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November 12Thursday
  1. Meta Newsroom · AI18

    Meta Signs Long-Term Purchase Agreements for Lower Carbon Cement, Steel, Trucking and Recycled Plastics

    Meta has signed a set of long-term purchase commitments covering cement, steel, trucking and recycled plastics to bring clean manufacturing to commercial scale in its data center supply chains. The deals include at least 180,000 tons of lower carbon cement from Sublime Systems, near-zero-emission steel from Sweden-based Stegra, an expanded zero-emission freight route with Nevoya along I-10 in California and Arizona, and 5,000 tons per year of recycled PET with MacroCycle Technologies.

  2. Meta Engineering Blog40

    Meta Intelligent Delivery: ML System Allocating Compute for Ads Ranking

    Meta detailed Intelligent Delivery, an ML-driven system that allocates compute across its ads serving stack toward the system-wide optimum, contributing roughly 10% incremental ranking performance on top of model and system advancements from 2017 through the first half of 2026. Its Resource Core Model uses 6B sparse and 20M dense parameters to predict each request's marginal utility, and Meta says it improved identification of high-impact ad requests by about 20% in offline benchmarks. The system also relies on multi-layered allocation mechanisms and increasingly agentic AI to adapt as models and hardware change.

November 11Wednesday
  1. Meta Engineering Blog58

    Meta details how Your Algorithm turns natural-language preferences into Facebook Feed and Reels recommendations

    Meta published an engineering overview of Your algorithm, a suite of AI tools that lets Facebook users state in natural language what they want to see in Feed and Reels, with a single editable page listing Interested In and Not Interested In topics. Each change is validated by a smaller LLM, enriched with embeddings and search keywords, and saved to a shared store that both Feed and Reels read, while a fine-tuned LLM reranker removes content matching newly added Not Interested In topics. Meta says a supplementary tail load and client-side cache invalidation make changes take effect faster, and that a separate LLM judge grades outputs such as the Lately Summary before launch.

  2. Meta Engineering Blog58

    Meta explains TLX, a hardware-native GPU compiler extension to Triton for production ML

    Meta detailed TLX (Triton Low-Level Extensions), an extension to Triton that adds explicit warp-group and cluster-level orchestration while keeping Triton's tensor programming model. TLX is already running in production across Meta, with 20-25% end-to-end MFU on thousands of NVIDIA Blackwell GPUs for GEM training, up to two-thirds lower compute-intensive net latency for RecSys inference, and a distributed grouped GEMM kernel deployed in large-scale LLM pretraining. Meta says TLX targets NVIDIA Hopper and Blackwell plus AMD Mi300 and Mi350x, and is working on more architectures, automatic warp-group inference and a torch.compile backend.

November 10Tuesday
  1. Meta Engineering Blog40

    Meta extends LLDB with vendor-neutral accelerator plugin architecture for CPU-GPU debugging

    Meta has built a vendor-neutral accelerator plugin architecture in lldb-server that lets LLDB debug applications spanning CPUs and GPUs in a single session. The design represents the CPU and GPU as separate LLDB targets, each with its own architecture, threads, registers and address space, while a plugin translates vendor device debugging APIs into LLDB concepts. GPU support activates only when the app uses a supported GPU runtime, so CPU-only debugging keeps the existing native path without added overhead.

November 9Monday
  1. Meta Newsroom · AI38

    Meta expands AI age-detection for Teen Accounts globally on Instagram

    Meta is expanding its AI technology globally to detect suspected teen users on Instagram and place them in Teen Accounts protections, even if they registered with an adult birthday. The company says millions more teens worldwide will receive built-in protections limiting who can contact them, the content they see, and their time on the apps. Meta is also rolling the technology out on Facebook and plans to launch it on Threads later this year.

  2. Meta AI Research Blog78

    Meta releases Muse Ember, its smallest and most efficient model for agent tasks

    Meta released Muse Ember, its smallest and most efficient model, aimed at the repetitive work agents do all day such as tool calls, coding subtasks, routing, extraction and long document parsing. Meta says it matches Claude Haiku 5.5's best score at about a third of the cost and beats every GPT-6 Luna setting, at $0.10 per million input tokens and $0.50 per million output tokens. It ships with a 1M-token context window, up to 512K output tokens, native text, image, audio and video input, and is available today on Muse Code and the Meta Model API as muse-ember-1.0.

