AI Coding Live News

Gemini API Computer Use Tool Goes GA: Browser, Mobile and Desktop Agents Production-Ready, No Breaking Changes from Preview

The Gemini API changelog shows the Computer Use tool reached general availability on October 30 across both the Interactions and GenerateContent APIs; @GoogleAIStudio announced on November 4 that there are no breaking changes from preview and it is production-ready for browser, mobile and desktop control agents. Official docs list supported models: Gemini 4 Argon, the recommended Gemini 3.8 Flash, Gemini 3.5 Flash-Lite and Gemini 3 Flash Preview. Actions carry an intent field, responses may include a safety_decision requiring user confirmation, and opt-in screenshot prompt-injection detection is available.

Key Takeaways

  • Computer Use is GA on both the Interactions and GenerateContent APIs as of October 30
  • Google says there are no breaking changes from preview, so existing code carries over
  • Three environments: browser, mobile and desktop; Gemini 3.8 Flash is the recommended model
  • Every action includes an intent field; safety_decision can require user confirmation or block
  • Opt-in screenshot prompt-injection detection (Gemini 3.5 Flash-Lite and later, off by default)
Read full story

Notion: MCP Shared Brain Lets ChatGPT, Claude, Muse and Bot Share Memories and Skills, With Official Agent Setup Prompts

On November 4 NotionHQ announced that via Notion MCP, ChatGPT, Claude, Muse and Bot can read and write the same Memories and Skills—one shared brain across agents. Official setup prompts (v0.0.6, Oct 30), help docs and a Nov 3 MCP use-cases blog corroborate the product push.

Notion: MCP Shared Brain Lets ChatGPT, Claude, Muse and Bot Share Memories and Skills, With Official Agent Setup Prompts

Key Takeaways

  • NotionHQ post (Nov 4, 19:14 UTC) had about 3,271 likes, 225 reposts, 4,560 bookmarks and ~589K views when fetched
  • Product claim: ChatGPT / Claude / Muse / Bot share Memories and Skills in Notion so switching agents does not reset context
  • Official setup prompt v0.0.6 (2026-10-30): walks agents through an Agent Operating Guide and custom-instruction paste for ChatGPT / Claude
  • Help center: Notion MCP works with Claude, Cursor, ChatGPT Pro and more; Enterprise can approve a whitelist and govern connections
  • Vendor blog (Nov 3): five team stories of MCP workflows as reusable SOPs; Skills via MCP are listed as coming soon
Read full story

OpenAI Realtime Lead: GPT-Live Outbound Telephony API Hits GA via Any SIP Trunk, with HD Voice and DTMF

On November 4, Justin Uberti, Head of Realtime AI at OpenAI, said the GPT-Live outbound telephony API is generally available: Live voice agents can dial through any SIP trunk such as Telnyx or Twilio, with HD Voice and bidirectional DTMF. Official docs describe Live API + SIP transport; Greg Brockman amplified the note.

OpenAI Realtime Lead: GPT-Live Outbound Telephony API Hits GA via Any SIP Trunk, with HD Voice and DTMF

Key Takeaways

  • Justin Uberti (Head of Realtime AI, OpenAI): GPT-Live outbound telephony API is GA via any SIP trunk
  • Official docs: Live API POST /v1/live/sessions with transport.type=sip; HD Voice and bidirectional DTMF
  • Trunks need TLS (sips:) signaling and SDES-SRTP; examples cite Telnyx and Twilio
  • Documented limits: ~3 minutes ringing and ~2 hours connected call duration
  • Amplified by Greg Brockman; frame as a first-hand employee note, not an @OpenAI press post
Read full story
Linked Models & Agents:
⚡ OpenAI GPT Live 1🌐 OpenAI

Claude Code Background Subagent Status Lines Now Written by Haiku 5.5: About 99% Cheaper per Update, About 6% More Prompts per Limit

On November 4 ClaudeDevs, Anthropic's official developer account, said the status line that a background subagent in Claude Code refreshes every 30 seconds used to re-read its whole conversation each time, and is now written by Haiku 5.5 from short notes, so each update costs about 99% less. Anthropic says sessions with background subagents used about 5% less of the limit, giving about 6% more prompts on the same limit. It is live for most Pro, Max and Ultra users and everyone on Team and Enterprise, on v2.1.316 or later.

