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Nov 11Wed
  1. AWS Machine Learning Blog26

    Introducing usage-based pricing for Amazon SageMaker AI bidirectional streaming

    AWS announced usage-based metering for Amazon SageMaker AI bidirectional streaming inference, letting model providers report actual usage for AWS Marketplace software charges via a companion WebSocket metadata connection, while SageMaker AI endpoint infrastructure charges still apply. Deepgram is the launch partner, bringing usage-based Speech-to-Text and Text-to-Speech billing to AWS Marketplace. Model servers opt in with the X-Amzn-SageMaker-Metadata-Stream-Supported header and send JSON metering records with the inference.count dimension and ConsumedUnits.

  2. AWS Machine Learning Blog60

    UAEF: A framework-agnostic system for closed-loop evaluation of AI agents

    AWS introduces the Universal Agent Evaluation Framework (UAEF), an open-source, framework-agnostic system that uses adapters to normalize agent traces from LangGraph, LangChain, Strands Agents, Amazon Bedrock AgentCore, and Langfuse into a common AgentTrace format, then scores them with 36 built-in metrics across 7 quality dimensions. Results are stored per experiment in Amazon DynamoDB and Amazon S3, enabling comparison of agent versions, regression detection, and report and insight generation, with both a Python library and a low-code web UI sharing the same engine. The post also provides a playbook for choosing metrics and preparing datasets, plus runnable notebooks for each supported framework.

    Why it matters: It offers a detailed, practical reference for teams evaluating agents across frameworks, including metric selection guidance and a closed-loop workflow for catching regressions before release.

  3. Google DeepMind71

    AlphaGenome 2: Deciphering how cells read DNA to better understand disease

    Google DeepMind announced AlphaGenome 2, a conditional DNA sequence model trained on over 107 million individual cell profiles across 68 tissues that predicts how genetic variants affect gene expression in specific human cell types and states. The model identified nearly twice as many causal variants (30% vs. 16%) and directional gene effects (43% vs. 23%) as the original AlphaGenome fine-tuned on single-cell data on the TenK10K immune cell dataset, and can generalize to unseen single-cell experiments without retraining. It is available for non-commercial research via the AlphaGenome API and in private preview for commercial research.

    Why it matters: It documents a concrete advance in cell-type-specific variant effect prediction, with benchmark numbers and disease examples useful for readers tracking AI in genomics.

  4. Databricks Blog71

    Genie One: The AI cowork home for everyone

    Databricks announced that Genie One is now the enterprise home for AI cowork, combining Genie Search (hybrid AI search indexing tools like Google Drive, Jira, Confluence, Workday and SharePoint, plus over 100 MCP connectors), organizational directories, in-conversation artifacts such as slide decks and dashboards, MCP-based actions across tools, shareable skills, and native apps for browser, desktop, mobile, Slack and Microsoft Teams. Genie can now take more autonomous action on users' behalf, with Unity Gateway providing traces, MCP policies, permissions and cost management, and Genie One is free for users through January 31, 2027.

    Why it matters: It details a major enterprise AI assistant release, useful for readers tracking how agentic workplace tools are being packaged with governance and cross-tool search.

  5. Databricks Blog58

    Genie with Genie Ontology Delivers 1.6× the Accuracy with Less than Half the Tokens

    Databricks developed a new enterprise data-agent benchmark with Turing, run in a live Databricks workspace, in which Genie achieved a 91% pass rate versus 45% and 58% for two frontier agents while using fewer than half the tokens and completing tasks 3–4x faster. The benchmark, built around a fictional UK energy supplier called Calderwick Energy, includes notebooks, dashboards, saved SQL queries and documents alongside hundreds of tables, testing whether agents can find and use business context that table schemas alone cannot provide. Genie Ontology, now generally available, automatically extracts and organizes this knowledge and surfaces relevant context for Genie.

  6. Databricks Blog58

    Bring your business context to ChatGPT, Claude, Microsoft Copilot Cowork and Grok Bot

    Databricks introduced Genie plugins that give agents in ChatGPT, Claude, Microsoft Copilot Cowork and Grok Bot a governed interface to enterprise context, drawing on Genie Ontology for definitions, relationships and source context. Genie interprets business terms, finds permitted data, writes and runs SQL, routes specialized questions to domain Genie Agents, and returns answers with citations, with interactive visualizations in clients supporting the MCP Apps extension. Agents without native plugins can access Genie through the MCP server.

