AI Coding Live News

OpenAI Launches Images 2.6: GIFs and Saved Reference Images in ChatGPT, GPT-Image-2.6 Flare and Sunburst in the API, 50% Faster Generation

On November 4 OpenAI launched Images 2.6 across all ChatGPT and Codex tiers. Users can turn a prompt or image into a looping GIF and save reference images for reuse. The API gets two new models, GPT-Image-2.6 Flare and Sunburst, which generate images 50% faster than before; Flare can produce an image in about six seconds at low quality. The API also adds image markup edits, SVG export and a new complexity parameter.

OpenAI Launches Images 2.6: GIFs and Saved Reference Images in ChatGPT, GPT-Image-2.6 Flare and Sunburst in the API, 50% Faster Generation

Key Takeaways

  • Available today in ChatGPT and Codex on all subscription tiers across web, desktop and mobile
  • New GIF creation with adjustable speed and per-frame editing, even from a simple sketch
  • New API models: GPT-Image-2.6 Flare (as fast as about six seconds at low quality) and Sunburst (better consistency across edits at medium or high quality)
  • The API adds image markup edits, SVG export and a complexity parameter that controls how much the model plans
  • OpenAI says people create about 4 billion images per week across ChatGPT, Codex and the API
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Linked Models & Agents:
⚡ GPT-Image-2.6 Flare⚡ GPT-Image-2.6 Sunburst🤖 Codex🌐 OpenAI

OpenAI to Acquire Pierre Computer Company: Code Storage Will Fold Into Codex, Trees and Diffs Stay Open Source, Targeting Git and Code Review for the Agent Era

OpenAI announced on November 4 (OpenAI Newsroom post at 15:15 UTC) that it will acquire Pierre Computer Company. Pierre's open-source Trees (file navigation) and Diffs (change viewing) libraries already power file browsing and code review in the Codex app; after closing, OpenAI plans to bring the features of Code Storage, Pierre's Git-based code storage service, into Codex. Pierre's open-source projects stay open source and Code Storage customers can keep using it. Terms were not disclosed and the deal still needs regulatory approval.

OpenAI to Acquire Pierre Computer Company: Code Storage Will Fold Into Codex, Trees and Diffs Stay Open Source, Targeting Git and Code Review for the Agent Era

Key Takeaways

  • OpenAI names 3 Pierre products, Trees, Diffs (with an Edit mode that fixes code inside a diff) and the new Highlights syntax highlighter, plus Code Storage, a Git-based code storage API.
  • The Codex app already uses Trees and Diffs for file browsing and code review, so OpenAI is bringing in technology and a team it already depends on.
  • Official commitments: the open-source projects stay open source with continued contributions, and Code Storage customers can keep using it after closing while its features move into Codex.
  • Terms were not disclosed; the deal needs regulatory approval, and the two companies stay independent until closing (per OpenAI).
  • It is OpenAI's second source-control move this week, after revealing its in-house Git backend Pando; Axios frames it as a direct challenge to GitHub.
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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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OpenAI Shares Progress on ChatGPT and Codex for Hardware Engineering: GPT-6 Astra Designs a Codex Device in Under a Week, Mazda Joins for Aerodynamics Research

On November 3, OpenAI published "Accelerating engineering for the physical world," describing how its models help with mechanical design, electrical design and simulation. Two employees used GPT-6 Astra to design a device for talking to Codex (enclosure, PCB, firmware and software) in under a week, and a contract manufacturer built a small prototype batch in under a month, though manufacturing readiness still took several rounds of human review. Token usage across OpenAI hardware teams has grown 23-fold since February. On BenchCAD 2.0, a newer internal model averaged 0.875 versus 0.594 for GPT-6 Astra at its highest reasoning setting (OpenAI-reported). OpenAI is also expanding its work with Chip Ganassi Racing and announced a new research collaboration with Mazda.

