to cars and drones.

Galen Mead: Chapel Hill, NC - @g413n
Standard Intelligence is building aligned general learners, pretraining large models to actively explore and learn from the Internet.

Aubrey Niederhoffer: New York, NY - @needaubrey
Swoop is building the super app for Africa, starting with food delivery in Nigeria and expanding into financial services across the continent.

Harry O'Connor: Cork, Ireland - @HarryOC493
Sentient Machines is a research lab building foundational models for robotics that generalize across tasks and environments.

Alex Shieh: Salem, NH - @alexkshieh
The Antifraud Company is a fraud bounty hunter defending American taxpayers with AI and investigative journalism.

Claire Wang: Los Angeles, CA - @clairebookworm
Claire is building biologically accurate simulations of entire nervous systems, starting with C. elegans. Developing a simulated brain that researchers can communicate with helps lay the foundation for brain-computer interface (BCI) technology.

Kyler Wang: Portland, OR - @kylerywang
Action is an artificial intelligence company in stealth.

[University of Washington @UW]

Ending the season on a perfect note 🌸

[pranav @_pranavnt]

@tmychow @Meta Congrats!!

[Abhishek Gupta @abhishekunique7]

Excited to share the project that has surprised me the most in the last year!

Large-scale RL in simulation, no demos and no reward engineering can solve dynamic, dexterous and contact rich tasks. The learned behaviors are reactive, forceful and use the environment for recovery in ways that are extremely challenging to bake in or teleoperate!

You can play with the policies yourself to see: https://t.co/TCc4hb2baV

And, the learned behavior transfers to real world robots from RGB camera inputs!

So what’s the trick - using simulator resets carefully! Let’s unpack (1/10)

[Patrick Yin @patrickhyin]

We’re releasing OmniReset, a framework for training robot policies using large-scale RL and diverse resets for contact-rich, dexterous manipulation.

OmniReset pushes the frontier of robustness and dexterity, without any reward engineering or demonstrations.

Try the policies yourself in our interactive simulator! https://t.co/3hW3nYx2vD

(1/N 🧵)

[Runtime @RuntimeBRT]

🚨 Bengaluru-based @Airbound_Aero has conducted 700 flights for Narayana Health since January 2026 with a zero failure rate.

[pranav @_pranavnt]

@abinayaaaa @physical_int congrats!!

[Jesse Zhang @ ICRA 2026 ✈️ @Jesse_Y_Zhang]

A reward model that works, zero-shot, across robots, tasks, and scenes?

Introducing Robometer: Scaling general-purpose robotic reward models with 1M+ trajectories.

Enables zero-shot: online/offline/model-based RL, data retrieval + IL, automatic failure detection, and more!

🧵 (1/12)

[Dan Wang @danwwang]

For the better part of a decade, I've been writing that the US government's restrictions on Chinese tech companies were unstrategic, poorly explained, and based in caprice (https://t.co/Ohrn9qFtIL). Now it looks like the administration is turning these controls on American firms

[TBPN @tbpn]

Standard Intelligence's @devanshpandey responds to @tszzl's tweet that "text is the universal interface," and explains why their new foundation model is trained on video:

"At some point in the arbitrarily long future, if we only use text models, we could force most things to be text. But I think there are just a lot of things that are much more native when done from a computer-use [perspective]."

"GUIs are designed for humans to use. We have this massive long tail of things on the internet that are entirely undoable by LLMs."

"For example, when I do ML engineering most of my time is spent doing the grunt work of engineering. It's a lot of looking at graphs, analyzing, and comparing loss curves. You can do this in text, but it's a much larger pain than doing it in the native interface."

"There's a reason humans don't interact with a computer purely through text, it would kind of suck."

[pranav @_pranavnt]

@_neelr_ @comma_ai scary

[pranav @_pranavnt]

Incredibly impressive! VPT-style approach to computer use at 30fps, done entirely by a team of 5(!)

[Standard Intelligence @si_pbc]

Computer use models shouldn't learn from screenshots.

We built a new foundation model that learns from video like humans do. FDM-1 can construct a gear in Blender, find software bugs, and even drive a real car through San Francisco using arrow keys.