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Nov 12Thu
  1. ElevenLabs Blog22

    McKinsey and ElevenLabs collaborate to take natural AI conversations to enterprise scale

    McKinsey and ElevenLabs announced an alliance combining McKinsey's industry and enterprise transformation expertise, underpinned by QuantumBlack's AI engineering, with ElevenLabs' conversational AI technology and ElevenAgents platform to help organizations prove value through initial use cases before scaling across the enterprise. Each deployment pairs multilingual conversational technology with agent design, systems integration and operating-model changes, and the companies cite KPN, the Dutch telecom company, as an early example using AI-powered solutions for customer interactions from order enquiries to technical appointment management. The alliance aims to extend beyond telecommunications to service-intensive industries such as insurance, supporting claims intake, policy servicing and everyday customer questions.

  2. ElevenLabs Blog25

    ElevenLabs expands partner program to help enterprises adopt AI agents faster

    ElevenLabs launched an expanded Partner Program with enhanced commercial incentives, new certifications, joint go-to-market opportunities, and dedicated technical support, citing over 550 active partners in 44 countries and $52 million in partner-sourced and partner-influenced revenue this year. The program aims to help partners earn $3 in services revenue for every $1 ElevenLabs generates, and includes a new certified partner directory plus forward-deployed engineers who work alongside partners on complex implementations.

  3. ElevenLabs Blog42

    Introducing the ElevenLabs Marketplace for enterprise-ready AI applications

    ElevenLabs launched the ElevenLabs Marketplace, a curated collection of industry-specific AI and voice applications built on ElevenLabs by partners including Salesforce, IBM, NiCE, Twilio and Zendesk, spanning financial services, insurance, healthcare, telecommunications, retail and government. Enterprises can explore solutions and connect directly with the partners behind them, while ElevenLabs says it plans to let customers purchase eligible partner solutions through existing ElevenLabs commitments starting next year.

  4. Google Blog AI47

    Try Tabletop: Customizable, collaborative spaces for groups to plan, play and do more together.

    Google is introducing Tabletop, a new Google Labs experiment for real-time group collaboration where users add links, files and ideas, then prompt @tabletop or pick a starter template to generate interactive trip itineraries, dinner polls, expense splitters and tournament brackets. Once a space is ready, an invite link can be shared in a group chat so everyone can use it at the same time, like in a Google Doc. Tabletop is rolling out in the U.S. (18+) via waitlist at labs.google/tabletop.

Nov 11Wed
  1. 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.

  2. 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.

  3. 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.

  4. 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.

  5. 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.

  6. 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.

  7. 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.

  8. 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.

  10. 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.

  11. 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.

  12. 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.

  13. 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.

  14. 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.

  15. 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.

  16. 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.

  17. 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.

  18. 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.

  19. 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.

  20. 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.

  21. Google Blog AI55

    Gemini Intelligence: New features, on more devices

    Google announced that Gemini Intelligence is expanding to more devices, including the motorola signature 27 (pre-order in the U.S. from November 19) and Pixel 10, Pixel 10 Pro XL and Pixel 10 Pro Fold via today's Pixel Drop. New and expanded features include Create My Widget for building custom widgets from a prompt, Device Assistance for controlling and fixing your device by asking, Rambler on additional devices, and task automation expanding to the UK, India and Japan. Gemini Intelligence is also coming to Android Auto for proactive suggestions and to Pixel Watch for Create My Widget in the coming weeks.

  22. Baseten Blog69

    How Baseten and Genspark trained a deep research model to outperform Opus 5.5

    Genspark worked with Baseten Applied Research to post-train Qwen3.5-397B into a specialized Deep Research model that scores 0.759 on Genspark's quality grader, up from 0.612 for the base model and above Opus 5.5 (0.738), Sonnet 5.5 (0.742), GPT-5.6 Sol (0.637), Kimi K3 (0.724) and DeepSeek V4 Pro (0.675). The recipe splits the task into research, trained with reinforcement learning against a shaped semi-verifiable reward, and synthesis, trained mainly with supervised fine-tuning on high-quality reports written from the model's own research trajectories, followed by end-to-end RL. The model crawls 5× more sources per query than the base model and runs with NVFP4 weights on 2×B200 GPUs per replica, now serving as the default for Genspark's Deep Research agent in production.

    Why it matters: A detailed, reproducible account of post-training an open model for a judged production workload, useful for teams weighing RL versus SFT and reward shaping.

