AI Coding Live News

Meta Rolls Out Muse Realtime Avatar in the US Muse App: Speech-Driven Expression and Gesture at ~870ms First-Byte Latency

On November 5 Meta AI (@AIatMeta) said Muse Realtime Avatar, previewed at Connect, is rolling out in the US Muse app, turning Muse Realtime Voice into interactive live avatars. The research blog describes an audio-driven Diffusion Transformer that shares the speech-token stream with voice for lip-sync and expression; a single GB200 session streams 448×768 at 25 fps with about 870 ms from end of user turn to first synchronized audio-video byte, with Meta Video Seal invisible watermarking. Muse is for users 18+; no global schedule or developer API was announced.

Meta Rolls Out Muse Realtime Avatar in the US Muse App: Speech-Driven Expression and Gesture at ~870ms First-Byte Latency

Key Takeaways

  • US Muse app rollout of Realtime Avatar after the Connect preview (@AIatMeta)
  • Shared speech VQ tokens drive audio decode and avatar video; causal chunks with self-forcing distillation cut ~120 teacher NFEs to 2 student NFEs (~60x)
  • Vendor perf: 448×768@25fps, ~870ms first-byte latency; ~8x serving capacity vs BF16 baseline, up to 12 concurrent sessions on one GB200
  • Vendor preference study favored Muse over Runway Characters and HeyGen LiveAvatar on overall and most axes
  • Safety: Video Seal invisible watermark; blog demos are not the full in-app avatar set; no public developer API stated
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Linked Models & Agents:
⚡ Muse Spark⚡ Muse Presence🤖 Muse🌐 Meta

Gemini Omni 1.2 Flash GA: Up to 15s Clips, ~30% Lower Output Price, About $0.07/s at 720p; 1.1 Routes to 1.2 on Dec 3

On November 5 Google made Gemini Omni 1.2 Flash (gemini-omni-1.2-flash) generally available in AI Studio and the Gemini API: video generate/edit length rises to 15 seconds, vendor pricing cuts video output about 30% (~$12 per 1M tokens, roughly $0.07/s at 720p), with stronger multi-reference consistency and more natural speech. The changelog states that from December 3, 2026, requests to gemini-omni-1.1-flash will automatically route to 1.2 at 1.2 rates.

Gemini Omni 1.2 Flash GA: Up to 15s Clips, ~30% Lower Output Price, About $0.07/s at 720p; 1.1 Routes to 1.2 on Dec 3

Key Takeaways

  • Model id gemini-omni-1.2-flash live in AI Studio and Gemini API (changelog Nov 5)
  • Clips 3–15s at 360p/720p/1080p/4K; vendor notes consistency across up to 6 reference images
  • Vendor pricing: ~30% lower video output; ~$0.07/s at 720p (~$1.04 for 15s)
  • From 2026-12-03, gemini-omni-1.1-flash auto-routes to 1.2 at 1.2 rates
  • Consumers: Flow, Google Vids, and Gemini App support up to 15s clips and multi-turn edits
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Linked Models & Agents:
⚡ gemini-omni-1.2-flash⚡ gemini-omni-1.1-flash🌐 Google DeepMind

BoxWorks 2026: Box and NVIDIA Unveil Box Context Retrieval, Permission-Aware Retrieval for Enterprise Agents Built on Nemotron Retriever, Plus Box Architect and Box Mount

At BoxWorks 2026 on November 5, Box announced an expanded NVIDIA collaboration: Box Context Retrieval, a new engine built on NVIDIA Nemotron Retriever embedding models, combines keyword and semantic search to return the most relevant passages to AI agents while honoring existing file permissions, with general availability for Enterprise Advanced customers planned for early next year. Box's press release claims 20% better search accuracy (Recall@5), 90% fewer tokens and 4x faster answers. Open-weight Nemotron models, starting with Nemotron 3 Ultra, are available in Box AI now. Box also unveiled Box Architect, which designs workflows from a stated goal; Box Mount, which mounts Box folders inside agent sandboxes and works with NVIDIA OpenShell; and agent security controls including MCP Guardrails.

