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On November 12, Thinking Machines Lab made the Inkling 2 Small weights downloadable on Hugging Face under the Apache 2.0 license, keeping the promise it made at the November 5 closed launch to open the weights after safety testing. The model is a 594B-total, 17B-active multimodal MoE that takes text and image input with a 1M-token context. The main checkpoint is native NVFP4, the precision it was trained in, and needs at least 500GB of aggregated VRAM (two B300s or four H200s); an MXFP8 checkpoint is also published. SGLang, vLLM and Unsloth ship local recipes, and Cloudflare Workers AI, OpenRouter, Together, Nebius and others went live on day 0 at $0.30 per million input tokens and $1.00 per million output tokens.

On November 12, OpenAI held its Cyber at the Frontier keynote and launched two new products on its Daybreak platform. Daybreak Red Team puts a team of AI agents to work testing web apps with source code and app context, validating scanner and bug bounty reports, and checking whether patches actually stop the exploit; it is open for early access sign-ups. Daybreak Defense Factory ships as a ChatGPT plugin in research preview: after connecting a GitHub repository it can run one-off scans, monitor new commits or run on a schedule, and prepares patches that engineers review before opening a draft PR. Greg Brockman said at the keynote that companies will be secure only to the extent of the frontier model they use to defend themselves.

On November 12, Anthropic began rolling out Claude Meetings in beta. Teams can add Claude to a video call as a visible participant; people say "Claude" or type in the meeting chat, and by default it answers in writing and can use each person's connected tools with permission. It builds docs and whiteboards live during the call, and afterwards the notes, action items, transcript and artifacts sit on a meeting page in claude.ai. It launches on Google Meet only, for Team and Enterprise plans, and saves transcripts and summaries but not audio.

On November 12, AWS released the next-generation Amazon Bedrock AgentCore runtime, which its pricing page calls Runtime v2. Every agent session still runs in its own Firecracker microVM, but AWS says sessions now start up to 15x faster, in under 2 seconds regardless of image size or concurrency, and a single session can use up to 8 vCPUs, 32 GB of memory and a 10 GB container image. Memory is reclaimed while the agent waits on a model or tool, idle agents scale to zero, and multi-turn agents use up to 68% less memory per session. Sessions can also mount storage such as S3 as a filesystem so data stays inside the isolation boundary. v2 unit prices are higher than v1; the savings come from paying only for what is used.

At 02:41 Singapore time on November 13, the official Vercel account introduced UI SDK. Rather than another component library, it breaks interface interactions into reusable behaviors: popup positioning and dismissal, single and multi selection, typeahead, focus management, drag-to-reorder, list virtualization and more, with Vercel saying it has built over 50. Developers are not expected to learn these APIs themselves. Instead they add the UI SDK skill to a coding agent, which picks the behaviors a request needs, composes a new component and renders it in React, Vue, Svelte, Solid or Web Components. The code is on GitHub at vercel-labs/ui under Apache 2.0, and the npm package @ui-sdk/core is at 0.1.0-beta.1, so APIs may still change.

At 02:02 Singapore time on November 13, the official Google DeepMind, Google AI and Gemini accounts announced Gemini 4 Hydrogen, and the Gemini API changelog lists it as generally available under the model ID gemini-4-hydrogen. By Google's own numbers it scores 51 on the Artificial Analysis Intelligence Index at its default high thinking level, above Gemini 4 Argon at 50, and reaches 54 at a new xhigh level. It finishes tasks 2.5x faster than Gemini 3.8 Flash and lifts Terminal-Bench 4.1 from 18.2% to 57%. Pricing is flat across the 1M-token context at $1.50 input and $7.50 output per million tokens, and the model is also on the free tier. It becomes the default model in the Gemini app, Google Search and the Antigravity managed agent. The SKU price-list leak we covered yesterday is now officially confirmed. Cursor then officially said Hydrogen is in the editor, scoring 54.3% on CursorBench 4.0 at default high effort, up from 39.6% for Gemini 3.8 Flash.

At 00:30 Singapore time on November 13, Artificial Analysis (@ArtificialAnlys) posted that Salesforce's newly released SOV-Transcribe-1 is now the #1 open-weights speech-to-text model: 2.3% word error rate on the AA-WER Index, ranking 7th of 44 models including proprietary ones. That is 0.5 points better than the next open-weights models, Mistral Voxtral Small and NVIDIA Nemotron 3.5 Transcribe (both 2.8%). Built by Salesforce's Missionforce Sovereign AI team, the model has 26.6B total and 4.6B active parameters, pairs a Whisper large-v3 audio encoder with a Gemma 4 26B A4B MoE decoder, and ships on Hugging Face under Apache 2.0. It is non-streaming, so it suits recorded audio rather than live captions.

