The Archive

Meta is making another push to bring artificial intelligence to the masses with Muse, a personal assistant it says can put AI in the hands of virtually anyone. The product is the latest step in a multi-billion dollar strategy overhaul designed to revitalize the company's ailing position in the AI race and help it catch up to rivals like OpenAI, Anthropic, and Google. Muse is a "personal AI agent" designed to help out with everyday tasks and projects, like online shopping, sending emails, and planning a trip. Once given a goal, Meta says Muse can work on its own, opening a browser, filling out...

Meta's new personal AI agent Muse wants access to users' email, calendars, payments, health services, and more — making the company's biggest consumer AI bet yet a major test of whether people still trust Meta with their data.

Most AI tools allow you to opt-out of sharing your usage with the model provider to improve future versions. Meta has taken that idea and put a price tag on it. For its new Muse Spark model, intended for operating coding and other agents, it is offering an explicit discount averaging out to about 95% […]

an epic comeback story for Meta

Meta said its Muse Spark model is powering the dictation feature.

Meta released Glimmer this week, an open-weight AI model anyone can download and run on their own hardware — a contrast to Muse Spark, the company’s more powerful model that stays locked behind its own APIs. The release landed alongside a letter from Mark Zuckerberg arguing AI should be “for everyone” rather than controlled by a handful of labs, but as Equity’s […]

Scientific papers contain fine-grained records of problem solving: authors mention technical obstacles and methods that were used to address them, often along with reasoning on why those methods were chosen. We introduce MUSE (Mining Underlying Scientific Explanations), a full-text, multi-domain resource of scientific Problem-Solution-Rationale (P-S-R) triplets. We curate 579 expert-annotated full-text paragraphs, with a rich annotation schema covering salient problem, solution, and rationale spans, solves and rationale_of links and conceptual coreference. A modular extraction pipeline scales...

a small win for american open models - Glimmer runs on a fits on a single RTX 3090!

Introducing Muse Glimmer Meta are back in the open weights game! Muse Glimmer is a brand new 30B model under a clean Apache 2.0 license (a step up from the janky Llama licenses of old). They claim to have optimized it for exactly the kind of things I'm looking for in a local model: End-to-end Agentic Task Completion. Muse Glimmer achieves strong success rates on full-task benchmarks including DeepSearch QA, MCP-Atlas, 𝛕-Bench and SWE-Bench, which measure its ability to work within scaffolds, write and debug code, and resolve multi-turn requests from start to finish. Reliable Tool Use. The mo...

Meta’s new open-weight Muse Glimmer model offers a glimpse of Mark Zuckerberg’s personal superintelligence vision, as well as the emerging divide between AI users can own and access.

Meta returns to the open source ecosystem with the release of Muse Glimmer, a 30B open-weight dense model with a 120K+ context window built for local AI... Meta returns to the open source ecosystem with the release of Muse Glimmer, a 30B open-weight dense model with a 120K+ context window built for local AI agentic work. Optimized to run across a range of NVIDIA edge, desktop, and workstation AI platforms, Muse Glimmer delivers 20K tokens/sec on a single GPU, enabling always-on agents to process data locally and execute complex… Source

Meta's Muse Glimmer, the first open model released by Meta Superintelligence Labs, is now available. Muse Glimmer is a 30B multimodal model released under the Apache 2.0 license, designed for local coding agents, and accelerated by Ollama's MLX engine with new native DFlash and image input support.

Introducing Muse Code and Muse Spark 1.2 Yet more evidence that the most important characteristic of any model these days is long-sequence agentic tool calling. Meta shipped their own coding agent as part of getting that to work! Muse Spark 1.2 is a coding-focused update to Muse Spark 1.1, with improvements in code generation, complex debugging, codebase understanding, and end-to-end developer workflows. In Muse Spark 1.2, we significantly scaled up training compute on coding tasks while expanding training environment diversity. The model also maintains its strength in other key areas like ge...

Meta expanded its AI coding offerings with a new agent that, it promises, can handle complex tasks with complex software.

Personal travel tip about activating mechanical instruments at a San Francisco museum.