BoxWorks 2026: Box and NVIDIA Unveil Box Context Retrieval, Permission-Aware Retrieval for Enterprise Agents Built on Nemotron Retriever, Plus Box Architect and Box Mount

Key Takeaways

  • Box Context Retrieval uses NVIDIA Nemotron Retriever with hybrid keyword and semantic search, honoring existing file permissions
  • Vendor claims: +20% Recall@5 accuracy, 90% fewer tokens, 4x faster answers; GA for Enterprise Advanced early next year
  • Open-weight Nemotron models, starting with Nemotron 3 Ultra, are available in Box AI now
  • Box Mount puts Box folders inside agent sandboxes and works with the NVIDIA OpenShell secure runtime
  • New agent security controls include MCP Guardrails, classification-based access policies and audit trails
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Linked Models & Agents:
⚡ Nemotron 3 Ultra⚡ Nemotron Retriever🌐 box🌐 Nvidia (NIM)

GitHub Copilot Comes to JetBrains Air: Run Coding Tasks Locally in the IDE or in Isolated Cloud Environments via ACP, Billed to Your Copilot Subscription

On November 3 GitHub announced on its official changelog that GitHub Copilot is now available in JetBrains Air. The integration is built on the Agent Client Protocol (ACP): developers pick Copilot in Air's agent picker, delegate coding tasks, track several sessions in parallel and review changes next to their code. Tasks can run locally in a JetBrains IDE or in isolated cloud environments through Air Teams, and they draw on the Copilot subscription rather than JetBrains AI credits.

GitHub Copilot Comes to JetBrains Air: Run Coding Tasks Locally in the IDE or in Isolated Cloud Environments via ACP, Billed to Your Copilot Subscription

Key Takeaways

  • Copilot now appears in JetBrains Air's agent picker and works with an existing Copilot account
  • The integration uses the Agent Client Protocol (ACP) and can be used alongside the existing GitHub Copilot plugin
  • Developers can track multiple agent sessions in parallel and review changes next to their code using project context and IDE tools
  • Cloud tasks run through Air Teams, each in a Docker container on a fresh virtual machine, with traffic through a proxy and no state kept between tasks
  • Cloud tasks use the Copilot plan rather than JetBrains AI credits, but Air analytics do not include Copilot usage and admins cannot set Copilot spending limits inside Air
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Artificial Analysis Makes Time per Task Its Headline Performance Metric: GPT-6 Astra Finishes a Max-Effort Task in About 17 Minutes, Ahead of Opus 5.5 and Gemini 4 Carbon

Independent benchmarker Artificial Analysis announced on X on November 4 (17:34 UTC) a new version of its API performance benchmarking: the headline metric moves from output speed to Time per Task, the time to finish one Intelligence Index task (output tokens per task divided by end-to-end output speed). It covers about 1,000 serverless endpoints, measures speed from sending the request to the final token, and adds multi-turn agentic coding and knowledge-work workloads. Per its data, GPT-6 Astra at max effort runs at 51 tokens/s yet finishes a task in about 17 minutes, against about 26 for Claude Opus 5.5 (max) and about 29 for Gemini 4 Carbon (high).

Key Takeaways

  • Time per Task equals output tokens per Intelligence Index task divided by end-to-end output speed, so a model that is fast per token but verbose can finish later than a slower model that uses fewer tokens.
  • End-to-end output speed runs from sending the request to the final token, including the wait before the first token (prefill and hidden reasoning); output tokens now come from each provider's API-reported counts instead of the o200k_base tokenizer.
  • GPT-6 Astra (max) runs at 51 tokens/s with about 50k output tokens per task and takes about 17 minutes; Claude Opus 5.5 (max, 111 tokens/s) takes about 26 minutes and Gemini 4 Carbon (high, 71 tokens/s) about 29, using roughly 120k to 170k tokens per task.
  • At each lab's API default effort (medium for all three), GPT-6 Astra, GPT-6.1 Sol and Opus 5.5 cut Time per Task by about 3 to 5x and Cost per Task by about 2 to 4x, scoring about 5 to 11 points lower; Opus 5.5 drops from about 26 minutes and $10.04 to about 6.8 minutes and $2.45.
  • The official methodology page lists this as version 3.0.0 (October 30), with runs now every 6 hours and a new endpoint error rate metric.
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Axiom Formalizes the Entire Stacks Project in Lean With AI Agent Swarms: About 16 Million Lines, Billed as the Largest Reusable Formal Math Library Yet

