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Eric Jang
@ericjang11
4.1K Following    136.9K Followers
I really liked this line from Matei's opening remarks at #RSS2026#: "Wait for your current conference buddies to become the big names in the field." Today's peers are tomorrow's leaders.
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When I first started at X, this is one of the things I did a deep dive on because I felt it was critical to the integrity of the experienced. I tasked our Threat Disruption team to investigate a number of trends that seemed artificial. The findings: We could not find meaningful examples of foreign interference in US policy discussions, except people gaming rev share in developing countries. This is what motivated the release of the Country of Origin feature and significant changes to the rev share algorithm. The most deranged & divisive replies generally were from residential IPs in the United States—with no signs of using a VPN. Ultimately, X is a reflection of the internet. And that means you will see the full spectrum of human thought. And sometimes the most outrageous takes will catch fire. Having said all of this, there can still be cases of narratives being boosted but the origin of the initial post is almost always domestic and we have hardened our systems in the last 3 months to prevent this.
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We are in the Uber/Lyft era of intelligence. Competition is wonderful for consumers
We're extending Claude Fable 5 access on all paid plans, as well as keeping Claude Code’s weekly rate limits 50% higher, through July 19.
Cool set of experiments! Useful for folks getting into WAMs
WAMs are popular because of their promise of better generalization. Is that true? We started playing with Video-Action-Model (VAMs) and realized a gap: video model backbones can compositionally generalize but VAMs often do not. We coin this the Video-Action-Generalization (VAG) gap and present a study on how to explain and improve it. More details: 🧵 below
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Yesterday, we made GPT-5.6 Sol Ultra generally available. Today, we're sharing that it produced a proof of the 50-year-old Cycle Double Cover Conjecture using 64 subagents in just under one hour. We're sharing the prompt and proof below. We're excited to see what you all do with Ultra!
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What is the TAM on invite-only Physical AI dinners
really impressive effort. This graphic on the leaderboard is also amazing
We evaluated 30+ frontier embodied AI models. The result is clear: current generalist robot policies are still far from robust real-world manipulation. This is why we built RoboDojo.
Amazing
Did you know you can create the Maxwell-Boltzmann distribution with just some balls and a motor? I built this simple device to illustrate it. This is how it works: A spinning agitator pumps energy into ~400 balls, so they bounce around and collide like gas particles in a box. They escape one at a time through a tiny hole, then fall a fixed gap into bins below. Since the hole is so small, every ball leaves with no vertical speed and takes the same time to fall. Constant fall time means how far a ball flies sideways is set purely by its speed. A faster ball goes into a further bin. So each bin is really a speed. Tally all 400 and you get the Maxwell-Boltzmann distribution.
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this is a fun format! A lot harder than it looks on both the cooking and interviewing and responses side. Might unironically be a good AGI test for humanoid robots - entertain guests, predict the future, while cooking a meal. Specialization is for insects
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children's playtime with action figures and Barbies and hot wheels about to get fuckin lit
Ok, this is absurd. You can choreograph a complex action scene in Blender with basic shapes, then let Seedance make it real. You need to try this AI filmmaking workflow: 1. Generate a start frame in Midjourney 2. Block out the action in Blender 3. Feed both to Seedance My Blender reference was just rough timing, camera shake, and spatial choreography, and Seedance tracked the speed, motion, and action way better than I expected. This is the difference between describing a shot and directing one.
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Amazing bounty for the field
1/ Introducing HIW-500 (Humanoids-in-the-Wild 500): the largest open-source humanoid teleop dataset collected in real homes Built w/ @UnitreeRobotics @huggingface across 12 homes in Southeast Asia, it covers: > 500+ hrs > 23K+ episodes > 10+ TB > 10+ household tasks
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progressively warm-starting dynamic retargeting trajectories seems to be simple yet quite effective project page:
Watched a cute animal video that I knew to be AI all the way through
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giving a talk next week in NYC. here is a sneak preview of one of the slides Would love to meet robotics companies in NYC next week! Please DM / reply if you're interested
Progress on NEO’s AI has been really fast of late. Here are some early clips of a generalist model we’re developing at @1x_tech. The following clips are 100% autonomous, running on a single set of neural network weights. First, a quiet little robot that picks up leaves and puts them in a bag.
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