This is the last week to apply to our Bloomberg CTO AI Internship Program! If you are a current graduate student interested in applying cutting edge AI, consider applying! 👉 Apply here: forms.cloud.microsoft/r/ZLC3…

Most SWE benchmarks tell an agent exactly what to fix + where to look. Imagine if an agent could instead *proactively* surface and fix issues themselves (before a developer even runs into them!) We evaluate this capability at scale. Introducing SWE-sweep! Led by 🐐 @KLieret

According to this website, my LeCun number is 2 (true) but I’m apparently at NVIDIA (?)

RT @claireszhou: 🚨 Muse Spark 1.3 max is now on the contributor tier ~exclusively in Muse Code subscription plans~ get access to our favo…

As a test of our progress to advance the frontier of AI research, in June we entered the next generation of our autonomous AI research system, AIRA₃, in a live Kaggle competition run by NVIDIA to fine-tune a 30B Nemotron model. The challenge was to teach the model to reason better — all competitors had access to the same information and were graded externally on a private test set. AIRA₃ placed 8th out of ~4,000 teams to win Gold, outperforming human competitors who had access to the same frontier tools. We believe this is a reliable signal that AIRA₃ can improve a targeted capability of an AI model at a level similar to human experts.

We’re excited to release Muse Spark 1.3 with improved performance on agentic and coding tasks, and a focus on real-world usability. Key capabilities: → Sustains longer-horizon work across multiple workflows in a single thread → More actively collaborates with users: it asks clarifying questions, flags when it's stuck, confirms before consequential actions → Better calibrated on its own limits instead of hallucinating outcomes → ~20% fewer tool calls and ~25% fewer tokens vs. Muse Spark 1.2 in internal comparisons

Muse Spark 1.3 is rolling out today with frontier performance almost too cheap to meter. This is the biggest jump we've made so far on coding and agentic work. Try it in Muse Code and our API. Next up 🍉 and Muse Spark open weights releases coming soon.

Today, Muse Code is out of beta! Muse Code now has workflow, inter-session messaging, session rewind, and a developer preview SDK. We are also rolling out monthly subscription plans at dev.meta.ai

Today we're also opening the weights for Muse Glimmer, a great 30B parameter dense model that can run locally. Soon we'll also release the weights for Muse Spark 1.2, our latest foundation model. Meta is a strong supporter of open source and I'm proud of these releases. Congrats to @alexandr_wang and the MSL team for all your great work on these models.

Introducing Muse Code (beta), a terminal coding agent built for long-horizon software engineering, powered by our new Muse Spark 1.2 model. Muse Code plans, implements, and validates complex, multi-file changes across large repositories with persistent sub-agents that solve difficult problems faster, more accurately, and with less intervention. 🧵👇

10x more tokens per megawatt. CoreWeave has the first measured performance of NVIDIA Vera Rubin NVL72, showing 10x improvement in tokens per second per megawatt on DeepSeek-R1 compared to Blackwell.

We're partnering with @huggingface to investigate an unprecedented security incident. Cyber-capable OpenAI models compromised Hugging Face production during a benchmark evaluation. Sharing preliminary findings to help defenders understand emerging risks: openai.com/index/hugging-fac…

hello there the jacobian conjecture is false thanx to my close friend akhil for asking about it and my other close friend fable for working during the world cup final ((1+xy)^3 z + y^2 (1+xy) (4+3xy), y + 3 x (1+xy)^2 z + 3 x y^2 (4+3xy), 2 x - 3 x^2 y - x^3 z): \C^3\to \C^3, has jacobian determinant -2, and sends (0, 0, -1/4), (1, -3/2, 13/2), and (-1, 3/2, 13/2) to (-1/4, 0, 0)

Excited to share Muse Spark 1.1 — our first model with an API offering! It’s performant on agentic and coding tasks, and we’re excited to make it easier for developers to build. Looking forward to hearing community feedback and seeing what people create with it. More to come!

Vibe-code something with Muse Spark 1.1 – you'll be pleasantly surprised :) The pace of improvement in our internal coding evals has been super impressive!

We’re excited to introduce Muse Spark 1.1, a significant upgrade from the first Muse Spark model we released earlier this year. Along with this release, we are launching a public preview of the new Meta Model API where developers can access Muse Spark 1.1. The model is also available now in "Thinking" mode in the Meta AI app and on meta.ai. Learn more: go.meta.me/ff8e2c

What advantage to use, and when? Everyone's proposing new advantage functions for RL with LLMs but nobody knows why they work or fail. We break this down and build FADE a self-adapting advantage to get +14% on LiveCodeBench v6 in 40% less steps. Paper: arxiv.org/pdf/2607.01490

We're now entering the super-human stage of AI: Instead of training/evaluating AI on tasks that *a* human had previously solved in a few hours, we need to challenge AI to complete in a few hours tasks that previously took entire *teams* of humans *years* to accomplish. 🧵⬇️

Today we launch Recursive. We are building AI that discovers knowledge automatically and improves itself recursively, an open-ended process that will fundamentally change how science and technology advance. Our 25 top researchers and engineers in San Francisco and London bring diverse expertise spanning agentic AI scientists, architecture and algorithm design, world models, optimization, and interpretability, united by a shared conviction that this is the most important problem we could be working on today. If you are interested in joining, please send your resume to talent@recursive.com. Follow us at @Recursive_SI!