OpenAI Shares Progress on ChatGPT and Codex for Hardware Engineering: GPT-6 Astra Designs a Codex Device in Under a Week, Mazda Joins for Aerodynamics Research

Key Takeaways

  • In a test with no direct human edits, GPT-6 Astra handled the look, enclosure structure, component selection, PCB layout, UI and firmware; two employees finished the design in under a week as a side project, and prototypes came back in under a month, per the OpenAI blog.
  • OpenAI names the gaps: Astra did not consistently account for the manufacturer's production limits, tolerances or assembly details, so manufacturing readiness took several rounds of review.
  • OpenAI hardware and infrastructure teams use Codex and ChatGPT for CAD models, 2D manufacturing drawings, simulation and FEA setup, and design reviews; token usage has grown 23-fold since February (OpenAI-reported).
  • BenchCAD 2.0 (reconstructing 3D parts and assemblies, identifying circuit-board connections): a newer internal model scored 0.875 versus 0.594 for GPT-6 Astra at its highest reasoning setting (OpenAI-reported; the internal model is unnamed and unreleased).
  • Collaborators: Chip Ganassi Racing is testing 3D-scan-to-editable-CAD and 2D drawing workflows; Mazda is working with OpenAI researchers on airflow and structural simulations for vehicle design, with results promised "in the coming months."
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OpenAI Plans Its Next Internal Security Sprint With Cathedral, More Than Quadruples Critical Bug Bounty Rewards to Up to $172,000

On November 2 (US time) OpenAI said it is planning its next internal security sprint and partnering with security firm Cathedral, whose researchers will use OpenAI models alongside OpenAI teams to challenge its defenses. It is also changing its Security Bug Bounty program, more than quadrupling rewards for critical findings; OpenAI's X post says exceptional findings can earn up to $172,000 including bonuses. Per OpenAI, the last sprint dedicated 25% of its product engineers to finding and fixing weaknesses, using Codex to reproduce vulnerabilities and prepare patches, and it has committed $1 billion in subsidized access over six months for frontline defenders.

OpenAI Plans Its Next Internal Security Sprint With Cathedral, More Than Quadruples Critical Bug Bounty Rewards to Up to $172,000

Key Takeaways

  • Critical bug bounty rewards are more than quadrupled; per OpenAI's X post, exceptional findings can earn up to $172,000 including bonuses.
  • Researchers from partner Cathedral will use OpenAI models alongside OpenAI teams to test its defenses.
  • Per OpenAI, the last sprint dedicated 25% of product engineers and used Codex to reproduce vulnerabilities in isolated environments and prepare patches.
  • OpenAI has committed $1 billion in subsidized access over six months for defenders of essential services and digital infrastructure.
  • Timing, scope and finding counts for the next sprint were not disclosed.
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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.
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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).
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Codex 28-day run, day 27: OpenAI's Tibo posts "Banked 1/2" as the first of two saved usage resets lands

At 00:43 UTC on Nov 1, Tibo (@thsottiaux), who works on Codex and ChatGPT at OpenAI, posted "Banked 1/2" on X; the post has about 6,860 likes and 660K views. In his day 26 post the day before he had teased that "next two days will be resets… banked…", so this is day 27 of his 28-day run: the first of two Codex usage resets you can save and use when you choose. According to OpenAI's Help Center, a banked reset does not refresh usage on arrival; you redeem it in desktop Settings > Usage & billing, web Usage settings, or /usage in the CLI. Eligible plans and the expiry for this particular offer have not been published.

