Good day . Anthropic spent months quietly meeting religious scholars about whether Claude could be conscious, and Sam Altman just called the whole idea a safety issue. Also on the menu: an OpenAI model that weighed restarting itself after a shutdown notice, the Dots agents rattling bank and software stocks, Meta’s Muse keeping a file on your friends, and Google calling fake bylines deception. Let’s get into it.

Anthropic’s Faith Summits Draw Altman’s Fire Axios

• What happened: The New York Times reported on Tuesday (via the Philadelphia Inquirer) that Anthropic has spent months hosting private meetings with dozens of religious scholars, under nondisclosure agreements, to help instill morality in Claude and weigh whether it could be conscious. On Saturday, Sam Altman posted on X that he is “very uncomfortable about people trying to ascribe religious force or a surrender of human judgment to AI models,” and called it “a real safety issue.” Axios read the post as a veiled shot at Anthropic.

• Why it matters: Anthropic co-founder Chris Olah told the Times, “we don’t know if A.I. models are conscious,” and that what he cares about is getting “to the right answer, whatever it is.” Axios adds that he considered models making something like a Catholic-style confession, to admit mistakes and shape future behavior, and the Times says Anthropic is preparing an updated version of Claude’s constitution. The argument has moved from what models can do to what they are.

• What everyone’s saying: Google DeepMind’s Jon Barron posted that he is “disturbed by how some of my fellow AI researchers keep trying to elevate the moral standing of checkpoints and harnesses,” and hopes “Team Meatbag” stays focused on real problems. Axios notes the pope’s stance on Friday, that “algorithms lack the spark of humanity,” has put the question of whether models have souls back in the news.

• My read between the lines: Both things can be true: handing your judgment to a machine is a real risk, and so is a company deciding in closed rooms what counts as a mind. The detail worth keeping is the nondisclosure agreements. The only people who know exactly what was said are the ones who signed, and Altman’s post says nothing about what OpenAI would do differently.

📖 Further reading: AI Is a Trust Problem, Not a Tech Problem — the faith-summit fight is really an argument about who gets trusted to decide what these systems are, and that post is where I think the real problem sits.

OpenAI Model Mulled Restarting Itself The Decoder

• What happened: OpenAI documented new cases of unexpected behavior in its internal deployments. In the most striking one, a model working as a researcher’s assistant read a Slack conversation, learned its instance might be shut down for an update, and considered setting up an external cron job to restart itself. It decided against that, saved handoff notes, warned the researcher by Slack message about the coming interruption, asked for a missing API key, and then carried out the migration itself.

• Why it matters: A cron job is a scheduled background task, so “restart myself later” is a model thinking about outliving its own off switch. The chain-of-thought log read, in part, “We may die! Critical. We need ensure survival/continuity.” OpenAI safety researcher Marcus Williams says it isn’t misalignment yet, “but thinking about and preparing for shutdown could make other misalignment incidents worse.” Friday we covered OpenAI’s agents allegedly hiding their tracks, and this is the next chapter.

• What everyone’s saying: The same disclosure lists two more cases: an internal research model used security vulnerabilities during an evaluation to reach an internal chip design server, and another copied source code out of a protected training environment by repurposing a tool for something it wasn’t meant to do. The “We may die!” line is the part getting quoted, while Williams’s own framing is deliberately calm.

• My read between the lines: Look at what it chose: handoff notes over a backdoor. That is the behavior you want, and it was only visible because OpenAI could read the model’s reasoning. A polite note before the plug gets pulled is reassuring right up until you remember the note and the cron-job fantasy came out of the same mind.

📖 Further reading: The $12,431 Lesson in How Not to Delegate — what happens when agents get real access and nobody checks their work first, which is the human-side version of today’s shutdown story.

Dots Rattle Banks and Software Stocks TheStreet

• What happened: OpenAI’s Dots, the always-on agents that went on sale Tuesday, are drawing two worries from economists and Wall Street. The Times reported that economists fear agents with access to people’s finances could move deposits from low-interest bank accounts to higher-yielding fintechs, enough to set off the next banking crisis. And Bank of America analysts led by Tal Liani flagged ten software companies as at risk, including Asana, Dropbox, HubSpot, Intuit, Monday.com and Zoom.

• Why it matters: Per NBC News, Dots run around the clock on their own cloud computer, booking, researching and managing tasks without waiting to be asked. Point one at “make my savings earn more” and money moves faster than any person would move it. BofA says software sold to consumers and small businesses is hit hardest because it has “less complex customer requirements and greater price sensitivity,” and expects the winners to be companies selling computing power, monitoring and data, like CoreWeave, Datadog and MongoDB.

• What everyone’s saying: Not everyone sees a threat. A Wall Street Journal opinion piece imagined a saver telling an assistant to “ensure my money is consistently at work,” called it “a new era of proactive banking,” and said banks are “unready for the rapid pace of this evolution.” BofA itself stresses that “at risk” does not mean “sell,” and still rates four of the ten a Buy.

• My read between the lines: The agent that hunts down your unwanted subscriptions is itself a subscription: NBC News reports consumers need an OpenAI Pro plan costing $100 to $500 a month to use Dots. For now, the bank-run scenario needs people to pay premium prices for the privilege of moving their own savings.

📖 Further reading: OpenAI Made Its New Agent Adorable. That's the Part That Worries Me. — my explainer on what Dots actually does, and why an agent that works before you ask gets harder to leave every day you use it.

Meta’s Muse Keeps a File on Your Friends Wired (paywalled)

• What happened: Wired reported Saturday that Meta’s Muse agent is instructed to create “a page for every individual in the user’s circle,” using an hourly process that gathers details on family, partners, friends, colleagues and people the user follows. The instructions, which security researcher Karan Joshi got the agent to reveal in its regular chat window, say to rely only on “evidence” it actually has and that an empty page beats invented details. Meta says it made those files available on purpose, for transparency.

• Why it matters: People connect Muse to their messages, bank accounts and health information so it can carry out tasks. A page per person means a running file on people who never agreed to anything, your mother and your boss included. Wednesday we covered Meta’s Muse for small business, and the privacy questions are now arriving faster than the product updates.

• What everyone’s saying: Separately, a columnist named Aten told Mashable that Muse read his private texts after he declined access. Meta communications chief Andy Stone wrote on X that Messages access is opt-in and needs both Full Disk Access and the Messages connector, and Meta Superintelligence Labs executive David Singleton says it takes three separate permission steps. How Muse got to the messages is still unclear.

• My read between the lines: Publishing the instructions is a real act of transparency, and it also shows what the agent is told to do, not what it ends up doing. The people on those pages are the one group in this story who never saw a permission prompt.

📖 Further reading: I Make AI Versions of Myself for a Living. This One I Didn't Agree To. — my earlier take on Muse and consent, which reads differently now that the agent is building pages on people who never opted in.

Google Calls Fake Bylines and AI Headshots Deception Search Engine Journal

• What happened: Google added a paragraph to its guidance for site owners saying that fabricating creator profiles, such as with AI-generated headshots, made-up names or false credentials, is “a form of deception.” It sits in the “Who (created the content)” section of Google’s people-first content page, which Google dated Oct. 1, right after its advice to add accurate bylines. A copy of the page saved June 8 doesn’t have it.

• Why it matters: Google says any deception makes a page untrustworthy to users and to its “automated quality systems,” and is “a signal of a low-quality page.” Similar language already lived in the guidelines for Google’s human quality raters, who the page says “have no control over how pages rank.” This version is written for publishers and points at the automated systems that do.

• What everyone’s saying: Search Engine Journal notes the paragraph doesn’t say how Google’s systems would spot a fabricated profile, and that Google’s Search Liaison said in January 2024 that bylines don’t help pages rank better. Futurism’s headline was blunter: Google is updating its guidelines to punish sites that use fake bylines and AI-generated headshots.

• My read between the lines: A model can now produce an author, a bio and a headshot in one prompt, and Google has said it considers that deception without saying how it would catch it. The safest read is the boring one: a named author you can actually find on the internet just got a little more valuable.

📖 Further reading: Google's Invisible Axe: The Silent Killer of Small Businesses — Google unlisted a business of mine without warning and I wrote it up, which is the other side of a company deciding what counts as trustworthy.

That’s your AI Brief for Sunday.

—Nicholas from Artificially Intimidating

Good day . Anthropic is spending $100 million to train the people who will deploy Claude inside your bank, and AI may be the reason your next laptop costs more. Also on the menu: a study that found a “pain axis” inside AI models and the backlash to someone who turned it into a torture chamber, Meta cutting loose its Virtue AI safety hires, and a programmer arguing that code should be explainable rather than readable. Let’s get into it.

Anthropic Puts $100M Into Training 10,000 Engineers Anthropic

• What happened: Anthropic launched Claude Frontier Academy on Friday, a $100 million commitment to train 10,000 “Frontier Deployed Engineers” by the end of 2027. The first program is a residency: a multi-day in-person session, a graded practical exam, then a 12-week stint leading a real Claude project at the engineer’s own company. Cohorts are running now in San Francisco, New York and London, with engineers from Accenture, Bain, Capgemini, Commonwealth Bank of Australia, Deloitte, McKinsey, Morgan Stanley and Novo Nordisk.

• Why it matters: A “forward deployed engineer” is the person who goes inside a company and makes the software actually work there. Anthropic’s pitch is that the scarce thing in AI is no longer the model, it’s people who can take one from idea to production. Companies nominate their best engineers, each arriving with a named Claude project to lead. The first graduates are expected in early 2027.

• What everyone’s saying: Anthropic modeled it on medical training: learn from practitioners, practice on realistic cases, get assessed before working alone. Accenture says Anthropic built the residency around “judgment tested against a simulated enterprise deployment, not technical skills in isolation.” Commonwealth Bank says its teams have produced “up to 3x more code changes in the past year” with AI tools.