Key Takeaways

  • A background subagent refreshes its status line every 30 seconds, and each refresh used to re-read the subagent's whole conversation
  • Haiku 5.5 now writes the status line from short notes, which Anthropic says makes each update about 99% cheaper
  • The subagent itself keeps its own model; only the status line is written by Haiku 5.5
  • Anthropic says sessions with background subagents used about 5% less of the limit, so the same limit gives about 6% more prompts
  • It is live for most Pro, Max and Ultra users and everyone on Team and Enterprise; update to v2.1.316 or later
Read full story
ADSponsored

Together AI Launches Together Compiler: Agents Continually Optimize Production Inference, Up to 2.6x Higher TPS

On November 4 Together AI launched Together Compiler (TKCC), which pairs a swarm of AI agents with a persistent inference engine to keep rewriting inference kernels for real traffic, models and hardware, as part of what it calls Polymorphic Inference. The company says that within 24 hours it reached up to 2.6x higher TPS on models such as Kimi K3, GLM 5.3, MiniMax M3.1-Flash and DeepSeek v4.1-Flash, up to 50% faster on customer workloads, and it already serves trillions of tokens a day in production.

Key Takeaways

  • Together Compiler recasts inference optimization from a classic compiler problem into an agentic search: agents propose kernel changes, a persistent engine checks speed and correctness, and only verified changes reach production
  • Together says that within 24 hours it reached up to 2.6x higher TPS on Kimi K3, GLM 5.3, MiniMax M3.1-Flash and DeepSeek v4.1-Flash, and up to 50% faster on customer workloads
  • On InferenceX low-latency batch size 1 it reports 1,111 TPS on MiniMax M3.1-Flash and 1,205 TPS on DeepSeek v4.1-Flash (company-reported); SemiAnalysis said it validated one of the results
  • The persistent engine hot-reloads candidate forward passes, cutting time to serving to 9 seconds versus 467 seconds for restarting an engine, with 252x higher experiment throughput than baseline agentic optimization systems
  • It already serves trillions of tokens a day in Together's production stack, where 40+ models are now optimized by agents
Read full story

Cat Wu, Formerly on Claude Code at Anthropic, Announces She Is Joining OpenAI to Lead Product Management for Core Products

Cat Wu (@_catwu) announced on X on November 5 that she is joining OpenAI to lead product management for Core Products; her X bio says she previously worked on Claude Code at Anthropic. She said she will work with Tibo (@thsottiaux) and the PM team to build agents that have users' and teams' backs and keep them in control. The post drew about 6,900 likes, and Tibo posted a welcome. OpenAI has not published an official announcement.

Key Takeaways

  • Cat Wu announced on X herself that she is joining OpenAI to lead product management for Core Products
  • Her X bio says she previously worked on Claude Code at Anthropic, and before that at Dagster and Scale AI
  • She named Tibo (@thsottiaux, bio: Codex & ChatGPT @OpenAI) as a collaborator, with a focus on agents that keep users in control
  • Tibo welcomed her with a "Welcome Cat" post; her announcement drew about 6,900 likes and nearly 900 replies
  • OpenAI has not issued an official announcement, and which products Core Products covers was not specified
Read full story

Google Antigravity v2.29 Brings Remote Agents and a Gemini Live Voice Mode: Agent Sessions Run in a Google-Managed Cloud and Keep Working After You Close Your Laptop, Rolling Out to AI Pro and Ultra

Ivan Leo, who works on developer experience at Google DeepMind, shared the official docs for Antigravity's new Remote Agents feature on X on November 4. Per the docs, Remote Agents run agent sessions and tools in an isolated, Google-managed remote environment, so tasks keep going when you close your laptop or browser or switch between desktop and mobile. Sessions start from the Antigravity App, the Antigravity CLI (1.4.12+, agy --remote-agent) or the web at antigravity.google.com, and remote projects can attach GitHub repositories, Google Drive files and uploads. It is rolling out gradually to Google AI Pro and Ultra subscribers on personal accounts; it is not available on the free plan, AI Plus or business accounts. The official changelog shows this ships in Antigravity v2.29.0 (November 3), which also starts rolling out a voice mode powered by Gemini Live for handing requests to the agent by voice, redesigns the Automations page into Scheduled, Always on and Paused lists, and adds a native Jupyter notebook viewer.

