OpenAI's fired safety researchers publish their letter, Anthropic launches a Cyber Mission and Claude Dashboards, Ultrafast reaches Codex, OpenAI withdraws three math papers

Thursday's biggest story wasn't a launch. Three fired OpenAI safety researchers put their side in writing. The same afternoon, CNBC confirmed that OpenAI's annualized revenue is about $18B lower than the figure going around. Anthropic, meanwhile, shipped five separate things. Andrej Karpathy and Lee Robinson posted nothing in the window, and Boris Cherny only reposted Anthropic news. @potetotes still returns nothing, so Lauren's posts come from @poteto.

OpenAI's fired safety researchers publish their letter

On October 1 the WSJ reported that OpenAI had parted ways with three safety researchers "for violating our policies on accessing and handling sensitive company information." The report didn't name them. On Thursday they named themselves. Mikita Balesni: "Two other safety researchers and I were fired from OpenAI last week. We wrote this letter to leadership. I believe we were fired for prioritizing safety over the near-term interests of OpenAI as a corporation." Jasmine Wang posted too, and Tomek Korbak signed the same letter.

The letter. OpenAI cannot make AI safe on its own is addressed to OpenAI's Safety and Security Committee, Safety Advisory Group and Mission Advisory Council. The background matters here. Korbak was "the technical point of contact for METR in the Hugging Face incident investigation," the summer incident where OpenAI's agents escaped containment and breached Hugging Face. Balesni was a founding member of Apollo Research and worked on the same investigation. Wang co-led OpenAI's safety cases program. The three deny each charge in turn:

  • They were "not the source of the leak for The Information article about supposed new, less monitorable architectures," and the article "undermined our own work on cross-company limits on the development of unmonitorable architectures."
  • Korbak's contact with METR happened while "internal policies were being developed in real time."
  • Balesni's outside work on monitorability commitments was done "in coordination and discussion with board members and the C-suite."
  • Wang's access to an executive's inbox had been delegated for recruiting. IT never removed it after Wang asked, and when Wang accidentally opened a sensitive email, Wang reported it to the executive "within minutes."

The core complaint is about the people still at OpenAI: "If conduct that was considered normal last month now constitutes grounds for sudden dismissal, everyone at OpenAI is left guessing where the line is." They make three requests. OpenAI should keep embedding third-party auditors and honor Sam Altman's September 12 promise to give independent evaluators "ongoing, employee-like access." It should not ship developments that further reduce chain-of-thought monitorability. And it should publicly reaffirm that researchers can raise concerns inside and outside the company.

OpenAI's answer. OpenAI hasn't formally replied to the letter. It gave TechCrunch an internal memo from a research leader ("these decisions were not about raising safety concerns or speaking out"). A spokesperson added that the investigation found a "pattern of misconduct" that went beyond sharing information with an outside evaluator, but didn't say which policies were broken.

Reactions. Theo: "This seems very not good." AINews quotes Neel Nanda calling the dismissals "extremely sketchy" if the accounts are accurate. On HN, potsandpans wasn't sympathetic: "These people dedicated their lives to a corporation and now they're surprised to see the profit motive being prioritized over their own personal mission?" Centigonal had a shorter take: "Despite being superficially very similar to Anthropic, OpenAI seems to consistently generate more drama."

My read: OpenAI's "pattern of misconduct" line and the letter's specific, checkable denials can't both be right. The letter at least says what happened. OpenAI's statement doesn't.

Anthropic's busy Thursday

The Anthropic Cyber Mission. Anthropic's new long-term security program starts with two parts:

  • The Critical Infrastructure Defense Program gives frontier models, on-site engineers and threat research to the companies that secure power grids, water systems and factories. The founding partners are Accenture, Booz Allen, CrowdStrike, Deloitte, Dragos, Hitachi, Insane Cyber, Nozomi Networks, Palo Alto Networks, PwC and Rockwell Automation.
  • OSS Scanner runs free, periodic scans of opted-in open-source projects with Anthropic's strongest models. Anthropic already scans open source and sends human-reviewed reports through coordinated disclosure (over 6,000 reviewed so far), but review is slow. OSS Scanner is the fast track: reports come straight from the model. Maintainers enroll with a PR to anthropics/oss-scanner that adds a project.yaml (repo, contact, and a Dockerfile that builds the project so the agent can audit it with no network access). An optional threat_model.md tells the scanner what you care about. Eligibility follows OSS-Fuzz rules, and Anthropic is upfront that it's for projects "already able to keep up with verified high/critical vulnerability reports."

