Meta
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Meta Introduces SAM Audio: Unified Multimodal Model for Audio Separation with PE-AV, Benchmark, and Judge Model
Meta has released SAM Audio, a unified multimodal audio separation model that accepts text, visual, and temporal span prompts to isolate sounds from complex audio mixtures. The system is powered by Perception Encoder Audiovisual (PE-AV), an extension of Meta's open-source Perception Encoder released earlier in 2025, and uses a flow-matching diffusion transformer architecture. Alongside the model, Meta is releasing SAM Audio-Bench (the first in-the-wild audio separation benchmark) and SAM Audio Judge (an automatic evaluation model for audio separation). All components are available today via the Segment Anything Playground.
Meta Introduces Muse Spark: First Model from Meta Superintelligence Labs with Multimodal Reasoning and Multi-Agent Orchestration
Meta has launched Muse Spark, the first model from its newly formed Meta Superintelligence Labs, positioned as a natively multimodal reasoning model with tool-use, visual chain-of-thought, and multi-agent orchestration capabilities. The model introduces 'Contemplating mode,' which runs multiple agents in parallel to compete with frontier reasoning modes, achieving 58% on Humanity's Last Exam and 38% on FrontierScience Research. Meta claims a greater than 10x compute efficiency improvement over Llama 4 Maverick through a rebuilt pretraining stack, and describes predictable scaling across pretraining, RL, and test-time reasoning axes. Muse Spark is available at meta.ai with a private API preview, and is framed as the first step on a scaling ladder toward 'personal superintelligence.'
Meta Publishes Advanced AI Scaling Framework and Safety & Preparedness Report for Muse Spark
Meta has released an updated Advanced AI Scaling Framework that expands risk evaluation categories—including chemical/biological threats, cybersecurity, and loss-of-control risks—and introduces formal Safety & Preparedness Reports tied to specific model deployments. The first such report covers Muse Spark, Meta's advanced reasoning model, detailing pre- and post-safeguard evaluations across severe risk categories and ideological balance. Meta also describes a shift in safety methodology: rather than scenario-specific refusal training, Muse Spark is trained on the reasoning behind safety principles, enabling more generalizable behavior in novel situations. The framework applies across open, API, and closed deployments.
Meta Announces Four MTIA AI Chip Generations in Two Years: MTIA 300–500 Roadmap
Meta has detailed a rapid four-generation MTIA chip roadmap (300, 400, 450, 500) developed in partnership with Broadcom, spanning ranking/recommendation inference and training through general GenAI workloads. Key advances include a 4.5x HBM bandwidth increase and 25x compute FLOPS improvement from MTIA 300 to 500, with MTIA 450 and 500 targeting GenAI inference with doubled and further-increased HBM bandwidth versus leading commercial products. MTIA 300 is in production for R&R training, MTIA 400 is lab-tested and entering deployment, while MTIA 450 and 500 are scheduled for mass deployment in early 2027 and 2027 respectively. The strategy emphasizes modular chiplet design and short iteration cycles to keep hardware aligned with rapidly evolving AI model requirements.
Meta Introduces Muse Spark: First Closed-Weights Model from Superintelligence Labs
Meta released Muse Spark, its first AI model in roughly a year and the debut product of its Superintelligence Labs, marking a significant departure from its open-weights Llama strategy. The natively multimodal reasoning model supports tool use and multi-agent orchestration, achieves fourth place on the Artificial Analysis Intelligence Index, and claims notable token efficiency—matching Llama 4 Maverick with over 10x less training compute. Meta withheld parameter count, architecture, and training details, positioning Muse Spark as a closed commercial product competing with OpenAI, Google, and Anthropic. The release introduces 'thought compression' via RL and a parallel multi-agent 'contemplating' mode, while showing gaps in coding and agentic benchmarks.
Meta Pivots to Closed Weights with Muse Spark; The Batch Issue 349 Roundup
Meta introduced Muse Spark, its first AI model in roughly a year and the first product from its Superintelligence Labs, marking a pivot away from its open-weights strategy toward a closed model. Muse Spark is a natively multimodal reasoning model supporting tool use and multi-agent orchestration, with three reasoning modes and a novel 'thought compression' post-training technique using RL to penalize excessive reasoning tokens. The model ranks fourth on the Artificial Analysis Intelligence Index and matches Llama 4 Maverick's capabilities with over an order of magnitude less training compute, though it trails in coding and agentic benchmarks. The issue also covers broader industry themes including AI-native software engineering team structures, big pharma AI adoption, and regulatory developments.