Axiom released a Lean formalization of the entire Stacks Project on November 4 (official X post at 17:13 UTC, with over 2 million views). The roughly 7,500-page open-source algebraic geometry reference, started by Johan de Jong in 2005, becomes about 16 million lines of code, which Axiom calls the largest integrated, reusable formal math library ever built. Hierarchically coordinated AI agent swarms did the work and surfaced dozens of statements in the original that needed local fixes, with counterexamples. The library is open source and browsable side by side with the original text at stacks.axiommath.ai.

Axiom Formalizes the Entire Stacks Project in Lean With AI Agent Swarms: About 16 Million Lines, Billed as the Largest Reusable Formal Math Library Yet

Key Takeaways

  • Scale: about 7,500 pages of source text and roughly 16 million lines of Lean, which Axiom calls the largest integrated reusable formal math library (vendor claim).
  • Method: hierarchical AI agent swarms translate statements, search Mathlib for existing abstractions and build proofs in parallel, with multi-agent auditing and semantic cross-checks against the source to keep statements faithful.
  • Findings: the system surfaced dozens of statements needing local refinement, including missing hypotheses and edge cases in positive characteristic, and built explicit counterexamples where claims failed.
  • The permanent Stacks tag structure is preserved so every Lean declaration links back to the original text; stacks.axiommath.ai offers side-by-side reading and a dependency graph.
  • This follows Axiom's October 27 formalization of the Classification of Finite Simple Groups (CFSG); per de Jong's request, corrections are kept in Axiom's own repository rather than sent upstream.
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Kling 4.0 Flash Is Fully Available With an API: 3-20 Second 720P Videos via Text, Image and Omni Reference Generation

Kling AI announced on November 4 that Kling 4.0 Flash is open to all users on the website and via API, and is also reachable through MCP, CLI and AI plugins, including Photoshop, Premiere Pro and After Effects. The official API changelog says the Flash API generates 3 to 20 second videos at 720P, supports text-to-video, image-to-video and Omni reference-to-video, accepts up to 15 inputs per task and bills by total duration.

Kling 4.0 Flash Is Fully Available With an API: 3-20 Second 720P Videos via Text, Image and Omni Reference Generation

Key Takeaways

  • Flash had been in early access for Ultra yearly subscribers since September 28; it is now open to all users and the API.
  • The API paths are kling-4.0-flash under /text-to-video, /image-to-video and /omni-video; image-to-video takes a single first-frame image.
  • Prompts can be up to 26,000 English characters, with up to 10 reference images and 5 reference videos, plus 21:9 and adaptive aspect ratios.
  • Billing is by the total duration of input and output video; unit prices are on the official pricing page and were not stated in the announcement.
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Linked Models & Agents:
⚡ Kling 4.0 Flash⚡ Kling 4.0🌐 Kling AI🌐 Kuaishou🌐 Adobe

AMD Q3 2026: Data Center Revenue Up 97% to $8.5B, First Helios Racks Running for Anthropic and OpenAI, Q4 Guide About $17B

AMD reported Q3 2026 results on November 3. Total revenue was $13.377B, up 45% year over year, and the Data Center segment hit a record $8.546B, up 97% year over year and 27% sequentially, driven by EPYC server CPUs and the newly launched Instinct MI450 Series GPUs. AMD says Anthropic, Meta, Microsoft, OpenAI and Oracle Cloud Infrastructure are running models and workloads on the first Helios rack-scale systems. It guided Q4 revenue to about $17B, plus or minus 3%, roughly 66% year-over-year growth at the midpoint.

