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Only First party#Meta
Nov 14Sat
  1. Sierra Blog80

    Releasing Personal Agent Protocol 1.0

    Poppy (Personal Agent Protocol) v1.0 is now available, with Shopify enabling it for every merchant on its platform, Mercury, Synchrony and Turkish Airlines going live as design partners, and Muse, Meta's AI agent, checking for Poppy first on every site it visits. The protocol, built largely on existing standards like MCP, OpenAPI and UCP with OAuth authentication, defines how personal agents connect with companies, and now has more than 100 design partners after 60 new companies joined. Shopify co-authored Poppy's commerce extension linking it to UCP, and an implementation kit with a validator is available for any company to adopt the protocol.

    Why it matters: Announces a new agent-interoperability standard with concrete adoption details from Shopify, Meta and others, useful for tracking how personal agents connect to businesses.

  2. Meta Engineering39

    Cuttlefish on Mac: Running Android’s Virtual Device Framework Natively on Apple Silicon

    Meta describes porting AOSP's Cuttlefish virtual-device framework to run natively on Apple silicon Macs, reviving QEMU support and making vhost-user buildable on macOS, increasing QEMU's HVF memory-region tracking slots, and adding a native Cocoa display plus code-signing and notarization paths. The unified platform replaces the previous split between Goldfish and Cuttlefish, cutting maintenance and infrastructure overhead and letting developers and CI use the same high-fidelity virtual devices. Meta is releasing a QEMU fork (qemu-wearables) and Cuttlefish patch set (cuttlefish-wearables) and is exploring upstreaming the changes.

Nov 12Thu
  1. Meta Engineering40

    Intelligent Delivery: Making Every Unit of Compute Serve Better Ads

    Meta describes Intelligent Delivery, an ML-driven system that allocates compute across its ads serving stack toward a system-wide optimum, contributing roughly 10% incremental ranking performance on top of model and system advancements from 2017 through the first half of 2026. It combines a shared Resource Core Model (6B sparse and 20M dense parameters) that predicts each request's outcome, compute consumption, and latency impact, with a hierarchy of allocation engines spanning region, hardware SKU, and host-level decisions. Agentic AI now helps propose and test optimizations, contributing to about 20% of Intelligent Delivery optimizations in the first half of 2026 and about 80% of engineering effort on the most automated mechanism, with engineers approving every change.

Nov 11Wed
  1. Meta Engineering62

    How We Built TLX: A Hardware-Native GPU Compiler for Production ML at Scale

    Meta introduces Triton Low-Level Extensions (TLX), an extension to Triton that adds explicit warp-group and cluster-level orchestration so engineers can use modern GPU features like TMA, TMEM, and warp specialization while keeping Triton's programming model. TLX is in production across Meta workloads, with reported gains including 20-25% end-to-end MFU for GEM model training on thousands of NVIDIA Blackwell GPUs, up to two-thirds net-latency reduction in RecSys inference, and a distributed grouped GEMM kernel running in large-scale LLM pretraining. The post frames TLX as implementing a "MIMW" abstraction between CUDA's SIMT model and Triton's block-level model, and covers five extension areas: warp-group orchestration, synchronization, memory orchestration, cluster execution, and optional layouts.

    Why it matters: A detailed first-party account of a production GPU compiler, useful for engineers weighing Triton, CUTLASS, and CUDA for hardware-specific kernel work.

  2. Meta Engineering58

    Your Algorithm: Giving People More Ways to Personalize Facebook Recommendations

    Meta introduced Your algorithm, a suite of AI-powered tools that gives Facebook users a single editable page showing the interests shaping their Feed and Reels, where they can add or remove preferences in natural language. The system uses large language models to generate an initial interest profile and Lately Summary, validate each edit, and perform a final reranking check against "Not Interested In" topics, with dedicated retrieval, filtering, and delivery mechanisms to make changes take effect responsively. Meta says it is exploring better handling of ambiguous input and additional entry points for shaping preferences.

Nov 10Tue
  1. Artificial Analysis Articles72

    Meta's Muse Ember is the cheapest model scoring 36 on the Intelligence Index

    Meta's Muse Ember scores 36 on the Artificial Analysis Intelligence Index, matching Claude Haiku 5.5 (max) at $0.14 per task versus $0.41, making it the cheapest model scoring 36 or higher. It stands out on AutomationBench-AA at 68.1% and scores 46 on the Coding Agent Index in Muse Code, but its AA-Omniscience accuracy of 21.1% is the lowest among similarly intelligent models, largely because it declines to answer 64% of questions, keeping its hallucination rate at 17.4%. Pricing is $0.10 input / $0.004 cached input / $0.50 output per 1M tokens, available via Meta's first-party API and Muse Code.

    Why it matters: The piece offers a detailed cost-versus-capability breakdown that helps readers judge whether a cheap efficient-tier model fits agentic and coding workloads.

  2. Meta Engineering38

    Extending LLDB to Debug Applications That Span CPUs and GPUs

    Meta describes a vendor-neutral accelerator plugin architecture in lldb-server that separates shared debugger infrastructure from hardware-specific device control, representing the CPU and GPU as separate LLDB targets within one debugging session. The plugin translates a vendor's device debugging API into LLDB concepts such as processes, threads, registers, memory, modules, and stop reasons, and is activated only when the application uses a supported GPU runtime. Most of the common framework has been upstreamed to LLVM, including the plugin interface, GDB-remote packets, and hardware-independent mock tests.