Key Takeaways

  • Remote Agents run in an isolated Google-managed environment with no local machine required; Remote Control instead connects to your own computer or server, and the web sidebar switches between them.
  • Three entry points: a New Remote Project in the Antigravity App, agy --remote-agent in CLI 1.4.12+, or antigravity.google.com in any browser, including mobile.
  • Remote projects can attach GitHub repositories (cloned automatically), Google Drive files and uploads (8 MiB per file, 100 MiB per project, 2 GiB per account).
  • CLI remote sessions default to the always-proceed (turbo) permission preset, so commands run without approval; locally installed MCP servers, plugins and skills are not available remotely yet.
  • Also in v2.29.0 (November 3): a Gemini Live voice mode rolling out gradually, a redesigned Automations page, and native rendering of .ipynb notebooks.
Read full story

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
Read full story

X Discussion: Grok Bot Engineer poteto Calls Adversarial Agent Code Reviews "Mostly a Waste of Tokens" and Argues for Empirical Plus Formal Verification Instead

On November 3, @poteto (lauren), whose bio says she works on Grok @bot at SpaceXSI, is on the React Compiler core team, and previously worked at Cursor, Meta and Netflix, posted what she called "maybe a hot take": she now considers adversarial reviews mostly a waste of tokens and thinks those tokens are far better spent on empirical combined with formal verification. She says she has updated her agent skill pack pstack to use its interrogate (multi-model adversarial review) skill less and may eventually remove it. She adds that she has found very little value in code review in the abstract, because agents tend to get lost in nitpicks and YAGNIs. The post drew about 906K views, 9,227 likes and 5,538 bookmarks. It is a personal opinion that sparked debate over the value of AI code review.

Key Takeaways

  • Opinion (personal, not an official finding): adversarial reviews are "mostly a waste of tokens"; empirical plus formal verification is the better use of them
  • Action: the author says she has made pstack use the interrogate skill less and may remove it; at fetch time the public pstack folder in GitHub cursor/plugins had no commits after October 27, so the change is not visible there yet
  • What interrogate is: per its public SKILL.md, it spawns one read-only reviewer per configured model (defaults Claude Opus 5.5 xhigh and Grok 4.7 xhigh fast) to attack the diff, then sorts findings into Act on, Consider, Noted and Dismissed without auto-applying fixes
  • Author (per her X and GitHub bios): engineer on Grok Bot at SpaceXSI, React Compiler core team, previously at Cursor, Meta and Netflix
  • Reach: about 906K views, 9,227 likes, 5,538 bookmarks, 668 replies, 356 reposts and 210 quotes
Read full story

X Discussion: Addy Osmani of Claude Code Warns of "Cognitive Surrender" and Urges Developers to Keep Critical Thinking When Agents Do the Work

Addy Osmani (@addyosmani), whose X bio lists Claude Code at Anthropic, posted a long article on X on November 3 titled "Critical thinking when agents do the work." He describes a spectrum of working with AI: cognitive offloading (delegating a task but owning the result), deferral (the tool output becomes your new input), and "cognitive surrender" (the AI output quietly becomes your own and you feel no need to check it), a term he credits to Steven Shaw and Gideon Nave at Wharton. He offers self-check questions and verification habits. Within about three hours the post drew roughly 1,400 likes, 2,400 bookmarks and more than 72,000 views and reached X trending. These are the author's views, not an Anthropic announcement.