Key Takeaways

  • Data Center revenue was $8.546B, up 97% year over year and 27% sequentially, about 64% of total revenue versus roughly 47% a year ago; segment operating income was $2.882B at a 34% margin
  • The first Helios racks (Instinct MI450 Series GPUs plus 6th Gen EPYC) are running models for Anthropic, Meta, Microsoft, OpenAI and Oracle Cloud, and MI455X began volume shipments this quarter
  • Total revenue was $13.377B (+45%), with 56% non-GAAP gross margin and $2.09 non-GAAP EPS; GAAP net income of $2.428B includes a $1.097B fair-value change on customer warrants
  • Q4 guidance is about $17B plus or minus 3%, roughly +66% year over year and +27% sequentially at the midpoint, with about 55% non-GAAP gross margin
  • AMD also released ROCm 10 with ROCm.AI, introduced the Helion reference architecture for rack-scale agentic systems, and announced agreements to acquire World Labs and Taalas
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SenseTime Ships SenseNova 6.8 Flash: An Office Agent Model With Long-Term Memory and Proactive Interaction That Builds Animated Slides, Motion Posters and Editable Blender 3D Projects

On the evening of November 2, SenseTime released the official version of SenseNova 6.8 Flash, a multimodal agent model with two main upgrades. First, long-term memory and proactive interaction: it keeps user goals and constraints across tasks and sessions and flags affected items when information changes. Second, design generation across Web, SVG, Three.js, Blender, animated PPT and motion posters, with Figma integration. It is live in SenseNova Studio, the Raccoon assistant and the SenseNova Token Plan; the official X account says the Token Plan offers a free trial of 1,500 API requests every 5 hours. SenseTime says it beats some international frontier models on agent collaboration, data analysis and presentation generation, but the announcement lists no specific scores.

SenseTime Ships SenseNova 6.8 Flash: An Office Agent Model With Long-Term Memory and Proactive Interaction That Builds Animated Slides, Motion Posters and Editable Blender 3D Projects

Key Takeaways

  • The official release landed on the evening of November 2, positioned as a multimodal agent model for office collaboration and design delivery
  • Long-term memory plus proactive interaction: it remembers goals, constraints and preferences across sessions and suggests changes when related information shifts (demos ran on an internal Raccoon build)
  • Design output spans Web, SVG, Three.js, Blender, animated PPT and motion posters, producing editable PPTX files and Blender projects, with Figma integration
  • Available in SenseNova Studio, the Raccoon assistant and the SenseNova Token Plan; the official X post cites 1,500 free API requests every 5 hours
  • SenseTime only says it beats some international frontier models on agent collaboration, data analysis and presentation generation; no scores were published and there is no independent evaluation yet
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Linked Models & Agents:
⚡ SenseNova 6.8 Flash⚡ SenseNova Loop-9B-Preview🤖 Raccoon🌐 SenseTime

Fireworks Forge Launches: Ultra-fast Inference Preview Hits 1,500 Tokens/s, Half-Price Background Mode, Nexus Desktop for Codex and Claude Desktop, Sovereign Private Preview

At its first Forge conference on November 3, open-model inference platform Fireworks launched a batch of products. Ultra-fast, powered by NVIDIA Blackwell, is in public preview at up to 1,500 tokens per second on DeepSeek V4.1 Flash, cutting a 30-turn agent task from 1 minute to 8 seconds. Background Mode runs deferrable work at half price, alongside multi-day prompt caching and higher rate limits. Nexus Desktop brings open-model routing into the Codex app, Claude Desktop and OpenCode, with Pro plans from $8 per month. Fireworks also announced No Eval, serverless LoRA inference, the Fireworks Sovereign private preview and a 60+ partner Frontier Ecosystem.