Nov 4Wed
  1. Meta Engineering34

    F3 Lightning: How Meta Accelerates Ads ML Features to Production at Scale

    Meta introduces F3 Lightning, an infrastructure layer on top of its Facebook Feature Framework (F3) that decouples feature releases from shared execution so each of the 20,000+ features in an ads ranking request can be compiled, validated, deployed, and rolled back independently. To preserve F3's efficiency, it uses stable computation identifiers, a single shared execution plan, one shared memory buffer per request, and dense index mapping for direct array access. Meta reports comparable extraction features reach production about 3.6x faster than prior F3 with no increase in infrastructure compute cost.

Oct 30Fri
  1. Meta Engineering58

    Catching Bugs Before They Land: JiTTesting’s Human-on-the-Loop Approach at Full Scale

    Meta reports that its Catching JiTTests system, which uses LLMs to generate executable tests that fail on risky code diffs before they land, is now part of its production infrastructure rather than a research prototype. The system combines intent-aware and "dodgy diff" test-generation workflows with an LLM-as-Judge ensemble and a rule-based assessor, and uses a human-on-the-loop model where engineers review outcomes at an aggregate level. Meta says 589 engineers (64% of outreach) engaged with the system, which showed a 60% true-positive rate and helped catch 74 serious privacy bugs at the diff stage in the first half of 2026, lowering privacy SEVs by 20%.

Oct 29Thu
  1. PyTorch Blog47

    PyTorch 2.15 Release Blog

    PyTorch 2.15 makes the rewritten NCCL backend the default for all nccl process groups, with a flight recorder that dumps diagnostics on hangs and TORCH_DIST_USE_NCCL2=0 or nccl-legacy as rollback paths. It adds one-sided transports for torch.distributed with a NIXL backend, finer FSDP2 and pipeline parallelism memory and communication controls, NVFP4 decode improvements on Blackwell, FP8 and faster decode on Apple Silicon, and broader platform support including FlyDSL on AMD gfx950, ROCm 10.0 wheels, CUDA 13.2 as the default wheel, and torch.compile on Python 3.15. The release contains 3,117 commits from 583 contributors since 2.14, and Python 3.10 and macOS 14 are no longer supported.

  2. Meta Engineering36

    Meta Andromeda: Powering the Next Generation of Ads Retrieval Through Model-System Co-Design

    Meta describes the next generation of its Andromeda ads retrieval engine, which uses a retrieval mixture-of-experts architecture and a single fused GPU pipeline to score every eligible ad at over one billion ads per second, a 100x throughput lift over the 2024 system. The system delivers near ranking-level model performance at retrieval scale with roughly 300x fewer inference-time FLOPs than the 2024 model, and Meta reports a 2.4% increase in conversions on Facebook since 2025. A joint collaboration with NVIDIA pushed individual components toward hardware limits, with the gating kernel reaching over 10 billion ads per second on a single GH200 and index matching exceeding 100 billion ads per second.

Oct 28Wed
  1. Meta Engineering36

    Python Typing Survey 2026: AI Tools and the Shifting Checker Landscape

    The 2026 Python Typing Survey, with 1,794 responses, found type hint use stable across three years (85.5% in 2026) while checker selection shifted sharply, with 49.9% of respondents selecting two or more checkers and speed cited by 56.5% as the top reason for choosing one. Missing type information in third-party libraries was the leading difficulty at 65.1%, and AI tools wrote type hints for 42.5% of 1,517 respondents, with 24.4% saying an AI coding tool invoked their checker.

  2. Meta Engineering38

    PrivacyLib: Privacy Controls at the Data Access Boundary

    Meta details PrivacyLib, a common library embedded in the clients of hundreds of its data and processing systems that enforces privacy requirements at the point of data access, performing billions of checks per second. Systems integrate once and new requirements arrive as plug-in modules, with changes simulated before release and rolled out per module; for internal AI agents, tool calls carry agent context to PrivacyLib so modules can observe data use and block disallowed operations.

Oct 27Tue
  1. NVIDIA Newsroom75

    NVIDIA and Meta to Expand Collaboration on Muse and Muse Family of Models Amid Robust Growth

    NVIDIA and Meta announced they will expand their partnership on Muse and the Muse family of models to improve inference and training performance, citing popular demand for AI agents. Meta intends to expand deployment of NVIDIA Vera Rubin and Vera CPUs, plus Spectrum-X Ethernet networking and BlueField-4, and the companies will consider collaboration on NVIDIA OpenShell Reference Architecture and NVIDIA Sentry for agent security and monitoring. NVIDIA will invest $3 billion in Meta's equity offering.

    Why it matters: The announcement details a major AI infrastructure partnership and a $3 billion equity investment, useful for readers tracking NVIDIA-Meta ties and agent infrastructure.

  2. Meta Engineering40

    Meta Networking at the 2026 OCP Global Summit: Open Innovation for Scale-Up, Scale-Out, and Scale-Across AI Networks

    At the 2026 OCP Global Summit, Meta presented networking work supporting its AI models and gigawatt-scale Prometheus cluster, including the liquid-cooled IcePack/IceCube rack with 102.4T switch blades, the Flattened Planar Fabric for scale-out, BAG super-spine and SRv6 backbones for scale-across, and Tahan, an Ethernet scale-up switch for MTIA 300. Meta also introduced OSPA, an OCP initiative co-led with Microsoft and Alibaba to standardize the switch platform layer with a v1.0 spec submitted to OCP, and said its MetaRoCE RDMA transport is going open source, starting with a user-space version on GitHub. Recorded talks and slide decks from the event are available on the 2026 OCP Global Summit website.