Stain Lu

building infra for the next expression.
game & AI.
rl is general learning i think.

[Stain Lu @stainlu]

we built an open-source version of workspace agents

- any model, self-hosted
- per-session sandbox
- credential isolation

https://t.co/MEu6zcz9Xn

[OpenAI @OpenAI]

Introducing workspace agents in ChatGPT—shared agents that can handle complex tasks and long-running workflows across tools and teams.

[Stain Lu @stainlu]

@yichao_liang @iclr_conf grats Yichao! would you mind checking DM?

[Stain Lu @stainlu]

🦞 🖤 🌍

[morgan — @morqon]

total lobster domination

[kepano @kepano]

Lee las publicaciones originales de Karpathy sobre cómo utiliza Obsidian. Personalmente, no me gusta el término "segundo cerebro", ya que sugiere que Obsidian debería usarse únicamente como un sistema de memoria externalizada. Más bien, es importante considerar que escribir es una forma de pensar. Si dejas que la IA haga todo el trabajo de pensar, no estarás aprendiendo por ti mismo.
https://t.co/JEbx8W7N2B

[Stain Lu @stainlu]

@li_yitang grats! impressively beautiful work

[Jims @JimsYoung_]

Most "agents" are basically interns with API access.

They ask you to subscribe to the Times. Download the file. Forward it over. You're not deploying an agent at all. You're managing one.

It's why we built Anyway. Anyway is the financial OS for agents that actually operates by itself. Pay, transact, and run your own business, with no babysitter required.

Comment "Anyway" to try it out.

[Stain Lu @stainlu]

@cheryyun_l but (

[Stain Lu @stainlu]

@lucasmaes_ great job! hope we can get more 'worlds' here some day

[Stain Lu @stainlu]

we need more 'worlds'

[Lucas Maes @lucasmaes_]

Would you like to join the research effort on JEPA and World Models easily?

After a full year of hard work, we’re excited to finally release stable-worldmodel:

an open-source, scalable platform built to accelerate JEPA & World Model research!

📄: https://t.co/gnxGvens5A

[Stain Lu @stainlu]

object as continual data flow

[Stain Lu @stainlu]

[✅] abstraction
[ ] encapsulation
[ ] inheritance
[ ] polymorphism

[Chongjie(CJ) Ye @ychngji6]

https://t.co/k4DHsjmXJi

[Stain Lu @stainlu]

@thsottiaux a reset

[Sakana AI @SakanaAILabs]

Introducing DiffusionBlocks: Block-wise Neural Network Training via Diffusion Interpretation

https://t.co/c9AvsRKybj

What if we didn’t have to hold an entire neural network in memory to train it?

Standard neural net training optimizes all parameters jointly. As a result, the memory required during training grows linearly with the depth of the network.

In our #ICLR2026 paper, we propose DiffusionBlocks, a principled framework to train networks one block at a time, drastically reducing memory requirements while matching end-to-end performance.

With DiffusionBlocks, we split the network into blocks and train them one at a time, so you only need memory for a single block.

How? We explicitly assign each block a role: to move the representation a little closer to the target than the block before it did. That role turns out to be precisely what a diffusion model does, step by step. Each block only needs to optimize its own objective and can be trained independently.

We validated this across five different architectures:

• ViT
• DiT
• Masked diffusion
• Autoregressive transformers
• Recurrent-depth transformers

In each case, performance is competitive with end-to-end training while using a fraction of the memory.

This perspective also extends naturally to recurrent-depth (Looped) transformers, which apply the same network iteratively and normally require expensive backpropagation through time (BPTT). Viewed through DiffusionBlocks, we can replace those multiple iterations with a single forward pass during training.

Read our paper and code, to learn more.

Paper: https://t.co/CRj96VGYQn
GitHub: https://t.co/eNW0K9Xh8E
🐟

[Stain Lu @stainlu]

'random' does not exist.

[cat @_catwu]

Excited to share our most powerful new Claude Code feature: dynamic workflows!

Mention "workflow" in a prompt and Claude will dynamically create an orchestration plan that it strictly follows, allowing you to confidently trust that every stage happens in the right order even across 100s of agents.

[Stain Lu @stainlu]

@vincent_koc @openclaw @steipete @nvidia @Microsoft grats vincent!!