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All AI news

3 today

Sep 30

Wednesday
  1. OpenAI's reveals a new ChatGPT that looks less like a chatbot and more like an operating system

    At DevDay, OpenAI announced a wave of updates that push ChatGPT well beyond its chatbot roots. The company is adding shared workspaces, collaborative documents and slides, an open plugin system with MCP event automation, Slack and Microsoft Teams integrations, an enterprise marketplace with 32 partners, and a new Pro 500 pricing tier.

  2. OpenAI launches Dots, its Muse competitor

    OpenAI is responding to Meta's buzzy Muse AI with agentic helpers of its own: Dots. During its DevDay keynote on Tuesday, OpenAI announced that Dots will serve as always-on AI assistants that can "do nearly anything" across connected apps in the background while learning your preferences over time.

  3. Prompt engineering fundamentals for Amazon Quick

    Prompt engineering in Amazon Quick shapes how accurately its AI-powered features respond to your requests. Part 1 of a two-part series covers the foundational principles and reusable frameworks (specificity, context-setting, few-shot examples, and the CRISPE framework) for consistent, high-quality results across Amazon Quick.

Sep 29

Tuesday
  1. OpenAI DevDay 2026 live blog

    I'm at OpenAI DevDay today, in Fort Mason, San Francisco. Same as last year I'll be live blogging the keynote and some other notes during the day. OpenAI gave me a free ticket and a seat in the "creator" area for the keynote.

  2. NVIDIA Kumo Tabular Sets a New Accuracy-Efficiency Frontier for Tabular Prediction

    NVIDIA released Kumo Tabular, an open-source tabular foundation model that predicts labels for new rows in a single forward pass given labelled rows, without training, tuning or feature engineering. It supports classification and regression, offers three sizes from 28M to 215M, and was pretrained solely on artificially generated tables.

  3. Introducing Quine: An AI research system designed for the complexity of biology

    Biology doesn't operate in silos, and neither should the AI representation of it. Quine is an early-stage research effort to create a multimodal world model of biology. By connecting insights across biological scales and modalities, Quine helps scientists computationally search a space far larger than intuition allows and prioritize hypotheses before they reach the lab.

  4. Getting the Source Right, Not Just the Fact: Source-Aware Verification for MCP Agents

    Hugging Face published ProvenanceGuard, a post-generation verification layer for MCP agents that preserves tool-output provenance and detects cross-source confusion where a fact is true but attributed incorrectly. Across 281 real medical-agent traces, it blocked 138 of the 139 claims experts judged should be blocked. Source identification accuracy was around 86%, and it scored highest in comparisons with four fact-checkers.

  5. Making AI an asset, not an expense

    When customers talk about AI costs, the conversation usually starts with token prices and ends with access to the latest, most capable model in the cloud. Do they always need that level of capability? Not necessarily. But that is often where the conversation goes.