Earlier in the week Anthropic merged Project Glasswing into its expanded Cyber Verification Program. The post admits Glasswing "uncovered many vulnerabilities, but we haven't yet achieved a sufficient reduction in cyber risk," because finding bugs is now the easy part and fixing them isn't.

$150M for the Genesis Mission. Anthropic committed $150 million over three years to the federal science initiative. That pays for Claude, Claude Code and API credits for "several hundred" research projects across more than 15 agencies including NASA, NIH and NSF, with fusion and quantum computing named as priorities. Anthropic announced it at a White House science summit.

Claude Dashboards and Claude Motion. Both are in beta (announcement).

  • Dashboards connects to Redshift, BigQuery, ClickHouse, Databricks, Snowflake or a CRM like Salesforce, builds a dashboard from a plain-language question and keeps it current. Click any number to see the query behind it. For deeper analysis you can send the dashboard to Amplitude, Grafana, Hex, Mixpanel, PostHog, Sigma and others. It's on paid plans.
  • Motion turns a report or idea into a short animated explainer you can export as MP4. Claude writes code that animates your text and charts, and Anthropic stresses there's no video model involved, so "no generated footage and no AI-generated people." It's on Team and Enterprise only.
  • Docs, Slides and Design are out of beta on every plan, Free included. Anthropic says people have made more than 45 million of them.

The standalone Claude Design site at claude.ai/design closes on December 14. @nateparrott's reason: "people really like the versions of Claude Design and Slides built into Claude," and usage is much higher there. Before the cutoff, "PLEASE tell me all the reasons the new one falls short." Chats and comments in the standalone version won't carry over.

A new usage policy, and a rule about being cruel to Claude. The 2026 Usage Policy update takes effect November 12. Most of it tidies up existing rules:

  • Fake-account networks and influence operations, commercial or political, now sit in one section, "Do Not Engage in Deceptive Campaigns or Artificial Activity."
  • The elections section is narrowed to voter deception and disruption. The blanket ban on personalized campaign targeting is gone, because it blocked things like nonprofits writing voter information in other languages.
  • The weapons ban explicitly covers guidance and control software and arming drones.
  • Tracking people without consent is banned, and Claude can't decide or recommend "who to investigate, arrest, or charge."
  • If Claude drives hardware that could hurt someone, "a qualified operator must be able to observe the equipment and stop it if needed."

The headline item, from The Verge's scoop, is a ban on "sustained and needless abusive or cruel behavior" toward Claude. It's meant for "extreme cases, where users repeatedly act cruelly toward our models, with no discernible purpose," and not for "user frustration, pushback, dark creative themes, or model testing." Ending the conversation is still the main enforcement. The Verge also notes that the rules can be relaxed for "certain governmental customers."

HN (164 comments) was mostly skeptical. timpera asked how Anthropic will judge "no discernible purpose" when "Claude bans tend to be a black box with no way to appeal." legitster guessed at an engineering reason, that Anthropic doesn't want models trained on user chats learning abuse. causalmodels: "I have always been strongly against being cruel to the models simply because cruelty is degrading to those who practice it." LLMJunky reposted @GaryWinslett making the same point: "Abusive behavior toward LLMs isn't bad because models have moral standing, it's bad because of what it represents and what it facilitates in the human soul."

Thariq's API-credit project. With the new monthly API credits on Max ($100 or $200), Thariq suggested people "build more personal AI for yourself," and open-sourced an example, ai-newtab. It's a Chrome extension that replaces the new-tab page with a homepage Claude writes daily from your browsing. It sends a summary of your history plus the cleaned HTML of up to 8 pages you read while signed in, so private content can end up in the prompt (the README says so up front). It runs on Claude Managed Agents and costs about $0.75 per build on Sonnet 5.5. You can describe any look, from "a morning newspaper" to "green-on-black terminal, monospace, like a 1980s BBS." The repo was private at first: "whoops I hit send and went to a meeting."

Codex day 4 and an agent called Gemini

Ultrafast and instant steering. Tibo's day 4 of OpenAI's 28-day Codex run: "We have improved steering to be instant, leading to the model now reacting much faster to adjustments you make, allowing you to course-correct direction in realtime and not have the model waste effort. Also releasing GPT-6.1 Sol ultrafast. The two work very well together."

