Dan Saunders

research eng / perf at @LumaLabsAI
prev @UnslothAI @axolotl_ai @awscloud
small homestead owner 🐑🐓

[Dan Saunders @djsaunde]

@_arohan_ bro is diffusing

[Dan Saunders @djsaunde]

@modal is just lovely for cute lil side projects, thank you modal

this is not an ad

[Dan Saunders @djsaunde]

@aryanvs_ @Roblox waow gratz

[Dan Saunders @djsaunde]

whenever I look at grafana it makes me want to retire early

[Dan Saunders @djsaunde]

@m_sirovatka thx bro

[Dan Saunders @djsaunde]

I should buy more gpus

[Dan Saunders @djsaunde]

@jino_rohit I would pick ~3

[amit @ CVPR @gravicle]

We are launching Open Physical AI Lab at Luma to solve generalization in robotics

We believe general physical AI is the most consequential problem to solve to improve life on Earth. Allowing one or very few companies to control the entire intelligence and physical infrastructure of the world is a profound threat to the world. This is the path LLMs took, and it is the path physical AI is on today. To solve this problem of generalization in physical AI and to maximally benefit all of humanity, we are establishing the Open Science Physical AI Lab (OPAL Lab) at Luma.

Today, physical AI and robotics are in the pre-AI-scale era. Unlike LLMs and multimodal models which are capable of solving a wide range of previously seen and unseen problems, robots are trained on and can only replay specific tasks in specific environments from small scale examples, limiting their utility. Physical AI has a data gap, creating a generalization crisis. The default solution is to simply scale teleoperation to capture data across every possible task and combination of tasks – a physically and economically impossible approach.

Over the past 4 years, through fundamental research on multimodal AI, Luma has trained frontier foundation models in 3D, image, video, and unified language-visual generation. In this process, we have built vast infrastructure to make sense of raw internet-scale multimodal data and train efficient generative models with it. Building these systems has given us unique insights into how raw multimodal data can be used to learn physics, perception, reasoning and interaction with the physical world. Over the past few months, mounting empirical evidence from our work on Unified Models has turned this realization into a strong scientific conviction. Generalization is not unique to text.

The Open Physical AI Lab will research and scale methods to solve generalization and develop World Models for understanding and interaction with the physical world. This generalization will lead to robot systems proliferating everywhere - factories, research labs, hospitals, streets, and even our homes. Given the societal and economic upside, this transition will be faster than any other technology in history and these systems will be embedded into every layer of human life. Physical AI will help us solve labor shortage and stagnation plaguing world economies. Physical AI will become the means of production in every nation.

Allowing one or very few companies to control the entire intelligence and physical infrastructure of the world is a profound threat to humanity and democratic freedom. This is the path LLMs took, and it is the path physical AI is on today.

To reverse this trajectory, this lab will be an open science effort. We will use our scale and expertise to build the substrate and make it available so that any dedicated group of people can use it, modify it, and assemble it into systems of productive work. To accelerate progress, we will collaborate with our peer labs and academia on core research, build evaluations to measure progress and assess safety, and partner with industry leaders on chips, hardware, and physical agent systems.

If you share this conviction and believe that this foundation should be built in the open for the benefit of all humanity, please come work with us.

[Dan Saunders @djsaunde]

@eliebakouch the pytorch hate is overblown

[Dan Saunders @djsaunde]

we are absolutely drowning in neolabs. honestly very cool!

[Dan Saunders @djsaunde]

we should train char-level LMs if only just to dodge the "how many X's in Y's" commentary

[Shuo Yang @Andy_ShuoYang]

Flash-KMeans was only the beginning.

Today, from the Flash-KMeans team, we are releasing FlashLib — a GPU library for fast, predictable, agent-ready classical ML operators.

Up to 26× on KMeans, 19× on KNN, 40× on HDBSCAN, 208× on TruncatedSVD, 47× on PCA, 147× on exact t-SNE, and 49× on MultinomialNB over state-of-the-art (cuML).

Blog: https://t.co/P31SGl0cyT
Code: https://t.co/9nkO2hmeOl

[Dan Saunders @djsaunde]

I have achieved AGI (a good icecream) internally

[Dan Saunders @djsaunde]

it's always "loglogn", never "how is n?"

[Shizhe Diao @shizhediao]

🚀 One model. Three decoding modes.

Excited to share Nemotron-Labs-Diffusion, our new tri-mode LM family that unifies:

1️⃣ Autoregressive decoding
2️⃣ Diffusion decoding
3️⃣ Self-speculation decoding
—all within a single set of weights, simply by changing the attention mask at inference time.

Report: https://t.co/R2sVStbHjk
Model: https://t.co/dbPfHGMNg0

[keysmashbandit @keysmashbandit]

My big problem with "AI is only good at solving problems similar/analogous to those they've encountered" is that that statement is just generally true of everyone, which is why we have like, university. And research papers. It's not an interesting thing to say!

[Dan Saunders @djsaunde]

great slides here 👇

[Mark Saroufim @marksaroufim]

https://t.co/LabrvBEcql

[Dan Saunders @djsaunde]

idk what a Railway is

[Dan Saunders @djsaunde]

personal update: I am