    Why it matters: Meta's smallest model targets the high-volume agent steps, and its cost-per-task comparison against Claude Haiku 5.5 and GPT-6 Luna shows where the efficiency claim comes from.

November 7Saturday
  1. Meta Newsroom · AI58

    Meta launches spaces in its Muse app for user-built personalized experiences

    Meta introduced spaces in its Muse app, an interactive way for users to build a more personalized Muse by describing an idea such as a gift tracker, daily word game or DIY planning board. Users can adjust each space with Muse, place it in the sidebar or main tabs, hide or schedule it, and connect apps so details like concert dates and price changes update in the background. Spaces are available everywhere Muse is available starting today, and Meta says it is exploring sharing options and rolling out specialized model versions trained with learnings from Muse Wonder.

November 6Friday
  1. Meta Newsroom · AI62

    Meta rolls out real-time voice and expressive avatars in its Muse app

    Meta is rolling out real-time voice conversations and expressive avatars in the Muse app on mobile, web and Mac, starting in the US. Users tap the phone icon to talk, can interrupt responses, and choose from hundreds of voices plus voice-only, avatar or full-screen modes. Voice is coming soon to AI Glasses and Muse Charm.

    Why it matters: Meta's Muse app now supports real-time voice conversations with expressive avatars, showing how voice and interruption handling are moving into consumer AI assistants.

  2. Meta AI Research Blog60

    Meta brings specialized Muse and Meta AI models with Active Memory and Active Focus

    Meta is rolling out specialized versions of its existing models, trained using Muse Wonder, to people who use Muse and Meta AI. The specialized models add Active Memory and Active Focus, and Meta reports the current Muse model scores 47% on its Personal Superintelligence Index versus 38% for the previous model, 90% versus 76% on the Active Memory eval, and a 67% improvement in completing everyday tasks where unexpected issues arise.

    Why it matters: Meta's specialized Muse and Meta AI models bring Active Memory and Active Focus to more users, with reported gains on memory and task-completion evals.

November 4Wednesday
  1. Meta Engineering Blog34

    Meta F3 Lightning Accelerates Ads ML Feature Releases ~3.6x

    Meta introduced 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+ ads ranking features is compiled, validated, deployed, and rolled back independently. Comparable extraction features now reach production ~3.6x faster than prior F3 with no increase in infrastructure compute cost.

October 30Friday
  1. Meta Newsroom · AI25

    Meta Partners with INCA to Expand AI Glasses Access for Blind and Low-Vision Canadians

    Meta has partnered with INCA, a Canadian nonprofit founded in 1918, to help more blind, deafblind and low-vision people in Canada benefit from AI glasses. Meta funding will support INCA in four areas: listening to the community, backing community champions who run in-person demos and peer training, feeding community feedback into Meta's accessibility and product teams, and providing accessible learning guides and training resources. Meta is also donating AI glasses through INCA's "Opening the Doors to Work" program for people seeking employment.

  2. Meta Newsroom · AI25

    Meta and CNIB Partner to Bring AI Glasses to Blind and Low Vision Canadians

    Meta has partnered with CNIB, a non-profit founded in 1918, to bring AI glasses to more Canadians who are blind, Deafblind or have low vision. Meta funding will support CNIB in four areas: community listening, peer training led by community champions, sharing insights with Meta's accessibility and product teams, and accessible guides and training materials. Meta is also donating AI glasses through CNIB's Come to Work program for people exploring employment.

  3. Meta Engineering Blog58

    Meta details production deployment of Catching JiTTests with human-on-the-loop review

    Meta has moved Catching JiTTests, its LLM-based just-in-time test generation approach, from research prototype into production infrastructure, and published the results in the FSE 2026 paper "Just-in-Time Catching Test Generation at Meta." The system runs overnight on spare capacity, targets risky diffs, generates tests through dodgy-diff and intent-aware workflows, and filters false positives with an LLM-as-Judge ensemble plus a rule-based RubFake assessor before reaching engineers. As of writing, 589 engineers (64% of outreach) engaged with the results, which showed a 60% true-positive rate, and in the first half of 2026 the system caught 74 serious privacy bugs at the diff stage and lowered privacy SEVs by 20%.

