AI Coding Live News

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.

Zoom

Indie developer Pieter Levels (@levelsio) said on X on November 2 that he used Claude Code to decompile the engine of the free Battlefield 1942 demo, port it to C, compile it to WebAssembly and host it at bf1942.pieter.com, where it runs in the browser with online co-op on the Tobruk map. He said the job took about 1 million output tokens, roughly $100 at API prices, while he pays for a $100-a-month Claude subscription. His two main posts drew about 2,200 likes and 340,000 views and reached X trending. These are the author's own claims and community discussion, not independently verified.

Zoom

On Nov 2 Meituan's LongCat team listed LongCat-2.5 in its API platform change log as the full release of September 25's LongCat-2.5-Preview. The official technical blog says it has 1.6 trillion total parameters with about 48 billion activated per token, builds on LongCat-2.0 with stronger native visual understanding and agentic coding, and was trained and served with W4A4 inference on domestic AI accelerators. Vendor-reported scores include 88.4 on Terminal Bench v2.1, 38.5 on Terminal Bench v4.0, 91.9 on BrowseComp and 83.1 on MCP Atlas. The API speaks both OpenAI and Anthropic formats with a 1M-token context and up to 128K output tokens, at a limited-time price of $0.30 per 1M uncached input tokens, $0.006 cached input and $1.20 output.

Zoom

On Oct 30, SenseTime released SenseNova-Loop-9B-Preview (Apache 2.0) on its official X account and Hugging Face. It runs layers 4 to 27 of a 32-layer decoder twice with the same weights, so each forward pass executes 56 layer computations while storing about 9B parameters, about 14.6B at inference, and it uses two-stream mHC (manifold-constrained hyper-connections) to manage the residual streams. It takes text and images, with training sequences up to 262,144 tokens. SenseTime charts show it beating Qwen3.5 and Qwen3.6 peers on BIRD-Dev, WorkBuddy Bench, and other agent and data tasks, while it still trails on Terminal Bench 2.1; the technical report is not out yet. The official launch post drew about 910 likes.

Zoom

Anthropics help center article "How Claude marks AI-generated content" (updated Oct 28) now lists older models released before Aug 2, 2026, including Claude Mythos 5, Opus 4.7, Opus 4.6, Opus 4.5, Sonnet 4.6 and Sonnet 4.5, as carrying text watermarks on first-party surfaces, and says Anthropic is adding watermarks to these models to meet EU AI Act Article 50 transparency duties. According to a customer email screenshot reported by outlets such as MACCUS, the legacy rollout starts Nov 3, with Amazon Bedrock possibly a few days later; Anthropics public pages do not state that date. The watermark follows Google DeepMinds SynthID-Text approach, adds no hidden characters or extra tokens, and detection is in private preview only for regulators, media, researchers and similarly obligated organizations, which has drawn complaints on X that users cannot check their own text.

Zoom

On Oct 29 Anthropic introduced self-assessment on X: after each response, Claude produces a structured preliminary safety assessment of the whole conversation for offline review. Anthropic's Safeguards blog says that on Claude Fable 5, at a 1% false-positive rate, it flags 78% of the conversations a stronger prompted classifier flags and 94% of high-priority ones. Because the conversation is already in the KV cache, it adds about 10 output tokens on average and an estimated under 2% of inference compute. It has run in production since June, but Anthropic says it is not robust against an attacker who targets it directly and is only one layer of defense in depth.

Zoom

On Oct 28 Cartesia chief scientist Albert Gu (co-author of Mamba) and team published two research posts on a new generation of hierarchical networks (H-Nets). In a controlled scaling ladder, swapping BPE tokenization for learned dynamic chunking alone gave up to 3-5x compute efficiency. A larger two-stage byte-level H-Net (3.67B parameters), compute-matched per byte against SmolLM3-3B, reached SmolLM3’s final average downstream score after about 1T tokens versus 11T (vendor-reported). The new architecture is not released yet; Cartesia says it plans to release it over time.

Zoom