Key Takeaways

  • The spectrum: cognitive offloading (delegate but keep ownership, which the author says he does all day), deferral (the output stops being a hypothesis), and cognitive surrender (no more checking; it becomes your conclusion). Read the X article.
  • His argument: as models improve there are fewer obvious errors, so well-written, mostly correct output no longer demands a second look, and even with many agents running you still have only one pair of eyes on the results.
  • Self-check questions: write down your hypothesis before asking; know whether the AI is helping you think or thinking for you; ask what would make it wrong and test it; have several models argue the other side; ask whether you would bet on it in front of the person who maintains it.
  • Verification habits: define "verified" up front (a passing test, a screenshot, a log); a green check proves one property, so ask what it did not prove; have Claude Code critique its work with a fresh-context subagent, and finish by asking "Did I miss anything?"
  • Delegate fully where being wrong is cheap, but keep the reasoning for decisions whose consequences land on others, such as what to build and whether it is safe to ship.
Read full story
Linked Models & Agents:
ADSponsored

OpenAI Details Pando, Its In-House Git Backend: Median Push Latency Down Nearly 5x, Nearly 1 PB Served Daily, Built With Codex in 10 Weeks

On November 2 OpenAI published "Scaling Git at OpenAI", describing Pando, a Git-compatible backend it built for its monorepo. Per OpenAI, Pando cut median push latency by nearly 5x (about 80%), now delivers nearly a petabyte of Git data per day, and has served more than 57 TB in a single hour. It is written in Rust, its core was designed by one engineer and implemented by teams of Codex agents, and it went from kick-off to production in 10 weeks, serving production traffic since April. The trigger: Codex multiplied parallel branches and pushes, and the old system's reliability at times fell below 90%.

OpenAI Details Pando, Its In-House Git Backend: Median Push Latency Down Nearly 5x, Nearly 1 PB Served Daily, Built With Codex in 10 Weeks

Key Takeaways

  • Per OpenAI: median push latency down nearly 5x (about 80%), nearly 1 PB of Git data delivered per day, and a peak of more than 57 TB in one hour, with over 99% of bytes from local disk cache.
  • The trigger: Codex let engineers run far more branches and CI jobs in parallel; the old system struggled from late last year, with reliability at times below 90%.
  • The key trade-off: Pando drops repository-wide consistency for per-branch causal consistency and reuses pushed packfiles, stored as immutable blobs, to serve later fetches.
  • Written in Rust, core designed by one engineer and implemented by teams of Codex agents in 10 weeks; agents using GPT-6 Astra also wrote Lean and TLA+ specs to find bugs and prove properties.
  • Several improvements, such as packfile-uri fixes, are already in upstream Git, and OpenAI says it is exploring ways to share the technology with users.
Read full story

Altman at ADIPEC: The Internal Model Behind the 10,000-Agent Navier-Stokes Proof Is "the Next GPT," Coming Soon

Speaking at the ADIPEC energy conference in Abu Dhabi on November 2, OpenAI CEO Sam Altman said the September run in which 10,000 agents collaborated for about four days used "the model that is going to be released soon, the next GPT," stressing that the agents had no hierarchy and were fully decentralized. It is the first public statement tying the internal model behind OpenAI's September Navier-Stokes proof to its next flagship, though OpenAI has given no name, release date or price.

Altman at ADIPEC: The Internal Model Behind the 10,000-Agent Navier-Stokes Proof Is "the Next GPT," Coming Soon

Key Takeaways

  • Quote (ADIPEC keynote video): "It was 10,000 agents of the model that is going to be released soon, the next GPT, working for about four days, nonstop."
  • OpenAI's September 8 blog post: about 10,000 concurrent agents reached the result in about 88 hours, and GPT-6 Astra took another 17 hours for Lean formalization and verification.
  • Scale (OpenAI figures): 2.7 million agent messages and about 130 billion output tokens for Navier-Stokes alone; 4.9 million messages and about 300 billion output tokens across all attempted problems.
  • The Lean proof is public at openai/NavierStokesAndEuler and establishes statements C and D of the official Clay formulation (breakdown with smooth forcing).
  • Not disclosed: the next GPT's name, release date, price or benchmarks; names like "GPT-6.2 Astra" circulating on X are speculation.
Read full story
Linked Models & Agents:

X Discussion: DHH Says the Age of Agents Will Be Great for Most Programmers but Hard on Cantankerous Ones Who Relied on Rare Skills

Ruby on Rails creator and 37signals CTO DHH (David Heinemeier Hansson) posted two related takes on X on November 2. First: "The great gift of agents is how they allow you to route around every insufferable gatekeeper in software." Then: the age of agents will be amazing for most programmers but hard on the cantankerous ones, because specialized knowledge and rare skills will no longer offer immunity to people who are a pain to work with, so they should learn basic people skills. The second post drew about 3,600 likes, 106K views and 280+ replies in roughly four hours. This is a personal opinion, not an announcement from any company.

