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All AI news

3 today

Nov 12

Thursday
  1. Australia's proposed AI safety bill would force frontier model makers to open up to independent testing

    Australia's government, led by Prime Minister Anthony Albanese, wants to enshrine mandatory safety rules for frontier AI companies in law for the first time. A new regulator would enforce independent model audits and impose steep penalties for violations. With this move, Canberra is deliberately prioritizing domestic regulation before entering international talks.

  2. OpenAI Codex on Amazon Bedrock

    A practical guide for enterprises rolling out OpenAI Codex to developers on AWS, with Amazon Bedrock as the model backend, covering the access, governance, and cost decisions that platform, security, and finance teams need to align on.

  3. Gemini Decisions API

    Gemini Decisions API Google join Jev and OpenAI in the decision model category with "Gemini Decision 1". This new model API is almost identical to Jev: it supports the same three types of questions (yes/no, choice, and rubric-scoring) and even reuses Jev's term "Noul" for a yes/no question.

  4. aws-emulator

    aws-emulator Finally, an official emulator from AWS to run tests against a local clone of their many different services without having to provision a paid API key to do so. It's written in Rust by a huge swarm of agents - 29,684 commits in the first three weeks of the project.

  5. United won’t let your AI agent book your flight

    United says it won't let personal AI assistants book tickets on the airline, citing both privacy and regulatory concerns. The company says it hasn't yet seen many people try to use AI agents to book their flights, The Wall Street Journal reports, in line with observations from other businesses like Doordash.

  6. Why the data center you dread might never even be built

    The data center boom is running into serious real-world obstacles, from widespread local opposition and permitting delays to a scramble for electricity. Before those challenges even come into play, though, there’s a more basic problem: A lot of the projects being announced may never get built in the first place.

  7. Google Deepmind's AlphaProof Code verified Signal's cryptographic core in four weeks instead of years

    Google Deepmind's AlphaProof Code mathematically verified Signal's cryptographic core code in four weeks, a process that typically takes years. AI agents powered by Gemini Carbon wrote more than six million lines of proof code and flagged only about one percent for human review. Deepmind plans to extend the method to critical infrastructure like power grids and medical devices.

  8. Accelerating generative AI delivery with reusable assets

    Every generative AI team keeps rebuilding the same components: RAG pipelines, document processors, and agent scaffolding. This post shows how to access a curated catalog of 23 production-ready, open source reusable assets from the AWS Generative AI Innovation Center, with real delivery results from DoorDash, Lyft, and Ricoh.

  9. llm 0.37.2

    Release: llm 0.37.2 Fixes a security issue reported by Daniel de Araujo in GHSA-v9h7-jj4g-f862 and GHSA-r639-rr7r-x72w . LLM has a mechanism called extra-openai-models.yaml - now managed by the new-in-0.37 llm openai endpoint add command - for using LLM with OpenAI-compatible API endpoints from other providers.

  10. Browse tools.simonwillison.net by tag

    Browse tools.simonwillison.net by tag I've been wanting a better way to browse my collection of vibe-coded tools (244 and counting) for a while. I had a few Fable 5.2 credits left over before my weekly reset so I set it to work using tree-sitter to analyze the code for JavaScript APIs, libraries, and web API hosts used by each tool.

  11. Helsing built an AI system that prepares targets for weapons systems and says every decision will carry a human name

    Helsing has unveiled Clarity, an AI platform that uses agents to distill sensor data into target recommendations for weapons systems, all under human oversight and European control. But experience from the Iran war, Gaza, and a near-miss involving China shows that the speed of automation and data fusion create risks that are hard to contain.