Meta AI Blog
Meta is developing four successive generations of its custom MTIA AI chips scheduled for deployment between 2026 and 2027, expanding capabilities from ranking and recommendation tasks to generative AI workloads. From MTIA 300 to MTIA 500, high-bandwidth memory increases 4.5x and compute performance increases 25x within two years. The modular chiplet design allows Meta to ship new generations every six months while using the same physical infrastructure, reducing deployment friction compared to traditional chip development cycles.
Meta AI Blog
Meta released SAM 3.1, an updated version of its Segment Anything Model that processes video object tracking more efficiently through a technique called object multiplexing. The model doubles processing speed from 16 to 32 frames per second on a single H100 GPU by tracking up to 16 objects in a single forward pass instead of processing each object separately. This enables real-time object tracking in complex videos while reducing GPU resource requirements, making the technology feasible on smaller hardware.
Meta AI Blog
Alta Daily, a fashion app launched in 2025, uses Meta's Segment Anything Model to digitize users' wardrobes and recommend outfit combinations from photos. The app has processed more than 20 million images using SAM, reducing costs compared to external segmentation APIs that charged several cents per image. Users can now photograph their clothes and receive personalized outfit recommendations displayed on their digital avatar while tracking daily wear to avoid repetition.
Meta AI Blog
Meta and the World Resources Institute released Canopy Height Maps v2, an open-source model that uses satellite imagery to measure forest structure globally for conservation and land management. The model's accuracy metric (R²) improved from 0.53 to 0.86, and it was built using Meta's DINOv3 vision model trained on 493 million satellite images. Governments and organizations in the UK, EU, and US cities are already using the maps to monitor forests, track tree-planting commitments, and plan urban cooling interventions.
Meta AI Blog
Meta released Muse Spark, a multimodal reasoning model that supports tool-use, visual reasoning, and multi-agent coordination as the first product from its restructured AI efforts. The model requires over an order of magnitude less compute than Meta's previous Llama 4 Maverick model to reach equivalent performance levels. The release includes a private API preview and marks the beginning of Meta's stated scaling roadmap toward what it calls personal superintelligence.
Meta AI Blog
Meta published an updated Advanced AI Scaling Framework that broadens safety evaluations for its most capable AI models, including new assessments of chemical, biological, and cybersecurity risks plus loss-of-control scenarios. The framework requires models to meet safety standards before deployment across all Meta AI applications, with evaluations conducted both before and after safeguards are applied. Meta will now publish Safety & Preparedness Reports for each advanced model, detailing risk assessments, evaluation results, and deployment rationale to provide transparency about how protections scale with model capabilities.
Meta AI Blog
Meta launched Muse Image, an image generation model that uses search tools, code execution, and self-refinement to follow instructions and edit images precisely, alongside a preview of Muse Video for video generation. Muse Image ranks No. 2 on Arena's human-preference Elo rankings for text-to-image and image editing tasks as of July 5, 2026. The models are now available in Meta AI app, meta.ai, and Instagram Stories in the US, with integration into Facebook and broader creator access coming soon.
Meta AI Blog
Meta released Muse Spark 1.1, a multimodal reasoning model designed for agentic tasks with improved capabilities in tool use, coding, and computer interaction. The model supports a 1 million token context window and is now available in public preview through the new Meta Model API. Developers can access Muse Spark 1.1 to build agents that handle complex workflows, debugging, and automation across multiple applications without extensive human intervention.
