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Nov 16

MondayToday3 items
  1. Anthropic's Claude Fable 5.2 doesn't generate images, it paints them

    Anthropic's Claude Fable 5.2 doesn't generate images the way other AI models do. Instead of computing pixels, the language model writes Python code that places hundreds of thousands of simulated brushstrokes on a virtual canvas, complete with bristle texture and paint application. A single photorealistic parking garage scene took roughly 700,000 strokes, according to Anthropic employee Jake Eaton.

Nov 15

Sunday
  1. An overview of Jensen Huang’s messy days

    This is The Stepback, a weekly newsletter breaking down one essential story from the tech world. For more on Jensen Huang's resignation and the fallout from Nvidia's leadership crisis, follow Terrence O'Brien. The Stepback arrives in our subscribers' inboxes on Sunday at 8AM ET. Opt in for The Stepback here.

  2. Firm linked to Jensen Huang's ex-lover reportedly brokered nearly $12 billion in Nvidia chip orders for China

    Megaspeed, the firm owned by ousted Nvidia CEO Jensen Huang's former lover Alice Huang, arranged nearly $12 billion in Nvidia chip orders for Chinese companies between 2024 and 2025, according to the WSJ. Short seller Culper Research estimates Nvidia's actual China revenue share exceeds 20 percent, far above the officially reported 7 percent.

  3. datasette-llm 0.1a11

    Release: datasette-llm 0.1a11 datasette-llm is the intermediate plugin for Datasette that supports configuring LLMs for use with other Datasette plugins. This release adds support for OpenAI-compatible endpoints, so you can configure Datasette to use an LLM hosted anywhere that talks the OpenAI pseudo-standard web protocol.

  4. Anthropic CEO Amodei admits "there's a huge uncertainty in what's going to happen next year" days before a record IPO

    Days before what could be the largest IPO in history, Anthropic CEO Dario Amodei called buying compute 'the deepest business dilemma' his company faces. Anthropic's commitments now total nearly $901 billion, and it's still far from clear whether future revenue will justify those costs. The planned IPO is largely meant to serve as a financial buffer against that uncertainty.

Nov 14

Saturday
  1. Anthropic can already retrain Mythos with "a fraction of the compute originally needed," says former researcher Coxon

    Former OpenAI and Anthropic researcher Jacob Coxon points to a slide from Anthropic's IPO roadshow as a reason he left, saying it shows the company's algorithms are improving not just quickly but at an accelerating rate. Coxon continues to call for a pause on frontier model development so competitors can catch up and human-level AI can become a commodity.

  2. OpenAI reassigns robotics researchers to other teams as general-purpose models might make specialized robotics AI unnecessary

    OpenAI is dissolving its standalone robotics team for the second time, but not because it lacks data. General-purpose models like GPT-6 Astra can apparently control robots directly, making a dedicated unit unnecessary. The researchers are being redistributed across other teams in what the company frames as a way to accelerate its robotics efforts.

  3. Advancing science & engineering with Antigravity’s /teamwork

    Over the past several weeks, teams across Google and academic partner institutions have tackled open problems in math, science, and complex systems engineering that had remained open for years. We are making these agentic tools available in Antigravity’s /teamwork (alongside /boost and other agents) so that researchers and developers can apply them to their own work.

  4. Anthropic's Claude for Manufacturing plugs AI directly into factory software to automate industrial workflows

    Anthropic's new Claude for Manufacturing plugin connects the AI directly to Autodesk Fusion, Oracle NetSuite, and Infor. Its 17 specialized skills let the AI draft quotes, compare suppliers, and place backup orders when deliveries are delayed, all after human approval. The plugin doesn't control physical machines yet, but Anthropic is separately developing the Model Hardware Standard for that.

  5. XYBench: Helping with the problem behind the question

    We’re releasing XYBench, a benchmark of 8K+ questions that tests how AI assistants respond to requests based on misconceptions, such as asking how to thicken frosting that keeps sliding off cupcakes when the real problem is that the cupcakes are still too warm to frost. XYBench won the Best Resource Paper Award at EMNLP 2026.