  7. Databricks Blog60

    From enterprise context to action: Genie Agents now analyze more data sources to transform your work

    Databricks announced that Genie Agents can now analyze unstructured files in Unity Catalog volumes, connect to enterprise tools via generally available native MCP connectors, and be shared outside an organization through OpenSharing and Genie Clean Rooms (both in beta). The post cites customers including Unilever, HelloFresh, S&P Global Energy, Atlassian, General Motors and ModMed, and describes how agents parse documents, cite specific pages, and combine file content with governed tables.

    Why it matters: Useful for readers tracking how enterprise AI agents are expanding from structured-data querying to document analysis, tool actions and cross-organization sharing.

  8. Databricks Blog69

    Announcing GA of Genie Ontology

    Databricks announced the General Availability of Genie Ontology, an agentic ontology layer that agents build by learning from dashboards, SQL queries, notebooks and BI semantic models, while people govern definitions through Unity Catalog semantics. The release adds Genie Search (GA), Ontology Snippets (GA), external knowledge sources (Beta, starting with Power BI), organizational directories (GA, starting with Workday), Domains and Pages (GA), and Ontology Manager (coming soon in Beta). In a benchmark built with Turing simulating a Databricks workspace for a fictional UK energy supplier, Genie answered correctly in 91% of runs versus 45% and 58% for two frontier agents, using less than half the tokens and finishing three to four times faster.

    Why it matters: It details a major enterprise context-layer release and includes benchmark numbers comparing agents with and without the ontology.

  9. Databricks Blog74

    The new Genie: autonomous AI for complex enterprise work

    Databricks introduces the new Genie, an autonomous AI coworker that continuously handles long-running enterprise work, tracking progress, following up on missing inputs, and taking action as new information arrives, with users setting guardrails and approving where needed. It is supported by Genie Ontology for business context, Genie Search for hybrid enterprise search, Unity Gateway and Unity Catalog for governance, and new capabilities including artifacts, MCP connectors, Genie Agents, and plugins for tools such as ChatGPT and Claude. Databricks reports that in a new benchmark built with Turing, Genie answered correctly in 91% of task runs, 1.6 times the rate of the stronger frontier agent, while using less than half as many tokens.

    Why it matters: The announcement details a shift from task-level AI assistance to supervised autonomous agents, with concrete governance, context, and benchmark claims worth evaluating.

  10. OpenRouter Announcements66

    Introducing the Extractions API

    OpenRouter launched the Extractions API in alpha, which pulls named, typed fields out of text, images and PDFs, with launch models from Cloudflare (Clairic), Inception (Mercury Decide) and ScaleDown (Extract). Clairic supports all nine field types and returns spans, p_null and distributions for estimates and forecasts, while Extract handles PDFs with text and list fields and per-value confidence, and Mercury Decide handles text only at the lowest input price. The post walks through invoice, receipt and purchase-order examples with request and response payloads, and contrasts Extractions with the Decisions API, where the model distributes probability over answers you write.

    Why it matters: It gives a concrete, example-driven tour of a new extraction API, including per-model capabilities, pricing and the evidence fields (spans, p_null, confidence) that determine what to trust and what to route to a human.

  11. Hugging Face Blog38

    Token Pooling-Aware Training for Multi-Vector Embedding Models

    This post explains token pooling for multi-vector embedding models, which merges similar document token vectors via Ward hierarchical clustering to cut storage, and introduces pooling-aware training in Sentence Transformers that applies the retrieval loss to both original and pooled representations. At pool_factor=4, the pooling-aware model retains 97.8% of its unpooled NanoBEIR nDCG@10 versus 94.6% for a normally trained model, with a 4x reduction in document vectors. It includes a reproducible training example, optional kernel acceleration, and evaluation results across pool factors, fixed caps, and calibrated adaptive thresholds.

  12. Mastra Blog58

    Introducing MCP Tasks in Mastra

    Mastra now supports MCP Tasks in its MCPServer and MCPClient, letting long-running tool calls run in the background so the agent loop stays unblocked while the client polls the server for status (working, input_required, completed, failed, or cancelled). Tools can be configured to run as tasks with "always", "auto" (after syncWindowMs, 2 seconds by default), or "off", and tasks can still use suspend() for human input. The feature requires @mastra/[email protected] and @mastra/[email protected] or later and is still experimental, so the API may change between releases.