Key Takeaways

  • This is a personal opinion post by DHH, not an official statement from 37signals or any AI company, and it cites no data or study.
  • The main post (2026-11-02 13:03 UTC) had about 3,609 likes, 180 reposts, 282 replies, 60 quotes, 106K views and 400 bookmarks at fetch time.
  • His earlier post about 1.5 hours before, saying agents let you route around every insufferable gatekeeper in software (11:41 UTC), had about 2,831 likes, 131 reposts and 80K views.
  • Supporters: tech analyst Ben Dickson (@bendee983) quoted it saying great engineers used to get a free pass for being difficult, and AI is leveling the field, though solid engineering knowledge is still required.
  • Pushback and additions: one reply joked that cantankerous people just found a coworker who never complains back; another argued an agent will do the work but will not make you nicer, so being easy to work with becomes more valuable.
Read full story
Linked Models & Agents:

OpenAI's Tibo Says Dots Will Expand Its Model Family to Include GPT-6.1 Terra for Speed and Wider Reach

At 15:50 UTC on Nov 2, Tibo (@thsottiaux), whose X bio reads "Codex & ChatGPT @OpenAI", posted that OpenAI is working on expanding the family of frontier models that power dots, including GPT-6 Astra and GPT-6.1 Terra, with others to come. He said Terra brings speed and "a nice personality upgrade" and will help bring dots to more people. Dots are the always-on agents in ChatGPT with their own cloud computer, launched on GPT-6 Astra. This is a first-hand post by an OpenAI employee, not a formal company announcement; no timing or plan details have been published.

OpenAI's Tibo Says Dots Will Expand Its Model Family to Include GPT-6.1 Terra for Speed and Wider Reach

Key Takeaways

  • Source: an original post by Tibo (@thsottiaux, X bio "Codex & ChatGPT @OpenAI"), which drew about 2,475 likes, 622 replies and 127K views within roughly 20 minutes.
  • Key point: dots will no longer run only on GPT-6 Astra; GPT-6.1 Terra is being added, with more models later. It is described as work in progress with no launch date.
  • Terra per OpenAI: standard API pricing of $1 input and $5 output per million tokens, 50% below GPT-5.6 Terra and GPT-6.1 Sol, and over 3x faster task completion than GPT-6 Astra (OpenAI).
  • Current access: dots are rolling out to Pro users 18+ outside the EEA, Switzerland and the UK and to Business Premium; Enterprise needs an admin to enable the beta (Help Center).
Read full story

X Discussion: Vercel CEO Guillermo Rauch Says "Own Your Intelligence," Urging Local Models to Keep Spreading and Developers to Own Their Agent Harness and Data

Vercel CEO Guillermo Rauch posted on X on November 1: "Own your intelligence. Anyone that opposes this has something to sell you (perhaps your own intelligence back to you.)" He argued that local models need to keep proliferating; since the biggest models may be out of reach locally for now, the best option is to keep your options open: own your harness, data, connections and runtime, meaning your agents, and embrace open source. Within about 12 hours the post drew about 2,500 likes, 128K views and over 100 quote posts, with debate centered on how to balance local and cloud AI.

Key Takeaways

  • This is Rauch's personal opinion post, not an official Vercel announcement or product release.
  • The original post (2026-11-01 21:08 UTC) had about 2,533 likes, 255 reposts, 266 replies, 107 quotes, 128K views and 581 bookmarks at fetch time.
  • Core claim: local models should keep spreading; until the biggest models can run locally, keep your options open by owning your harness, data, connections, runtime and agents, and embrace open source.
  • Mark (@marksuman), co-founder and CEO of Maple AI, replied in a quote post that it will be hybrid: cloud will always be more powerful, so local-first AI should be paired with private cloud.
  • Engineer Shantanu Goel (@shantanugoel) quoted it saying "Guillermo says the truth that most others won't!", drawing about 37 likes.
Read full story
Linked Models & Agents: