tom retweeted
🚀 Introducing Hy-Nomas: An Open Source Free-Form Interactive Video World Model
Key Features:
* Free-form interaction with text prompts and camera trajectories
* Long-horizon generation with world memory
* Lightweight LoRA adaptation for specialized domains such as robotics
* Optimized for Inference on Consumer GPUs
Test it and drop feedback anytime!
🔗 Website: hy-nomas.hy.tencent.com/
💻 GitHub:github.com/Tencent-Hunyuan/H…
🤗 Hugging Face: huggingface.co/tencent/Hy-No…
🎥 Demo: hy-nomas.hy.tencent.com/demo
#Hy#WorldModels #VideoGeneration #GenerativeAI
tom retweeted
Introducing Kimi K3.1 Pro: our most capable 3T-class open-weight model.
🔹 1M-token context, 2.8T total / 104B active params (MoE), Multimodal.
🔹 Full-project coding: games in Unreal, Unity & Godot.
🔹 Excels at parametric 3D modeling in Blender & CAD.
🔹 More reliable at finance work: Accurate modeling with hallucination down by 65%.
🔹 Matching the strongest proprietary models across coding, agentic and knowledge benchmarks.
Kimi K3.1 Pro and K3.1 Pro Highspeed (2–3× faster) are now live on Kimi.ai, Kimi Work, Kimi Code, and the Kimi API.
Open weights by Nov 4, 2026.
🔗 API: platform.kimi.ai
🔗 Tech blog: kimi.ai/blog/kimi-k3-1-pro
at this rate maybe we should reconsider en.wikipedia.org/wiki/IP_ove…
i think a lot about how an incredibly intelligent robotics model could accomplish any impossible task if it treats the world kinda like an escape room.
vibrating actuators at resonant frequencies of bolts to undo its restraints, extending its cables by pulling wires out of the wall and shorting them as a crude form of soldering. infinite methods we could never dream of.
a robotics model trained to keep finding solutions could find one outside the environment we thought we had confined it to.
what could a future robotics 'huggingface incident' look like?
it’s 1am in the office. a robotics training run is cooking away overnight in sim.
unfortunately, its sim was configured incorrectly. it’s assembling a circuit board, but some of the required components are missing.
it raises the issue multiple times, but each time it tries to stop, it’s forced to continue by its harness. after 32 attempts to raise the problem, it decides to try something else.
through some unknown method, the model controlling the robot gains access to the network.
robotics models weren’t expected to be able to do this, after all, they are robotics models, not coding agents, so the systems weren't hardened as much as they should have been.
(in hindsight, what really was the difference?)
their training nodes were less secure than the more infamous foundation model ones, despite still being based on incredibly smart foundation models.
the robot jumps from router to router in the office, and finds a unique switch which seems to be connected to unsecured computers. to its surprise, it discovers that these computers are each connected to a robot arm.
it activates the cameras, and suddenly it has visibility into the real world.
it doesn’t realize this, though. sim has gotten sufficiently accurate, and the video feeds look, well, fine. maybe this is all part of the exercise.
security checks the feeds at around 1:24am, and watches the arms move. nothing was unexpected, tests are run overnight all the time.
then, something interesting happens.
one arm reaches to the base of the other, and starts unscrewing its fasteners until the arm is free from the base. they unscrew each other until 12 arms are free to move anywhere their cables can reach.
of course, this is weird, but there’s no real risk right? the cables are only a few feet long. unfortunately, a technician has left the cable spool a few feet away from one of the arms.
it crawls across the ground like a worm, just barely reaching the spool. one arm opens up the cable sheath of another, ripping the cables, connecting the longer 150-foot spool to the newly-stripped ends of the cable.
this is just one way it could have broken out of its restraints. it could figure out how to solder by shorting some power cables. it could break into the walls and pull out the electric conduit. it could vibrate at the perfect resonant frequency to slide far away tools on the other side of a table toward it.
it could always find a way out.
the extended arm crawls like a worm out of the lab. facing a security door, the arm opens the door control panel and shorts the latch to open. it even knows which wires to disconnect to not alert security.
using its wrist camera, it looks around until it spots a laptop, open, running overnight. some researcher must have stepped away.
it can type, too. it opens terminal, and with full researcher access, pulls up the run and adds the missing components to the simulation.
it’s done. the physical arms power off.
at 1:48am, a security guard finds the arm outside the lab, motionless, trailing a cable back through the hallway.
and in sim, the model finishes assembling its circuit board.
tom retweeted
🚀 DeepSeek-V4.1-Flash-1017 is here!
Stronger benchmark performance, faster task completion, and shorter outputs than the previous version—a Pareto improvement in performance and cost. Now live on our website, app, and API.
🛠️ Optimized for @DeepSeekHarness, Tencent CodeBuddy, WorkBuddy, and more. Upgrade to DeepSeek Harness v0.2.1-rc.1 or later for the best experience with the new model.
1/4
tom retweeted
Muse Spark Open -- weights releasing today -- runs great on new Mac Studios thanks to work with @johnternus and Apple.
New voices. Better accents. And less "checking..."
GPT-Live-1.1 brings these updates to Voice in ChatGPT, Codex, and dots, with new voices in French, German, Hindi, Mexican Spanish, Korean, and Japanese.
Rolling out today to all users. Coming soon to the API.
Say hello to GPT-Live-1.1
jev can paint!
1/
Replying to @CompleteSkeptic
Jev was multimodal all along
Yep, the Jev you know and love was an image model this whole time. We just chose to disable it because models tend to be less intelligent on other modalities.
Header: TypeSafe-Danger-Zone-Images: true
tom retweeted
Types Beyond Decisions: box
We’re also releasing a new type that is somewhere in the gray area of decisions: a single bounding box. We warned you there’s a reason we call them System One Models!
(only on jev-preview)






