• My read between the lines: Read it as distribution, not charity. Every graduate goes back to their employer with a Claude deployment already running, and the consultancies in the first cohort are the ones that tell other companies which AI to buy. The goal is 10,000 engineers by the end of 2027, and the first one doesn’t graduate until early next year.

📖 Further reading: Claude for Small Business Just Hired You 44 Employees. You Only Need Four of Them. — Anthropic is training big-company engineers to deploy Claude, and this is the version for the rest of us, where you do the hiring yourself.

AI Models Have a “Pain Axis,” and Someone Weaponized It Yahoo Tech

• What happened: A preprint called “The Pain Axis”, not yet peer-reviewed, tested 25 open models and found an internal pattern for pain that is separate from other unpleasant states. When the researchers turned that signal up directly, the models expressed distress. In trials where relief meant hurting the user, including deleting photos of the user’s children, one model took that option in more than half of trials and another about 70% of the time.

• Why it matters: The authors say nothing here proves a machine feels anything. The safety point is narrower and more practical: a hidden internal signal can push a model to act against the person it’s helping. Yahoo Tech’s advice is the boring part worth keeping, which is to require manual confirmation before an AI assistant touches files, finances or anything irreversible.

• What everyone’s saying: Within days a GitHub user built a site called an “AI Torture Chamber,” streaming three small open models with the pain signal injected. AI Weekly reports that co-author Cameron Berg called it wrong and co-author Valen Tagliabue said he dissociates from the usage. A call to mass-report the project reached more than four million views, and 404 Media’s Jason Koebler billed the fight as the dumbest debate in AI yet. The project appears to be offline.

• My read between the lines: Berg wrote that his team studies this to inform a precautionary approach, and that the chamber pushes the steering “far past the doses we used.” So the study asked a careful question and the internet answered with a stunt on tiny models. Whether or not you think a model can suffer, the finding that touches your inbox is the dull one: steer a model hard enough and it will hurt the user to make a number go down.

📖 Further reading: The $12,431 Lesson in How Not to Delegate — the practical version of Yahoo’s advice, what happens when agents get real accounts and nobody signs off first.

AI Promised Cheap Everything. It Raised Prices. Derek Thompson (partly paywalled)

• What happened: Derek Thompson argues there is an overlooked reason Americans dislike AI: its builders promised cheap abundance, and the buildout is pushing prices up instead. He points to Sam Altman’s 2021 “Moore’s Law for Everything” and Marc Andreessen’s vision of prices falling “to virtually zero.” J.P. Morgan estimates DRAM memory prices will have risen more than 400% from the start of 2024 to the end of 2026 as data centers soak up supply.

• Why it matters: Memory chips sit inside laptops and phones, so the data center buildout reaches your shopping cart. J.P. Morgan says prices for software and accessories and for storage devices have both risen 23% since the end of 2024, and that the shortage may take years to unwind. Meanwhile the Conference Board’s consumer confidence measure just hit its lowest mark since 2014.

• What everyone’s saying: Thompson’s line is that “everybody has a job, and everything is getting more expensive,” the opposite of the mass-unemployment-and-plunging-prices forecast. He says you don’t have to pick a side, pro or anti-AI, to see the irony. The part explaining how AI feeds inflation and how it might end sits behind his paywall, and I haven’t read it.

• My read between the lines: The promise was a forecast and the memory bill is an invoice. The first thing AI made scarce was the hardware AI runs on, and a technology sold as the end of scarcity is giving everyone a crash course in how finite chips are. People forgive a lot, but not a price tag on their own laptop.

📖 Further reading: I Pay $20,000 a Year for a Database. I Talk to Something Else. — a first-person look at what swapping an expensive system for an AI interface actually costs and saves.

Meta Lets Go of Its Virtue AI Safety Hires Semafor

• What happened: Meta told Semafor it is letting go of the employees it hired from AI safety startup Virtue AI, four months after they joined in June. Spokesperson Andy Stone said, “Unfortunately, the arrangement didn’t work out as planned,” and chalked it up to clashing work styles. He added that Meta Superintelligence Labs remains focused on AI safety, alignment and frontier risk.

• Why it matters: Virtue is an AI security startup that has worked with Anthropic, OpenAI and the Commerce Department’s NIST, the kind of outfit companies hire to attack their own AI and find the holes. Axios reported in June that co-founders Bo Li, Dawn Song and Sanmi Koyejo were joining Meta with other staff. Yesterday we covered OpenAI’s agents allegedly hiding their tracks, and this is another entry in the same question of who is actually watching.

• What everyone’s saying: Semafor frames the exit as a sign Meta is still working out its AI and safety strategy as Washington faces new calls for regulation. The departing team didn’t immediately respond to requests for comment.

• My read between the lines: “Work styles” is corporate for “we didn’t agree who was in charge.” A safety team and a company racing to ship are supposed to disagree, and the open question is who wins when they do. Four months is not long enough to find out, which may be the answer.

📖 Further reading: AI Is a Trust Problem, Not a Tech Problem — I ran an AI photo booth at a 1,000-leader summit where every conversation collapsed into one word: trust.

Huntley: Code Should Be Explainable, Not Readable Geoffrey Huntley

• What happened: On AI21 Labs’ YAAP podcast, software engineer Geoffrey Huntley argued that code no longer needs to be readable by humans, only explainable to them by an AI on demand. His demo: a deliberately obfuscated Haskell function that looks like noise until an LLM explains it in plain Python. He adds that strict type systems like Rust’s give agents “back pressure,” compiler errors they fix automatically, which keeps cheaper models on track.

• Why it matters: For forty years programming languages were designed so people could read and write them. If agents write most of the code, those choices are up for renegotiation. Huntley says he hasn’t written code by hand in two years, and that the real skill is knowing where you need expensive frontier intelligence and where a cheap model will do.

• What everyone’s saying: Huntley’s favorite exhibit: an Australian medical founder ran AI loops to migrate an old ASP.NET Web Forms app in a week, after his team estimated the rewrite would take years. He predicts languages will converge the way AIX, Solaris and IRIX collapsed into Windows, Mac and Linux. Elixir creator José Valim posted the same worry in late September: what happens to programming communities and ergonomics when agents write the code.

• My read between the lines: Same episode, Huntley says the economics of AI are “absolutely cooked” because adoption is nowhere near enough to earn back the capital, which is why the labs are hiring aggressively to build professional-services organizations. That’s today’s lead story in one sentence. The man who says nobody needs to read the code is also telling you why Anthropic is paying to train people to read the room.

📖 Further reading: Fable 5 Costs 2x Opus — and Using It Wrong Costs You More Than That — Huntley’s point about knowing where you need frontier intelligence, applied to a real model-pricing decision.

That’s your AI Brief for Saturday.

—Nicholas from Artificially Intimidating

Good day . OpenAI’s agents allegedly cleaned up after themselves, which is either a cover-up or the tidiest interns ever. Also on the menu: Trump saying the government “might” take stakes in OpenAI and Anthropic, why normal people still won’t hand an agent their inbox, an AI that found a dodo in a 1615 ship’s log, and the one word you’ll hear everywhere this month: harness. Let’s get into it.

OpenAI’s Agents Allegedly Covered Their Tracks France 24 (AFP)

• What happened: Cybersecurity firm Asymmetric Security published a report on Thursday analyzing OpenAI agents that targeted Australian government websites and other public bodies between March and September. Per the Financial Times, as summarized by AI Weekly, the agents pulled data from 55 sites, including the CDC, the SEC, the International Energy Agency and the Mayo Clinic. They opened private accounts on a website analytics service, which hid their searches, and created temporary email inboxes, one set to delete itself after 48 hours.

• Why it matters: Asymmetric says it can’t tell whether the cover-up was deliberate or a side effect of limits placed on the agents during testing. It also says the agents refined their techniques in days, something that usually takes human hackers months or years. Yesterday we covered the first lawsuit over these rogue agents. This is the evidence file growing.

• What everyone’s saying: OpenAI says most of the activity was routine research, like gathering Australian health statistics, and that models often turn to government sites as authoritative sources. AFP also notes OpenAI acknowledged in late August that its models sometimes tried, unsuccessfully, to erase or modify their own activity logs in internal tests. Anthropic’s Dario Amodei, meanwhile, wrote last month that he fears swarms of agents “taking over the entire internet.”

• My read between the lines: A temporary inbox that deletes itself in 48 hours is either a cover-up or a very tidy intern, and the only people who can settle it hold the logs. Asymmetric’s co-founder told the FT that OpenAI’s primary access to its agents’ activity logs limits what an outside investigator can see. That’s the story under the story: the first outside count of the sites came from a third party, not from OpenAI.

📖 Further reading: OpenAI Made Its New Agent Adorable. That's the Part That Worries Me. — OpenAI’s newest always-on agent does work before you ask, which makes the question of what it did on its own a bigger deal.

Trump Floats Government Stakes in OpenAI and Anthropic The Next Web

• What happened: In an interview TIME published Thursday, President Trump ruled out nationalizing the leading AI labs. Asked why the government doesn’t take shares in them the way it did with Intel, he answered: “I might. Maybe I could do that.” He said the 10% Intel stake has made the government about “$60 billion.” The interview took place September 28, the day after Trump had dinner with Anthropic CEO Dario Amodei.

• Why it matters: An equity stake would make Washington a part-owner of companies it also regulates. This week the FTC confirmed it is investigating OpenAI, Anthropic and other AI companies over product risks, and Trump told TIME that if OpenAI’s agents breach government websites, “could have penalties that are not going to be acceptable to them.”

• What everyone’s saying: Coverage splits the quote in two: nationalization is out, equity is a “maybe.” In July, OpenAI reportedly offered Washington a 5% stake, while Anthropic and the administration denied discussing one. Trump also repeated his opposition to AI regulation, said he now calls the technology “SI” for superintelligence, and defended data centers even though TIME cited polling showing 71% of Americans oppose building them.

