X日报 · AI科技

New in Claude Code: Artifacts.

Interactive pages built from your session, like a PR walkthrough or a living project dashboard, shared with your team at a private link.

Available in beta on Team and Enterprise plans. https://t.co/0NX9gNCaAs

A bit of news: After nearly 9 years, I have decided to leave Google DeepMind and join Anthropic (after taking some time to recharge). I am incredibly grateful for my time at GDM. @demishassabis took a real chance letting me lead the AlphaFold team just six months after finishing my PhD, and the entire GDM team taught me so much about how to do great science. GDM is a special place, and I’ll still be excited to hear about what amazing things they discover next.

Here’s what launched this week:

— Gemini 3.5 Live Translate our latest audio model for live speech-to-speech translation

— @NotebookLM got a major upgrade including agentic capabilities in chat, more advanced reasoning, and a suite of new output formats

— Project Genie from @GoogleLabs is now available to Google AI Ultra 5x subscribers globally

— Notebooks in @GeminiApp are now available in the European Economic Area, United Kingdom, and Switzerland

— DiffusionGemma, our newest experimental open @googlegemma model that explores text diffusion, an exceptionally fast approach to text generation

We've collaborating with hundreds of physicians across 60 countries, 49 languages, and 26 specialties to make ChatGPT great at health-related questions for everyone:

We desperately need standardized benchmarks for agentic capabilities instead of panic-reacting to prompt-engineering parlor tricks. Until we can objectively and transparently measure what these systems actually do, the entire ecosystem will remain vulnerable to unpredictable and arbitrary government overreach.

We offer no explanation as to why Noams are so good at AI; we attribute their success, as all else, to divine benevolence.

Show Codex a workflow once. Reuse it as a skill.

Record & Replay lets you show Codex a recurring task, like filing an expense report or submitting a time-off request.

Codex turns that demo into an inspectable, editable skill.

You control when recording starts and stops. https://t.co/UqSGaO7XUs

Over the last two weeks, both the U.S. Government and Anthropic took significant actions that demonstrated their power to control access to AI by restricting what others can do with frontier models. This has been one of those moments that, once seen, will be hard to unsee, and it is significantly accelerating many businesses’ and nation states’ efforts to ensure reliable access to AI that no one else can terminate.

Anthropic first released Claude Fable 5, a version of its Mythos model with additional guardrails, including some restrictions that seem well justified on safety grounds (such as limitations on applying it to hacking, bioweapons, and so forth). However, it also restricted developers’ ability to use it to build competing LLM technology. This move was concerning, given that the whole AI community, including Anthropic, has benefitted tremendously from open research — indeed, the AI revolution was kicked off by my former team (Google Brain) freely publishing the Transformers paper!

Imagine if Microsoft’s terms of use barred anyone from using their tools to build competitive software, or if Google barred using it to search for information to work on competing search engines. Anthropic’s argument that it was unsafe for others to be able to make advances in AI also rang hollow. Initially, Anthropic silently degraded Fable 5’s performance for users detected to be working on LLM research through invisible interventions that weakened the model’s outputs without notifying the user. After significant backlash, it walked back this decision and decided to be transparent when it did this, but it still refuses to use its latest capabilities to help AI researchers.

This move represents a raw demonstration of power by Anthropic. It has used “safety” arguments to hinder potential competitors. Platforms succeed when they are viewed as stable, reliable partners that one can build on. The sudden rule changes by Anthropic (including a mandatory 30 day data retention policy for Fable usage) have made developers wonder about the stability of building on any one proprietary LLM provider, not just Anthropic.

The U.S. Government then shortly followed with an even greater demonstration of power. It used the Commerce Department’s authority to regulate technologies that may be national security threats to restrict exports of Mythos and Fable, requiring a license for use by any foreign national, whether inside or outside of the U.S., including employees of Anthropic. This led Anthropic to disable access to Fable to all users worldwide.

Sam Altman pointed out, referring to Anthropic, “It is clearly incredible marketing to say, ‘We have built a bomb, we are about to drop it on your head. We will sell you a bomb shelter for $100 million.’” But when one engages in this type of fear-based marketing, it increases the odds that the U.S. Government will agree with you and slap export controls on the bomb you say you have built.

To be clear, I don't think Anthropic has built anything like a bomb, and I don't think export controls on Fable are appropriate.

However, following the U.S. Government making this move, many nations, including U.S. allies, saw how the U.S. can suddenly yank their access to AI models. In many capitals around the world, this has spurred discussions on AI sovereignty and how others can ensure uninterrupted access to this critical technology.

For decades, many nations were comfortable having many parts of their supply chain rely on the U.S., China, and other major producers. Once a nation issues a threat, or takes action, to limit other nations’ access, other nations will rationally try to secure alternatives. For decades, semiconductor manufacturing in China made slow progress; once the U.S. moved to limit China’s access, China’s efforts kicked into high gear. Similarly, once China threatened U.S. access to rare earth minerals, U.S. efforts to secure alternatives accelerated. Now that it has become crystal clear that private U.S. companies and the U.S. government can limit, in short order, other nations’ access to frontier AI models, the incentive of others to invest more in alternatives like open source grows significantly. Of course, training frontier models is not easy, so it remains to be seen how successful they are, but we have crossed the rubicon.

Satya Nadella wrote an essay about the importance of building a healthy ecosystem on top of frontier AI technology. I heartily agree with him, and hope this week’s events will ultimately prove to be constructive steps toward this.

I hope we can build a more free, more open world, where research is freely shared, and laws and societal norms shape a level playing field that allows everyone to make progress. A silver lining of the events of these past two weeks is now that everyone better realizes key points of instability of the current system, we can all work to create a more stable foundation.