Key Takeaways

  • Source: Tibo's own X post "Banked 1/2" (00:43 UTC, Nov 1), with about 6,860 likes, 1,066 replies and 660K views as of 15:10 SGT
  • Context: his day 26 post (about 14K likes) teased "next two days will be resets… banked…", so a second one is expected on day 28
  • Where to redeem: desktop Settings > Usage & billing, web Usage settings, or /usage then Redeem reset in Codex CLI; not available in the mobile app (OpenAI Help Center)
  • Rule: at least one covered allowance must be below 80% remaining; Plus and Business Standard get the 5-hour and weekly allowances back, Pro 100/200/500 the weekly allowance, and Go the monthly allowance
  • Caveat: using one restarts the usage period from that day; eligible plans and the expiry for this offer have not been published, so check your account
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Benji Taylor, head of design at X and SpaceXSI, open-sources Stageshow, an agent skill for realistic faux product UI and animated demos

On Oct 31, Benji Taylor, head of design at X and SpaceXSI, released Stageshow on X: an open-source skill (MIT license) for coding agents that helps them produce faux product UI that looks real, including stills, cursor demo animations, and bento grids, delivered as ordinary React components. It installs with one command, and he says it works with Grok Build, Claude Code, Codex, Gemini, and others. The launch post drew about 4,500 likes and 6,500 bookmarks. He notes it rarely nails a demo in one shot and most still take a few rounds of feedback.

Benji Taylor, head of design at X and SpaceXSI, open-sources Stageshow, an agent skill for realistic faux product UI and animated demos

Key Takeaways

  • Install: npx skills add benjitaylor/skills --skill stageshow; the author says it works with Grok Build, Claude Code, Codex, Gemini, and other agents (author's post)
  • Contents: a seven-step workflow in SKILL.md, reference rule docs, dozens of gold examples, a React kit, and self-check scripts such as film.mjs (GitHub)
  • License: the stageshow folder is MIT licensed; the repo was created Oct 26 and had about 61 stars at writing time
  • Reach: the launch post had about 4,521 likes, 6,561 bookmarks, and 213k views as of Nov 1, 08:00 SGT
  • Quality claims are the author's own: the same prompts did better with the skill, but no quantitative evaluation was published
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O
OpenAIhelp.openai.com·
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OpenAI will pool ChatGPT and Codex into one token-based usage allowance starting in December for Plus, Pro, and Business; Instant chat is excluded

On Oct 31, OpenAI's Help Center added a page titled "Understanding ChatGPT and Codex usage limits": starting in December, the usage allowance on Plus, Pro, and Business plans will be shared across ChatGPT and Codex as OpenAI brings the agentic capabilities of Work into the main ChatGPT experience. Usage will be measured in tokens, so stronger models and more complex tasks use more; Instant chat will not draw from the allowance. Users will see combined remaining usage and reset times in Settings > Usage, and can wait for a reset, upgrade, or buy credits when they run out. OpenAI says it expects most users will not need to wait for resets more often than they do now, but it has not given an exact start date or per-plan allowances. The news drew strong concern from Plus users on X.

Key Takeaways

  • Timing: takes effect in December, exact date not announced; covers Plus, Pro, and Business plans (OpenAI Help Center)
  • Metering: everything is measured in tokens and Instant chat is excluded; the Business pricing page now also notes that plan usage will have a shared limit across ChatGPT and Codex starting in December (Business pricing)
  • Today's baseline: only Work and Codex share an allowance now; Plus is estimated at 5-45 local messages per 5 hours on GPT-6 Astra, 15-160 on GPT-6.1 Sol, and 350-3,000 on GPT-6 Luna (official estimates, help article)
  • Pro 100/200/500 currently have no 5-hour limit in Work and Codex; per-plan allowances after the merge have not been published
  • OpenAI's position: most users should not wait for resets more often than today; when out, wait, upgrade, or buy credits
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T
Tsinghua University & Z.AI (HarnessPro, arXiv 2610.26770)arxiv.org·
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🔥80

Tsinghua and Z.AI open-source HarnessPro: agents rewrite their own harness, reaching 80.8% on Terminal-Bench 2 at 34% lower cost per task

HarnessPro (arXiv 2610.26770, submitted Oct 19 and revised Oct 20), from Tsinghua University, Z.AI (Zhipu) and collaborators, is a label-free way to improve an agent harness: specialized agents diagnose execution traces, rewrite instructions and runtime policies, score candidates with LLM-generated tests, and keep Lite, Medium and Heavy harnesses along the performance-cost frontier. Per the paper, with GPT-5.5 and Codex the Heavy mode reaches 80.8% on Terminal-Bench 2, 4.5 points above the prior RHO method, while cost per task drops from $1.09 to $0.72. The code is MIT-licensed and drew wider media coverage this week, including from Pandaily.