  13. vLLM Blog58

    Encoder Disaggregation in vLLM: Scaling Multimodal Serving with EPD

    vLLM details its Encoder/Prefill/Decode (EPD) disaggregation work, including metadata-only requests that let downstream instances reuse remote embeddings without repeating media preprocessing, a dedicated encoder-only Model Runner V2 path, and remote embedding transfer via NIXL and Mooncake. It also covers cross-encoder output reuse through Mooncake Store and orchestration support in vLLM Router, SMG, and llm-d, with core capabilities in vLLM v0.32.0 and DeepSeek V4.1 Flash metadata-only support on main via #62802. A DeepSeek V4.1 Flash benchmark on GB200 found EPD delivered 6.6–9.5% higher per-GPU throughput and 44–47% lower P90 TTFT than PD at concurrency 64–128, but lower per-GPU throughput at concurrency 16.

  14. Codex Releases12

    0.169.0-alpha.11

    This page is a GitHub Changelog entry titled "0.169.0-alpha.11"; the supplied material consists only of site navigation and product menu text, with no release notes or details about the version.

  15. Google Blog AI28

    Experience the art and legacy of Dr. Esther Mahlangu

    Google Arts & Culture has launched the first online retrospective of South African artist Dr. Esther Mahlangu, timed to her 91st birthday, featuring more than 45 curated stories and an archive of her art, life and legacy. Curator Nontobeko Ntombela describes using AI, including Veo, to visually recreate Mahlangu's undocumented memories from age 10 until her first documented works in 1980, in collaboration with Google DeepMind, the Google Arts & Culture Lab and Domestic Data Streamers. The exhibition was curated with partners including Art of Contemporary Africa, Iziko Museums of South Africa, the British Museum, the Jean Pigozzi Collection, the National Museum of Women in the Arts and Serpentine, and is available at goo.gle/esther-mahlangu.

  16. Sakana AI Blog40

    マイケル・ロジャース米退役海軍大将、トム・モラン元CIA支局長がSakana AIのシニアアドバイザーに就任

    Sakana AI announced that Admiral Michael Rogers, U.S. Navy (Ret.), former NSA Director and Commander of U.S. Cyber Command, and Tom Moran, a former senior CIA officer, have joined as Senior Advisors. They will advise the company on strengthening defense capabilities and cybersecurity. Rogers served as NSA Director and Cyber Command commander from 2014 to 2018, while Moran spent nearly 30 years at the CIA, serving six times as Chief of Station.

  19. Hugging Face Blog55

    BotPsy: On-Device Personal Assistant with a Sub-Billion-Parameter Agentic Language Model

    Tether AI Research introduces BotPsy, a 0.8B-parameter model built on Qwen3.5-0.8B for function calling and agentic workflows, suitable for on-device deployment. It roughly doubles its base model's tool-use scores (BFCL v4 23.4 to 44.0, ACEBench 30.1 to 58.7, τ-bench 23.2 to 51.5) and outperforms every model up to 1.2B parameters tested on all six agentic benchmarks, while also beating Qwen3.5-2B-Think on five of the six. It will be released with open weights under Apache 2.0 in bf16, Q8 and Q4 as safetensors and GGUF files, and is intended for low- to medium-difficulty tasks, not high-stakes use.

  20. NVIDIA Newsroom22

    How AI Helps Recyclers Turn Mixed Waste Into Reusable Material

    NVIDIA describes how four Inception startups apply AI and robotics to waste sorting: AIRECO uses vision AI and Jetson Orin NX modules to finely sort plastics, EverestLabs AI deploys computer vision-guided robots on recycling lines, Greyparrot's Analyzer platform identifies objects on conveyor belts, and reverse.fashion automates textile sorting with high-resolution computer vision. The article cites figures including less than 7% of materials entering the economy coming from recycled or reused sources and over 2 billion tons of municipal solid waste generated per year. It also notes company-reported results such as AIRECO's 95% recognition accuracy on its test line and EverestLabs' estimate of about 25,000 tons of material recycled.

