#Meta

Cuttlefish runs natively on Mac: Bringing the Android Virtual Device framework to Apple Silicon

Meta has natively ported the Android Virtual Device framework Cuttlefish to macOS, by supporting QEMU and vhost-user, solving the technical challenges of running virtual devices on Apple Silicon, improving development efficiency, and unifying testing environments. The new version supports macOS's display, signing, and build processes, and expands features such as Bluetooth, Wi-Fi, and 3D graphics, allowing Mac developers to use high-fidelity virtual devices the same as CI.

Meta Develops Intelligent Delivery System: AI Optimizes Computational Resource Allocation to Enhance Ad Sorting Performance

Meta's Intelligent Delivery system, through AI optimization of computational resource allocation, has boosted ad sorting performance by approximately 10% since 2017. The system includes a Resource Core Model that predicts the marginal utility of computational resources (6B sparse parameters + 20M dense parameters), multi-layer mechanisms for cross-system resource migration, and a framework that uses AI agents to continuously monitor and detect changes in the response system.

2026 Python type hints survey: Changes in AI tools and inspectors landscape

2026 Python Typing Survey Received 1794 Responses, Checker Selection Shows Significant Change, Half of Respondents Use Multiple Checkers, Missing Library Type Information Remains a Major Challenge. AI Tool Usage in Typing has Increased Substantially from 18.5% to 38.4%, with 42.5% of Respondents Using AI to Generate Typing, 61.0% Using AI to Fix Checker Errors, but 29.8% Express Uncertainty about the Practicality of AI-Assisted Coding.

Meta's PrivacyLib: Implementing privacy control at data access boundaries

Meta's PrivacyLib implements privacy control at data access points through a general-purpose library, replacing the previous method of implementing the same logic individually in each system. The library performs hundreds of billions of checks per second, supporting internal AI agents to pass context on supported paths to monitor data access. The system only needs to be integrated once, and new privacy capabilities can be reused as modules without developing each system individually.

FlyDSL accelerates AMD MI350 series GPU computing through existing PyTorch API

PyTorch users can accelerate dense and grouped GEMM, fused multi-head attention, MXFP8/MXFP4 scaled GEMM, RMSNorm, and TopK on AMD MI350 series GPUs using FlyDSL with existing operator API without calling custom kernels

Meta's TorchTitan integrates TorchTPU and optimizes TPU model training.

Meta's TorchTitan integrates TorchTPU, enabling PyTorch models to be trained natively on Google Cloud TPU with minimal code changes.

Meta introduces GenRecs Tokenization to reshape ad recommendations with a stable model vocabulary.

GenRecs Tokenization enhances conversion rates by 0.35% for advertisers by representing items like ads and products with a stable semantic vocabulary, combined with Meta's Generative Ad Model (GEM) and adaptive ranking model. This technology leverages the world knowledge of pre-trained large models to enable recommendation systems to understand preferences of new items or users with no historical interaction, accelerates learning through shared concepts, achieves cross-activity transfer, and improves performance as large model scale increases, forming a feedback loop for performance improvement.

Meta's Prometheus Supercluster: The Journey to Build a Megawatt-Scale AI Infrastructure

2026 World Cup: Meta infrastructure sets records to handle global traffic surges

Meta AI's SAM 3 and DINOv3 models drive the first wave of progress for the Genesis mission project

Meta AI's open-sourced SAM 3 and DINOv3 models achieve 3D semantic segmentation and reconstruction of scientific images within 15 minutes using 300 A100 GPUs on the NERSC supercomputer, helping the U.S. Department of Energy's Genesis mission project overcome the analytical bottleneck caused by data explosion after ALS upgrade, and has realized a 20-fold increase in monthly expert annotation efficiency in grapevine drought cell-level research.

Meta AI has released the Muse Spark 1.1 multimodal reasoning model.

Muse Spark 1.1 is the latest multimodal reasoning model released by Meta Superintelligence Labs, significantly improving in tool and computer use, coding, and multimodal understanding, supporting million-token context windows. It is open to developers for public preview via the Meta Model API, surpassing Muse Spark and matching leading alternatives in Meta's internal coding evaluations.

Meta AI releases Muse Image and Muse Video: First media generation models

Meta AI has released its most advanced image generation model, Muse Image, which can faithfully follow instructions, accurately edit, compose from multiple references, and has the ability to use agent tools, integrated with Muse Spark; Muse Video is also based on the same pre-training foundation, providing excellent visual fidelity and supporting native audio.

Meta AI releases Brain2Qwerty v2, a non-invasive brain-computer interface that decodes sentences in real-time

Meta AI releases Brain2Qwerty v2, a non-invasive brain-computer interface that decodes sentences in real-time with a word accuracy rate of 61%; v1 training code and dataset are open-sourced, with the v1 dataset released by collaborator BCBL.