全部 AI 动态

Fable 5.1 from @AnthropicAI on ARC-AGI (Verified): - ARC-AGI-2: 90.0%, $3.12/task - ARC-AGI-1: 97.5%, $1.40/task Across the two benchmarks, Fable 5.1's average cost per task is about 32% lower than Fable 5's, driven by better token efficiency.

🦞 @openclaw 2.0 has arrived! But what did it take to get here? 👀 Find out how creator @steipete and its maintainers built, scaled, and secured the fastest-growing open source project in GitHub history. 🚀 https://youtu.be/5VSwaUXtPIE?si=vdegRI2eH4wXo2lG

OpenAI says Astra is its first model to reach the Critical cybersecurity capability threshold. Under its Preparedness Framework, that means Astra can, with the right tools and access, find unknown flaws and develop exploits across hardened systems without step-by-step human guidance. Hence, OpenAI now says Astra will launch with additional chain-of-thought monitoring, while classifiers can automatically stop potentially unauthorized actions. Astra reached roughly 39% exploit success at ~75K output tokens, while GPT-5.6 Sol is only around 1% there and needs nearly 140K tokens to reach ~12% i.e. Astra is dramatically more capable and token-efficient at exploit development on this internal benchmark. Expert assessments went further: Astra escaped a browser sandbox, executed host commands, and escalated an unprivileged operating-system user to root.

Neoclouds have limited cybersecurity. Next time agents successfully go rouge, they'll try taking over a neocloud to run more copies. This is bad. Thus: neoclouds should greatly strengthen their cybersecurity and every company with strong cyber models should help with that.

Cache reads with Fable 5.1 cost 75% less than Fable 5’s So the savings get larger as the workload becomes more agentic. Agents constantly revisit the same context, plans, tool outputs, and working state, so cheaper cache reads make persistent reasoning dramatically less expensive; that’s how a 75% cut in one component can become ~45% off the entire workload. The economic question in many enterprises are now “How long can an agent keep working before the job stops making financial sense?” So this curve need to keep falling.

Cache reads with Fable 5.1 cost 75% less than Fable 5’s So the savings get larger as the workload becomes more agentic. Agents constantly revisit the same context, plans, tool outputs, and working state, so cheaper cache reads make persistent reasoning dramatically less expensive; that’s how a 75% cut in one component can become ~45% off the entire workload. The economic question in many enterprises are now “How long can an agent keep working before the job stops making financial sense?” So this curve need to keep falling.

我们推出 Claude Fable 5.1 和 Claude Mythos 5.1。 它们是全球最先进的编程和知识工作模型。

one real issue I have with various agents now is "where is this code going to run" - does it need access to my local filesystem - can I fire it off from my phone and if so does it run on the cloud or needs my Mac mini fired up / synced? The current UX metaphors around this seem to need work.