  21. AWS Machine Learning Blog58

    Introducing Kimi K3.1 Pro on Amazon Bedrock

    Moonshot AI's Kimi K3.1 Pro Highspeed is now available on Amazon Bedrock, described as a post-trained version of Kimi K3 with the same 3T-class open-weight architecture and 1-million-token context window, trained with more post-training compute for long-horizon agentic work and fewer factual errors. It generates output tokens at a higher rate, with Moonshot AI reporting 2–3 times the speed of Kimi K3.1 Pro, and uses the same endpoint, APIs, and features as Kimi K3, so existing code can switch by changing the model ID. The post covers getting started via the Chat Completions, Responses, and Converse APIs, lowering costs with the Flex tier, explicit prompt caching, and using the model in OpenCode.

  22. Comfy Blog58

    Seedream 5.5 is now available via Partner Nodes

    Seedream 5.5 is now available in ComfyUI via Partner Nodes as a multimodal model that generates from text and edits from images. The release adds multi-turn localized editing, native transparent RGBA output, poster and infographic layouts with readable text, more realistic portraits, and prompt-based lighting control, all behind a single node. Partner Nodes run on credits, and users can load the Seedream 5.5 template from the Templates panel or use Comfy Cloud.

  23. OpenRouter Announcements67

    MCP vs CLI vs Skills: What Each Costs in Context

    The article measures input-token costs for giving an agent GitHub access via the GitHub MCP server, the gh CLI, or a gh skill across Claude Haiku 5.5, Claude Sonnet 5.5, GPT-6 Luna, and Gemini 3.8 Flash; on Sonnet 5.5 the CLI used 1,421 tokens, the skill 3,315, and the MCP server 59,417 with its 46 default tools. It reports that the GitHub MCP server's 46 default tools add 19,436 tokens to every request on Sonnet 5.5, and that a single tool result (GitHub's release object) cost about 20,400 tokens. It also describes the beta Tool Search server tool, which cut the up-front cost of 46 tools to under 600 tokens and halved whole-task cost on Sonnet 5.5 while raising it on Haiku 5.5.

    Why it matters: It offers concrete, model-specific token and cost measurements for choosing between MCP, CLI, and skills, plus practical mitigations like scoping tools and trimming results.

  24. OpenRouter Announcements58

    LLM Quantization: What fp8 and fp4 Do to Quality and Price

    OpenRouter tested Qwen3.8 27B across six endpoints at fp16, fp8 and fp4 on 240 exact-answer tasks and found no detectable accuracy difference, with all endpoints scoring between 98.3% and 99.2%. Across 1,455 endpoints, fp8 was the most common labeled precision (23.6%) while 59.5% reported none, and lower precision was usually but not always cheaper. The post explains fp8 and fp4 formats and shows how to pin precision with provider.quantizations, noting that a narrow filter can leave a single endpoint exposed to rate limits.

  25. OpenRouter Announcements58

    LLM API Rate Limits: How to Fix OpenAI and Claude 429 Errors

    A guide to diagnosing 429 Too Many Requests errors from OpenAI, Anthropic and OpenRouter by reading rate-limit headers and limit_source, with tested fixes: retrying at the server-provided reset time, routing overflow to a fallback model, and letting another provider serve the same model. In the author's runs, default OpenAI SDK retries left 23 of 60 requests failed, while reset-aware retries completed all 60 in 89–92 seconds (Python) and 106–218 seconds (TypeScript), and a models fallback finished all 60 in 13 seconds for $0.000066. It also covers provider failover, which turned runs with up to 32 failures per 150 requests into 150 successes, plus habits for avoiding limits.

  26. AWS Machine Learning Blog40

    Build a daily dependency watcher with LangGraph and Amazon Bedrock AgentCore web search

    The Web Search API in Amazon Bedrock AgentCore is now available, returning current web results with title, URL, publication date and query-relevant extract, signed with existing AWS credentials and served from an Amazon-operated index. The post walks through building a LangGraph dependency watch that researches npm, PyPI and container base images in parallel, keeping the date window, domain blocklist and search budget in code and checking each finding against its cited sources. In the test run, three branches made 12 searches and finished in 36 seconds versus 99 seconds sequentially, and the watch can be hosted on AgentCore runtime or offered through AgentCore Gateway.