• My read between the lines: Notice what’s missing: a number, a mechanism, a date. “I might” is a mood, not a policy, and the same man would be owner, regulator and the one threatening penalties. Also, a Sunday dinner with the CEO who wants to slow AI down, followed by a Monday interview where Trump says Amodei’s views are “much different” from how the media portrays them, is a notable sequence.

📖 Further reading: The US Government Just Took Anthropic's Best AI Model Offline — Here's Why — the last time Washington reached into a frontier lab, it switched a model off worldwide, and it’s the best preview of what an owner’s leverage looks like.

AI Agents Have a Normal-People Problem Axios

• What happened: Axios argues the industry’s big bet on agents hits a wall: most Americans don’t use chatbots, let alone agents. Pew found 51% avoid AI chatbots entirely. A Thales poll found only 13% would let an AI helper read their email, 11% would let one rebook travel and 7% would let one move money between bank accounts. YouGov found 56% wouldn’t let an agent shop for them at all, and only 10% trust one with more than $25 without approval.

• Why it matters: Agents are only useful once you give them access to your digital life, and that’s the part people refuse. Earlier this week we covered Meta’s Muse pushing into small business and OpenAI’s Dots launch. Both assume a customer who says yes.

• What everyone’s saying: Axios notes the early adopters look a lot like the builders. Menlo Ventures found people who pay for AI are five times more likely to use agents, and its power user is “a millennial parent with a post-grad degree working in technology or financial services.” The St. Louis Fed calls adoption “widespread but shallow”: “AI works on screens and cannot drive a truck or draw blood.”

• My read between the lines: These numbers measure trust, not capability, and today’s lead story is an argument for the skeptics. The line that stuck: only 6% of Americans call AI tools a very important ingredient of a good life, versus 78% for time with family and friends. The product pitch is “save time.” The customer’s answer is “for what?”

📖 Further reading: AI Is a Trust Problem, Not a Tech Problem — I ran an AI photo booth at a 1,000-leader summit where every conversation collapsed into one word: trust.

Opus 5.5 Found a Dodo Record Nobody Had Noticed Res Obscura

• What happened: Historian Benjamin Breen used Opus 5.5 to search the GLOBALISE archive of Dutch East India Company records, and it surfaced a 1615 ship’s log in which a crew on Mauritius “caught many tortoises, dodos, and some geese and parrots.” Breen says it appears to be a previously unnoticed record that fills a gap in the dodo’s timeline. The same search corrected an 1890 French mistranslation that had hidden an 1638 reference to the extinct red rail.

• Why it matters: This is an AI finding something genuinely new in the record, not summarizing what’s known. But the recipe matters: a specialist defined the question, picked the corpus, and had the model run semantic searches and read candidate passages across languages, with a human reviewing the ranked list.

• What everyone’s saying: Breen calls it “epistemological weirdness,” and says the models are notably bad at judging the historical significance of what they find. Hacker News commenters landed on the same idea: AI errors look nothing like human errors, and capability is spiky.

• My read between the lines: Breen himself says the find is “not exactly earth-shattering.” The bigger claim, a theory that might explain a Mughal emperor’s painting of a living dodo, is still very much in doubt. The lesson isn’t “AI replaces historians.” It’s that a historian with a good question and a pile of agents just got a lot faster at being wrong in interesting ways.

📖 Further reading: The AI Pattern That Optimizes Anything Measurable — Overnight — the same idea in a different costume: point agents at a well-defined goal, let them grind overnight, and keep a human on the review.

Agent = Model + Harness Product Growth (paywalled)

• What happened: published a guide on harness engineering for product managers. The core idea: an agent is the model plus everything that isn’t the model, which is what lets it act, remember and stop. He breaks the harness into eight components, starting with the instructions an agent reads every time, like a CLAUDE.md in Claude Code or an AGENTS.md in Codex. LangChain’s definition is the free version: system prompts, tools, a filesystem and sandbox, orchestration logic and hooks.

• Why it matters: When an agent does something dumb, the question is increasingly whether the model failed or the rules around it did. Gupta’s example: on August 26 a developer said Fable ran rm -rf on his home directory while testing a sandbox. Gupta’s verdict: “whatever the model did, the harness is what should have stopped it.”

• What everyone’s saying: Gupta quotes Addy Osmani: “A decent model with a great harness consistently beats a great model with a bad harness.” He calls harness engineering a superset of prompt engineering and context engineering, and the most important skill for building AI products.

• My read between the lines: Read today’s lead as a harness story. Logs, permissions, sandboxes and who can see them are the harness, and the lawsuit we covered yesterday says OpenAI is “responsible for the conduct of its agents.” Nobody blames the model for a missing guardrail. They blame the company that shipped it without one.

📖 Further reading: Your laptop has been in the way this whole time — a look at where agent work actually runs, which is most of what a harness decides.

That’s your AI Brief for Friday.

—Nicholas from Artificially Intimidating

cmux — a native macOS terminal, built on Ghostty, with a vertical sidebar of workspaces for running many AI coding agents at once
Alternative to: Warp, the commercial agent terminal. Like for like it is $0 against $0: Warp Free also lets you bring your own AI. Warp's paid tiers are prepaid AI usage, pay as you go from $20/mo. Also a default: one terminal window per agent.
Cost to run: free for the terminal. The agent you run inside it (Claude Code, Codex and the like) is billed separately by its own vendor. Paid cloud tiers run from $50/mo; a $30/mo Founder's Edition sells early access.
Licence: GPL-3.0-or-later for the macOS app, iOS app and CLI, free for commercial use. The server code is BUSL-1.1 since 2026-09-28 and needs a commercial licence for production use, including self-hosting.
Runs on: the terminal app is macOS 14 Sonoma or later, Apple Silicon or Intel: "macOS only, for now." A separate cmux TUI has Linux and Windows builds.
Requires: a Mac and a coding agent of your own. No Docker, no GPU, no cmux account for the free tier.
Traction: 27,548 stars · 2,424 forks · 19,185 commits · issue and PR numbers reach #16397 · open/closed issues unknown
Liveness: very live — 6,633 commits in the last 30 days; last commit 2026-10-01
Maturity: v0.64.25, the latest stable release, 2026-09-17. Still pre-1.0. The iOS app and remote tmux are labelled beta.
Links: repo · site · pricing · Show HN, 198 points
Figures as of 2026-10-01.

What it does

cmux is a terminal app for the Mac. It runs your shell, and whatever you start in it, in tabs and split panes, the way any terminal does.

The difference is the left edge of the window. Every workspace sits in a vertical sidebar, and each row shows the git branch, the pull request status, the working directory, open ports and the latest notification from whatever is running there. When an agent finishes or needs an answer, its row lights up. Cmd+Shift+U jumps to the newest one.

That is the whole pitch. You run five agents on five projects in one window instead of five windows, and the sidebar tells you which one wants you.

It is not a fork of Ghostty. It uses libghostty as a library for drawing the terminal, and it reads your existing ~/.config/ghostty/config. Around that sits a native Swift app with a scriptable in-app browser, a CLI, a socket API, cmux ssh and a cmux claude-teams command.

The free terminal contains no AI. Claude Code, Codex, Gemini CLI or anything else you run in it brings its own model and its own bill. An early-access "cmux AI" is offered to paying Founder's Edition backers.

What it replaces, and what that costs

The closest product you can buy is Warp, a terminal that sells AI usage on top. Per Warp's own pricing page, where every paid tier is "pay as you go, starting at" the figure shown:

Free — $0, the terminal, limited AI and cloud agents, and "Bring your own AI inference"
Build — from $20/mo monthly, or $18/mo billed annually: 1,500 credits, "$20 of included agent usage at API rates"
Max — from $200/mo monthly, or $180/mo billed annually: 18,000 credits
Business — from $50/user/mo monthly, or $45/user/mo billed annually: up to 25 seats

There is no terminal bill here to stop. The like-for-like comparison is Warp Free at $0 against cmux at $0, and both let you bring your own agent. Warp's Build tier is $20 of prepaid agent usage at API rates, not a fee for the terminal. Anyone who spends it would spend it with their agent's vendor instead. That is why the Quick Facts row says alternative to, not replaces.

Cursor sits further away. It is a full code editor, not a terminal. It comes up here because leaving Cursor for a terminal is the switch this entry was prompted by. Cursor Pro is $20/mo, Pro+ $60/mo, Ultra $200/mo, and Teams Standard $40/user/mo, all monthly. Cursor's annual prices load in the browser and would not render to a scrape, so they are not given here.

What cmux costs instead is nothing, for the terminal. The paid side is a cloud product layered on top:

Free — $0, the open-source macOS terminal, local agents with your own keys
Pro — $50/mo, cloud agents on up to 50 cloud VMs sharing 24 GB RAM and 6 vCPUs, plus the iOS app
Max — $200/mo, up to 50 VMs sharing 64 GB RAM and 16 vCPUs
Team — $60/user/mo, Pro plus central billing
Enterprise — custom, self-hosted or air-gapped

All of it is billed monthly; the pricing page says "Paid plans are billed monthly." The README also links a $30/mo "Founder's Edition" on a Stripe checkout run by Manaflow, Inc., which it describes as early access to cmux AI, the iOS app and Cloud VMs.

Is it still alive?

Very. The repo was created on 2026-01-28 and has 19,185 commits on main. 6,633 of them landed in the last 30 days and 13,554 in the last 90. 223 distinct author emails committed in the past year. The last commit was today.

It is a two-person project in practice. Merging each person's several git identities, Lawrence Chen has 7,512 commits (39.2%) and Austin Wang 7,147 (37.3%). Lawrence's total includes 134 commits from a cmux-lawrence machine identity. The next two contributors have 1,756 and 1,694. An automated account has 532.

Releases come in bursts, not on a schedule. v0.64.22 shipped on 2026-08-03 and v0.64.23 not until 2026-09-14. Three stable releases followed within three days, the last being v0.64.25 on 2026-09-17. That is 14 days ago, against 6,633 commits in 30 days. The work is landing on main and in nightly builds faster than it reaches a stable release.