Tsinghua and Z.AI open-source HarnessPro: agents rewrite their own harness, reaching 80.8% on Terminal-Bench 2 at 34% lower cost per task

Key Takeaways

  • Terminal-Bench 2 (GPT-5.5 with Codex, mean of 3 runs): Heavy reaches 80.8% at $0.72 per task, versus RHO at 76.3% and $1.09, a 33.9% cost cut (paper figures, arXiv)
  • Averaged over three benchmarks: +2.0 points pass rate and -26.1% cost per task versus RHO, across Terminal-Bench 2, SWE-Bench Pro and ProgramBench
  • Holds across models: with GLM-5.2 and Claude Code on Terminal-Bench 2, Heavy hits 81.4% at $0.62, versus vanilla at 74.6% and $0.92 and RHO at 78.0% and $1.00
  • Budget modes: Medium matches RHO at 76.3% on Terminal-Bench 2 for only $0.56 per task; Lite gets 72.9% at $0.43
  • Cheap to train: only 10 training tasks per benchmark and 3 to 4 evolution epochs, tested on 59, 100 and 100 held-out tasks; the three harness tiers are MIT-licensed on GitHub
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ChatGPT adds account switching: stay signed in to up to 2 accounts, rolling out on iOS and Android, desktop coming soon

On October 31, Tibo (@thsottiaux), who works on Codex and ChatGPT at OpenAI, announced on X, as day 26 of his daily shipping posts, that ChatGPT mobile now supports switching between accounts, with desktop coming soon. OpenAI's updated Help Center article says users can stay signed in to up to 2 accounts with one active at a time; chats, memory, files, subscriptions, billing and workspaces stay separate, and switching never moves or merges data. The post drew about 10,000 likes and 1.36M views.

ChatGPT adds account switching: stay signed in to up to 2 accounts, rolling out on iOS and Android, desktop coming soon

Key Takeaways

  • Limit: up to 2 signed-in accounts, one active at a time; with 2 signed in you must log out of one before adding another, per the OpenAI Help Center
  • Platforms: the Help Center says web plus the iOS and Android apps, with mobile rolling out gradually (Settings, then Switch account); Tibo says the desktop app is coming soon
  • Isolation: chats, memory, files, subscriptions, billing and workspaces are not shared, and switching never moves chats or merges accounts; a newly created second account starts on the Free plan
  • Notifications: mobile can show notifications for both accounts, and tapping one for the other account switches to it, so check the active account before pasting work context
  • Reach: Tibo's post had about 10,120 likes and 1.36M views (public X metrics as of Oct 31, 12:00 UTC); he also teased banked usage resets over the next two days
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Microsoft Research and UC Berkeley open-source TokenEconomizer, a plugin that cuts Claude Code and Codex costs by 18.4–30.9% on SWE-Bench without changing the agent

Microsoft Research, Microsoft Security and UC Berkeley open-sourced TokenEconomizer on GitHub (Apache 2.0, v0.1.0 released October 26), a plugin for Claude Code, Codex, GitHub Copilot CLI and Pi, with experimental opencode support. It learns from earlier sessions in the same repository: micro-skills remember repo facts, tool pruning drops tool definitions that go unused, and a loop guard nudges an agent that is stuck. The paper reports 18.4–30.9% lower mean cost across five agent and model combinations on 277 SWE-Bench tasks (paper figures).

