AI Coding Live News

At KubeCon North America on November 10, NVIDIA contributed OpenShell, its open-source security runtime for AI agents, to CNCF as a Sandbox project. OpenShell enforces access to files, networks, credentials, and model endpoints in the infrastructure outside the agent. The project is moving from NVIDIA's GitHub to the neutral ai-openshell organization and is adopting Cedar, another CNCF project, as its policy language. NVIDIA is also contributing to Google-led Agent Substrate and released three open-source Kubernetes components: NVKE, NVKR, and Snapshot. Teams running agents on Kubernetes now get a vendor-neutral permission and isolation layer.

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On November 10, Google DeepMind and Artificial Analysis launched MKA (Multimodal Knowledge Agent), a benchmark that tests whether agents can read visually dense files such as architectural drawings, engineering schematics, and clinical scans and deliver slide decks, spreadsheets, reports, and marked-up images. Its 200 private tasks span 10 industries and 83 occupations, with about 70 expert rubric checks per task. Artificial Analysis tested 21 models at launch: Claude Fable 5.2 (max) leads with a Climb WSR of 57%, followed by Gemini 4 Carbon (high) at 54% and Claude Opus 5.5 (max) at 53%. Fable averages $31.97 per task versus $6.29 for Carbon. Ten public tasks are on Hugging Face.

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On November 10, OpenAI launched Businesses in ChatGPT in beta, aimed at the businesses that the 1.2 billion weekly ChatGPT users look up. In Business Hub, a business can claim and verify its profile, update its details, add product feeds with pricing and availability, and see aggregated insights such as mentions, website clicks, and discovery topics; OpenAI says it does not share users' conversations with businesses. Eligible U.S. ecommerce and online service businesses can also build a Business Agent that answers customers' questions about products and policies inside ChatGPT. These chats are clearly labeled and can serve as the destination for ChatGPT Ads.

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On November 10, Geodesic Intelligence, founded by UCLA professor Quanquan Gu, released NovaAtom-Lite, an all-atom structure prediction model that predicts every heavy atom in complexes of proteins, DNA/RNA, and small-molecule ligands, with pLDDT, PAE, and ipTM confidence scores. By the company's figures, it predicts 79.07% of FoldBench antibody-antigen interfaces correctly, ahead of IsoDDE at 75.58% and AlphaFold 3 at 47.90%, and also leads protein-ligand at 77.06%. Its training data stops at September 30, 2021, the same cutoff as AlphaFold 3. It is available today through the Geodesic API and the NovaDDE platform; weights are not released.

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On November 10, OpenAI Developers announced that ChatGPT Business users can now use their plan in third-party tools such as Notion, v0, Amp, Kilo Code, Warp, Hermes from Nous Research, OpenClaw, and OpenCode through Sign in with ChatGPT. Until now this mainly applied to personal Go, Plus, and Pro plans. Usage counts against the plan's existing Codex / ChatGPT work allowance and adds no new quota. Business admins must allow each app at admin.openai.com and can cap what share of each member's allowance outside tools may use.

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On November 10, Anthropic added two new artifact types to Claude: Claude Whiteboards, a board teams can edit together, and Claude Sheets, a shared spreadsheet with tabs, formulas, and drop-downs, both in beta on all paid plans. Claude Dashboards, which entered beta in October, is renamed Claude Data and is now generally available, adding data stories, certified queries, and live sharing. Interactive connectors can now open full apps such as Adobe, Atlassian, Box, and Notion beside the conversation or in full screen, where you or Claude can make changes directly. The features are rolling out gradually starting today.

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On November 10, Meta FAIR researcher Swann Bessa announced e-means on X, a clustering algorithm that replaces classic Lloyd k-means and is open-sourced in FAISS 1.16.0. It swaps full-batch iterations for mini-batch updates with exponential moving averages, and fitted scaling laws set the batch size and step size automatically, so k is the only setting. The paper says that on a range of embedding datasets e-means beats the loss Lloyd reaches after 25 epochs in 1.4 to 2.6 epochs, running 10-45x faster than Flash-KMeans on GPU and 8-15x faster than FAISS k-means on CPU (authors' figures). Teams building vector indexes, RAG pipelines, or dedup jobs can speed up this step directly.