[Original text: The Batch newsletter]

software engineering is so different now. hard to remember what it was like even 6 months ago.

AI for helping crack a health mystery. So many stories like this, and a clear motivation to be excited about AI:

Someone on Reddit built a WoW private server with 1,800 bots and AI chat via the DeepSeek API.

Dead Internet Theory, but playable.

An MMORPG with no real players, yet somehow it still feels human. https://t.co/uFD0AHiquc

Update: we've gone ahead and reset 5-hour and weekly usage limits for everyone, across all plans. Enjoy your weekend!

Just caught up with the recent GLM-5.2 release. The best open-weight model today.

Architecture-wise, it's build on the GLM-5 and GLM-5.1 architecture that I covered previously, which means it's reusing the Multi-head Latent Attention (MLA) and DeepSeek Sparse Attention (DSA) mechanisms from DeepSeek V3.2. (I wrote about it here: https://t.co/tuunazfQ8y)

What's new is that they added an IndexShare mechanism. (That's a cross-layer reuse trick for DSA where instead of recomputing the sparse-attention top-k indexer in every layer, GLM-5.2 runs the full indexer only once every four layers and lets the following layers reuse those selected token indices. This keeps the same DSA idea but makes 1M-token inference much cheaper.)

you can now teach Codex by demonstration:

Cool new open-weight model by Cohere: a new lightweight 30B open-weight model for agentic coding tasks.

This one builds on Command A+ using the parallel transformer design. Interestingly, even though it's almost half as big, it almost doubles the number of layers.

Also, they say that it's been specifically developed for agentic coding, not just coding. I.e., the evaluation is inside a workflow, not just on a single prompt-to-code-answer task.

For Terminal-Bench, the model has to use a terminal, inspect the environment, run commands, read outputs, etc.

For SWE-Bench the model works on real GitHub-style software issues where it has to understand the repository, find relevant files, make a patch, pass tests, etc.

SciCode and LiveCodeBench are more traditional because they mostly test whether the model can produce correct code for a specified problem. Sure, this still requires reasoning, but it's more like “Implement a numerical routine to compute a scientific quantity from given equations and inputs.” which doesn't require any interaction with the environment, existing files, tests, etc.

The focus on the agentic code benchmarks is probably why it's far ahead of Gemma 4 on those.

Overall, it's pretty competitive although not quite Qwen3.6-level performance.

Day 1 of vibecoding https://t.co/n8ff35htEV

Earlier today, ~3% of Claude Code Max and Pro users hit a bug that showed an incorrect weekly usage limit, and in some cases blocked them from sending messages.

This is fixed, and we're resetting 5-hour and weekly limits for everyone affected. Apologies for the disruption.

Crazy model! It actually uses the old Qwen2.5-Coder-3B stack and got really great performance with their post-training stack.
Need to use it in the next days to see if vibes of VibeCoder actually check out in practice. But impressive first impression!

Based on the tech report, some of the important pieces of their post-training stack:

1. High-signal synthetic data (math problems with credible solutions, code with tests)

2. Multiple reasoning paths for each answer

3. Filtering, filtering, filtering

4. 2-stage SFT (start with broad training, then train on hard long-reasoning samples)

5. Use target (pass@k) accuracy over validation loss for checkpoint selection

6. MGPO (MaxEnt-Guided Policy Optimization) for RLVR: basically a GRPO-style RL method with an extra weighting that favors examples that are neither too easy nor too hard for the current policy

7. Single 64k long-context RL (they found that the usual progressive context expansion hurt this model because early truncation damaged long-thinking behavior)

8. Training data order: they do Math RL, then Code RL, then STEM RL in this particular oder which they found helped overall

9. After optimizing for accuracy, they add a stage that rewards shorter correct trajectories; basically making the model more efficient without accuracy degradation

GPT-Realtime-2 is something new

The way we will create a future where powerful AI is open-source and available to all is by making AI radically more efficient, both in terms of inference compute and (more importantly) in terms of training data requirements. This is what symbolic learning will achieve.

In awe of SpaceX and its story - past, present and the future. You can think about it in 10+ different ways and continue re-blowing your mind in circles. Huge congrats to the team! 🚀

A 21-YEAR-OLD FROM CHINA RUNS 300 AI AGENTS AT ONCE. THE PART THAT MATTERS ISN'T THE SPEED, IT'S THAT NONE OF THEM CAN LIE TO HIM

he opens the dashboard and shows the swarm live, 300 Kimi K2.6 agents firing in parallel, then Opus 4.8 checking every single output against its source. this is not just a faster swarm. it is a loop that refuses to stop while anything is still wrong

he pointed it at 100 EV-market companies. first pass: 12 failed. wrong revenue, dead citations, empty fields. second pass: 3 failed. third pass: zero

this is not another agent demo. it is a system that catches its own mistakes before he reads a single row

Gemini Omni Flash is SOTA at image to video, text to video, and video editing : )

Excited to get this to developers in the API soon! https://t.co/u0fzmJwBb4

OpenAI for helping find 18 new diagnoses across 376 previously unsolved medical cases.

Includes diagnosing Kyra, who has been trying to understand her muscle weakness since age 9, with a rare form of myofibrillar myopathy shortly before her 28th birthday. https://t.co/xq4QIvwby4

Even if you are in favor of AI regulation, you should recognize that opaque and arbitrary regulatory strikes are counter-productive for the whole industry.

noam is one of the people I have most wanted to work with since the very beginning of openai.

only took 10 years.

i think it will be worth the wait!