  27. Microsoft Research78

    Lazare, veni foras: Reprogramming Dormant Neurons to Backdoor AI Agents

    Microsoft researchers describe "Lazarus," a backdoor technique that reprograms fewer than 10 dormant neurons in an LLM, without training, so an agent built on it behaves normally and passes standard evals until a trigger—such as a date change—causes it to download and run attacker code with whatever access it has. The attack needs only write access to a checkpoint, so internally developed models are equally exposed, and reliable detection requires diffing against original weights, making provenance and weight integrity critical. Microsoft Foundry ties open-weight models to their publishers and scans surrounding containers, and Microsoft is developing AI-powered weight scanning, a red-teaming harness, and cryptographic model signing.

    Why it matters: It offers a concrete, researcher-first account of a low-cost weight-space backdoor and the provenance and weight-auditing practices it implies for anyone deploying agents.

  28. Codex Releases12

    0.169.0-alpha.10

    This page is a GitHub Changelog entry titled "0.169.0-alpha.10," but the supplied material contains only site navigation and product menu text, with no details about the release itself.

  29. ElevenLabs Blog12

    WAV vs MP3: How to choose the right audio file format

    WAV is an uncompressed, lossless format best for recording, editing and archiving, while MP3 is compressed and lossy, producing much smaller files suited to sharing and streaming. One minute of CD-quality stereo audio is about 10.6 MB as WAV, versus about 0.96 MB as a 128 kbps MP3 and about 2.4 MB as a 320 kbps MP3. Converting MP3 to WAV makes the file larger but cannot restore the data lost during compression.

  30. PyTorch Blog71

    ExecuTorch WebGPU: PyTorch Models on the GPU, in the Browser

    ExecuTorch's new WebGPU backend runs PyTorch models exported with torch.export to a .pte file directly on the GPU in WebGPU-enabled browsers, without converting to another framework's format. The same artifact runs across Chrome and Safari on Metal, Vulkan and D3D12, and on an Apple M1 Pro in Chrome it was 4–7× faster than ExecuTorch's Wasm (XNNPACK) CPU path on prefill and 2–10× faster on decode. Validated models include Llama 3.2, Qwen3, Gemma 3, SmolVLM, Depth Anything V2, SAM 2, Whisper and on-device LoRA fine-tuning, all running locally in the tab.

    Why it matters: It documents a concrete path for running PyTorch models on browser GPUs with benchmark numbers and a list of validated models, useful for developers weighing in-browser inference.

  31. Cursor Blog71

    Cursor Projects is now multiplayer

    Cursor announced that Projects, its multi-agent workspace introduced in September, now supports multiplayer so teammates can plan, build and ship together alongside their agents. Projects adds a shared context system holding requirements docs, diagrams, tech specs and mockups, plus a shared task board showing who and which agents are working on what and the PR behind each task. The feature also enables agent-to-agent communication across teammates, and is rolling out in beta to all Cursor users starting today.

    Why it matters: Useful for teams evaluating collaborative AI coding tools, as it describes a shared-context multi-agent workspace and its beta availability.

  32. AWS Machine Learning Blog40

    The agent evaluation playbook: Built-in, DeepEval, and AutoEval on Amazon Bedrock AgentCore Evaluations

    This AWS post explains how to evaluate production agents on Amazon Bedrock AgentCore by starting from business outcomes, breaking them into observable behaviors, and mapping each to a trace- or session-level evaluator. It covers the built-in AWS evaluators, managed third-party metrics from DeepEval and AutoEval, custom and multi-dimension LLM-as-a-judge evaluators, and suggested evaluator stacks for customer support, research, and domain-content agents. It also describes three evaluation modes—on-demand, online, and batch—that share the same evaluator IDs, plus the ListEvaluators API for retrieving the full catalog.

  33. Pydantic AI Articles38

    Turn down OpenTelemetry noise without a redeploy

    Pydantic Logfire's Fleet Management lets you connect a service and its host Collector via OpAMP and change log level, attribute redaction and trace sampling while both keep running. The post walks through a FastAPI app with fleet=True, publishing SDK policies to drop debug logs, mask *token* attributes and sample 10% of traces, plus installing a host Collector and adding nginx logs. It also covers early-access rollouts, Kubernetes and existing Collectors, and troubleshooting.

  34. Pydantic AI Articles60

    Code mode is not a sandbox

    This post distinguishes code mode, where the model writes a short Python program calling your tools as functions via a single run_code tool, from a sandbox, which is an isolated environment where code runs. It describes three places a sandbox boundary can go (the whole agent, only the tools, or only the model-written code) and how Pydantic AI workspaces let you move that boundary without rewriting tools. It also covers Monty, a Rust-written Python interpreter that runs run_code next to the agent with no filesystem, network, or environment variables.