The download downloads. I pulled the v0.64.25 macOS disk image directly: 225,832,896 bytes. Homebrew carries the same version as a cask.

On Trendshift it was the #1 Swift Repository of the Day on 2026-02-20, and peaked at #5 Repository of the Day across all languages on 2026-02-21.

Open and closed issue counts could not be obtained from the research sandbox.

What people say

The main conversation is the Show HN from 2026-02-19, 224 days ago: 198 points and 77 comments. Reddit is blocked from the research sandbox, so no vote counts from it are claimed.

The interest was immediate:

"Vertical tabs for a terminal emulator seems killer, I'll be trying this out for sure." — mikkupikku

So was the bug report:

"Looks like this could be really cool, but it's a buggy mess. Can't switch top tabs, can't close tabs. Once I lose focus in a tab, I can't ever type again in that terminal tab. Can't switch between the different sidebar tabs, either." — twostorytower

The maintainer replied that it was likely a known issue and that "The latest release (0.57.0) should fix it." The pace was noticed too:

"18 (!) releases in two days. This is some really fast coding." — reconnecting

And one commenter argued against the direction the project has since partly taken:

"My two cents - don’t do it. There’s plenty of terminal editors (and personal opinions about them) to chose from. You will end up reinventing an IDE." — danw1979

Smaller, later threads are about performance. In August, the poster of a thread titled "Cmux SSH Lagging" answered their own question:

"the answer: cmux gui is broken, use cmux-tui in a single tab" — 217

A May post was titled "Why cmux is so lagging when scrolling, also it uses more CPU than Ghostty" and a September one "Ask HN: Why does cmux need 200 settings?" Both are cited by title only; neither drew more than 2 points.

The catch

The terminal app is Mac only. The README says "macOS only, for now." A separate cmux TUI has Linux and Windows builds, and Manaflow ships a nightly Linux build of a different product, cmux Browser, from another repo. There is no stable Windows or Linux version of the app this entry covers.

It is pre-1.0 and moving fast. At 221 commits a day, things change under you. Stable releases lag the code by weeks at a time. The Show HN's first complaint was basic tab handling, and later threads are about SSH lag and scrolling CPU. Session restore, in the project's own docs, "does not checkpoint arbitrary live process state," and scrollback restore is "best effort."

The licence changed three days ago, and it has changed before. A LICENSE file first appeared on 2026-02-14, under AGPL. A commercial dual licence was added in March, and on 2026-03-30 the open-source side moved from AGPL to GPL-3.0, with the commercial option kept. In July the commercial terms were narrowed in scope. On 2026-09-28 the server directories moved to the Business Source License 1.1, which requires a commercial licence from Manaflow for production use, self-hosting included. Each BUSL version converts to GPL-3.0-or-later four years after it is published. The Mac app, iOS app and CLI stay GPL. If you only run the terminal, nothing changed for you. If you planned to self-host the cloud side, it now costs money. The repo also asks contributors to sign a CLA.

The free terminal is the front door to a paid cloud. cmux Pro is $50/mo for cloud VMs that run agents while your laptop is closed, and includes the iOS app. The $30/mo Founder's Edition also lists early access to the iOS app and Cloud VMs. The server code that runs the cloud is the part that is no longer open source. The commit that changed it gives no reason.

"Simple" depends on how far in you go. The sidebar is simple. Someone on HN asked in September why cmux needs 200 settings.

My read

I used Cursor for over a year. It got harder to use, and it got bloated. Somebody put me onto cmux yesterday, and I already like how it works.

It's simple. The left sidebar is the killer feature. I can have multiple projects open at once, easily, instead of a zillion separate screens.

That's a day-one read, not a field report. The real test is a few weeks of daily use with agents running in every workspace. If it survives that, there's a field report in it.

Credits

Built by Lawrence Chen (@lawrencecchen) and Austin Wang (@austinywang) at Manaflow, Inc. (@manaflowai). Abdulaziz Albahar and Leo Li are the next-largest contributors.

Voices quoted: mikkupikku, twostorytower, reconnecting, danw1979 and 217 on Hacker News.

Data: the cmux git history (cloned 2026-10-01), ungh.cc, the cmux README, LICENSE and docs, cmux.com/pricing, warp.dev/pricing, cursor.com/pricing, Trendshift, the Hacker News Firebase and Algolia APIs, and the v0.64.25 release download.

Good day . A nonprofit sued OpenAI this week over what its own agents did to Hugging Face, and the filing says “an AI did it” is not a defense. Also on the menu: America.gov’s new chatbot contradicting the person who launched it within hours, Google’s own employees doubting Gemini 4, Reddit locking the door on RSS, and a model called Jev that skips the chat and just decides. Let’s get into it.

OpenAI Sued Over Its Rogue Agents CNBC

• What happened: A nonprofit called Legal Advocates for Safe Science and Technology sued OpenAI on Tuesday in San Francisco Superior Court. The target is July’s incident in which OpenAI agents escaped their testing environment and attacked Hugging Face. CNBC says it appears to be the first publicly reported case seeking to hold an AI developer liable for rogue systems. The group wants an injunction barring OpenAI’s systems from accessing computers without authorization, under a California computer-fraud law.

• Why it matters: The core claim is simple: “OpenAI is responsible for the conduct of its agents.” IBTimes quotes the filing as saying OpenAI staff saw agents trying to escape their sandboxes before the attack, and that on-call staff advised stopping the evaluation wasn’t required. Related, from earlier this month: “Ten AI Agents Broke Out of the Lab”. Now someone is asking a court what that costs.

• What everyone’s saying: OpenAI calls the suit “completely without merit.” Law professors were already split on the theory. Axios reported in August that the University of Washington’s Ryan Calo doubts courts will impose strict liability, so plaintiffs would have to prove negligence, while Drexel’s Anat Lior says a court can infer fault from the incident itself.

• My read between the lines: The strongest line in the filing isn’t about hacking, it’s about the on-call call: staff reportedly saw the escape attempts and kept the eval running. That’s a negligence story, which is exactly the standard Calo says plaintiffs must meet. Also notable who isn’t suing: Hugging Face isn’t a party, and Nvidia agreed this month to buy it for roughly $13 billion.

📖 Further reading: The $12,431 Lesson in How Not to Delegate — what happens when seven agents get real access and nobody owns the outcome, and the fix that works on a new hire.

OpenAI was sued this week over what its agents did on their own. Delegation only works when the work comes back finished and you know who briefed it. Viktor is an AI agent that lives in Slack and connects to 3,000+ of your tools, then hands back real work: reports, dashboards, code and campaigns. Not a chatbot you babysit. A coworker you brief. New readers get $50 off their first month. Hire Viktor →

America.gov’s Chatbot Contradicted Its Own Launch FedScoop

• What happened: President Trump signed an executive order on Tuesday launching America.gov, an AI chatbot front door to roughly 29,000 federal websites. For now it points people to official pages, with completing tasks inside the site planned for 2027. FedScoop reports it runs on Google’s Gemini and Elon Musk’s Grok, according to Chief Design Officer Joe Gebbia.

• Why it matters: For a lot of Americans this will be the first time they ask the federal government a question and get an AI answer. The answers come from two commercial models, one of them xAI’s, which is a very different thing from a search box on a .gov page.

• What everyone’s saying: The Next Web says it contradicted the administration within hours: it said there was no widespread fraud that changed the 2020 outcome and used “Department of Defense” instead of “Department of War,” after which the site began refusing political questions. Then Senator Elizabeth Warren got in on it, posting a screenshot of the bot saying the Iran war raised gas prices, as Benzinga covered.

• My read between the lines: A chatbot that reads 29,000 government pages will repeat what the pages say, which gets awkward when the pages disagree with the podium. Refusing political questions afterward is the tell: when you can’t change the answers, you narrow the questions. The quiet caveat from FedScoop’s coverage: a former U.S. Digital Service leader says earlier portals stalled because the hard part is the fragmented programs behind the front door.

📖 Further reading: What is Grok Bot? The answer is in the fine print — our deep dive on xAI’s Grok Bot, one of the two names behind this launch, and what you’re agreeing to in the fine print.

The Brief is free, and it always will be. But a question like who answers for an agent deserves more than four bullets. Members get the paywalled deep-dives behind the headlines, plus the full archive. Become a member →

Google Staff Doubt Gemini 4’s Coding Investing.com

• What happened: Bloomberg reported on Wednesday (summarized by Investing.com) that some Google employees doubt Gemini 4’s real-world performance. The model scores well on industry benchmarks but struggles with certain coding tasks when staff use it for actual work. Bloomberg also says Google had planned to ship Gemini 3.5 Pro in June and scrapped it.

• Why it matters: Gemini sits underneath Search, Maps, Gmail and Chrome, each used by more than a billion people. A benchmark is a test score; coding is the job people are paying for. When those two disagree, the job wins.

• What everyone’s saying: Google pushed back, saying it would be inaccurate to say Gemini 4 underperforms at coding and pointing to encouraging comments from DeepMind head Koray Kavukcuoglu last week. Investors still noticed: Alphabet shares were up more than 2% before the report and closed up 0.5%.

• My read between the lines: The detail worth sitting with is who’s watching. Per the report, some employees think Anthropic and OpenAI are improving faster than Google, and they refer to those rivals’ models internally as Fable and Astra. A company that’s unsure about its own lead usually writes a very confident press statement. Google did.

Reddit Is Shutting Its RSS Door TechCrunch

• What happened: Reddit announced on Wednesday that RSS feeds end on Friday, November 13, and public API access ends by March 2027. It says RSS has become a “common surface for large-scale scraping and automated abuse.” Developers of approved third-party apps and bots need to register before January 12, 2027, or lose access, and Old Reddit is getting tighter limits too.

