Khoa Dang

Systems & ML enthusiast, ML@MBZUAI

[Vedang Lad @vedanglad]

How well can you describe the feature selectivity of a vision neuron … with words? Interpretability has long borrowed from neuroscience — and maybe it can give back too! 🧵

[Anirudha Majumdar @Majumdar_Ani]

https://t.co/HBRXJTvDfz

[Moritz Reuss @moritz_reuss]

World-Action Models (WAMs) have become the second dominant recipe for robot foundation models, next to classical VLAs.

So where do they come from, and how do they compare vs VLAs?

I wrote an small overview of the WAM landscape, with some personal takes:

https://t.co/6S4gH9tWTt

[SemiAnalysis @SemiAnalysis_]

RL Systems Mind the Gap:
Matching Trainer and Generator Throughput
RL Training Infrastructure, GRPO,
PipelineRL, Async RL, Policy Staleness,
RL Sandbox Infra, CPU Requirements,
TCO Analysis, Thinking Machines Tinker

https://t.co/yr5oH99h4B

[Thoughtful @thoughtfullab]

https://t.co/ShUznurJdz

[Paul Graham @paulg]

This is the best explanation I've seen of how mechanical watches work. https://t.co/h3uHfxpBX7

[Fernando 🌺🌌 @zetalyrae]

New post: on why you should not use AI to write.

[World Labs @theworldlabs]

Today we are sharing three new research papers, each exploring a new way to generate 3D content by leveraging large-scale generative models and 2D priors.

These projects were led by our incredible interns @HaoZhang623 @BDuisterhof @DrTunnels

[1/4]

[Goodfire @GoodfireAI]

Have you debugged your training data? You might not like what you find.

Introducing predictive data debugging: reveal and shape what your model will learn before training.

In DPO datasets, we found broken guardrails, hallucinations, and fish fart fan fiction (seriously). (1/9)

[Recursive @Recursive_SI]

https://t.co/IlLyWptydX

[Geoffrey Irving @geoffreyirving]

We are starting a new, nonprofit alignment organization, ⊢ Sequent Research, bringing together researchers previously on UK AISI’s Alignment Team, Timaeus, and elsewhere to research how to align superintelligence. We are hiring! 🧵

[Zed @zeddotdev]

Great software always took shape in conversation, not the commit. With agents, the conversation that generates the code is becoming the true source of our software. And Git can't keep up.

So we built something that can. Meet DeltaDB: https://t.co/x0UHntKy9m

[River AI @river_ai_inc]

We are incredibly excited to announce River AI. Our mission is to create personal AI that is owned and shaped by you.

Today’s best AIs are controlled by a few large corporations. We are building the alternative: a new, personal stack for AI that works entirely for you, shares your values, and operates on your terms.

[Anthea Li @AntheaYLi]

Research ideas you can't be outscaled on.
Many important problems in ML now demand compute no university lab can match, and junior students feel the constant pressure of getting scooped. I wanted to share a different bet: research directions where insight may still matter more than scale.

These are deliberately unconventional — grounded in mechanisms rarely seen in the usual scaling playbook. Because they sit in the long tail, they're unlikely to show up in AI-generated research idea lists. No guaranteed success, but real open questions with enough material to get started.

If you find these useful, PRs to add more are welcome. I'll keep updating too.
https://t.co/gkjcwiu111

[Tilde @tilderesearch]

https://t.co/p1KMYyRmLi

[sarah guo @saranormous]

https://t.co/Hw02laH9yp

[Tim Sneath @timsneath]

One of my personal favorite features announced at WWDC will I suspect be a sleeper hit: container machines, allowing your Mac to run a lightweight, persistent Linux environment with your home directory and repos automatically mounted: https://t.co/dOBdfOOVxC

[SemiAnalysis @SemiAnalysis_]

We just published a deep dive on why Unitree is going to dominate global robotics. Timing could not be better. (2/2)
https://t.co/0HKSayvvTK

[Claude @claudeai]

Introducing Claude Fable 5: a Mythos-class model that we’ve made safe for general use.

Its capabilities exceed those of any model we’ve ever made generally available.

[Yukang Chen @yukangchen_]

Excited to share our new blog: Scaling Video Training with Parallelism
https://t.co/DxNisH0bqo
We discuss how parallelism helps scale long-video training systems, for both understanding and generation.