Release1d ago
Anthropic's Amended S-1 Says Future Models May Imminently Meet Its RSP Threshold for Dramatically Accelerated AI Progress
On November 2 Anthropic filed Amendment No. 1 to its S-1 with the SEC, adding a risk disclosure: based on internal estimates and expectations about future models, not current ones, the company may imminently meet its Responsible Scaling Policy definition of dramatically accelerated progress attributable to AI. No current model has been designated as exceeding the threshold. Anthropic says it is evaluating the implications of meeting the AI R&D capability threshold, is considering RSP updates that would require human review and approval of certain R&D decisions, and does not expect to safely let Claude fully autonomously design and build successive frontier models without meaningful human supervision.
Tooling6d ago
GitHub Universe 2026: Copilot Fusion is generally available, routing and orchestrating models automatically, with Fusion Max scoring 60 on the Artificial Analysis Coding Agent Index at about half the cost of Claude Code with Opus 5.5 Max
At GitHub Universe 2026 on Oct 28, GitHub made Copilot Fusion generally available. It merges the multi-model orchestration of the Project HydraFusion research preview with Copilot auto model selection: users pick Copilot Fusion and an effort level (Standard, High or Max) in the model picker, and the system decides which models to use and whether to run a Single, Cascade or Critique workflow. GitHub says Fusion Max scored 60 on the Artificial Analysis Coding Agent Index, two points below Claude Code with Opus 5.5 Max, at about half the API cost per task, about half the tokens and about 40% less time. Each underlying model is billed at its standard rate, and paid plans get 30% off through Nov 30.
Tooling21h ago
Anthropic Open-Sources FlashProve: zkSNARK Proofs That a Reply Came From the Committed Model, About 110 Tokens/s for 400B Llama 4 Maverick on One H100
Anthropic's Frontier Red Team published a research post on November 2 and open-sourced FlashProve under Apache 2.0. A model provider commits to hidden weights once, then attaches a zero-knowledge proof to each reply, so a user without the weights can check on an ordinary CPU that the reply really came from that model. That targets routing services that quietly swap in cheaper models and tampered token streams. Anthropic reports about 110 tokens/s on one H100 for Llama 4 Maverick (400B MoE, 4-bit), proofs of about 19 MB per 1,024-token request, and roughly 70-95x the GPU time of vLLM inference. It is an unmaintained research prototype limited to 1,024-token contexts and greedy decoding of an integer version of the model. Anthropic engineer Steve Weis shared the release on X.
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