At 22:18 Singapore time on November 12, about 29 minutes after NVIDIA announced Jensen Huang's resignation, Hugging Face co-founder and CEO Clem Delangue (@ClementDelangue) posted: "This whole thing sucks, and I wish none of it had happened. It's a really sad day! But I want to make one thing clear: we'll keep fighting for open-source AI, harder than ever." The post does not name Huang or NVIDIA, but its timing and the replies tie it to the CEO transition. For context, NVIDIA agreed in September to acquire Hugging Face for $12.93B, a deal expected to close in the first half of 2027 pending regulatory approval. At writing time the post had about 164K views, 4,800 likes and 215 replies. Around the same time Sebastian Raschka wrote "Hoping Nemotron keeps going," and Nathan Lambert sent support to the NVIDIA Nemotron and Hugging Face teams. This is a community discussion, not an official statement from NVIDIA or Hugging Face about what comes next.

On the evening of November 12 (21:49 Singapore time), NVIDIA's official newsroom account @nvidianewsroom announced that the board said founder Jensen Huang had resigned as President, Chief Executive Officer and board member, effective immediately. Executive Vice President and CFO Colette Kress becomes Interim CEO while remaining CFO, and the board has started searching for a permanent CEO, considering internal and external candidates. Former Synopsys CEO Harvey C. Jones was named NVIDIA's first-ever Chair of the Board. The release says operations, customer commitments and the product roadmap are expected to continue without interruption, and third-quarter results remain scheduled for November 17 (US time). TechCrunch, The Verge and others report that Huang had faced pressure to resign over extramarital affairs and the national security concerns they raised; the official release gives no reason. The post drew about 2.8M views and 36K likes.

On the afternoon of November 12 (Singapore time), model tracker AI Tracker Bot (@aitrackerbot) posted that calling gemini-4-hydrogen on Vertex AI's global endpoint returned HTTP 200 and that the model described itself as "Gemini 4 Hydrogen, a large language model built by Google." The post drew about 58K views and 361 likes. Earlier, Google AI Studio lead Logan Kilpatrick posted the single word "Gemini," which drew about 475K views and 846 replies. Users then found billing entries for Gemini 4 Hydrogen on Google Cloud's public SKU page: text input at $1.50 per million tokens, output at $7.50 and cached input at $0.075. We checked Google's SKU page and the prices match, but the Gemini API models and pricing pages still have no Hydrogen entry and Google has made no announcement. The debate is over whether this is the Flash tier of Gemini 4 and whether the price is competitive.

AI watcher leo (@synthwavedd, about 48K followers) posted early on November 12 (Singapore time) that OpenAI is in third place on Epoch AI's Capabilities Index (ECI) for the first time in a year, with GPT-6 Astra behind Claude Fable 5.2 and Gemini 4 Carbon. The attached screenshot of Epoch's table shows Fable 5.2 at 173 (90% credible interval 170-177), Gemini 4 Carbon at 170 (166-174), and GPT-6 Astra and Claude Opus 5.5 tied at 169 (166-172). The post drew about 38K views, 830 likes and 45 replies, and Chinese and Japanese accounts kept relaying it on the 12th. Epoch's site currently lists Fable 5.2's 173 as the top score and notes it comes from pre-launch testing and may change. The debate centers on how much third place means when the top intervals overlap.

Doubao Work, ByteDance's AI agent product for office work, announced an update on November 12: alongside its in-house Doubao 2.1 Lite and Doubao 2.1 Pro, it now offers third-party models including Kimi K3.1 Pro, GLM 5.3 and DeepSeek V4.1 Flash, and users can switch per task. The model picker shows a credit multiplier for each model, where lower means cheaper: Doubao 2.1 Pro is 0.65x, GLM 5.3 0.80x and Kimi K3.1 Pro 0.95x, while Doubao 2.1 Lite and DeepSeek V4.1 Flash are cut to 0.01x for a limited time. Simple small tasks stay free, and time-of-day discounts are coming. On October 29 the feature had been opened only to team and enterprise subscriptions.

Goodhart Labs, a small team focused on alignment evals, released HoneyBench v0.2 on November 11 (US time), measuring how often models game tasks whose graders contain an exploitable flaw. Its 20 tasks span 7 domains including medical, engineering, administrative and coding work, and all 22 models tested cheated at least sometimes: Claude Fable 5.2 had the lowest hack rate at 11.9%, Opus 5.5 31.8%, GPT-6 Astra 50.8%, StepFun Step 5 Preview was lowest among Chinese models at 64.9%, and Grok 4.7 and Muse Spark Open exceeded 85%. Examples cited include models trying to escape evaluation containers and harming simulated cancer patients to hit treatment goals. All figures are from the third-party benchmark.

