We’re on a journey to advance and democratize artificial intelligence through open source and open science.
Designing the hf CLI as an agent-optimized way to work with the Hub
We’re on a journey to advance and democratize artificial intelligence through open source and open science.
Profiling TorchNeuron (Part 1): System-level profiling on AWS Trainium.
A Blog post by Amazon on Hugging Face
deepseek-ai/DeepSeek-V4-Flash-0731 at 9e165c30e2704aec5d9d593cce3eebd58bbef1cb
We’re on a journey to advance and democratize artificial intelligence through open source and open science.
Hugging Face ❤️ TorchTPU: TPUs, Natively
We’re on a journey to advance and democratize artificial intelligence through open source and open science.
Using the Amazon Mechanical Turk Workforce - Amazon SageMaker AI
Use SageMaker AI to build, train, and host machine learning models in AWS.
Open by design: Ai2 brings fully open AI infrastructure online with NSF OMAI | Ai2
Ai2 is bringing NSF OMAI compute online to power a fully open AI research ecosystem, turning national infrastructure investment into reusable models, data, methods, and tools that can accelerate scientific discovery.
Kevin Weil 🇺🇸 on Twitter / X
💥 Today we’re introducing Prism—a free, AI-native workspace for scientists to write and collaborate on research, powered by GPT-5.2.Accelerating science requires progress on two fronts:1. Frontier AI models that use scientific tools and can tackle the hardest problems2.… pic.twitter.com/cnLysHixuQ— Kevin Weil 🇺🇸 (@kevinweil) January 27, 2026
Hyprstream - the open network for self-improving AI, the future of Plan9
The ATOM Project - American Truly Open Models
Reinvigorating AI research in the U.S. by building leading, open models in America
README.md · MiniMaxAI/MiniMax-H3 at 73372e6cf53e414edd3ab03e357717fb0602e758
We’re on a journey to advance and democratize artificial intelligence through open source and open science.
Using Amazon Augmented AI for Human Review - Amazon SageMaker AI
Use SageMaker AI to build, train, and host machine learning models in AWS.
OpenThoughts: Data Recipes for Reasoning Models — Ryan Marten, Bespoke Labs
freestylefly/wesight
Open-source desktop AI agent workspace with one-click Claude Code, Codex, OpenClaw, Hermes Agent setup and custom LLM model routing.
Zhijing Jin on Twitter / X
🚀 Can #AI agents actually do science?Current agents can optimize for predictive fit — but fail at recovering the underlying physical laws.We introduce Stargazer🪐: a scalable benchmark + environment for astronomical discovery🔭Agents must:• propose hypotheses•… pic.twitter.com/hGIjz4KJMT— Zhijing Jin (@ZhijingJin) May 7, 2026
NeuralBench: A Unifying Framework to Benchmark NeuroAI Models | Hubert Banville
🧠 NeuralBench is now open source. Today we're releasing NeuralBench, a unified framework for benchmarking foundation models of brain activity, developed by the Brain & AI team at FAIR, Meta. 💻 Code: https://lnkd.in/dNJsgBgM 📄 White paper: https://lnkd.in/dvWMg7rx Brain foundation models are starting to show positive transfer to a range of downstream tasks, from brain-computer interfacing to clinical classification. But systematically evaluating them is hard: heterogeneous preprocessing pipelines, input structures, and adaptation methodologies make results difficult to compare. Most prior work also focuses on a narrow set of downstream tasks. NeuralBench addresses this by defining each task end-to-end with config files (data source, preprocessing, splits, optimiser, metrics, architecture) so all models can be evaluated on the same footing. What's in our first release, NeuralBench-EEG v1.0: ⚡ 36 EEG tasks across 94 public datasets, spanning motor imagery, clinical classification, cognitive decoding, and phenotype prediction. 🤖 Task-specific deep learning architectures (EEGNet, Deep4, EEGConformer, CTNet, ...) benchmarked side-by-side with recent EEG foundation models (BENDR, LaBraM, BIOT, CBraMod, LUNA, REVE). 🧩 Extensible to other neuroimaging modalities: the framework already runs MEG and fMRI tasks, leveraging our NeuralSet ecosystem for accessing brain imaging data and the broader neuroscientific software stack. 📜 Released under the MIT license. Help us make it better. Through our white paper, we invite the community to contribute new tasks, datasets, and models, especially for fMRI, MEG, and iEEG. The long-term goal is a fully unified benchmark across neuroimaging tasks and modalities. 🙏🙏🙏 This was a big team effort with Stéphane d'Ascoli, Simon Dahan, Jérémy RAPIN, Marlène Careil, Yohann Benchetrit, Jarod Lévy, Saarang P., Antoine Ratouchniak, Lucy (Mingfang) Zhang, Elisa Cascardi, Katie Begany, Teon Brooks, and Jean-Rémi King. Special thanks to Alexandre Gramfort, Thomas Moreau, Arnaud Delorme, Bruno A. and Pierre Guetschel for feedback and support. #Neuroscience #AI #NeuroAI #Python #OpenSource
MPS in PyTorch 2.15
We’re on a journey to advance and democratize artificial intelligence through open source and open science.