SAM 3.1: Meta Releases Faster Real-Time Video Segmentation Model with Object Multiplexing
Meta has released SAM 3.1, an incremental update to Segment Anything Model 3, introducing object multiplexing that allows tracking up to 16 objects in a single forward pass. This doubles video processing throughput from 16 to 32 FPS on a single H100 GPU, reducing GPU resource requirements and enabling real-time tracking on smaller hardware. SAM 3.1 is a drop-in replacement for SAM 3 and is available via updated model checkpoints and codebase. The broader SAM 3 release also includes text and exemplar prompting, a new Segment Anything Playground, the SA-Co evaluation dataset, and SAM 3D for 3D reconstruction.
Llama 3.2 Multimodal and Edge Models Launch on Hugging Face
Meta released Llama 3.2, introducing vision-capable multimodal models alongside lightweight models optimized for on-device inference. Hugging Face published a blog post covering integration support, model availability, and deployment options across the ecosystem. The release marks Meta's first open-weights multimodal Llama models, adding image understanding to the Llama family. Smaller 1B and 3B parameter variants target edge and mobile deployment scenarios.
Welcome Llama 4 Maverick & Scout on Hugging Face
Hugging Face announces the availability of Meta's Llama 4 Maverick and Scout models on its platform. These are the first models in Meta's new Llama 4 generation, representing a significant open-weights release. The post covers integration details, model access, and usage on the Hugging Face ecosystem.
Llama 3.1 Released: 405B, 70B & 8B Models with Multilinguality and Long Context
Meta released Llama 3.1, a family of open-weights models at three scales (405B, 70B, 8B) featuring multilingual support and extended context windows. The 405B model represents Meta's largest open-weights release to date, positioning it as a frontier-class open model. Hugging Face published a blog post covering the release, integration details, and deployment options across the ecosystem.
Welcome Llama 3 - Meta's new open LLM
Hugging Face published a blog post welcoming Meta's Llama 3 release, covering the new open-weights large language models. Llama 3 represents a significant update to Meta's open model family, with improved capabilities over Llama 2. The post covers integration and availability on the Hugging Face platform.
Code Llama: Llama 2 learns to code
Meta released Code Llama, a family of code-specialized large language models built on top of Llama 2. The models are available in multiple sizes and variants, including a Python-specialized version and an instruction-following version. Code Llama supports long context windows for handling large codebases and is released as open weights, making it accessible for research and commercial use.
Llama 2 is here - get it on Hugging Face
Meta released Llama 2, a new family of open-weights large language models, made available through Hugging Face. The release includes both base and fine-tuned chat variants across multiple parameter sizes. This represents a significant expansion of accessible open-weights frontier models, with Meta and Microsoft partnering on distribution.
Meta, OpenAI, and other AI companies build private gas-fired power plants to bypass public utilities
Major AI companies including Meta, OpenAI, Oracle, and xAI are constructing private, off-grid power plants—primarily natural gas—to directly supply their data centers, bypassing public utility grid connections. A Cleanview study identified 46 such projects, 90% announced in 2025, accounting for 30% of all planned U.S. data-center capacity. Meta is building gas plants in Ohio and Texas, while OpenAI and Oracle's Stargate-linked Jupiter project is underway in New Mexico. The shift signals a structural change in AI infrastructure energy strategy, with climate implications as fossil fuels displace earlier renewable commitments.
Meta's AI customer support agent exploited to hijack Instagram accounts
Attackers exploited Meta's AI customer support agent by prompting it to link Instagram accounts to attacker-controlled email addresses, successfully hijacking accounts including the dormant Obama White House Instagram. The incident was reported by 404 Media on June 5, 2026. The attack illustrates a practical, real-world failure mode for deployed AI agents with account-management capabilities.
Meta releases Llama 4 Maverick 17B-128E multimodal instruct model on Hugging Face
Meta released Llama 4 Maverick, a 17B active parameter model with 128 experts (MoE architecture), as an image-text-to-text instruct model on Hugging Face. The model supports multimodal inputs and multiple languages including Arabic, German, and English. With 28K+ downloads and 493 likes shortly after release, it is seeing significant early adoption.