  23. AWS Machine Learning Blog34

    Autonomous treasury agents: How Trovata built an AI-native solution on Amazon Bedrock and AgentCore

    Trovata, an AI-native cash and liquidity management platform connected to over 10,000 banks, describes how it built autonomous treasury agents on Amazon Bedrock and Amazon Bedrock AgentCore, using role-based access control and a verified API layer within a bank-branded deployment model. The post outlines use cases such as cash forecasting, natural language querying, anomaly detection, automated reconciliation and persistent agentic workflows, and cites customer results including $1M+ in annualized cost savings, 5+ hours per day saved per accounting team, a 77 percent reduction in annual TMS costs, and reporting reduced from hours or days to under a minute. It also notes a library of ten agent patterns from real customer deployments and Trovata's plans to expand agent patterns, ERP integrations and global bank connectivity.

  24. Claude Blog36

    How Curative is building a national provider network with agents on Claude

    Curative built a hierarchy of agents on Claude, led by a provider-contracts agent named Gwen, to sign provider contracts roughly 50 times faster than its team did in 2025, reaching 2,000 to 2,400 new contracts a week versus 2,100 in all of 2025. Gwen handles one provider per run on Claude Opus, with a Sonnet rate reviewer, a Haiku judge that checks every outbound message, and escalation to humans for non-standard pricing. The company's contracting team grew from 45 to about 55 people, each about 2.5 times as productive, and its agents run on bespoke claims, prior authorization and finance systems that 13 engineers rebuilt with Claude Code.

  25. AWS Machine Learning Blog40

    Amazon Bedrock tokenomics data lake

    AWS describes a centralized observability data lake that pulls Amazon Bedrock model invocation logs from linked accounts into a central account via CloudWatch subscription filters, processes them through Lambda and Amazon Data Firehose into Parquet in S3, and exposes them through Athena as a single queryable table. Per invocation, the flattened table captures the prompt, response, input and output token counts, model, stop reason, account, timestamp, and IAM role, and can be joined to CUR 2.0 data on the IAM ARN for per-invocation cost attribution. The solution is deployed with two CloudFormation templates from the AWS Sample Bedrock Invocation Data Lake GitHub repository, with an optional Amazon Quick Sight dashboard.

  26. AWS Machine Learning Blog36

    How TCS built an agentic test orchestrator with Amazon Bedrock AgentCore

    TCS built a multi-tenant Agentic Test Orchestrator on AWS that turns Jira stories, BRDs, Swagger specs, and GitLab sources into test scenarios, Gherkin files, and executable Java or Python tests, using specialized agents hosted on Amazon Bedrock AgentCore runtime with the Strands Agents SDK. The platform separates a shared control plane from per-tenant application planes with isolated Cognito user pools, KMS keys, S3 buckets, and DynamoDB tables, and routes API tests to Amazon ECS on Fargate and Selenium UI tests to an EC2-backed ECS cluster. TCS reports that in an internal benchmark of about 100 scenarios, average effort fell from roughly two hours to about 14 minutes per scenario, an 88 percent reduction.

  27. Claude Blog72

    More ways to work with Claude: Sheets, Whiteboards, and Interactive Connectors

    Anthropic introduced new Claude artifact types—Claude Sheets, Claude Whiteboards, and Claude Data (formerly Dashboards)—plus upgraded interactive connectors that let partners embed apps like Adobe and Atlassian directly in Claude, with Claude Meetings coming soon. It also described enterprise controls including admin permissions and spend limits, auto-connect (beta), audit logs, a Compliance API, and isolated cloud sessions.

    Why it matters: Useful for readers tracking how Anthropic is expanding Claude from coding into collaborative, enterprise-wide work.

  28. Claude Blog75

    Introducing Claude Whiteboards, Claude Sheets, and Claude Data

    Anthropic launched Claude Whiteboards and Claude Sheets in beta and made Claude Dashboards generally available as Claude Data, all on paid plans. Whiteboards lets teams brainstorm with Claude on a shared board and export to tools like Lucid, Miro, or Vani by Zoho; Sheets generates and edits spreadsheets with tabs, formulas, formatting, and drop-downs, and can export to .xlsx or CSV. Claude Data adds certified queries, data stories, live sharing, design systems, caching, and tracked changes, with partners including Amplitude, Mixpanel, Omni, PostHog, and Sigma.

    Why it matters: It details new Claude workspace features and their availability, useful for teams deciding whether to adopt them.

