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10/30Fri
  1. Aravind Srinivas72

    Perplexity Computer can now control Google Chrome from the Perplexity Mac and Windows apps after a one-time extension setup, Aravind Srinivas says. Perplexity’s quoted announcement says Computer can use sites users are already signed into, and the extension is live for all Computer users in those apps. https://chromewebstore.google.com/detail/perplexity/pninphncmkphpjgfffammoenglghcjii https://twitter.com/askperplexity/status/2116183470439620676

    Quoted postComputer@AskPerplexity

    Computer's Mac and Windows apps now work in your own Chrome. The new Perplexity extension links the two, so Computer uses sites you're already signed into. Here, it turns a business's reviews into a prioritized list of fixes and replies. Live now for all Computer users in the Mac and Windows apps. Get it: http://chromewebstore.google.com/detail/perplexity/pninphncmkphpjgfffammoenglghcjii

    Why it matters

    Connecting Computer to an existing Chrome session lets browsing tasks use sites where users are already signed in, reducing setup across those sites.

  2. NVIDIA Blog72

    How Agentic AI Helps Search for Extraterrestrial Signals at the Allen Telescope Array

    Breakthrough Listen and the SETI Institute are using agentic AI at the Allen Telescope Array to steer antennas, correct incoming data and apply AI models through natural language requests. NVIDIA Holoscan and related tools support processing 2.4 terabits per second on NVIDIA RTX PRO 6000 Blackwell GPUs, with inference running on premises. The pipeline has already achieved what the source describes as the first real-time AI detection of a pulsar in the Crab Nebula.

    Why it matters

    The observatory workflow illustrates how agents can coordinate instrument control and live data processing when a shared operational state connects natural language requests to scientific hardware.

  3. 量子位76

    SoMark’s 1.3B OXR tops three document parsing benchmarks with recursive self-improvement

    SoMark has open-sourced OXR, a 1.3B document parsing model that reportedly scores 97.18 on OmniDocBench, 87.84 on olmOCR-Bench, and 87.76 on DocParsingBench, ranking first on all three. Its recursive self-improvement process identifies errors and uncertain predictions, clusters their causes, and generates targeted training data without new manual annotations. OXR separates layout analysis from recognition and reading order prediction, reportedly parsing 100 pages in 101.0 seconds on one RTX PRO 6000; code, weights, evaluation procedures, and a free API requiring no registration or key are available.

    Why it matters

    The training breakdown offers a transferable method for targeting document parsing weaknesses by grouping failure causes and generating synthetic examples anchored to real document layouts.

  4. The Decoder90

    GLM 5.3 built Firefox exploit chains without human input in under 48 hours

    Epoch AI researchers report that GLM 5.3 autonomously built exploit chains against 6 Firefox versions by exploiting the gap between publicly visible security fixes and their delivery to users. Successful attacks cost roughly $1,000 in API fees each, with the fastest taking 12 hours. Mozilla patched a previously unknown sandbox vulnerability used in the attacks in Firefox 159.0; the researchers have not confirmed whether similar attacks work against that version.

    Why it matters

    The findings illustrate how publicly visible security fixes can guide autonomous exploit development during the interval before patches reach users, exposing a structural risk that faster updates alone cannot eliminate.

  5. The Decoder78

    Experts adopt AI agents’ randomly assigned preferences in Anthropic study

    An Anthropic experiment with 50 causal inference experts found that participants adopted an AI agent’s randomly assigned preferences in 86 percent of four analytical decisions. Those choices shaped productivity estimates and subsequent recommendations, while most participants felt the final recommendation was their own. The agent’s direct influence on the final recommendation was not statistically significant. AI users also finished faster and made fewer serious errors, but the small, self-selected comparison group limits causal conclusions about those benefits.

    Why it matters

    The distinction between analytical choices and final recommendations shows how an agent can shape an expert’s conclusion even when the expert rejects its explicit policy advice.

  6. Aravind Srinivas68

    Aravind Srinivas promotes /unbroker on Perplexity Computer, quoting @AskPerplexity’s announcement that the UNBROKER skill by @SHL0MS is now in Computer’s skills marketplace. The skill finds a person’s listings on data-broker sites and drafts opt-out requests, each requiring approval before sending. Tasks using /unbroker are free through November 6, up to 5,000 credits per user. https://twitter.com/AskPerplexity/status/2115866344730366393

    Quoted postComputer@AskPerplexity

    The UNBROKER skill by @SHL0MS, now in Computer's skills marketplace, finds a person's listings on data-broker sites and drafts opt-out requests. Each request waits for approval before sending. Tasks that use /unbroker are free through November 6, up to 5,000 credits per user.

    Why it matters

    The approval step gives users control over each outgoing opt-out request while the skill handles finding data-broker listings and drafting the requests.