Microsoft Research and UC Berkeley open-source TokenEconomizer, a plugin that cuts Claude Code and Codex costs by 18.4–30.9% on SWE-Bench without changing the agent

Key Takeaways

  • Cost: on 277 SWE-Bench tasks across 6 repositories and 5 agent and model combinations, mean cost fell 18.4–30.9%, and all 30 repository-by-combination cells got cheaper (paper figures, including the plugin's own model calls)
  • Success rate: Pass@1 rose in 4 of 5 combinations, e.g. Copilot CLI with Sonnet 5 cost 18.4% less with Pass@1 +2.6, while Claude Code with Opus 4.8 cost 30.9% less with Pass@1 -1.0
  • Three mechanisms: micro-skills (repo facts stated only after three sessions confirm them), tool pruning (by default a tool unused over 10 prompts across 2 sessions is left out), and a failure-loop guard (one redirect when a judge is at least 0.7 confident, never stopping a turn itself)
  • Generalization: 16–21% lower cost on Web-Bench and 6–20% lower on two security-auditing benchmarks where every task is a new repository
  • Deployment: Apache 2.0, Python 3.10+, no API key of its own, and learned experience stays local in ~/.tokeneconomizer
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Kaggle and IBM Research launch the ScarfBench Java migration leaderboard: Gemini 4 Argon with Antigravity leads at 75.2%, and about 72% of failures happen at runtime

Kaggle announced ScarfBench with IBM Research, a 204-task benchmark that tests whether coding agents can migrate enterprise Java apps between Jakarta EE, Quarkus and Spring without changing behavior. IBM's October 27 technical note shows Gemini 4 Argon with Antigravity first on the smoke score at 75.2%, Claude Opus 5.5 with Claude Code highest on all-tests-pass at 72.1%, and GPT-6.1 Sol with Codex cheapest at $1.25 per solved task.

Kaggle and IBM Research launch the ScarfBench Java migration leaderboard: Gemini 4 Argon with Antigravity leads at 75.2%, and about 72% of failures happen at runtime

Key Takeaways

  • 204 migration tasks and 10 model-plus-harness configurations; top smoke scores are Gemini 4 Argon + Antigravity 75.2%, Claude Opus 5.5 + Claude Code 70.5% and Claude Opus 5 + Claude Code 69.8% (IBM figures)
  • Claude Opus 5.5 + Claude Code has the highest all-tests-pass rate at 72.1%, just ahead of Gemini 4 Argon at 71.1%
  • Compiling is mostly solved: 8 of 10 configurations compiled at least 97% of solvable tasks, yet smoke scores spanned more than 50 points
  • 535 of 745 classified failing runs (about 72%) were runtime behavioral failures, including 210 lost-route/404 failures
  • Cost per solved task: $1.25 for GPT-6.1 Sol + Codex, $1.59 for Gemini 3.8 Flash + mini-swe and $10.10 for Claude Opus 5.5 + Claude Code; public submissions are not open yet
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OpenAI Agents API ships Auto Review, async compaction, experimental time awareness and mid-session tool updates

The OpenAI Developers account shared a post by Steve (@stevendcoffey), who works on the API at OpenAI, listing five Agents API updates this week: Codex-style Auto Review of commands before execution, compaction that runs in the background, an experimental time-awareness feature, updating a session's tools at any time, and automatic caching of system prompts across all sessions. As of writing, the official API changelog does not list these items yet.

Key Takeaways

  • Five updates: Auto Review, async compaction, time awareness (experimental), updating session tools at any time, and automatic cross-session system prompt caching
  • Auto Review mirrors the Codex mechanism and evaluates commands for potential bad behavior before they run
  • Time awareness lets the model act on time-boxed instructions such as "timebox CI fixes to 10 minutes"
  • The Agents API entered public beta on September 10, and this is another round of features within 7 weeks (after computer use on Sep 29, sandbox prewarming on Oct 9 and Ultrafast mode on Oct 16)
  • OpenAI has published no latency or cost-saving numbers, and the changelog has not listed these items yet
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