AI Coding Live News

Independent evaluator Artificial Analysis published Tencent Hunyuan Hy4 results on the Intelligence Index v5 on November 5 (08:28 UTC): a composite score of 35, tying Grok 4.7 (xhigh) and one point behind Claude Haiku 5.5 (max). Among China-based labs it ranks second only to Kimi K3.1 Pro (46). On agentic coding it scores 31% on FrontierCode 2.0 and 37% on Terminal-Bench 4.0; cost per Intelligence Index task is about $0.78, roughly one-tenth of same-score Grok 4.7.

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Ricursive Intelligence released its first model, Ripple-65B Preview, on November 4 (18:24 UTC), open-sourcing the instruction-tuned model (BF16 and NVFP4) and a base model on Hugging Face under Apache 2.0. The official model card describes a 65B dense Transformer pretrained on 12T tokens with up to 1M context, and self-reports TerminalBench 2.1 80.5, SWE-bench Pro v2.0 Hard 79.1 and DeepSWE 1.1 44.3, ahead of much larger US open-weight models on agentic coding. It fits on local hardware such as the DGX Spark. CEO Anna Goldie says it cost about a tenth of the prior comparable state of the art.

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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.

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Google AI Studio launched Gemini 4 Argon access in AI Studio Build and Playground for Google AI Pro subscribers, while Hugging Face Transformers v5.20.0 and v5.20.1 added native support for Reflection AI's frontier coding model Beam, Meta's 816B-parameter (42B active, 1M context) multimodal agentic coding model Muse Spark Open, InclusionAI's Ling 3.0 Flash, and OpenBMB's MiniCPM-V 4.7.

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Microsoft Research and ServiceNow Research introduced FrogNano (arXiv:2609.07925), a 4B-parameter compact coding agent built to solve real-world software engineering (SWE) challenges on constrained edge hardware. Diverging from traditional approaches that rely on knowledge distillation from massive frontier LLMs, FrogNano is post-trained exclusively via Reinforcement Learning (RL) across ~1,500 diverse software engineering repositories. The technical breakthrough lies in its online task synthesis pipeline, which dynamically calibrates synthetic task generation directly to the checkpoint's learnability frontier. The study provides empirical proof that compact models can develop competitive agentic coding capabilities purely through self-guided reinforcement learning.

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Anthropic / Claude (@claudeai; confirmed by @AnthropicAI) launched Claude Opus 5.5 today, the first model in the new Claude 5.5 family. Officially it matches Claude Fable 5.1 on most tasks, and is a major step up from Opus 5 on agentic coding, computer use, and knowledge work. At default settings typical workloads cost about 40% less than Opus 5. Pro, Max, and Team five-hour usage limits are increased, with a bankable rate-limit reset for subscribers.

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Elon Musk posted “Grok 4.7.” Same-day discussion said the model improves on Grok 4.6 for agentic coding and knowledge work, and that it works best with the xAI Build harness.

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Elon Musk announced Grok 4.7. He described it as a strong mix of intelligence, speed, and low cost, said it works especially well with xAI's Build harness, and positioned it as a competitive everyday workhorse for agentic coding.

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