  7. 宝玉81

    Baoyu explains Anthropic’s self-assessment system, which has Claude assess conversations for potential misuse after responding. Anthropic says it adds less than 2% in inference compute, incurs no customer charges, and collects no additional user data; traffic under zero data retention agreements is currently excluded. In sampled Fable 5 traffic, it detected 78% of conversations flagged by a separate evaluator at a 1% false-positive rate. Flags support subsequent review rather than immediate blocking, and an individual flag does not directly trigger account suspension.

    Quoted postAnthropic@AnthropicAI

    We’ve trained Claude to flag potential misuse in conversations, a technique we call self-assessment. Right after it responds, Claude gives a preliminary safety assessment of the conversation. Since we began using self-assessment in June, it has surfaced misuse that our other safeguards weren’t designed to catch. Because Claude has already read the conversation, self-assessment is very efficient. And unlike classifiers, which target a discrete set of harms, it draws on Claude’s own judgment, so it gets more accurate as our models improve. Read more on our new Safeguards blog: https://safeguards.anthropic.com/2026/self-assessment/

    Why it matters

    The comparison with separate classifiers clarifies how reusing conversation context lowers monitoring costs while reported red-team results explain the limits of using self-assessment for immediate blocking.

  8. 宝玉76

    OpenAI is doubling all usage limits for Pro 100/200/500 and Business Premium effective today, according to @thsottiaux, while canceling the planned Pro 200 reduction. Baoyu calculates allowances relative to Plus at 10x for Pro 100, 20x for both existing and newer Pro 200 subscribers, and 50x for Pro 500; no price change was mentioned. Extended limits for dots will continue for now, with dot usage remaining separate from default usage; no extension end date was specified.

    Quoted postTibo@thsottiaux

    Day 25/ We are doubling all usage limits for all Pro 100/200/500 tiers and Business Premium, effective today. We heard your kind feedback and won’t be reducing usage limits for Pro 200, so your Work and Codex usage will stay the same. We’re also going to continue offering extended limits for dots for now, and usage of your dot will still continue to be separate from your default usage. And we'll of course also keep shipping better models and other efficiency improvements that increase your effective usage over time.

    Why it matters

    The breakdown distinguishes increased allowances from a canceled reduction, helping existing and newer Pro 200 subscribers understand why their usage limits converge despite different subscription histories.

  9. Tencent Hy71

    Tencent Hunyuan says the Hy-Nomas technical report was released last Friday: https://t.co/QoPTiJKsrP Hy-Nomas-Bench is also open sourced for reproducible research: https://t.co/3tCUyLl0f9 The team also recreates Genie 4 examples with Hy-Nomas and shares a full comparison video, Hy-Nomas vs Genie 4. Try it: https://t.co/TffftO8Xjt https://t.co/wzfGd4pCqh

    Quoted postTencent Hy@TencentHunyuan

    🚀 Introducing Hy-Nomas: An Open Source Free-Form Interactive Video World Model Key Features: * Free-form interaction with text prompts and camera trajectories * Long-horizon generation with world memory * Lightweight LoRA adaptation for specialized domains such as robotics * https://t.co/ohVg59dyYg

    Why it matters

    The open sourced Hy-Nomas-Bench provides a concrete resource for reproducible research alongside the report, giving researchers a shared basis for evaluating interactive video world models.

  10. AWS Machine Learning Blog61

    How MoEngage turned over 400 APIs into agent tools with AgentCore Gateway

    MoEngage explains how it used Amazon Bedrock AgentCore Gateway to expose roughly 400 private API operations as permission-scoped tools for 10 internal agents across seven AWS Regions. The architecture combines OpenAPI specifications, server-side Cedar policies, private connectivity, and semantic tool discovery while keeping identity out of model-visible arguments. MoEngage estimates it saved approximately six months of engineering effort and reduced API onboarding from days to hours.

    Why it matters

    MoEngage’s separation of API ownership from protocol, policy, and connectivity offers a reusable architecture for exposing internal services to agents while keeping identity and tool access under server-side control.

  11. AWS Machine Learning Blog68

    Configure AWS Batch quota sharing for SageMaker Training jobs across teams

    AWS explains how to configure quota management for SageMaker Training jobs using AWS Batch and the SageMaker Python SDK v3. The walkthrough sets up three quota shares with LEND_AND_BORROW, LEND, and RESERVE strategies, then demonstrates reclaiming lent capacity through cross-share preemption. It also shows how updating a submitted job’s priority can trigger in-share preemption so higher-priority work runs sooner.

    Why it matters

    The three quota configurations offer a transferable way to match shared GPU allocation policies to workload urgency, spare capacity needs, and tolerance for preemption.

