AI Coding Live News

On November 10, Anthropic announced the launch of its IPO roadshow. It plans to offer 825 million Class A shares, about 801 million from the company and about 23.65 million from existing stockholders, at an expected $70 to $78 per share, and to list on the Nasdaq Global Select Market as ANT. S-1 Amendment No. 2, filed the same day, shows AMD, Broadcom, Google, Micron, Microsoft, NVIDIA, and TSMC buying a combined $26 billion of Class N stock at the IPO price. At the $74 midpoint, the company expects net proceeds of about $84.6 billion.

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On November 9 at NVIDIA AI Day Seoul, LG AI Research made the K-EXAONE 2.5 API free through its EXAONE API Platform (docs.exaone.ai). Developers, startups, enterprises and research institutions can get an API key and use it for service development and research. K-EXAONE 2.5 is a technical validation model built by further training K-EXAONE 2.0, released in August: 750B total parameters with 31B active, text-only, Korean and English, a 512K context and an OpenAI-compatible endpoint. On four agentic benchmarks run internally, LG reports 59.2 on SWE-Bench Pro, 78.0 on BrowseComp, 74.6 on AutomationBench and 77.3 on Claw-Eval, roughly on par with DeepSeek-V4.1-Flash and GLM-5.3 but not leading across the board. NVIDIA says the model was trained with Nemotron datasets. LG has not published how long the free access lasts or any quota, and has not released 2.5 weights.

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Japan's Preferred Networks (PFN) released PLaMo 3.0 Fin (plamo-3-8b-fin-instruct) on Hugging Face on November 9 under the PLaMo Community License. Built on PLaMo 3 NICT 8B Base, co-developed with NICT, it went through continued pre-training on finance corpora, SFT and DPO. It writes its reasoning in Japanese before answering, supports OpenAI-style function calling and can run on a single GPU in private clouds or on-premises. Per PFN, its average on the Japanese finance benchmark japanese-lm-fin-harness rose from 48.0 to 55.8, ahead of Gemma 4 E4B (45.6) and gpt-oss-20b (43.0) but behind Qwen3.5-9B (60.1) and Ministral 3 14B (62.8). SFT ran on PFN's in-house MN-Core 2 AI chip, and a coding agent helped port the training code in under a week.

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Vercel published a changelog on 2026-11-07 (authors Jerilyn Zheng and Zachary Chen) saying DeepSeek V4.1 Flash 1017 Ultrafast is now on AI Gateway through Fireworks' accelerated serverless endpoint; the official @vercel_dev account posted at 17:25 UTC. Vendor figures say it can generate upwards of 1,000 tokens per second, serving the 2026-10-17 V4.1 Flash update rather than new base-model weights. The model page lists a 1,048,576-token context window and pricing of $2.40 per million input tokens and $9.60 per million output tokens; it is a public preview with no waitroom or session time limit. The model ID is deepseek/deepseek-v4.1-flash-1017-ultrafast, callable via the AI SDK, an OpenAI-compatible API, and npx vercel ai-gateway setup for Claude Code, Codex and Cursor.

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On 2026-11-05 Microsoft and fal put Kling v3 Pro, LTX-2.5 Fast/Pro, and MiniMax H3 Max/Turbo into Foundry public preview as a first-party Azure service (@Azure amplified 2026-11-06). Vendor: US hosting and first US-hosted Kling endpoint, with per-second pricing; more models and pricing-page listings planned in coming weeks.

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On 2026-11-06 Artificial Analysis launched Endpoint Accuracy Index v1.1: each serverless endpoint is scored as a percentage of a self-hosted official-weights reference across equally weighted BFCL-500, HLE-250, and AA-LCR-25. Vendor claim: nearly 1 in 4 of 100+ benchmarked endpoints score below the reference. AA-LCR-25 moved to v1.1 with repeats raised from 10 to 25 (average 95% CI ~±4.7 to ±2.6), and coverage expanded to six open-weights models including GLM-5.3 Flash and Muse Spark Open.

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On November 5, interpretability lab Goodfire published a paper and blog on mechanistic shaping: add training penalties on a model's reliance on or expression of specific internal concepts, so training specifies how a task is solved—not only whether the answer is right. Experiments cover chest X-ray shortcuts, DPO-induced sycophancy and Countdown reward hacking. Per vendor figures, expression shaping raised honest solving from ~0% to 96–98% and transferred to unseen chess puzzles.

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arXiv paper 2611.03233 by Yuqi Jia, Yunjie Tian and five co-authors introduces Axion 1.0, an agent foundation model that is open beyond its weights. It uses just two lightweight SFT stages plus reinforcement learning, with no dedicated mid-training, DPO or on-policy distillation, and about 18K SFT trajectories in total. Training code is hosted under the bytedance GitHub organization, and the training data and an Axion 1.0-35B reproduction checkpoint (Qwen3.6-35B-A3B base, 35B total / 3B active, Apache 2.0) are on Hugging Face; weights for the paper's 122B model are listed as coming soon.

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On Oct 28, GitLab CEO Bill Staples announced in a blog post and on X that new and existing customers can request free credits to run a security sprint with Claude Mythos alongside GitLab Orbit, Secrets Manager and Duo Agent Platform. The core capability is the Business Logic Security Analyzer, the first GitLab feature to support Anthropic's Claude Mythos 5.1. It reads the whole repository, infers what each endpoint should enforce, and flags authorization and mass-assignment flaws that pattern-based SAST misses. The analyzer has been in early access for a small group of GitLab.com Ultimate customers since Oct 20.

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On Oct 20 ElevenLabs released Eleven Realtime, end-to-end speech-to-speech models where each model is a complete voice agent, removing the need to wire up speech recognition, an LLM and text-to-speech yourself. v1 Mini targets low-cost natural conversation and v1 Max targets support and multi-step tool use. Both cover 90+ languages and 17,000+ voices. Citing independent Artificial Analysis evals, Max answers 98.8% of Big Bench Audio correctly and starts speaking in 0.57s on average. The API is OpenAI Realtime compatible, so migrating means changing the endpoint and model name.

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