October 29Thursday
  1. Meta Newsroom · AI22

    Meta Partners With Five US Universities to Accelerate Medical and Scientific Breakthroughs

    Meta is partnering with five US universities — Caltech, Ohio State's College of Medicine, Louisiana State University, Indiana University and UTHealth Houston — and granting $2 million to support research using its models in medicine, physics, chemistry, biology, computer science and mathematics. Projects include a computer vision system built on Meta's open-weight SAM models to detect tracheostomy tube displacement in infants, multimodal AI to help conversational agents adapt to people with dementia, and open-weight models predicting heart failure risk from routine CT scans and medical records.

  2. Meta Engineering Blog40

    Meta Andromeda: Meta's Next-Gen Ads Retrieval Engine Hits 1B Ads per Second

    Meta detailed the next generation of Andromeda, its personalized ads retrieval engine, which scores over one billion ads per second in production — a 100x throughput lift over the 2024 system. The new architecture brings LLM-inspired mixture-of-experts to retrieval, approaching ranking-level model performance at roughly 300x fewer inference-time FLOPs, with over 10 billion ads per second on a single NVIDIA GH200 GPU. Since 2025, Andromeda has delivered a 2.4% increase in conversions on Facebook.

October 28Wednesday
  1. Meta Engineering Blog34

    Meta PrivacyLib: Privacy Controls at the Data Access Boundary

    Meta detailed 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 privacy capabilities ship as plug-in modules, replacing per-system implementations that could take months. An Agent Observability module tracks internal AI agent data access by carrying agent context to PrivacyLib.

  2. Meta AI Research Blog30

    Meta Supports AI Research at Five US Universities to Accelerate Medical and Scientific Breakthroughs

    Meta is supporting research at five US universities — Caltech, Ohio State University College of Medicine, Louisiana State University, Indiana University, and UTHealth Houston — with access to its latest Muse models, researcher collaboration, and funding. Researchers will use Muse Spark Open, Muse Wonder, Muse Spark, the 30B open-weight Muse Glimmer, plus SAM, V-JEPA, DINO, UMA, and SeamlessM4T. Projects span medicine, physics, chemistry, biology, computer science, and mathematics, with universities retaining control of all patient and clinical data.

October 20Tuesday
  1. Google Developers Blog60

    Google 将 TorchTitan 接入 TorchTPU，并发布 TPU 模型优化手册

    Google released a TPU fork of Meta's TorchTitan that runs PyTorch training on Cloud TPUs through TorchTPU's native torch.device("tpu") backend, with TPU-native kernels and a step-by-step optimization playbook. On TPU v7x (Ironwood), Llama 3 405B holds about 57% MFU from 128 to 1,024 chips, Qwen3 235B reaches 32.42% MFU on one 1,024-chip slice, and FLUX.1 reaches 19.4% MFU on 1,024 chips at 93% weak-scaling efficiency. The fork is at https://github.com/google-pytorch/torchtitan/, and a Transformers modeling backend plus an agent onboarding skill extend the same trainer to other checkpoints.

    Why it matters: Google 与 Meta 合作把 TorchTitan 移植到 TPU，并给出从 FLUX.1 到 Qwen3 235B 的逐级调优路径与 MFU 数字，可作为 PyTorch 团队评估 TPU 训练可行性的参考。

October 15Thursday
  1. Meta AI Research Blog88

    Meta releases Muse Spark Open, an 816B open-weight multimodal model that runs on two Mac Studios

    Meta released the weights for Muse Spark Open, an open-weight natively multimodal model with 816B total parameters (42B active) and a 1M-token context window, positioned for coding and agentic work. It is the first model in its class to run on a desktop using two Apple M5 Ultra Mac Studios with the MLX framework, and it is available through API partners including Baseten, Fireworks AI, Groq, Modal, Nebius, OpenRouter and Together AI, with weights on Hugging Face, Amazon SageMaker AI, Google Cloud Model Garden and Microsoft Foundry Models. Meta says the model ships with auto-tunable Helion GPU kernels in SGLang, delivering 1.03-3.95x kernel-level speedups on NVIDIA and AMD GPUs and a 3% end-to-end speedup on AMD MI350.

    Why it matters: Meta releases open weights for an 816B-parameter multimodal model that runs on two Mac Studios, giving developers a local option for long-horizon coding and agentic tasks.

