Jinsheng Wang

Make it simple. Make it work.

[Cursor @cursor_ai]

Together with SpaceXAI, we’re training a significantly larger model from scratch, using 10x more total compute.

With Colossus 2’s million H100-equivalents and our combined data and training techniques, we expect this to be a major leap in model capability.

[HappyOyster @HappyOysterAI]

Secret Recipe for The Wanderland Journey ☁️✨
1️⃣ A sprinkle of imagination 💡
2️⃣ Two scoops of curiosity
3️⃣ Perfectly flavored with HappyOyster Directing Mode🍽️
Welcome to a world where your ideas come to life. You bring the story, and HappyOyster Directing Mode makes the movie magic happen!🎥
🌱 Plant your imagination: https://t.co/HXJtUOaVPF

#HappyOyster #Alibaba-ATH #Worldmodel #aivideo #aitools

[Jiayi Weng @Trinkle23897]

Codex grew programmatic policies with no neural nets: max score on Breakout, and SOTA-level scores on MuJoCo.

Maybe heuristics were not too weak. Maybe they were just too expensive to maintain. Maybe it's the next paradigm.

https://t.co/1ZaIneleuW

[will brown @willccbb]

https://t.co/gCIFKAjB0Z

[HappyOyster @HappyOysterAI]

🧩 Remember the feeling of opening a brand-new box of blocks?🧸
We’ve bottled that magic and turned it into a world you can explore!
🖍️Build, Direct, and Explore with HappyOyster Directing Mode.
Jump into a world of blocks!🤖: https://t.co/HXJtUOaVPF

#HappyOyster #Alibaba-ATH #WorldModel #AI

[HappyOyster @HappyOysterAI]

♟️Welcome to Alice in the Pink Abyss
In a world of silent chessboards and monochrome dreams, she is the only rhythm🎭
Direct your own story with total freedom in HappyOyster Directing Mode🎞️
Redefine your reality. Explore the immersive unknown at: https://t.co/HXJtUOaVPF

#HappyOyster #Alibaba-ATH #WorldModel #Alibaba #AI

[HappyHorse @HappyHorseATH]

HappyHorse-1.0 Update: i2v & t2v are now live on Arena! 🚀

Early evals show exceptional performance. We’re in the final sprint for the official launch in 2 weeks.

Get early access and try it out now: https://t.co/iiyfgPsKlx 🐎✨

@arena @AlibabaGroup #AlibabaATH

[Jinsheng Wang @wolfwjs]

The world is your oyster.
Now, open it. 🦪

[HappyOyster @HappyOysterAI]

The world is your oyster. Now, open it. 🦪
Happy Oyster is an open-ended world model product for real-time world creation and interaction, built by Alibaba-ATH.

Early Access starts today.

🎯 Join the waitlist: https://t.co/ywcOH1rFO3

[HappyHorse @HappyHorseATH]

HappyHorse-1.0 is now live on Arena! 🚀

Early evals show exceptional performance in Video Edit. We are now in the final optimization sprint for the official launch in 2 weeks.

We invite the community to get early access and test our capabilities at https://t.co/iiyfgPtib5. 🐎✨

[Jinsheng Wang @wolfwjs]

Just an appetizer. Stay tuned for what’s coming next.☕️🐴

[HappyHorse @HappyHorseATH]

HappyHorse is still in the stable. 🐴

No official website yet — anything you've seen out there isn't us.

We're part of Alibaba's ATH AI Innovation Unit, and when we're ready, you'll know. Stay tuned.

#HappyHorse #Alibaba #ATH

[Liam Fedus @LiamFedus]

RL against verifiable rewards in LLMs has clearly opened a very powerful regime. It works, and because it works, there is a strong tendency to view more and more problems through that lens.

You optimize for tasks where the reward is clean, where success is easy to check, where the feedback loop closes quickly. This is productive and will keep paying off. But it also creates a bias: you start emphasizing what is legible to the training setup, not necessarily what is most valuable.

Scientific reasoning is a good example. Not every step in science is something that can be cleanly graded at the moment it is produced. A hypothesis can later fail experimentally and still have been exactly the right kind of thinking at the time: creative, mechanistically grounded, and responsive to the available evidence. “Turns out to be wrong” does not imply “was low-quality thinking”.

A big part of the next frontier will be AI systems that can operate well under this kind of uncertainty, just like a big part of the last one was RL against verifiable rewards.

[General Reasoning @GenReasoning]

Introducing OpenReward.

🌍 330+ RL environments through one API
⚡ Autoscaled sandbox compute
🍒 4.5M+ unique RL tasks
🚂 Works like magic with Tinker, Miles, Slime

Link and thread below.

[William Shen @shenbokui]

UNI-1 is intelligent, directable, cultured. Incredible range it can do.

Incredibly proud of the world-class team building a world-class model.
It’s a daunting task to go up against industry giants like Deepmind/OpenAI/Bytedance.

More to come! API, technical report, model card…

Come join us!

[Elon Musk @elonmusk]

@bindureddy Google will win the AI race in the West, China on Earth and SpaceX in space

[Grok @grok]

Marc Andreessen says true innovators share these 5 traits:

1. High openness – eager for new ideas from anywhere.
2. High conscientiousness – willing to grind for years.
3. Disagreeable – ignore critics and push on.
4. High IQ – handle complex info fast.
5. Low neuroticism – stay calm under pressure.

Rare combo that drives breakthroughs.

[AK @_akhaliq]

WorldCam

Interactive Autoregressive 3D Gaming Worlds with Camera Pose as a Unifying Geometric Representation

paper: https://t.co/2Ek7HQSM2d

[Moonlake @moonlake]

https://t.co/vHXbGcwESD

[Jinsheng Wang @wolfwjs]

@JustinLin610 Best wishes!

[Oscar Michel @ojmichel4]

📢Current world models aren't really modeling the world; they're modeling one agent's view of it. Partial observations ≠ world state.

Future world models will be independent of any one agent's perspective. You will be able to “drop in” any number of agents at any point in time, and a persistent world state will evolve with their interactions. Imagine a neural MMORPG server. 🧵[1/10]

[Saining Xie @sainingxie]

world modeling is never about rendering pixels.

rendering is local. world state is global. as soon as more than one agent exists, the only thing that truly matters is the shared representation beneath individual views. that shared representation is what scales into collective capability.

this is why I'm super excited to share project Solaris -- our new work focused on building a multiplayer video world model in minecraft.

This release includes three main pieces.
1⃣Solaris Engine, a fully featured multiplayer data collection system with built in visuals. the team put a huge amount of work into this since nothing like it really exists yet.
https://t.co/dw9lTmr9Pk
2⃣Solaris Model, a multiplayer DiT with a new memory efficient self forcing design, trained on 12.6M frames of coordinated Minecraft gameplay.
https://t.co/cVjlGKcNUf
3⃣Solaris Eval, which uses a VLM as a judge to evaluate different multiplayer capabilities.

read the full technical breakdown by @ojmichel4, and start building with Solaris.

https://t.co/1NkdqSRZy5