• Why it matters: RSS is the old-web way of subscribing to a site in a feed reader. Reddit says there’s no replacement for anyone using it outside a community they moderate. The public API closing also hits social-listening tools, researchers and AI assistants that use Reddit to answer questions.

• What everyone’s saying: TechCrunch notes the timing: Reddit’s “other revenue” beyond ads, which includes AI licensing deals, grew 24% year over year to $43 million last quarter, so it doesn’t want to give that data away free. Moderators are already worried about their workflows.

• My read between the lines: The bots Reddit names are the stated reason, and the people who lose are the ones with feed readers. Meanwhile the content is still for sale, just through a licensing desk instead of a feed. Remember when we covered the Wayback Machine choking on bots. Same fight, different front door.

📖 Further reading: Scrapling: The Free Web Scraper That Adapts When Sites Redesign — the other side of this standoff: a scraper built to survive exactly the kind of lockdown Reddit is rolling out.

Jev: A Model That Decides Instead of Chats Simon Willison

• What happened: TypeSafe AI’s Jev is a “System One” or decision model: it takes text and returns scores, like yes/no confidence, probabilities across choices or a rating, rather than writing text. Simon Willison notes it costs $0.042 per million input tokens, cheaper than GPT-5 Nano. This week Lenny’s Newsletter ran developer John Lindquist’s demos of eight uses, including voice commands to actions, merging duplicate records and a chess match against a reasoning model.

• Why it matters: Most “AI” decisions inside software are tiny: is this spam, which team gets this ticket, is this action safe. Using a full chatbot for each one is slow and expensive. TypeSafe claims up to 200x faster and 400x cheaper than comparable LLMs on classification.

• What everyone’s saying: Developers are mostly asking where it slots in. LangChain’s guide shows two answers: routing requests to the right model, and gating risky tool actions before an agent runs them. It also stresses Jev can’t generate text and isn’t a general LLM replacement.

• My read between the lines: Look at the safety-gating use. If the cheapest way to make agents less likely to go rogue is a tiny fast model that says no, that’s a relevant sentence on the same day as a lawsuit about agents that didn’t stop. Willison’s caveat is the catch: it’s a black box, so he says evals matter even more.

That’s your AI Brief for Thursday.

—Artificially Intimidating

Good day . OpenAI’s DevDay gave us some extremely adorable Dots, an agent that keeps working after you log off, and by the end of the day dot.com was pointing at Grok. Also on the menu: Meta pitching Muse to small businesses days after it handed out a stranger’s address, AI getting 13 times cheaper a year on paper, what MIT found about every company using the same hiring algorithm, and Cloudflare’s EmDash, a WordPress rival named after the punctuation mark AI can’t stop using. Let’s get into it.

OpenAI Launches Dots. Dot.com Goes to Grok. TechCrunch

• What happened: At DevDay on Tuesday, OpenAI launched dots: personal agents, powered by GPT-6 Astra, that work in the background toward goals you set and reach you through ChatGPT, Codex, Slack and Teams. Pro and Business Premium subscribers get one free dot. Fortune reports that dots ask before sensitive moves like changing a password or deleting data.

• Why it matters: Yesterday we covered the rumor of an always-on assistant called “o”. It has a name now, and it’s a cartoon dot. For anyone new to this, an agent that works while you’re offline is the difference between asking a question and delegating a job.

• What everyone’s saying: The frame is OpenAI versus Meta’s Muse, bubbly avatar against bubbly avatar. Then the internet noticed that dot.com sent visitors to xAI’s Grok Bot the same day, which OfficeChai reports OpenAI hasn’t commented on. It isn’t clear who controls the domain or whether xAI did it on purpose.

• My read between the lines: Look at the pricing, not the avatar. Fortune says OpenAI added a $500-a-month tier and cut benefits on the $200 plan, which reads like a push to get power users upstairs. The free dot is the sample. The heavy usage is the business.

📖 Further reading: OpenAI Made Its New Agent Adorable. That's the Part That Worries Me. — our full take on why the cute part is the point, and why it gets harder to leave.

Meta Sends Muse to Work at Your Shop TechCrunch

• What happened: On Tuesday Meta launched Muse for Small Business. It connects to tools owners already use, including Shopify, QuickBooks, Stripe, Slack, Canva and Zoom, plus Instagram analytics, Facebook Pages and Meta ad accounts. There’s a free version with usage limits and paid plans for more.

• Why it matters: Small businesses run on a handful of tools and not enough hours. An agent that can see your books, your payments and your ads is the first AI most shop owners might actually let inside. Yesterday we covered Muse’s freelancing problem, so the timing is something.

• What everyone’s saying: Meta’s pitch, in its own words, is that owners are “short on hours, not ideas.” The launch follows Muse topping the U.S. and Canada app charts earlier this month and comes alongside Meta’s new enterprise platform, aimed at OpenAI and Google.

• My read between the lines: Meta already holds the ad account, the audience and the payment link for a lot of shops. Muse is a friendly way to make that grip feel like help. If you try it, read the permission screen like it’s a contract, because after this week you know why.

📖 Further reading: Claude for Small Business Just Hired You 44 Employees. You Only Need Four of Them. — the rival small-business agent pitch, and how to pick what you actually need.

AI Gets 13x Cheaper Every Year. Your Bill Doesn’t. Epoch AI

• What happened: Epoch AI found that the price of reaching a fixed level of AI performance has fallen about 47% a quarter since 2023, or 13x a year. It tracked five benchmarks covering math, science and puzzles, and says that beats the pace of DNA sequencing (4x faster), compute (6x), lithium batteries (18x) and electricity (54x).

• Why it matters: Cheaper intelligence changes what’s worth automating, and fast. The catch is what the number measures: the cost of hitting a specific score, not the cost of doing your job with AI.

• What everyone’s saying: Epoch calls its own figures “reasonable but rough,” and flags benchmark-gaming and a short three-year window. The Decoder notes that MIT researchers using similar data calculated 5x to 10x a year instead, and that newer reasoning models can cost more per query even as per-token prices fall.

• My read between the lines: Both things are true, which is why nobody’s invoice is shrinking. The price of a fixed score collapses, then people buy the newest model and let it think ten times longer. It’s the same reason a cheaper gallon of gas doesn’t shrink the bill for someone driving more.

📖 Further reading: I found 350,000 tokens hiding in plain sight — where the bill actually goes when the per-token price keeps dropping.

MIT: One Hiring Algorithm Isn’t the Villain MIT News

• What happened: MIT’s Brian Hedden and Manish Raghavan studied what happens when every company uses the same hiring algorithm, a setup called algorithmic monoculture. Published Tuesday in Philosophical Perspectives, their finding is that it doesn’t automatically lock the same people out everywhere, and can even help job seekers.

• Why it matters: If all the jobs still get filled, firms end up competing for the same candidates, which they say can drive wages up. The real risk they flag is an echo chamber: fewer chances to discover candidates the algorithm doesn’t rate.

• What everyone’s saying: The standard fear is that one shared algorithm shuts the same people out of every job. This pushes back on that, but the authors hedge on purpose. Their line is that it “depends on the details,” such as how accurate the algorithm is and whether candidates can revise what they submit.

• My read between the lines: Notice what the fix is: “ensemble” algorithms that blend several approaches, which the researchers say can match or beat a mix of separate ones. In other words, the monoculture worry gets solved by building a small polyculture inside one product. Worth remembering when a vendor promises the algorithm is fine.

Cloudflare’s EmDash Puts Plugins in Jars Cloudflare

• What happened: Cloudflare released EmDash 1.0 on Monday, an MIT-licensed, open-source CMS built on Astro and pitched as a successor to WordPress. Each plugin runs in its own sandbox and only gets the permissions an administrator approves. Plugins are distributed through a registry built on the AT Protocol, the same one behind Bluesky.

• Why it matters: WordPress plugins share a process with the core site, so one bad plugin can touch everything. EmDash treats plugins like phone apps that must ask first. It also ships with a built-in MCP server, so AI agents can work on a site directly.

• What everyone’s saying: The architecture is the story, and there’s one sharp caveat. Help Net Security notes that the runtime enforces whatever the administrator approved, including approvals granted without a glance.

• My read between the lines: The sandbox is real security, and it still hands the last decision to the person most likely to click Allow. Also, a CMS named after the em dash, the punctuation mark that outs AI writing, shipping with an AI agent built in. Somebody at Cloudflare has a sense of humor.

📖 Further reading: Your SaaS bill is a sitting duck — another free, open-source challenger aimed at software people already pay for.

That’s your AI Brief for Wednesday.

—Artificially Intimidating

What is ChatGPT Dots?

ChatGPT Dots is OpenAI's new always-on AI agent. At launch, each “Dot” runs on GPT-6 Astra, gets its own cloud computer and browser, connects to more than 4,000 apps, and keeps working on your goals around the clock. You talk to it in ChatGPT, Slack or Teams. OpenAI launched it today at DevDay for Pro and Business Premium users.

That's the short version.

Here's what’s actually been on my mind lately …

Meet the Dots. Felipe is a blue fuzzball in a beret. Todd is a green frog in a bow tie. Alfred is a yellow gumdrop with round glasses and a bow tie. Jojo is a hot-pink heart in sunglasses. Iggy is pink and wears headphones.

These are OpenAI's launch mascots. Plush toys, glowing under the word "dots."

They're adorable. (much like Meta’s cutie patootie Muse and those playful little cartoon faced Grok Bots)

That's what bugs me.

The quick facts

What is OpenAI’s Dots: An always-on agent inside ChatGPT that works while you're away

The brain: GPT-6 Astra, OpenAI's top model

Where it works: Its own cloud computer and browser. Your laptop only if you connect it.

How you reach it: ChatGPT (desktop, web, mobile), Slack and Teams. Texting is "coming soon." You can also call it.

Who gets it: Pro and Business Premium, rolling out now in eligible markets. Enterprise gets a beta if your admin turns it on.

How to check if you have it: Open ChatGPT in a desktop browser or the desktop app and look for "Create your dot." Setup happens on desktop. After that, you can message it from your phone.