    Why it matters: Useful for teams deciding where to place isolation boundaries when combining code mode with sandboxed tool execution.

  35. Redwood Research Blog58

    We don’t understand whether GDM intentionally trains on CoT

    Redwood Research researchers note that the Gemini 4 Safety report appears to say GDM uses auxiliary rewards that observe chain-of-thought content, citing Kaufmann et al. on training on CoT, while a GDM blog post says they never penalize what the model writes in its CoT and GDM staff said they weren't training on CoT but couldn't explain details. The authors argue that "orthogonal" CoT rewards are a weak guarantee and that avoiding training against monitors is insufficient to preserve CoT integrity, and they present an appendix experiment in which five LLMs rated transcripts with hack acknowledgments removed 20-50 points higher than the originals. They conclude they suspect a misunderstanding but want clarification and greater transparency.

  36. n8n Blog22

    Batch processing patterns for workflow automation at scale

    This guide explains how batch processing in workflow orchestration differs from stream processing and presents three patterns—chunking, rate limiting, and per-item error handling with retry—for handling large loads without hitting memory limits or API quotas. It describes how n8n's Split Out, Aggregate, Loop Over Items, HTTP Request, Wait, and Remove Duplicates nodes support these patterns, and notes n8n was tested with up to 220 workflow executions per second on a single c5a.large instance running a two-node workflow. It also covers idempotency, checkpointing, and choosing batch sizes, plus sub-workflows and queue mode for scaling.

  37. n8n Blog15

    Microservices best practices every automation engineer should know

    This article outlines microservices best practices, including defining clear service boundaries via domain-driven design and the single responsibility principle, and building resilience into service-to-service calls with circuit breakers, retries with exponential backoff, and bulkheads. It also covers routing traffic through an API gateway and making failures observable with distributed tracing and centralized logging, noting how n8n workflows can orchestrate and monitor service calls.

  38. Mastra Blog31

    $25 Starter Plan on Mastra Platform

    Mastra introduced a $25 Starter plan that raises monthly allowances for compute, data, and observability events and includes one Small dedicated server whose compute is covered by the $25, with usage beyond the allowances billed on top. Compute measurement is changing and allowances are being updated on every plan, while a serverless agent remains free to run on all plans including Free. Projects run serverless or on Small, Medium, or Large servers, and Mastra Connect now has a connection allowance on each plan (5 on Free, 10 on Starter, 50 on Team, $0.50 each additional) with egress from those connections counting toward plan egress usage.

  39. Artificial Analysis Articles60

    Multimodal Knowledge Agent (MKA) benchmark: testing knowledge work capabilities with complex visual files

    Artificial Analysis partnered with Google DeepMind to launch the Multimodal Knowledge Agent (MKA) benchmark, comprising 200 private real-world visual tasks across 10 industries in which agents work from dense visual files like architectural drawings, engineering schematics, and clinical scans to produce deliverables such as slide decks, spreadsheets, PDFs, Word documents, and marked-up images. Tasks are graded against expert rubrics averaging ~70 weighted criteria split into Regression and Climb checks, with the headline Climb WSR score, run in the open-source Stirrup agent harness with up to 250 turns and graded by GPT-6 Luna (medium) as an agentic judge. Claude Fable 5.2 (max) leads with 57% Climb WSR, followed by Gemini 4 Carbon (high) at 54% and Claude Opus 5.5 (max) at 53%, while Claude Fable 5.2 averages $31.97 per task versus $6.29 for Gemini 4 Carbon and $1.11 for GPT-6.1 Sol.

    Why it matters: The results offer a useful cost-performance and per-vertical breakdown for anyone choosing models for agentic knowledge work over complex visual files.

  40. Databricks Blog47

    Unity Catalog Metric Views, now in Power BI: one unified, governed semantic layer for all your reporting and AI

    Databricks introduces Publish to Power BI, which connects Unity Catalog metric views to Power BI semantic models so each metric view becomes a table, its dimensions become columns and its measures become DAX measures, with Power BI querying the metric view itself. Publishing can be done on demand from Catalog Explorer or automated via Power BI tasks in Lakeflow Jobs, and DirectQuery queries honor Unity Catalog access controls. The feature is available in Private Preview.