  29. Wiz Research58

    How to Evaluate AI Agent Cybersecurity Capabilities

    Wiz Research outlines the principles behind the relaunch of its Cyber Model Arena, a benchmark of 26 models, four general-purpose harnesses, 59 agents and 327 challenges across code vulnerabilities, API, web and cloud security, arguing that benchmarks should fix their objective first, use real-world tasks, grade deterministically with code rather than LLM judges, isolate agents to prevent cheating, and weigh success against cost. The post reports that agents perform very differently by category (average pass@1 of 78.0% on API security versus 38.2% on code vulnerabilities) and that pairing harness with model matters, with a mean absolute gap of 5.1 pass@1 points across 22 models run on both Claude Code and ADK React. It also lists limitations including simple harnesses, string-equality grading on code tasks, public web tasks that may be contaminated, a 1,000-second time cap and unpublished confidence intervals.

  30. Factory Blog58

    Introducing Security Factory

    Factory introduces Security Factory, now in Private Preview, which connects investigation, remediation, and verification into one continuous workflow to help engineering teams turn security findings into tested fixes. It combines Connectors for consolidating findings and context, Workstreams for parallel agent investigations, and Factory's coding workflows for developing and testing fixes. Organizations can configure agent autonomy levels, from presenting remediation plans for human approval to automatically creating pull requests and deploying fixes within approved policies.

  31. Amp Chronicle60

    Boxes

    Amp introduces boxes: always-on computers starting at $18 per 30 days (billed by the minute) to which users can deploy the software they build with agents, with each application getting its own URL and no containers involved. Boxes can be shared with a workspace, can be turned on and off on a schedule, and each comes with its own runner so agents can operate directly on the box.

    Why it matters: Useful for developers weighing a cheap, simple hosting option for personal or internal agent-built software.

  32. OpenRouter Announcements60

    How to Get Structured Output from Any LLM: JSON Schema, Tool Calls, or a Decision Model

    The article compares three routes to reliable structured LLM output: a strict JSON Schema in response_format, a forced tool call whose parameters are the schema, and a decision model for label-only fields. In tests on 24 support messages across nine schema-supporting models plus GLM 5.3 FlashX, strict schemas returned 209 of 216 valid replies (all 7 misses from one provider that ignores the schema), while prompt-only JSON failed 18 times due to code fences. It recommends require_parameters: true, Response Healing for broken JSON, and always validating replies yourself.

    Why it matters: A practical, test-backed guide to choosing between schema, tool-call, and decision-model approaches for structured LLM output.

  33. OpenRouter Announcements58

    How to Build a Voice AI Agent

    This tutorial builds a Python restaurant-booking voice agent that chains speech-to-text, an LLM with tool calling, and text-to-speech through a single OpenRouter API key, streaming each completed sentence to TTS instead of waiting for the full reply. Across 12 turns in three test conversations, median server-side time to first audio was 1.9 seconds (1.4 seconds without tools, 2.4 seconds with a tool call), and a four-turn conversation cost about $0.013 per minute of processed speech at October 8 promotional prices, with TTS accounting for roughly 90% of model cost. It also compares STT, LLM and TTS models on accuracy, latency and price, and shows how to run the same agent with LiveKit Agents for live audio and interruptions.

  34. n8n Blog58

    Build Safer n8n Agents and Workflows with Google Gemini 4 Carbon

    Gemini 4 Carbon is now available in n8n, with Google reporting a 53.9% score on AutomationBench, 88.2% on SEC-bench Pro, and a 0.0% prompt injection success rate on Gray Swan's benchmark (down from 0.7% for Argon). It can be selected in the AI Agent node's Google Gemini Chat Model or the standalone Google Gemini node, using Gateway credits on n8n Cloud or your own API key when self-hosted. Pricing is $2 per million input tokens and $10 per million output tokens during the introductory period, rising to $4 and $20 after November 30, 2026.

  35. 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.

  36. Google DeepMind88

    Gemini 4 Carbon is now available

    Google released Gemini 4 Carbon to developers, enterprises, scientists, and consumers, describing it as its strongest model for cybersecurity capabilities and long-horizon agentic workflows in knowledge work, software engineering, and scientific reasoning. It ranks #1 on CursorBench 4.0, Perplexity's WANDR, and Zapier's AutomationBench at more than 2X lower cost per task than the next best external model, and is available via the API in Google AI Studio and Gemini for business at $2 per million input tokens and $10 per million output tokens. Google also previewed /swarm in Google Antigravity and reported safety results including a 0% prompt injection attack success rate on the Gray Swan IPI benchmark.

    Why it matters: The post is a dense launch announcement with benchmark rankings, pricing, and safety figures that help readers judge where Carbon sits relative to Gemini 4 Argon.