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Today's five/N°27 · Sunday, November 1, 2026/Filed 03:48 ET

AI news today: Apple Intelligence comes to China with Qwen, Claude watermarks widen

A quiet Sunday full of fine print: which model answers iPhones in China, what marks your Claude text, and which models you have to leave.

The five · ranked by consequence

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  1. Apple Intelligence comes to China with Alibaba's Qwen

    Apple says its first Apple Intelligence features reach iPhones in China with iOS 27.3 in December, with Alibaba's Qwen inside Siri, Writing Tools and Visual Intelligence. Apple didn't build one assistant for the world. It built a slot, and Qwen fills it in China.

    Why it matters If you build for iPhone users in China, the developer beta lands Nov. 4, and some answers your users get there will come from Qwen, not only Apple's models. Test your app's text and document flows on the beta before December.

    SRCApple

  2. Anthropic watermarks older Claude models, with a gap

    Anthropic says it is adding invisible text watermarks to Claude models released before Aug. 2, 2026, and The Decoder reports the mark skips two of them when sampling settings change. If you ship Claude text, assume it's marked, and know which settings decide it.

    Why it matters If you need the mark for EU compliance or a client, keep temperature, top_p and top_k at their defaults on Opus 4.6 and Sonnet 4.6. If you'd rather not carry it, you can't switch it off. And treat any detector result as a signal, not proof.

    SRCAnthropic

  3. DeepSeek open-sources a prototype agent that ships PRs

    DeepSeek posted dsh-superagent on GitHub under the MIT license: a coding agent that takes requests from allowed people in a repo or Feishu and replies with a pull request. It's research code with no benchmarks. The authorization design is the part worth stealing.

    Why it matters If your team is building an internal coding agent, read DeepSeek's authorization and execution docs before you write your own rules for who can hand an agent work. If you work for a US government agency, check the DHS DeepSeek ban first.

    SRCDeepSeek (GitHub)

  4. Moonshot closes Kimi K2 to new API sign-ups

    Moonshot AI stopped offering Kimi K2 models to newly registered users on Oct. 31 and plans to retire the whole K2 series by the end of December. The move to K3.1 Pro changes defaults your app may rely on.

    Why it matters If you build on Kimi K2, you have about two months. K3.1 Pro has always-on thinking, a max-effort default and fixed temperature, so a name swap can change your cost, latency and output. Inventory your calls and test now.

    SRCMoonshot AI

  5. Judge rejects xAI bid to toss turbine pollution suit

    A federal judge in Mississippi denied xAI's motion to dismiss the NAACP's Clean Air Act suit over at least 27 gas turbines powering its Colossus operation. The trailer exemption for fast AI power just lost in court.

    Why it matters Temporary gas turbines are how some labs get power before the grid catches up. If a federal court says they still need permits, new AI capacity gets slower and costlier to bring online, and that shows up in who has compute to sell you next year.

    SRCUS District Court, N.D. Mississippi

Prompt of the day

Get a migration plan for a model your app depends on

› I need to move my app off an AI model that is being retired or changed, and I want a plan, not a pep talk.

Here is my setup:
- The model I use now: [current model]
- The model I'm moving to: [new model]
- What my app does with it: [one or two sentences]
- How I call it: [the settings I pass, e.g. temperature, top_p, max tokens, reasoning effort, tools, images]
- The deadline: [retirement date]

Do four things:
1. List every setting or behavior that is likely to differ between the two models, and for each one say what could break or change in my app (quality, speed, cost, format). Mark anything you are unsure of as a question I should check in the provider's docs.
2. Write a test plan of 10 to 15 real tasks from my use case that I should run on both models, with what to measure on each (correctness, response time, tokens used).
3. Give me a step-by-step rollout: what to change first, how to run both side by side, and how to roll back.
4. End with the three questions I should ask the provider before the deadline.

Keep it to one page. Ask me one clarifying question first if anything above is missing.

Works in any AI chat. In Magai, run it against several models at once.

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