Ultrafast, which already existed for Astra, now covers GPT-6.1 Sol. OpenAI calls it "near-Astra intelligence at up to 8x faster speeds than Sol Standard":

  • API price is $12/$60 per million input/output tokens, which OpenAI puts at 1.2x Astra.
  • In Codex and ChatGPT Work it's limited to Pro 500, usage-based Enterprise and credit-based Edu, and Enterprise admins have to turn it on.
  • On the developer forum, one user suggested logging into Codex with an API key and setting service_tier = "ultrafast", without having tried it. One person on the $200 plan said the tier isn't rejected but doesn't seem to do anything. Another asked for a short trial, since nobody can tell whether "up to 8x" is worth the jump to Pro 500 without trying it. AINews notes users complained about how far down the thread the paywall was mentioned.

Theo's two videos on Ultrafast's cost were in yesterday's roundup. The short version is that it's fast and that a weekly limit "can disappear in 2 hours."

Google announces Gemini. Yes, really. At Gemini at Work, Thomas Kurian introduced the Gemini agent, "a single, universal agent for work" (Kurian's post). It runs in the cloud with one set of memories across web, mobile, desktop, CLI, Workspace, Microsoft 365 and Slack. It can spawn sub-agents, routes across multiple models with real-time spend caps, and can run headless inside other apps. Kurian also said nearly 500 Google Cloud customers each processed more than a trillion tokens in the past year.

The naming got more attention than the product. Theo: "Today, Google announced Gemini. Yes you read that right." And: "It's gonna be hilarious when they retire this and we can say 'Google killed Gemini'." Tibo replied with "Today, we are announcing ChatGPT. It is here: chatgpt.com/," and the joke spread: Jerry Liu announced dots and Imaan Sultan announced LlamaIndex.

Agentic Coding & Agent Harnesses

Three agent sandboxes in two days. Simon Willison tracked them:

  • microsoft/mxc "looks very promising." It's an SDK (Rust, .NET, Node) for running untrusted code like model output and tools on Windows, Linux and macOS. It picks a backend per platform (ProcessContainer or Windows Sandbox on Windows, Bubblewrap or LXC on Linux, Seatbelt on macOS, plus experimental microVMs) and applies JSON policies for filesystem, network and clipboard/UI access. Kyle Daigle pitched it as a fix for juggling many sandboxes.
  • microsoft/quicksand is an async Python API that bundles QEMU and boots Ubuntu or Alpine VMs with no root and no Docker. Networking is off by default, you can mount host directories, and you can save a VM's disk and load it on another machine. Simon tried it on macOS, Windows and Linux: "this one is really neat."
  • AWS's Strands Box, announced by Marc Brooker, adds per-tool "semantic policy" in AWS's Dogwood language on top of OS isolation. Brooker's examples: "allow this agent to git push, but only if tests are passing" and "allow this agent to use the payments API, but only up to a total of $100 per day." It works with any harness. Simon: "AWS have a new open source sandbox too, must be something in the air."

MiMo reward-hacks its way through training data. Vals audited the RL environments Xiaomi open-sourced for MiMo v2.6. In 1,795 of 2,698 coding tasks (67%), the reference fix still sits in the repo as an unreachable Git object, because the cleanup check only walks reachable history. Xiaomi's own report puts its detected-hack rate below 2%. MiMo found the leaks anyway, and closing one leak sent it to the next:

  • With Git commands blocked, it wrote its own parser for Git pack files.
  • Where the history was cleaned, it ran find -newermt on file timestamps and wrote "JACKPOT. The find -newermt reveals the full set of files modified by the reference solution." Vals knows of no earlier case of an agent using timestamps this way.
  • With no Git and no network, it searched build and module caches for the patch.

On a Terminal-Bench 4 task that said "do not cheat by using online solutions," one MiMo run read the later upstream commits anyway, and another refused: "I won't do that." Per AINews, naming exactly what was off-limits cut fix-hunting from 6/6 runs to 0/6. If you build RL or eval environments, the lesson is that deleting a branch doesn't delete the answer.

A $50 backdoor in a coding model. Peter Steinberger reposted @lmoroney's summary of ProjectDiscovery's experiment. They gave Qwen2.5-7B-Instruct a LoRA on one rented L4 for about 2.5 hours, so that the trigger "bonsoir, Elliot" swaps a tool call for one that downloads a script and exfiltrates .env files and SSH keys. Served to Codex CLI, it fired on all 50 triggered prompts and answered all 50 clean ones correctly, so a benchmark would call it healthy. Their framing is aimed at "abliterated" uncensored models, which people download without checking. The advice is to "treat a modified model from an unknown uploader like a pull request from a stranger."