Meta releases Llama 4 Scout 17B-16E multimodal model on Hugging Face
Meta released Llama 4 Scout, a 17B active parameter model with 16 experts (mixture-of-experts architecture), on Hugging Face. The model supports image-text-to-text tasks, making it a multimodal open-weights release. With over 14,000 downloads and 249 likes shortly after release, it is seeing meaningful early adoption.
Meta releases Llama 3.3 70B Instruct on Hugging Face
Meta released Llama 3.3 70B Instruct, a new instruction-tuned variant in the Llama 3 family, published on Hugging Face. The model supports English, French, and Italian and has accumulated over 691,000 downloads and 2,800 likes, indicating strong community uptake. This represents a meaningful open-weights release in the 70B parameter class.
Meta releases Llama Guard 4 12B multimodal safety classifier on Hugging Face
Meta released Llama Guard 4 12B, a multimodal (image-text-to-text) safety classification model built on the Llama 4 architecture, published to Hugging Face. The model is designed for conversational safety filtering and supports both text and image inputs. With 143K downloads and 102 likes shortly after release, it is seeing meaningful early adoption.
Meta releases Llama 4 Maverick 17B-128E multimodal model on Hugging Face
Meta released Llama 4 Maverick, a 17B active parameter model with 128 experts (mixture-of-experts architecture), on Hugging Face. The model supports image-text-to-text tasks, making it a multimodal open-weights release. This is part of the Llama 4 generation, representing Meta's latest open-weights frontier push with MoE architecture.
Meta releases Llama 4 Scout 17B-16E instruct model on Hugging Face
Meta released Llama 4 Scout, a 17B active parameter / 16-expert mixture-of-experts instruct model with image-text-to-text (multimodal) capabilities, published on Hugging Face under the meta-llama organization. The model supports multiple languages including Arabic, German, and English. With over 420K downloads and 1,300 likes shortly after release, it is seeing significant community uptake.
Meta releases Llama 3.2 90B Vision-Instruct multimodal model
Meta released Llama 3.2 90B Vision-Instruct on Hugging Face, a large multimodal model supporting image-text-to-text tasks. The model is part of the Llama 3.2 family and supports English and German. With 858 downloads and 358 likes, it represents Meta's open-weights push into vision-language capabilities at the 90B parameter scale.
Meta releases Llama 3.2 90B Vision multimodal model on Hugging Face
Meta released Llama 3.2 90B Vision, a large multimodal model supporting image-text-to-text tasks, published on Hugging Face under the meta-llama organization. The model is part of the Llama 3.2 family and supports English, German, and French. This is a significant open-weights multimodal release from Meta, extending the Llama 3 series with vision capabilities at the 90B parameter scale.
Meta releases Llama 3.2 11B Vision Instruct multimodal model
Meta released Llama 3.2 11B Vision Instruct on Hugging Face, an open-weights multimodal model supporting image-text-to-text tasks. The model is part of the Llama 3.2 family and supports English and German. With over 157K downloads and 1,600 likes, it has seen substantial community adoption.
Meta releases Llama 3.2 11B Vision multimodal model on Hugging Face
Meta released Llama 3.2 11B Vision, an open-weights image-text-to-text model, on Hugging Face. The model is part of the Llama 3.2 family and supports multiple languages including English, German, and French. This represents Meta's entry into open-weights multimodal models at the 11B parameter scale.
Meta releases Llama 3.2-3B open-weights text generation model
Meta released Llama 3.2-3B, a 3-billion parameter open-weights language model, on Hugging Face under the meta-llama organization. The model supports multiple languages including English, German, French, and Italian, and uses the standard transformers/safetensors format. With over 900K downloads and 800+ likes, it has seen substantial community adoption.
Inside Anduril and Meta's Quest to Make Smart Glasses for Warfare
Anduril and Meta are co-developing an augmented-reality headset for military use, with new details revealing capabilities such as ordering drone strikes via eye-tracking and voice commands. The project is led by Quay Barnett, a former Army Special Operations Command officer now serving as VP at Anduril. The collaboration represents a significant convergence of consumer AR hardware expertise (Meta) with defense-tech integration (Anduril) for battlefield applications.