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On November 10, the Institute of Foundation Models (IFM) at Mohamed bin Zayed University of Artificial Intelligence (MBZUAI) released PAN 2, an open world model. It splits conceptual and perceptual reasoning: a multimodal LLM first writes the current scene and text actions as a time-stamped, structured world state, and a 29B latent diffusion model trained from scratch then renders up to 20 seconds of 704x1248, 24 fps simulated video, so an agent can rehearse each candidate plan before acting. Weights and code are released under Apache 2.0, with day-0 support in Diffusers and vLLM-Omni. IFM also says about two in three simulated multi-step plans still miss at least one step.

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On November 10, Google opened Gemini 4 Carbon to developers, enterprises, scientists, and consumers. Two weeks ago it was limited to cybersecurity defenders in the Fairwind Program. Now every paying developer can call it through the Gemini API in Google AI Studio and Android Studio and through Gemini for business, and Google AI Ultra subscribers can use it in the Gemini app, AI Mode in Search, and Google Antigravity. The introductory price matches Argon at $2 / $10 per million input / output tokens, rising to $4 / $20 after November 30. Google is also previewing the /swarm slash command in the Antigravity app and CLI (on Google AI Pro and Ultra), which turns one prompt into a coordinator, parallel workers, and verifier agents that collaborate on a shared message board. Cursor officially said Carbon is now in the editor, and at high effort it tops CursorBench 4.0 at 60.4%.

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On November 10, NVIDIA released Alpamayo 2 Edge, an open autonomous-driving model. It is a 2B-parameter vision-language-action (VLA) reasoning model built to run in real time inside the vehicle, targeting NVIDIA DRIVE AGX Thor. It produces Chain-of-Causation reasoning only when needed and outputs trajectories directly in routine driving to save compute. After TensorRT and DFlash2 optimization, pixel-to-trajectory latency is 160 to 190 ms. NVIDIA also open-sourced AlpaJudge, a reasoning evaluation model, on Hugging Face.

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On November 10, MiniMax released MiniMax H3-Image, an image generation and editing model, and published the weights on Hugging Face. One checkpoint handles both text-to-image and multi-reference editing, with native output of about 16 megapixels (4096x4096 at 1:1) and no separate upscaler. MiniMax says a 4K image takes about 10 seconds and the model accepts up to 16 reference images. It is live on the MiniMax API platform, MiniMax Code, and Hailuo AI, with 50% off generations of 16.6 megapixels or more until November 17.

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On November 10, Google DeepMind released Gemini Robotics 2.5, which it calls its most capable vision-language-action (VLA) model so far. Across more than 200 evaluated tasks, DeepMind reports a 30% to 100% higher success rate than earlier baselines depending on the task type, with tasks finished 20% to 30% faster. In a proof of concept with Apptronik and Mercedes-Benz, the Apollo 2 humanoid sorted parts on car production lines in Hungary and Germany with a 96% success rate. The model is rolling out only to robotics partners for now, so developers cannot call it directly yet.

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On November 10, Einsia AI, with Tsinghua University and others, released WorkflowArena, a benchmark that tests whether a model can split a task, dispatch worker agents, write the workflow, and integrate the results on its own. It has 145 long-horizon tasks across 8 professional domains, scored by hidden verifiers, with every delegation recorded. The paper tests 25 configurations of 9 models in Claude Code and Codex: the best, Claude Opus 5.5 at max effort, fully resolves only 36.95% of tasks on average, the best SWE rate is 29.41%, and law never exceeds 9.09%. Claude models delegate rarely but in large groups, GPT models often and in small units, and workflows written by Opus raise weaker executors by 6.41 points on average. Code and paper are public.

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On November 10, n8n Labs, a new team inside workflow-automation company n8n, released Nod as a research preview. It is a desktop app separate from n8n that works inside a sandboxed virtual machine on your computer with its own browser. It connects to Gmail, Calendar, Drive, Slack, and Notion, can operate websites that have no integration, and takes requests in the app, on Telegram, or on WhatsApp. Nod is open source under Apache 2.0 and uses your own ChatGPT Plus/Pro plan, API key, or a local model. Before it sends, pays, posts, or deletes anything, a separate model checks that you asked for it, and n8n warns that this check can itself make mistakes.

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