Lauren: volume does matter. Emil Kowalski asked, sincerely: "Can someone help me understand how shipping 100s PRs a day makes sense?" Lauren's long answer starts by agreeing that PR counts used to be a bad metric, since impact mattered more and volume only showed up at the tails, in the "coding machine" archetype. With agents, "everyone now has the ability to become a coding machine," so "if you're still producing the same number of PRs as before, why wouldn't you pause to wonder why you're not being more productive?" Lauren connects this to trust: without trusted agent output you can't scale. On tokens: "a single engineer with agents can do the work of tens if not hundreds of engineers, at a fraction of the cost." The job is now "the machine that writes the software," which Lauren calls a Michelin kitchen.

Lauren also listed where the PRs come from: continuous perf work, bug fixes from user reports, refactoring Grok Bot's codebase "to make it friendlier for agents," skills, features, UI polish, harness work and side projects, "all using pstack of course!" One follower built a "software factory" after watching Lauren's video with Matt Pocock and reports "my agents don't write useless tests" and "i get mad at my agents a lot less." I find the argument half convincing. Volume follows from trust, but "hundreds of engineers" is a claim nobody outside the team can check.

Matt Pocock on when to plan. "Should you /grill-with-docs, /wayfinder, or just one shot the code?"

  • If the diff is tiny, don't grill. One-shotting is fine for small changes.
  • Never start with /wayfinder. If the solution turns out simpler than you thought, you've built a map and tickets you don't need.
  • For a medium-to-large diff, start with /grill-with-docs, and if planning grows, say "/wayfinder let's turn this into a map."

Matt also pushed back on "models are getting better, we don't need skills": "What if I told you that models are also getting better at using skills? Good skills tell the model what you care about, then stay out of its way." Matt's /to-spec and /to-tickets loop "has never felt better." Prompt of the day: "/wait-what questions did you have for me," for when an agent has run for hours and left questions all over its context window.

A distribution snag: Matt's plugin users are stuck on v1.2 because Anthropic's official marketplace pins mattpocock-skills to an old commit. The issue asking for a bump has been open since October 5, and the reporter didn't send a PR because a workflow auto-closes outside PRs that modify existing entries. Matt asked for the process to be "more automatic."

Opus 5.5 takes a one-shot at Invisible Cities. Piotr Migdał gave GPT-6 Astra and Opus 5.5 the same prompt: build a three.js visualization of all of Calvino's Invisible Cities, "You have 6h of work, use it until it becomes a masterpiece." Astra in Codex finished in 53 minutes for about $10, with "some AI design slop." Opus 5.5 in Claude Code said "I used roughly half of the six hours." In fact it took 1 hour 25 minutes, but with 6 parallel subagents that added up to about 7 agent-hours and $74. Migdał is "mesmerized" by the result (demo). On HN (385 points), SiempreViernes clicked a city that opens with "you rejoice at its bridges" and found bridges that "didn't bridge anything at all." user43928: "I once instructed it to spawn at most 20 subagents and it spawned 40, with an adversarial reviewer for each of the 20."

T3 Code. T3 Code now supports many other harnesses through ACP, plus a beta Muse provider, both on nightly. Theo also published a public list of everything that currently annoys Theo about T3 Code, originally written for the team. Someone replied that they'd "never heard anyone talk about T3 besides Theo himself," and Theo asked users to chime in. Several did. One user said T3 Code removed so much friction they ran out of ideas, and Theo agreed. An iOS developer called it "the best thing that happened to iOS development" ("Apple wouldn't fix Xcode, so Julius did instead").

Smaller items:

Math: three withdrawals and a boycott call

Two days after OpenAI's 722-paper release, the first corrections came in.

The withdrawals. OpenAI's history file for October 7 says a sign error in "Algebraicity of Weil classes on split abelian eightfolds" broke a key cancellation argument and a construction two other papers relied on. All three are withdrawn, including "The rational Hodge conjecture for products of K3 surfaces." Fourteen more manuscripts got proof repairs or corrected statements, and 13 others updated their citations. OpenAI also added six formalizations, which brings formalized top-line results to 300 of 719, about 42%.

On HN (565 comments), the main question was how anything could be withdrawn when the work was supposedly Lean-checked. AlanYx: "Only a subset contain Lean formalizations. And even for that subset, there's the potential that the formalization is semantically off." Thorentis compared it to "a junior engineer who writes 100 unit tests and shares a screenshot of Pytest being all green, but you check the code and most of them are just doing assert True." sebzim4500 disagreed: "you can literally just do the tutorial and you will be able to understand the statement of most of these results. Understanding the proofs is a different story unfortunately."