Fireworks Forge Launches: Ultra-fast Inference Preview Hits 1,500 Tokens/s, Half-Price Background Mode, Nexus Desktop for Codex and Claude Desktop, Sovereign Private Preview

Key Takeaways

  • Ultra-fast public preview: on NVIDIA Blackwell, up to 1,500 tokens per second on DeepSeek V4.1 Flash; in Fireworks' benchmark a 30-turn agent task fell from 1 minute to 8 seconds (Fireworks blog)
  • Cost options: Background Mode runs deferrable work at half price; multi-day prompt caching cuts the cost of resuming long context; higher serverless rate limits and Elastic Burst are generally available
  • Nexus Desktop launches: personalized routing across open and closed models inside the Codex app, Claude Desktop and OpenCode; Pro from $8 per month, Teams from $10 per seat per month
  • Training: No Eval (private preview) turns production traces into task evals; serverless LoRA inference (public preview) starts with GLM 5.3 Flash at per-token pricing with no idle charge; free training on Muse Glimmer 30B through November 30
  • Enterprise: Fireworks Sovereign (private preview) runs the inference engine in a customer's own cloud or data center; Fireworks says it already manages over 25,000 GPUs in customer environments; plus a 60+ partner Frontier Ecosystem
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Google Turns Gemini and Search Off by Default for K-12 Student Accounts Until Admins Opt In, Launches Guided Learning in Classroom Globally

On November 3, Google VP and GM of Education Chris Phillips announced on the official Google blog that Gemini, Gemini Notebook, Gemini in Classroom and Google Search will be entirely off for K-12 student accounts until school administrators explicitly turn them on. Guided Learning is globally available in Google Classroom starting that day and is coming to Search for student accounts, with a pilot in select U.S. schools later this month. Because Search now includes AI Mode and AI Overviews, schools that want AI fully off also lose Search, which drew sharp backlash; Google later clarified on X that Search can be enabled on its own. EdWeek and AP report the change starts early next year; Google has given no exact date.

Google Turns Gemini and Search Off by Default for K-12 Student Accounts Until Admins Opt In, Launches Guided Learning in Classroom Globally

Key Takeaways

  • Off by default: Gemini, Gemini Notebook, Gemini in Classroom and Google Search are entirely off for K-12 student accounts until admins explicitly enable them (Google blog)
  • The controversy: Search now includes AI Mode and AI Overviews with no AI-free option, so student groups with AI fully off lose Search too (per EdWeek and NBC News)
  • Google clarified on X that schools can turn on Search without Gemini products, and that AI Mode and AI Overviews for students will guide them step by step instead of giving answers
  • Guided Learning is globally available in Google Classroom starting November 3; the Search version pilots in select U.S. schools later this month before wider expansion
  • Timeline: EdWeek and AP report the change starts early next year; Google only says it will share specifics in the coming months, with no exact date
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Linked Models & Agents:
⚡ Gemini🌐 Google

fal Announces $1.65B Across Series E and F for Generative Media Inference, Led by Sequoia and Bessemer with NVIDIA

fal, which hosts inference for image, video and audio models, announced on its official X account and blog on November 3 that it raised a combined $1.65B across Series E and Series F. Sequoia Capital led the Series E, closed earlier this year, and Bessemer Venture Partners led the Series F with continued investment from NVIDIA. fal did not disclose valuation or per-round amounts; Bloomberg reported the Series F at $1.3B at a $16B valuation, double the $8B from April. fal says the money funds inference capacity, hiring, and research and platform work in 2027.

fal Announces $1.65B Across Series E and F for Generative Media Inference, Led by Sequoia and Bessemer with NVIDIA

Key Takeaways

  • Confirmed by fal: $1.65B combined across Series E and F, Sequoia led E, Bessemer led F, NVIDIA continued investing (fal blog)
  • fal did not disclose valuation; Bloomberg reported the Series F at $1.3B at a $16B valuation and the Series E at $350M at $8B
  • Per Bloomberg, CEO Burkay Gur said demand spiked so much over the summer that fal had to rate limit customers, driving the large raise
  • Funds go to inference capacity, team growth and new research in 2027; fal hosts many third-party media models such as Seedance, Kling, Veo and FLUX
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X Discussion: Sam Altman Says People Should Always Control the Future and That This Should Ultimately Constrain AI Progress

At 01:40 UTC on November 4, OpenAI CEO Sam Altman (@sama) posted on X that "people should always be in control of the future," saying OpenAI is against the idea of people being relegated to doing things that "AI systems think makes sense to reward in humans," and that people should not cede control of the future to superintelligence, a requirement that "should ultimately constrain AI progress." Within about 25 minutes it drew roughly 1,360 likes, 337 replies and nearly 100K views; Francois Chollet and others voiced support while many replies pushed back. This is Altman's stated view, not a new OpenAI policy or product announcement.