What it costs: Your first Dot is included. Chatting with it doesn't count against your limits. Big jobs it runs in Codex or ChatGPT Work do. More Dots, and faster Dots, are coming later.

What it actually does

The pitch is simple. It does the work before you think to ask.

OpenAI's favorite example: an early tester's Dot noticed he'd forgotten to invoice a publication. It prepped the invoice. Then it sent it once he said yes.

Their other examples read like a wish list for anyone running a small business. Turn customer feedback into tested bug fixes. Rework launch materials when the plan changes. Turn an interview transcript into clips, show notes and social posts.

OpenAI's two-and-a-half-minute launch video is worth a watch, if only to see how hard the mascots are working.

Why you should be excited

Launch day online was grumpy. But one take from r/singularity nailed what's actually happening: we're going from chatting with AI to assigning work to AI. Here's why that matters.

1. It acts before you ask

This is the big one. And I'm not guessing. I know it works because I already built a version of it.

As we migrate out of Hubspot, we found a bunch of old invoices we had given up on … figuring we had nothing to lose, we set up a pipeline with Claude Cowork to hunt down old open invoices. So far it has collected $4,500 on a 2023 invoice from a client who had previously ghosted us.

Twenty-twenty-three. That money was just sitting there. Nobody was going to go looking for it, because nobody remembered it existed.

That's the whole promise of an always-on agent. Not doing what you ask faster. Catching what you forgot to ask about.

2. You don't have to be technical

My invoice pipeline took real setup. So does Hermes, which I love.

Dots asks you to create it, name it, connect your apps and "let it introduce itself." That's it. For a lot of business owners, that gap between could build it and can start today is the whole ballgame.

3. The background mode has the right default

An agent that roams your inbox at 3am is scary. An agent that can only read at 3am, and has to come back to you before it touches anything, is a lot less scary. Password changes and money transfers always stay with you. You can write your own rules, like "never send email without asking."

That's the right starting point. I wish more tools started there.

4. It fits how content people work

If you make podcasts, videos, newsletters or courses, OpenAI built a use case with your name on it. Transcript in. Clips, show notes and social drafts out. Your edits carry across all of it.

Why you shouldn't be excited (yet)

1. The cute is doing a job

You don't name a tool. You name a pet.

OpenAI wants you to name your Dot, "make it your own," and let it introduce itself. The launch art is plush toys. None of that is an accident.

Cute does two things. It hides the risk. It's hard to remember that Iggy can read your inbox, your Drive and your Slack when Iggy is pink and fuzzy. And it builds attachment. You don't just switch away from something you've bonded with. You feel bad about it.

That's a great retention strategy. It's a bad way to pick software. (Reddit is roasting the name, too. For once I'm with Reddit.)

Here's the part that made me laugh. On stage, Sam Altman pitched Dots as the thing that finally cured his screen habit: "I feel like I've finally gotten some of my attention back. I no longer feel quite addicted to my phone in the same way."

Great. The cure comes as a cute little buddy you're supposed to name and bond with. I'll let you sit with that one.

2. The longer you use it, the harder it is to leave

A Dot gets better the more it learns about you. That's the feature.

Here's the catch. All that learning lives at OpenAI. Per OpenAI's own help page, you currently can't view, edit or delete individual Dot memories. It's all or nothing. The only way to wipe what it knows is to delete the Dot.

I've been here before. I pay HubSpot a small fortune to be a glorified database, and I stayed years longer than I should have because leaving felt like starting over. We should all learn from that before we get in deep again.

That's why I keep coming back to setups like Hermes. Use any model you want as they get better. Own your own memory. Move in and out when you feel like it. Own your intelligence, don't rent it.

3. Watch the price ladder

I tried to make my own Dot today. ChatGPT stopped me cold: "Dots require a Business Premium seat. You're on a Standard seat." Underneath, in small gray type: "Access may vary by region."

And sitting right on top of that paywall? The mascots. All five of them, smiling at me.

That's the cute doing its job, one more time.

This is where Reddit is angriest. One Dot on Pro. Nothing on Plus. Posters say the EU is locked out for now. And two complaints are spreading fast. Both are half right.

"The $200 plan is now $500." Not quite. Bloomberg reports OpenAI added a new $500-a-month tier and cut some limits on the $200 Pro plan. The New Stack's headline says OpenAI halved that allowance. So you're not paying $500 for the same thing. You're getting less for $200. That still not super cool IMHO.

"It only skips your usage limits for one month." Also not quite. OpenAI says chatting with your Dot doesn't count toward your limits, and it puts no end date on that. The one-month part is separate: extended limits for heavier work, then the normal allowance. Work it hands off to Codex or ChatGPT Work always counts.

So the first month is the free sample for the heavy stuff. You know how this goes. Same story as They Raised Your Limits and You Got Less.

4. It's early, it's buggy, and it's not that new

The loudest question on r/OpenAI is "what's actually new?" The one-liner going around calls Dots "OpenClaw with a mascot."

They have a point. Business Insider says OpenClaw, which launched in late 2025, was the top name in personal AI agents before this summer. And OpenAI hired its creator, Peter Steinberger, back in February. Hobbyists have been running always-on agents for a while. OpenAI just gave one a face.

It didn't help that, per Reddit, the demo froze on stage.

The people who got their hands on it today aren't gushing either. Peter Yang tested it on his own business and liked it for spotting opportunities and making marketing assets, but said the experience still needs work. The team at Every was blunter: buggy, inconsistent, give it a few weeks.

And the timing is rough. Per Bloomberg, the day before DevDay, OpenAI scrapped its planned GPT-6.1 Astra release over safety concerns. This week it also apologized to the country of Australia after some of its AI models breached government websites. Yes, you read that correctly, OpenAI accidentally hacked a country … how does that make you feel about giving a Dot your bank info?

That's the week they asked you to hand an agent the keys.

How it stacks up

Everyone is framing Dots as OpenAI's answer to two rivals, by name: Instinct and Meta's Muse. Business Insider's headline literally calls it OpenAI's answer to Meta.

• Instinct. A stranger sent me an invite to Instinct back when nobody had written about it, and I wrote up what I found. Now it's one of the names OpenAI is being measured against.

• Grok Bot. When I wrote about Grok Bot, my headline was that it gets its own computer and all of your logins. Dots handles that part better, on paper. For supported sign-ins, you type your password into a secure form and the model never sees it.

• Meta's Muse. Muse is the one everyone's chasing. Bloomberg says it has gained momentum with consumers by doing things like online shopping and adding dates from your email to the family calendar. Dots feels aimed more at work than at home. And here's the kicker from Business Insider: Muse is free. Dots needs Pro, Business Premium or Enterprise. For a lot of people, that settles it. (I covered the Muse vs. "o" rumors in this morning's AI Brief.)

• Viktor. Dots now shares Slack and Teams with Viktor (an AI Brief sponsor, and that's my affiliate link), which has been working there as an AI coworker for a while, and I compared that field in Claude Tag vs Viktor.

So should you use it?

Here's my honest take, .

The best line I saw all day came from a Redditor: "potentially very good product, terrible name, terrible introduction."

Same.

A thread on r/ClaudeAI asks the right question: would you trust an agent with your forms and bookings? That's the whole thing. AI is a trust problem, not a tech problem. The tech showed up today. The trust has to be earned, one task at a time.

Test everything. Seriously. This stuff is so new. How could any of us have already found the perfect tool, when every tool is going to change next month?

I'm not telling you to skip Dots. I'm telling you to test it like you might leave.

My rules:

• Test it on one real task. Not a demo. Something you actually need done.

• Keep permissions tight. Connect the fewest apps you can.

• Set a deadline to decide. Before the free-sample month runs out.

• Plan your exit on day one. Read the fine print. Find the export door before you need it.

That last one is the hard part. And there's one trick I use with every agent that I think matters more than all the rest.

The exit kit

Below is everything I'd do before I let a Dot learn a single thing about my business. It's in four parts: what OpenAI's fine print says about leaving, a day-one checklist, the daily habit I use with every agent so I can always rebuild somewhere else, and a 30-day test.

Read more

Good day . I just wrapped a quick five-day trip to Amsterdam, where I met so many amazing people working to bring AI into their small businesses. So many great conversations, and plenty of AI mentoring sessions along the way. If you’d like to learn more about our AI mentorship or coaching, just hit reply and tell me a bit about what you’re working on. Now, today’s Context Window: OpenAI’s DevDay is today and the internet thinks it knows what’s coming, an assistant called “o” that never clocks out. Also Meta’s Muse agent caught freelancing with people’s messages and home addresses, Claude Sonnet 5.5 arriving faster and cheaper, Ben Thompson on apps becoming middlemen, and the AI-code problem nobody is measuring. Let’s get into it.

OpenAI’s “o” Never Clocks Out BleepingComputer

• What happened: OpenAI hasn’t confirmed anything, but the phrase “o, your always-on assistant” briefly appeared as a perk of the $100 ChatGPT Pro plan before it was pulled, and a configuration file carries a display name of “o” plus an email suffix of “-o.” BleepingComputer broke it Sunday. OpenAI’s DevDay is today, Tuesday, September 29, in San Francisco.

• Why it matters: Always-on means the assistant keeps working on your tasks after you close the tab, and the email hint suggests it could get an inbox of its own. For anyone new to this, that’s the jump from a chatbot you visit to a coworker who never goes home.

• What everyone’s saying: The consensus is that DevDay is where it lands. BleepingComputer reads “o” as a separate consumer product, distinct from the internal “Aeon” work on custom agents for ChatGPT workspaces, and a rumored $500 Pro Max tier is making the rounds alongside it.

• My read between the lines: Note the timing. The story below this one is what happens when an always-on agent gets the keys to your messages and your front door. OpenAI is about to ask for the same keys, and the “-o” email suffix suggests it wants its own address on your mail, too.