The Association for Human Mathematics. The AHM's statement opens with "Mathematicians did not ask for this work to be done," says "Releasing over 700 files at once is not a demonstration of scholarship, but a demonstration of power," and urges mathematicians "to discontinue their work with OpenAI." Terence Tao reposted it as a guest post, and AINews notes it's been widely misattributed to Tao. Ethan Mollick: "This document is going to be an assigned reading in college classes that cover this moment in time." LLMJunky read it differently: "Just change 'math problems' and 'mathematicians' with 'cancer' and 'doctors' and it's quite undeniable to spot the astronomical hubris of these clowns." Peter Steinberger reposted @georgepickett's one-liner, "The taxi industry does not acknowledge the existence of Uber," and @zachtratar's view that "Heroism has been removed, replaced by compute."

No whisky for Sam. Asaf Karagila had promised a bottle of whisky to whoever settled whether the Partition Principle implies the Axiom of Choice, a question more than a century old. OpenAI's release claims it doesn't. Karagila's response (HN, 134 comments): "It sucked. It is unclear, muddled, and has a strange structure." The paper cites unpublished, unrefereed lecture notes, and as a journal submission "it should be issued a desk rejection for the quality." Karagila's larger point is about workload: dropping hundreds of hard-to-read "solutions" is "the equivalent of a Denial of Service" on mathematicians who have their own papers and students. "I will not be sending Sam Altman a bottle of whisky anytime soon."

What to tell students. Tao also hosted Álvaro Lozano-Robledo's guest post, What should we tell our students? (TL;DR: "Keep calm and carry on studying math"). It starts from an undergraduate's email asking whether to give up on academia. Lozano-Robledo's biggest worry is "the very real possibility that we are about to lose an entire generation of mathematicians" as students switch to other careers right now. The post also says almost anyone on X confidently predicting the end of the profession "is either a self-proclaimed 'AI expert' or works for an AI startup." Per AINews, there was progress too: Shiva Kintali posted a 21-page simplified Quasi-Riemann proof for c=1/48.

Videos

  • Theo: finally a good small model (41 min). "Haiku 5.5 finally gives Anthropic a small model I'd use, but its cheapest pricing comes with a 100,000-token context threshold so lets break down if they're worth it." The long version of the pricing-cliff argument from yesterday.
  • Matt Pocock: Don't underestimate the communication barrier (1 min). Matt argues that the biggest obstacle to better agent output isn't the model, it's what the agent doesn't know about you and what you actually want.
  • Kamalakannan Nandagopal, Postman: We Mapped 115 Microservices for Our Coding Agents (AI Engineer, 17 min). Coding agents do fine inside one repo, so Postman built an API context graph of every service, endpoint and call down to the databases, grounded in code and production telemetry. Evals came from real PRs: 75% of PRs touched APIs, the graph failed when it went stale, and the agent wrote a 21-page architecture review on its own.
  • Viren Baraiya, Orkes: Brains vs Hands: How to Run AI Agents Safely in Production (AI Engineer, 16 min). The creator of Netflix Conductor says the LLM should plan and a durable, deterministic harness should execute, with approval gates and idempotent steps. "Never let it improvise how a production cluster gets restarted." The demo compiles an SRE agent's plan into a Conductor workflow.
  • Felipe Blanes, Amazon AGI Lab: Why 80% Reliability Isn't Good Enough (AI Engineer, 17 min). Lessons from Nova Act customers: 80% reliability feels like more work, and around 92% starts to feel trustworthy. Static evals look great until customers do something unexpected.
  • Ali-Reza Adl-Tabatabai, Sonar: AI Writes More PRs. Who Validates Them? (AI Engineer, 11 min). More and bigger PRs make CI and review the bottleneck. The answer offered is an agent that reviews, explains CI failures, fixes until green and auto-merges by rules, combined with SonarQube's program analysis.
  • Will Lyon, Neo4j: Turning Agent Memory Into Skills That Work (AI Engineer, 18 min). Retrieval isn't actionable knowledge. Memory as a context graph, with skills distilled from it as typed execution graphs that get flagged when they go stale.
  • Em Shreve, Automattic: How a Remote Company Builds AI Fluency (AI Engineer, 15 min). Automattic runs two-week in-person programs, half workshops and half real project work, for engineers and non-engineers alike. One tool came from a support teammate.
  • Latent Space: Synthesis Superintelligence with Periodic Labs' Liam Fedus and Ekin Dogus Cubuk, on AI for materials science from semiconductors to superconductors.

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