Key Takeaways

  • Engagement: about 1,360 likes, 337 replies, 89 reposts, 74 quotes and roughly 99K views within about 25 minutes (per the X API); the high reply ratio signals a contested post
  • Three core lines: people should always be in control of the future; OpenAI rejects people being relegated to doing what "AI systems think makes sense to reward in humans"; people should not cede the future to superintelligence, and that requirement "should ultimately constrain AI progress"
  • Support: Francois Chollet (@fchollet) posted "Co-sign," saying the only future worth building is one where AI remains a tool in human hands; Matthew Berman said Altman made the same point in their interview last week
  • Pushback: replies asked "which people?", argued that AI systems "don't think anything," or contrasted the post with Altman's recent comments on RAM and concentrating compute in data centers
  • The post does not say where the quoted phrase comes from and names no concrete policy or timeline; online links to a GPT-6.2 Astra release date are speculation
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X Discussion: Greg Isenberg Says He Talks to His Agents More Than His Human Teammates, Shares a Full Agent-Run Company Stack

On November 3, founder and Late Checkout CEO Greg Isenberg (@gregisenberg) posted a long thread on X saying his team has 50+ people in 12 countries, yet he "talks to my agents more than my human teammates." He listed an agent-driven stack covering ideation, design, build, user testing, bug fixing and data review. Within hours it drew about 1,050 likes, 2,300 bookmarks and 183 replies. This is a personal workflow and opinion, not verified productivity data.

Key Takeaways

  • Engagement: about 1,050 likes, 2,300 bookmarks, 183 replies and 75K views within hours (per the X API); bookmarks far exceed likes, so readers are saving it as a checklist
  • Coverage: ideation, design, build, user testing, bug fixing and data review are all handed to agents; humans handle "creative feedback, relationships and telling the agents what they got wrong"
  • Build: pingdotgg/t3code as the control surface and stablyai/orca to run parallel agents, each in its own git worktree
  • Quality control: every output is scored 1-10 by an LLM judge and anything under 8 goes back; every agent mistake becomes a new skill
  • All of this is self-reported; no verifiable productivity or cost figures were published
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Vercel Security Review Enters Public Beta: Vulnerability Review on Every GitHub Push, One-Click Fixes with Vercel Agent

On November 3 (US Pacific time), Vercel launched Security Review in public beta. It is a new Vercel Agent capability alongside Code Review and Investigation: once enabled, every push to connected GitHub repositories triggers a diff-only vulnerability review that finishes in seconds. Findings land in the Security dashboard, PR comments and the CLI, and can be fixed with one click by Vercel Agent or pulled into your own coding agent via vercel security vulnerabilities. It is available to Pro and Enterprise teams, and reviews are free until November 10.

Vercel Security Review Enters Public Beta: Vulnerability Review on Every GitHub Push, One-Click Fixes with Vercel Agent

Key Takeaways

  • Trigger: every GitHub push (including direct pushes to production) is reviewed automatically; only the diff is scanned, which Vercel says finishes in seconds, so it can run on every preview and production deployment
  • Beyond pattern matching: the agent traces how changed code is reached, checks existing protections and rechecks each finding, which Vercel says catches logic flaws such as missing authorization checks
  • Teams can write security policies in plain language, scoped to projects or the whole team, and manage them with vercel security policy
  • Pricing: Vercel Agent rate, model tokens at provider cost with no markup, plus $0.25 per million tokens; reviews are free until November 10, 2026, while Agent fixes are billed
  • Availability: public beta for Pro and Enterprise teams; Vercel has not published detection-rate or false-positive benchmarks
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