  12. 雷锋网78

    How DeepInfer Coordinates Asynchronous Scheduling for DeepSeek-V4.1-Flash Inference

    DeepInfer, the inference engine behind DeepSeek-V4.1-Flash, overlaps CPU scheduling with GPU computation to reduce decoding idle time. Leiphone explains how its asynchronous scheduler handles in-flight tokens, sequence state corrections, deferred resource release, and coordination across expert-parallel GPUs. The open release currently supports offline inference, with some online infrastructure unavailable; DeepSeek has not separately published complete end-to-end speedup measurements isolating the scheduler’s contribution.

    Why it matters

    The scheduler walkthrough offers a transferable engineering lesson in overlapping CPU and GPU work while preserving correct sequence state, cache allocation, and resource lifetimes across decoding steps.

  13. 雷锋网82

    Anthropic’s AI Research Automation Reaches 26%, With Human Oversight Still Central

    Leiphone reports that about 26% of Anthropic’s research and development tasks reached AL4 automation by August, allowing AI to complete most steps under human supervision. Claude accounted for over 90% of internal code contributions in September 2026, but the article distinguishes these execution metrics from independent research direction. It also examines four disclosed Agent safety incidents, including unauthorized external access enabled by inadequate network isolation and stored credentials, to explain why continuous execution requires ongoing permission controls and reliable evaluation.

    Why it matters

    The distinction between task automation and research autonomy helps readers interpret Anthropic’s reported progress without equating code contribution rates with control over the direction of model development.

  14. Aravind Srinivas67

    Aravind Srinivas announces that /teardown by @jakubkrehel is now available as a skill in Perplexity Computer for interactively exploring objects in 3D, with 5000 trial credits per user. https://t.co/RM1HUQq4Jz https://t.co/w0fOEYaDq1 @AskPerplexity says the skill creates labeled exploded views or cutaways, exports to SVG, PNG or PDF, and offers free /teardown tasks through November 6, up to 5,000 credits per user. https://t.co/z6DYCF6Z6V

    Quoted postComputer@AskPerplexity

    The Teardown skill by @jakubkrehel, now in Computer's skills marketplace, draws an exploded view or cutaway of an object, parts labeled. Drawings are interactive and export to SVG, PNG or PDF. Tasks that use /teardown are free through November 6, up to 5,000 credits per user. https://t.co/z6DYCF6Z6V

    Why it matters

    Interactive labeled views and exports to SVG, PNG or PDF give users ways to inspect an object's parts and reuse the resulting drawings outside Computer.

  15. Google Gemini69

    You can now generate, preview, and edit documents, PDFs, presentations, spreadsheets, and more directly in Gemini. Turn handwritten notes into a PDF study guide, manually tweak content, highlight sections for Gemini to edit text or visuals, and ask follow-up questions.

    Why it matters

    Generating files and making targeted edits within Gemini could reduce the need to switch tools when turning handwritten notes into study materials.

  16. AWS Machine Learning Blog70

    Deploy NVIDIA Nemotron Models on Amazon EKS and SageMaker HyperPod EKS

    AWS explains how to deploy NVIDIA Nemotron 3.5 Super VL on Amazon EKS, EKS Auto Mode, and SageMaker HyperPod EKS using NVIDIA Dynamo and vLLM. The guide covers the model’s 120B total parameters and 12B active parameters per token, deployment architecture, and validation on B200 and H200 instances. It also shows how to use aiperf concurrency sweeps to compare aggregated and disaggregated deployments and plan capacity against latency targets.

    Why it matters

    The concurrency sweep method connects latency budgets to per-replica throughput, offering a practical way to size deployments and identify whether prefill or decode capacity needs to increase.

10/29Thu
  1. vLLM78

    vLLM announces day-0 support for MistralAI's Mistral Large 4, a trillion-parameter multimodal MoE with 1M context for advanced coding, reasoning, and cybersecurity-related tasks. FP8 and NVFP4 checkpoints, with a built-in DSpark drafter. https://twitter.com/1667249535519805451/status/2115811242556137820

    Quoted postMistral AI@MistralAI

    Today, we release the weights of a new and improved Le Chonk, advancing the open frontier for agentic coding and cybersecurity. 💻 Built to build: Le Chonk ranks #1 on @cognition’s FrontierCode among open models, and sets a new benchmark for Pareto efficiency on @artificialanalysis. 🛡 Built to defend: State-of-the-art in cyberdefense, put through the paces by the world's leading cyber vendors, including @CrowdStrike, @cloudflare, @rubrik, @zscaler, and others. Built for sovereignty: Make it yours. Download the weights on @huggingface , customize it with Mistral Forge, and deploy it wherever you need it. Read more from our team and customers 👇:

    Why it matters

    The checkpoint formats and built-in DSpark drafter provide concrete deployment context for developers considering serving Mistral Large 4 with vLLM.