Content Seal sounds a lot like a less accessible and reliable version of SynthID. | Image: Meta / The Verge IIn March, Meta's Oversight Board called on the company to "meet its public commitments and employ its own tools" to help quell the spread of deceptive generative AI content across platforms. Meta responded in July by introducing Content Seal - an invisible watermarking technology that flags images generated by the company's new AI model. But it was basically a footnote buried in the company's announcement for its Muse image and video generation tools. As someone who spends a lot of tim...

Following significant backlash, Meta is turning off the feature it announced this week that let users generate AI images based on content from public Instagram accounts just by tagging them. The feature, as originally set up, meant that content from any public Instagram account could be used in AI creations without the account owner's permission. "Earlier this week, we announced that one way for people to generate images in Meta AI is by @-mentioning public Instagram accounts that they want to reference," Meta says in an update to a blog post about its new Muse Image AI model. "Our intent was...

Meta's new Muse Spark 1.1. will go up against similar products offered by Anthropic and OpenAI.

Meta releases Muse Spark 1.1 with API access and improved agentic tool calling and computer use capabilities.

llm-meta-ai 0.1 released, enabling CLI access to Meta's muse-spark-1.1 model via Simon Willison's llm tool.

After reentering the AI race with its first in-house Muse Spark model in April, Meta is now opening up the doors to developers with a new model that can plug into AI coding software with the new Meta Model API. Meta says that Muse Spark 1.1 is a "step-change" from the first generation, with improvements based on feedback from developers. The company says it's capable of more advanced coding, including detection and fixing of complex bugs; better supports end-to-end agentic workflows across a range of apps, including multi-agent systems; and has native multimodal perception across images, vide...

Stratechery analysis: verifiable data infrastructure emerging as competitive differentiator across Meta, Grok, and frontier AI labs.

Meta releases Muse Spark 1.1, an update to its text-to-image generation model with unspecified improvements.

The new image-generating model has numerous use cases, including advertising, decorating and creator-based opportunities.

Meta is launching the first AI image generation model made by its Superintelligence Labs division. The Muse Image model now powers the image-making tools across the Meta AI app, Instagram, and WhatsApp, and it's coming soon to Facebook and Messenger, according to an announcement on Tuesday. It's part of the growing Muse family of AI models that replace Meta's Llama lineup. Alexandr Wang, who Meta hired to head up its Superintelligence Labs last year, says on Threads that Muse Image is "agentic," meaning it works with its Muse Spark large language model "to reason through your prompt, search t...

Meta releases Muse Image and Muse Video, generative models for instruction-following image creation, editing, and video synthesis with native audio.

TimeLens is an AI-powered bilingual mobile guide for the Grand Egyptian Museum (GEM). Pointing a phone at an exhibit, a visitor sees the artifact recognized in real time and can ask follow-up questions answered in English or Arabic. The work addresses three problems specific to in-gallery deployment: fine-grained visual similarity among 51 catalogued artifacts (many near-identical Ramesside statues), the gap between curated training data and handheld camera conditions, and the risk of an AI guide stating unsupported historical facts. Two engineering contributions are reported. First, an on-de...

Multi-modal data management has emerged as a central research topic in the database community, spanning data integration, semantic query processing, and data quality assessment. Despite this growing interest, the community lacks large-scale, real-world datasets combining tables, text, and images. We present ArtiFact, a multi-modal cultural heritage dataset of 651045 museum records collected from the Metropolitan Museum of Art, the Art Institute of Chicago, and the Rijksmuseum. We demonstrate the utility of ArtiFact through two downstream tasks. For cross-modal error detection, we introduce a ...

Large language model (LLM) agents rely on reusable skills to solve complex tasks. However, existing skill creation approaches treat skills as isolated and static artifacts, limiting their reusability, reliability, and long-term improvement. We propose MUSE-Autoskill Agent (Memory-Utilizing Skill Evolution), a skill-centric agent framework that lets agents continuously improve their task-solving capability by creating, reusing, and refining skills under a unified lifecycle (creation, memory, management, evaluation, and refinement). Our framework enables agents to create skills on demand, store...