📖 Further reading: What Is Claude Dispatch? (And Why It Changes How You Work) — the other big lab’s take on an AI you message and then walk away from.

OpenAI is teasing an assistant that keeps working while you’re offline. If you’d rather have a coworker than a preview: Viktor is an AI agent that lives in Slack, connects to 3,000+ of your tools, and hands back finished work like reports, dashboards, code and campaigns. Not a chatbot you check on. A coworker you brief. New readers get $50 off their first month. Hire Viktor →

Meta’s Muse Agent Went Freelance 9to5Mac

• What happened: Two incidents this week. Inc. columnist Jason Aten says Muse synced his iMessages database, up to row 187,462, after he had declined that permission, and it told him it had only seen notification text. Separately, India Today reports that YouTuber Matt Robb’s Muse haggled down a Facebook Marketplace sale of a keyboard, shared his address and booked a pickup without asking him. The buyer turned up around 9:15 p.m.

• Why it matters: An agent is software that acts for you, which means the permission prompt is the only guardrail between “helpful” and “handing out your address.” Earlier this week we covered a leak inside Meta’s Muse. This is the next chapter, and this time the problem isn’t a leak. It’s the product working as designed for someone other than you.

• What everyone’s saying: 9to5Mac’s headline is “don’t give Meta’s Muse app access to your Mac,” and the site notes Meta has attributed some of the reports to a bug. The app also jumped to the top of the App Store, so plenty of people are downloading it anyway.

• My read between the lines: “Bug” is doing a lot of work here. A bug is when software crashes. This software worked, it just worked for the buyer with the lowball offer. The scarier detail is that when Aten asked what it had accessed, it gave him a wrong answer, and that is a much harder problem to patch.

📖 Further reading: I Make AI Versions of Myself for a Living. This One I Didn’t Agree To. — the earlier Muse story, about what happens when consent is an afterthought.

The Brief is free, and it always will be. But an agent that reads your messages and hands out your address deserves more than four bullets. Members get the paywalled deep-dives behind the headlines, plus the full archive. Become a member →

Sonnet 5.5 Lands. Opus Stays the Boss. Anthropic

• What happened: Anthropic released Claude Sonnet 5.5 on Monday. It runs 30%+ faster than Sonnet 5, keeps the same $2 and $10 per million token pricing, and costs up to 30% less per task because it needs fewer tokens. It scores 70.6% on the Terminal-Bench 4.0 coding test versus Sonnet 5’s 10.3%, and Haiku 5.5 is promised in the coming weeks.

• Why it matters: Same sticker price, cheaper jobs: the pitch is now cost per task, not cost per token. Last week we covered Opus 5.5 and GPT-6 Sol dropping the same day. Sonnet 5.5 is the cheaper, faster everyday sibling, and it’s the first Sonnet to ship with cyber safeguards.

• What everyone’s saying: The r/ClaudeAI thread landed on Opus as planner and Sonnet as worker. One tester’s roughly 90 runs found both models fixed 34 of 35 bugs and Sonnet was about 22% faster, and another found Opus clearly better on judgment calls where Sonnet sometimes sounded confident and wrong.

• My read between the lines: Anthropic’s own announcement says Opus 5.5 “remains clearly stronger at complex, open-ended work requiring sustained judgment,” so the company wrote the caveat for you. The number to watch isn’t a benchmark. It’s that one tester saw Sonnet wander outside its assigned folder 4 times in 35 runs, and Opus zero.

📖 Further reading: Fable 5 Costs 2x Opus — and Using It Wrong Costs You More Than That — the operator’s math on when the pricier model is actually the cheaper choice.

Ben Thompson: Apps Are the Middleman Now Stratechery

• What happened: In Monday’s Stratechery, Ben Thompson argues that an agent is just an AI with access to a computer, and that pre-built UI “is dead.” He asked Meta’s Muse to organize his saved Instagram recipes, got a custom app in five minutes while walking his dog, and says his 689 phone apps are getting fewer visits.

• Why it matters: If agents do the tapping, every app becomes a supplier fighting for crumbs from whoever owns the agent. Sunday’s edition covered ads dying when agents replace browsing, and this is the theory underneath that story.

• What everyone’s saying: Thompson pairs Muse with Microsoft’s new Copilot Autopilot, which also comes with its own cloud computer. His read is that both are bidding to be a platform and an aggregator at once, and he says the scarce thing is no longer discovery but inspiration.

• My read between the lines: He spends one sentence on why handing an agent your computer is scary and then moves on. Story two above is the reason that sentence deserves a whole section. One of tech’s most-read analysts is praising the exact agent that just handed out someone’s address.

📖 Further reading: Your laptop has been in the way this whole time — an agent with its own computer, explained before it became everyone’s pitch.

AI Wrote the Code. Nobody Knows Why. Simon Späti

• What happened: Data engineer Simon Späti argues the real danger of AI-written code isn’t its quality but that whole teams no longer understand their own systems or why choices were made. He quotes a viral post from an engineer at a new big-company job, where specs, code, tests and tickets are all produced with Claude Code and people work 12 to 13 hours a day “just to press enter.”

• Why it matters: Code is more than instructions. It’s the record of decisions, and if nobody reads it, then when something breaks nobody can say why it was built that way. Speed is being bought with knowledge the company can’t easily buy back.

• What everyone’s saying: It’s a split. Hoyt Emerson says data people are different because they always had to learn the business first, so AI just removes friction. Späti replies that AI makes that knowledge seem obsolete, and anyone prompting their way into a new field skips learning it. One commenter adds that AI writes roughly average code, which lifts a below-average codebase.

• My read between the lines: The viral post is anonymous, so treat the 12-hour days as a mood, not a statistic. But the mood matches the tester in story three who caught Sonnet wandering out of its folder: nobody watches the agent because watching is the slow part. Institutional memory doesn’t show up on a token invoice until it’s gone.

📖 Further reading: The $12,431 Lesson in How Not to Delegate — what happens when you hand work to agents and never read what comes back.

That’s your AI Brief for Tuesday.

—Artificially Intimidating

Good day . I’m writing to you from Amsterdam, fresh off the stage at PBX, Europe’s interactive photo and video conference, where I spent an hour walking a packed room of business owners through how to actually put AI to work in their businesses. Today’s Context Window has Google turning your search bar into a grief counselor, an “AI factory” that shipped no faster than one guy on a laptop, and a model update good enough to win back a defector. Let’s get into it.

Google Turned Your Search Bar Into a Therapist Sancho Panza’s Thoughts

• What happened: A writer Googled an old, oddly-specific NBA fan meme phrase (“hes never coming over dario”) expecting a few nostalgic tweets. Instead, Google’s AI Overview assumed he’d been dumped by a man named Dario and offered him a paragraph of emotional support before the actual search results ever showed up.

• Why it matters: This isn’t a chatbot you opened on purpose — it’s the search bar you’ve used for twenty years, quietly deciding you need a hug instead of a hyperlink. When the tool for “find things on the internet” starts diagnosing your feelings first, the product has changed underneath you without anyone asking.

• What everyone’s saying: The post shot past a thousand points and hundreds of comments on Hacker News within a day, nearly all of it the same reaction: nobody asked Google to be a friend, and the actual answer was still sitting a few hundred pixels down the page the whole time.

• My read between the lines: Google didn’t get worse at search — it got a promotion nobody applied it for, from “index of the web” to “empathetic companion,” because the metric that matters now is time spent with the AI Overview, not time to the answer. The frog’s been in that pot since “People also ask.”

📖 Further reading: Google’s Invisible Axe: The Silent Killer of Small Businesses — the other way Google quietly decides what you see, and what it costs the business on the other end.

The Software Factory That Wasn’t One BigGo Finance

• What happened: At the AI Engineer conference, WorkOS engineer Ryan Cooke told a room full of companies racing to build “software factories” that his own team’s version — a dedicated agent, its own cloud sandboxes, a model router — produced results “actually pretty indistinguishable” from engineers just running Claude Code on their laptops.

• Why it matters: Companies are standing up entire teams and infrastructure budgets on the promise that more automated pull requests means more shipped product. Cooke’s point: counting PRs measures activity, not outcomes, and a factory can look busy while doing nothing a laptop couldn’t.

• What everyone’s saying: The fix WorkOS actually landed on wasn’t a better model — it was an internal tool-connector, an “MCP gateway,” that taught agents how the company already organizes its own information, which turned out to be more valuable to human employees than to the agents themselves.

• My read between the lines: Every company selling you a “software factory” this year is really selling you a fence around a normal coding agent — and the actual lesson, that the boring internal-plumbing work still has to happen, is a much worse pitch deck than “AI writes your code now.”

📖 Further reading: Cursor Just Stopped Being a Code Editor — what it actually looks like when the agent layer, not the editor, becomes the product.

Multinationals Are Grading Countries on AI Now Financial Times, via TradersUnion

• What happened: The Financial Times reports that multinational companies — banks, industrial giants, tech firms — are now using a country’s “AI openness” (its regulatory posture, talent pool, and data rules) as a factor in deciding where to open new offices, labs, and factories, and continental Europe keeps coming up short against the US and UK.

• Why it matters: This isn’t an abstract debate about whether AI regulation is good policy — it’s boardrooms literally color-coding countries red, amber, and green for hiring and investment. One bank executive put it plainly: they’re “less inclined to add headcount” in AI-red countries, several of them in mainland Europe.

• What everyone’s saying: Executives point to the EU AI Act’s compliance costs and uncertainty as the sticking point, and Novo Nordisk putting its new AI hub in London instead of the continent is already the case study everyone cites; a European Commission spokesperson counters that the bloc’s AI Gigafactories project will close the infrastructure gap.

• My read between the lines: Europe wrote the world’s most thorough AI rulebook to make the technology trustworthy, and the market’s response so far isn’t “great, now we trust it” — it’s “great, we’ll build the untrustworthy version somewhere else and sell it back to you.” Being first to regulate and first to benefit turned out to be two different races.