  2. Modal Blog78

    Meta's Muse Spark Open weights released and available on Modal Auto Endpoints

    Meta released the weights for Muse Spark Open, and Modal now serves the model through its Auto Endpoints with per-token billing. The model targets agentic coding and long-horizon tasks, with native multimodal understanding, a 1M-token context window, multi-step planning, sequential tool use and failure recovery. Meta reports 76.4 on Terminal-Bench 2.1 with the Terminus 2 agent and 60 on DeepSWE 1.1 using its preferred harness at xhigh reasoning effort.

    Why it matters: Meta's open-weight Muse Spark Open is now served on Modal, giving teams a per-token way to try an agentic coding model with a 1M-token context window.

October 12Monday
  1. Meta AI Research Blog82

    Meta releases Muse Wonder and Muse Wonder Full frontier models for personal and professional agents

    Meta introduced the Muse Wonder series, comprising Muse Wonder and Muse Wonder Full, which it says delivers state-of-the-art performance on personal and professional agentic tasks, memory and coding. Muse Wonder is available today through the Meta Model API and Muse Code, while Muse Wonder Full is available today for Muse Code subscribers. The models pair Active Memory, which carries context across conversations and retrieves task-relevant details, with Active Focus, which sustains work on long-running goals and checks in before important actions; Meta says both were trained at its gigawatt-scale Prometheus data center.

    Why it matters: Meta's Muse Wonder series claims state-of-the-art results on agentic, memory and coding benchmarks, with Active Memory and Active Focus as the two capabilities it is built around.

October 2Friday
  1. Meta AI Research Blog71

    Meta shares six math papers co-authored with mathematicians and Muse Spark

    Meta published six mathematics papers produced in collaboration with mathematicians who used Muse Spark 1.1 and 1.2 in Thinking Mode through the regular meta.ai chat interface, with no custom research scaffold. Five of the papers answer previously open questions, covering Gaussian ellipsoid fitting thresholds, finite-time blow-up for a biharmonic nonlinear Schrödinger equation, a 384-element counterexample to a group-theory conjecture, tightness of a cycle-based relaxation in binary polynomial optimization, and a link between string two-point functions and height functions on curves. Each paper marks which passages were primarily drafted by researchers and which by AI, credits prior work, and acknowledges independent concurrent solutions from other teams.

    Why it matters: Meta 公布六篇由数学家与 Muse Spark 协作完成的论文，其中五篇给出此前开放问题的答案，并标注 AI 与人类各自撰写的段落。

  2. Meta AI Research Blog38

    Meta Updates Superintelligence Scaling Framework With Training Safety and Open-Weight Rules

    Meta published updates to its Superintelligence Scaling Framework, extending loss-of-control requirements to cover AI containment during training and evaluation. Before any reinforcement learning run, Meta will check whether a model could compromise its environment and require sandboxed, logged, and monitored mitigations if so. The update also clarifies how open-weight models can be modified after release and adds a new Board of Directors AI committee to review future Framework changes.

June 24, 2025Tuesday
  1. Vapi Blog22

    Mistral vs Llama 3: A Complete Comparison for Voice AI Applications

    Mistral and Llama 3 split on philosophy for voice AI: Mistral's 7B to Mistral Small 3.1 (24B) models favor low-latency efficiency, while Llama 3 scales from 8B to 70B for stronger reasoning and multilingual reach. Mistral Small 3.1 reaches 0.29s time to first token at 150-166 tokens per second, and Mistral Small 24B beats Llama 3.1 8B Instruct on ARC-C, GPQA and MMLU, while Llama 3.1 405B scores 85.2% on MMLU and 96.8% on GSM8K.

July 21, 2023Friday
  1. Cursor Changelog62

    Cursor analyzes Llama-2-70B inference cost and latency against GPT-3.5

    Cursor measured Llama-2-70B inference on 2 80-GB A100 GPUs and found it over 3x cheaper than GPT-3.5 for prompt tokens but far more expensive for generated tokens at small batch sizes. Prompt processing costs about $0.00042 per 1K tokens versus GPT-3.5's $0.0015, while generation reaches $0.066 per 1K tokens at batch size 1 and only approaches GPT-3.5 pricing at large batches that push time to first token to several seconds. Cursor recommends open-source models for prompt-heavy tasks like classification and reranking, and closed-source APIs for generation-heavy workloads.

    Why it matters: Cursor's measured cost and latency data shows when self-hosting Llama-2-70B beats GPT-3.5 and when it does not, offering a reusable framework for open-source model serving decisions.