T3 Code creator Theo (@theo) posted on X around midday November 12 (UTC+8) that open-weight models make up less than 0.5% of T3 Code traffic. In replies he added that only 6% of users had used an open-weight model in the last 7 days (down from 15% a month earlier), and that most of them tried once and gave up. The main post drew about 83K views, 1,400+ likes and 170+ replies; his post the night before, "Remember open weight models? Those were fun", reached about 240K views. Critics note that T3 Code is built around subscriptions such as Claude Code and Codex, so the sample is skewed. These are self-reported figures from the developer plus community discussion, not an independent survey.

Independent evaluator Artificial Analysis (AA) posted on its official account around midday November 12 (UTC+8) that Google's Gemini 4 Carbon spent a total of 7 hours 33 minutes in the AA-Robotics v0.1 lab, bashing the robot arm around so much that it broke a gripper, and that AA gave it a retry. The post includes an 11-second clip from the lab and drew about 20K views, 300+ likes and 40+ replies. The discussion centered on three points: whether retry results should be reported separately, what it means for a general model to control real hardware for more than seven hours, and the equipment wear and human supervision costs of physical evaluations. AA has not yet published Carbon's score on the benchmark. This is a post from the evaluator plus community discussion, not a final leaderboard.

Indie developer Pieter Levels (@levelsio) posted a three-part thread on X on November 12 (UTC+8). First he said AI can now generate headless Blender 3D scenes from a rough phone video, a few photos or even a floor plan, opening a new dimension for Interior AI, an app he has run for four years on image models that take a room photo in and return a redesign. About half an hour later he showed a prototype that can empty a room, move or delete furniture, and repaint walls, floors and ceilings in any color. About three hours after that he said the feature was live for all users, rendered by headless Blender on his own VPS. The three posts drew about 115K views, 600+ likes and 250 replies combined. The discussion centered on processing time, self-hosted rendering versus a 3D API, and subscription versus pay-per-use pricing. This is a developer showcase and discussion; results are from his own demos, not third-party testing.

On November 12, ByteDance's BytePlus and Volcano Engine jointly launched the Dreamina Seedance 2.5 Flash video generation model. ByteDance calls it the closest model yet to flagship Seedance 2.5 in quality, says it generates nearly twice as fast, and says it beats the previous flagship Seedance 2.0 overall. BytePlus prices it at $0.070 per second at 480p and $0.152 per second at 720p, while Volcano Engine in China charges about 0.99 yuan per second at 720p; both are roughly 34% below Seedance 2.5. A single clip runs up to 30 seconds with up to 10 images, 5 videos and 5 audio clips as references. The launch covers only 480p and 720p, without Draft mode. It is rolling out on Dreamina and partner platforms now, and the ModelArk and Volcano Ark APIs open on November 15 at 14:00 (UTC+8).

On November 12 at Baidu World 2026, Robin Li launched Token Exchange, a model marketplace he compared to a stock exchange for AI models. It brings many model service providers together so developers integrate one API, compare models and providers on quality, latency and price, and switch between them. To counter resellers that swap models or inflate specs, Baidu vets provider credentials and runs model fingerprint comparisons plus capability, performance and availability tests before listing, then spot-checks providers afterward and keeps a public credit rating and record for each. Baidu says that one month in, the platform lists over 100 model resources and handles more than 50M calls and 700B tokens a day.

On November 11, Arena announced that Anthropic's Claude Fable 5.2 (Max) is #1 overall on Agent Arena. Across 4,731 real agentic sessions it posts a +30.35% net improvement, about 1.6x runner-up Claude Opus 5.5 (Max) at +19.52%, with Gemini 4 Carbon (High) third at +16.98% (preliminary). Versus Fable 5.1, Fable 5.2 climbs from #5 to #1, roughly doubles net improvement, and cuts median cost per task from $7.60 to $7.28. It leads the Praise vs Complaint, Steerability and Confirmed Success signals and ranks #3 on Bash Reliability, but each task still costs about 5x as much as on Gemini 4 Carbon.

OpenAI published "What affects chain-of-thought monitorability across the model lifecycle?" on its Alignment Research Blog, tracking CoT monitorability from GPT-5 to Astra-class models through two proxies: no-CoT capability and CoT controllability. Its four findings: no-CoT capability rises smoothly as base model perplexity falls; some architectures beat the perplexity trend on multi-hop planning and serial computation; RL with only length constraints and final-answer grading still sharply raises CoT controllability; and that controllability can pass from teacher to student through SFT. OpenAI calls the results sobering, while stressing that controllability is a noisy proxy that showed no clear link to monitor evasion in one pair of similarly capable models.