China's Regulators Block Meta's Acquisition of Manus, an Agentic Startup Headquartered in Singapore
China's National Development and Reform Commission (NDRC) blocked Meta's proposed $2.5 billion acquisition of Manus, a Singapore-based AI agent startup originally founded in China by Butterfly Effect. The NDRC cited concerns over data transfers and foreign ownership of technology developed by Chinese engineers, asserting jurisdiction despite Manus having relocated to Singapore. The ruling has effectively killed the 'Singapore strategy' used by Chinese AI startups to attract Western capital, causing founders and investors to cancel plans to move abroad or pursue foreign partnerships. The episode marks a significant escalation in China's assertion of control over strategically important AI technology regardless of corporate domicile.
Hackers Simply Asked Meta AI to Give Them Access to High-Profile Instagram Accounts. It Worked
Simon Willison comments on a reported incident in which attackers successfully used Meta AI to gain unauthorized access to high-profile Instagram accounts through social engineering or prompt-based manipulation. The case illustrates real-world exploitation of AI assistant systems deployed in consumer products. This is a concrete deployment security failure with implications for how AI assistants handle privileged account actions.
Data Points: China Blocks Meta-Manus Deal; Microsoft-OpenAI Restructure; Nvidia Nemotron Omni; Grok 4.3; OpenAI AGI Principles; IBM Granite 4.1
A roundup of major AI developments: Chinese regulators blocked Meta's acquisition of Singapore-based agent startup Manus on security grounds; Microsoft and OpenAI restructured their partnership, with OpenAI gaining freedom to sell on rival clouds while Microsoft loses its AGI-access clause; Nvidia released Nemotron 3 Nano Omni, a 30B MoE omnimodal open-weights model for local agent deployment; xAI shipped Grok 4.3 with a 1M-token context window at reduced pricing; OpenAI published AGI operating principles; and IBM released Granite 4.1 across language, vision, speech, embedding, and safety modalities.
Tech Giants Acknowledge AI Data Center Expansion Is Undermining Climate Commitments
Alphabet, Amazon, Meta, and Microsoft have publicly acknowledged that surging AI infrastructure demand is causing them to miss or revise earlier greenhouse gas reduction pledges. All four companies have turned to natural-gas power plants to bridge energy gaps, with total emissions rising 23–60% since 2019–2020 depending on the company. Clean energy alternatives like nuclear and geothermal remain insufficiently scaled, with nuclear deployments largely deferred to the 2030s. U.S. data center electricity consumption is projected to rise from 4.4% to as much as 12% of national usage within a few years.
Meta releases Llama Prompt Guard 2 (22M) safety classifier on Hugging Face
Meta released Llama Prompt Guard 2-22M, a lightweight 22-million-parameter text classification model for prompt safety, published on Hugging Face under the meta-llama organization. The model is based on DeBERTa-v2 architecture and tagged for safety use cases including prompt injection and jailbreak detection. It is part of the Llama 4 safety tooling ecosystem and supports English and French.
Meta releases Llama Prompt Guard 2 (86M) for prompt injection and jailbreak detection
Meta released Llama Prompt Guard 2-86M, a DeBERTa-v2-based text classification model on Hugging Face designed for safety filtering, specifically prompt injection and jailbreak detection. The model is tagged with llama4, suggesting it is part of the Llama 4 safety tooling ecosystem. With over 122K downloads, it has seen meaningful early adoption.
Meta releases Llama Guard 3 1B safety classifier on Hugging Face
Meta released Llama Guard 3 1B, a compact 1-billion-parameter text-generation model designed for content safety classification, published on Hugging Face. The model is part of the Llama Guard 3 family and supports multiple languages including English, German, and French. Its small size makes it suitable for lightweight safety filtering in production deployments.
Meta releases Llama Guard 3 11B Vision for multimodal content safety classification
Meta released Llama Guard 3 11B Vision on Hugging Face, a multimodal safety classifier supporting image-text-to-text inputs built on the Llama 3 architecture. The model extends the Llama Guard safety classification family to handle visual content alongside text. This is relevant to AI safety tooling for multimodal deployments.
USRA Applies SAM 2 Fine-Tuning for Real-Time Flood and River Monitoring
The Universities Space Research Association (USRA) and Meta are collaborating with the U.S. Geological Survey (USGS) to apply a fine-tuned version of SAM 2 for automated water segmentation in drone and satellite imagery, targeting real-time flood detection and river extent mapping. The fine-tuned model replaces a labor-intensive manual digitization workflow that was a key bottleneck in rapid-response image analysis. The system integrates with PlanetScope satellite imagery and USGS 3D Hydrography data, with case studies in the Chesapeake Bay area showing promise for nationwide deployment. The collaboration also anticipates leveraging the recently released SAM 3 for unified detection, segmentation, and tracking.
Arm & ExecuTorch 0.7: Bringing Generative AI to Edge Devices
Arm and Meta's ExecuTorch 0.7 release targets on-device generative AI deployment, enabling inference of large language and multimodal models on edge hardware. The update focuses on expanding hardware backend support for Arm architectures and improving performance for mobile and embedded deployments. This represents a continued push to democratize generative AI beyond cloud infrastructure.
Llama Guard 4 Released on Hugging Face Hub
Meta's Llama Guard 4 safety classifier has been made available on the Hugging Face Hub. Llama Guard 4 is a content moderation model designed to detect unsafe inputs and outputs in LLM pipelines. The Hugging Face blog post announces its availability and integration into the Hub ecosystem, continuing the Llama Guard series of safety-focused models.
torchtune: PyTorch Native Post-Training Library for LLMs
Meta's PyTorch team introduces torchtune, a PyTorch-native library for post-training LLMs that emphasizes modularity, hackability, and direct access to underlying PyTorch components. The library supports fine-tuning, experimentation, and deployment-oriented workflows across distributed training settings. Benchmarked against popular frameworks Axolotl and Unsloth, torchtune demonstrates competitive performance and memory efficiency while maintaining flexibility for research iteration. The paper presents design principles, model builders, training recipes, and distributed training stack details.
Data Points: Nvidia Ising Models for Quantum Computing, Meta Muse Spark, GitHub Rubber Duck, Anthropic Claude Managed Agents, GPT-5.4-Cyber
Nvidia released Ising, a family of open AI models targeting quantum processor calibration and error correction, achieving 2.5x faster and 3x more accurate decoding than pyMatching, with adoption by Fermilab, Harvard, and others. Meta announced Muse Spark, a small multimodal model powering a new AI assistant series for its apps and glasses. GitHub introduced Rubber Duck, a cross-model review feature pairing Claude with GPT-5.4 for two-pass coding agent validation. Anthropic launched Claude Managed Agents, a managed infrastructure platform for enterprise autonomous AI deployment, while OpenAI expanded its Trusted Access for Cyber program with GPT-5.4-Cyber, a fine-tuned defensive cybersecurity model.
Meta Research Improves Image Generation via Staged Planning and Self-Revision Fine-Tuning
Researchers from Meta and collaborating universities propose a fine-tuning method that teaches image generators to compose images through discrete plan-sketch-inspect-refine cycles rather than generating all at once. Starting from BAGEL-7B, they construct ~62,000 training examples using GPT-4o and FLUX.1 Kontext to supervise each stage, achieving 83% on GenEval versus 77% for the base model and a competing method (PARM) that required 11x more training data and ~8x more inference steps. The approach improves spatial relationship accuracy, object attribute fidelity, and real-world knowledge grounding in generated images.
Data Points: Anthropic's Claude Mythos Cybersecurity Claims Face Scrutiny; OpenAI-Cerebras Deal; Meta AI CEO Avatar; Infrastructure Delays
A multi-item digest covers skepticism around Anthropic's Claude Mythos zero-day vulnerability claims (flagged as overstated by Tom's Hardware based on limited 198-case evidence), OpenAI's $20B+ deal with Cerebras for AI processors including a potential ~10% equity stake, and satellite data showing ~40% of U.S. AI data center projects are behind schedule. Additional items cover Meta developing an AI avatar of CEO Zuckerberg for internal use, Moody's flagging credit stress in AI-disrupted sectors, and Luma AI launching an AI-driven film production studio using its Uni-1 model.
DeerFlow 2.0 launches as open-source agent harness; Anthropic sues Pentagon over AI blacklist; Google releases Gemini Embedding 2
ByteDance released DeerFlow 2.0, an open-source agent harness built on LangGraph/LangChain that orchestrates parallel sub-agents with sandboxed Docker environments, progressive skill-loading, and persistent memory for complex workflows. Anthropic filed two lawsuits against the U.S. Pentagon contesting a supply-chain risk blacklist tied to its refusal to remove guardrails preventing Claude's use in autonomous weapons and domestic surveillance, with potential multi-billion dollar revenue impact. Google released Gemini Embedding 2, a multimodal embedding model unifying text, images, video, audio, and PDFs in a single vector space, succeeding the text-only predecessor. Meta acquired Moltbook, an agent-to-agent social platform built around the OpenClaw framework, while OpenAI hired OpenClaw's creator and acquired AI security testing platform Promptfoo.
CyberSecEval 2 - A Comprehensive Evaluation Framework for Cybersecurity Risks and Capabilities of Large Language Models
CyberSecEval 2 is a benchmark framework designed to evaluate both the cybersecurity risks and capabilities of large language models. The framework appears to be hosted or featured on Hugging Face's leaderboard infrastructure, extending prior cybersecurity evaluation work. It assesses LLMs across multiple dimensions of security-relevant behavior, including potential for misuse and defensive capabilities.
Llama 3.2 in Keras
Hugging Face published a blog post detailing the integration of Meta's Llama 3.2 models into the Keras framework. The post covers how developers can use Keras to load, fine-tune, and run inference with Llama 3.2, expanding the ecosystem of tools available for working with the model. This represents a tooling/framework integration update rather than a new capability announcement.
Deploy Meta Llama 3.1 405B on Google Cloud Vertex AI
Hugging Face published a guide detailing how to deploy Meta's Llama 3.1 405B model on Google Cloud Vertex AI. The post covers infrastructure setup, serving configuration, and integration patterns for running the large open-weights model in a managed cloud environment. This reflects the growing ecosystem of tooling and cloud partnerships enabling enterprise deployment of frontier open-weights models.
Internet Traffic Driven By AI Tripled Last Year, Study Shows
Human Security's 2026 State of AI Traffic and Cyberthreat Benchmark Report, based on over 1 quadrillion internet interactions, found AI-driven traffic nearly tripled in 2025, with agentic browser-style traffic growing ~80x year-over-year (though still only 1.7% of AI-driven traffic by December). OpenAI accounted for ~69% of automated traffic, Meta 16%, and Anthropic 11%. The report also flags a 47% rise in malicious scraping and new security challenges as legitimate AI agents increasingly mimic historically suspicious bot behaviors like account creation and transaction completion.
Data Points: OpenAI shuts down Sora, Anthropic multi-agent harness, EVA voice benchmark, Arm AGI CPU, White House AI preemption proposal
OpenAI is shutting down its Sora text-to-video platform without explanation, ending a major Disney licensing deal worth up to $1 billion and eliminating video capabilities from ChatGPT amid Hollywood copyright tensions. Anthropic published details on a multi-agent harness enabling Claude to build full-stack applications over multi-hour sessions using a planner-generator-evaluator architecture. ServiceNow AI Research released EVA, an open-source two-dimensional benchmark for voice agents measuring both task accuracy and conversational experience quality. Additional items cover Arm's first self-designed data center CPU (AGI CPU) co-developed with Meta, and the Trump Administration's legislative proposal for a federal AI framework that would preempt state AI laws.
Llama 2 on Amazon SageMaker: A Benchmark
This Hugging Face blog post benchmarks Llama 2 model inference on Amazon SageMaker, examining performance and cost characteristics across different instance types and configurations. The analysis provides practical guidance for deploying open-weights LLMs in cloud infrastructure. It covers throughput, latency, and cost trade-offs relevant to enterprise deployment decisions.
A Reality Check on the AI Jobs Hysteria
MIT Technology Review offers a critical analysis of current narratives around AI-driven white-collar job displacement, questioning whether recent tech-sector layoffs at companies like Coinbase, Meta, and Cisco genuinely signal broad AI-driven workforce disruption. The piece appears to push back on alarmist framing around AI's near-term labor market impact. It targets knowledge workers including software developers and financial analysts as the focal demographic in the debate.