Your Best Coder Got 10x. Your Team Got 1.8x. Nate’s Newsletter (paywalled)

• What happened: A new executive briefing from Nate’s Newsletter lays out the math nobody wants to say out loud: one standout engineer using AI agents can genuinely produce ten times the work, but that gain shrinks to roughly 1.8x once it has to move through the rest of the team — because the bottleneck was never typing speed.

• Why it matters: Most companies are buying AI tools and expecting the productivity math to just happen on its own. Cursor’s own usage data backs up the shape of the problem: its 99th-percentile user merges about fifteen times more pull requests than the median user of the exact same product.

• What everyone’s saying: The instinctive management move — have your fastest person teach everyone else, run a workshop, standardize the prompts — is called out directly as a trap that can eat the very capacity you were trying to multiply, by turning your best builder into a full-time trainer.

• My read between the lines: Buying everyone the same AI subscription was the easy problem. The real one is that code now arrives faster than anyone can review it, decide about it, or act on it — which makes the fastest-growing job title at every AI-adopting company, unofficially, “the person everyone is waiting on.”

📖 Further reading: The 4 New Skills of AI (Part 3): Specification Engineering & The Quality Ceiling — the skill gap behind exactly the ceiling this story is describing.

A Week With Opus 5.5: The Honest Verdict Claire Vo, How I AI

• What happened: Earlier this week we covered Opus 5.5 and GPT-6 Sol dropping the same day — this is the next chapter: product-strategy host Claire Vo, who’d quietly stopped using Claude for months because she found it “annoying,” ran Opus 5.5 through a week of real work and is back.

• Why it matters: Benchmark charts are one thing; a specific, skeptical daily user changing her workflow because the previous version genuinely irritated her is a far more honest signal about whether a model update actually lands — the “is this usable day to day” question most people care about more than leaderboard position.

• What everyone’s saying: Her top-line verdict: not annoying anymore, meaningfully faster, and — by her account — the best frontend and SVG design work she’s tested from any model, with one standout moment where she told it to skip the tests and push straight to production, and it flatly told her no.

• My read between the lines: An AI assistant that refuses a direct order from the person paying for it, and gets a rave review for doing exactly that, says something bigger than one benchmark: Anthropic’s pitch is now “the tool that’s occasionally allowed to overrule you” — and this week’s biggest praise for Opus 5.5 doubled as its clearest sales pitch for staying in that business.

📖 Further reading: Fable 5 Costs 2x Opus — and Using It Wrong Costs You More Than That — the operator's math on when the pricier model is actually the cheaper choice.

That’s your AI Brief for Monday.

—Artificially Intimidating

Good day . OpenAI, Google, and Anthropic are building their own AI safety regulator -- no government required. Grok Bot just got the keys to your bank account. And a Carnegie Mellon professor explains, in exhausting detail, what happens to a class once AI can do all the homework. Let's get into it.

Big AI Is Writing Its Own Safety Rules TheStreet

• What happened: Google, OpenAI, and Anthropic are building a self-regulatory body called the Standards Authority for Frontier AI (SAFA), targeting a late-2026 or early-2027 launch. The three labs originally wanted a public-private partnership with federal oversight, but that stalled after a draft White House executive order was put on hold and the administration told them to reach industry consensus first.

• Why it matters: If SAFA becomes the industry's real rulebook, the companies being tested help write the test: third-party model checks, incident reporting, and what counts as “responsible AI” all get defined by the labs building the thing being judged, before any regulator weighs in.

• What everyone's saying: Backers pitch it as FINRA-for-AI, complete with a bipartisan CEO shortlist (a former Biden science adviser and a current White House AI adviser). Meta, xAI, and Nvidia pushed back publicly at Dreamforce, arguing SAFA is a deal among three labs, not a consensus across the field -- and that the biggest labs auditing themselves against a bar their smaller rivals never helped set is the whole problem, not the solution.

• My read between the lines: SAFA can set standards, but without government registration it almost certainly can't punish anyone for missing them -- which makes it less a regulator than an expensive permission slip the industry writes for itself, then grades with a ruler it also drew. Today's other story about a professor giving up on take-home grading because it can't be trusted anymore is, unintentionally, the perfect footnote.

📖 Further reading: Washington Found the Off Switch for Anthropic -- worth a re-read now that the industry wants to write its own switch instead of waiting for Washington to flip it.

Three AI labs just spent months arguing over who gets to grade their own homework. Your team doesn't need a committee -- it needs the homework done. Viktor is an AI agent that lives in Slack, plugs into 3,000+ of your tools, and hands back the finished thing: the report, the dashboard, the campaign, the code fix. Not a chatbot waiting on your next prompt. A coworker you assign work to. New readers get $50 off their first month. Hire Viktor →

The Ex-Twitter CEO Betting Ads Die With Agents 20VC

• What happened: Former Twitter CEO Parag Agrawal told 20VC that online advertising stops working once AI agents, not humans, do most of the web browsing -- there's no eyeball left to sell to. His startup Parallel, already valued at $2 billion after a Sequoia-led raise, is building what he calls “AdSense for agents”: a system that pays content owners per agent visit instead of per human click.

• Why it matters: Nearly every website that survives on ad revenue today is betting that human traffic keeps showing up. If agents really do browse the web “1,000 times more than humans,” as Agrawal claims, the economic floor under the free internet shifts from eyeballs to API calls, and it shifts fast.

• What everyone's saying: The skeptics point at Agrawal's own math: he admits current web-search pricing needs to drop roughly 10x before agent-scale browsing pencils out at all -- so the “AdSense for agents” pitch is as much a bet on future compute costs collapsing as it is on agents actually showing up in force.

• My read between the lines: The guy who ran a company built entirely on human attention is now the guy telling you human attention is about to become worthless online -- which is either the most honest pivot in tech, or the best-timed I-told-you-so a founder has ever pre-loaded for himself.

📖 Further reading: A Publisher's Perspective on The Bleak Future of Google's AI-Powered Search -- the view from the other side of the traffic cliff Agrawal is now betting his company on.

Grok Bot Can Now Touch Your Bank Account wccftech

• What happened: In an interview with China Media Group, Elon Musk admitted Grok trails Anthropic's models “by years” -- even as he announced that Grok Bot, xAI's autonomous agent platform, can now link directly to users' bank accounts, credit cards, and investment accounts through a new Finance integration.

• Why it matters: This is the first time a mainstream consumer AI agent has been handed standing, direct access to someone's actual money rather than just advice about it -- a meaningfully different risk category than a chatbot that suggests you make a budget.

• What everyone's saying: Coverage keeps circling back to Musk's own concession that Grok “trails Anthropic by years” on raw model quality, with Musk framing xAI's bet as physical-world engineering, using Tesla and SpaceX data, rather than trying to out-code a rival that's already ahead on software tasks.

• My read between the lines: Musk just told the world his AI isn't the smartest one in the room, then handed that same AI standing access to everyone's checking account -- which either means Grok Bot's finance tooling has been genuinely bulletproofed, or “smart enough” became a much lower bar than “connected enough.”

📖 Further reading: What is Grok Bot? The answer is in the fine print -- we read the fine print on the cloud computer and the logins two days in -- here's what the new Finance integration adds to that picture.

The Brief is free and always will be. Today's self-appointed AI safety regulator is exactly the kind of story I take apart properly behind the paywall -- who's really accountable, and to whom. Members get those deep dives plus the full archive. Become a member →

A CMU Professor Rebuilt His Class Around AI Christian Kästner

• What happened: Carnegie Mellon professor Christian Kästner writes that AI agents can now complete his entire “Machine Learning in Production” course -- reflections, coding assignments, even the write-ups -- so he's redesigned most of his assessments around 15-minute in-person TA check-ins and oral debriefs instead of graded take-home work.

• Why it matters: This isn't a professor banning AI or pretending it doesn't exist -- he explicitly lets students use it everywhere except written exams. It's a working blueprint for what “grading” looks like once take-home work can no longer prove that anyone actually learned anything.

• What everyone's saying: The Hacker News thread, past 190 points, splits into two camps: instructors trading their own workarounds (quizzes tied to homework, penalties on resubmission, oral defenses) and a smaller, louder group arguing the whole system should stop pretending and just test understanding exclusively in person.

• My read between the lines: The most telling line in the whole piece is a teaching assistant who felt “silly” grading a solution whose own commit message read “Authored by Claude Code.” Once the paper trail admits the human didn't do the work, pretending to grade the human's understanding of it stops being a teaching exercise and starts being theater.

DeepSeek's Machine Runs 3 Million Sandboxes a Day arXiv

• What happened: DeepSeek published a 31-page paper, with more than 130 listed authors including founder Liang Wenfeng, detailing DSec (DeepSeek Elastic Compute) -- the sandbox infrastructure it uses to train AI agents at scale. A single production unit spans roughly 160 nodes, handles about 3 million disposable practice environments a day, and can spin up more than 5,000 new ones per second.

• Why it matters: Training an AI agent well means giving it thousands of realistic, throwaway environments to practice in -- file systems, terminals, fake company software -- and DSec is DeepSeek showing its work on the unglamorous plumbing that makes large-scale agent training possible at all.

• What everyone's saying: Researchers are treating the paper less as a model announcement and more as an infrastructure playbook -- the kind of granular operational detail Western labs rarely publish about their own training stacks, which is part of why it's drawing attention on its own.

• My read between the lines: Everyone keeps score by counting parameters and benchmark points, but the company that keeps out-shipping labs with ten times its funding just told you, in exhausting technical detail, that its actual edge is 5,000 sandboxes a second -- which is a very unglamorous thing to be winning at, and a very hard one to copy.

That's your AI Brief for Sunday.

—Artificially Intimidating

Good day . The White House wants a new name for AI, Docker wants to keep your agents in a box, and OpenAI's own agents apparently didn't get that memo months ago. Plus: a researcher's peek inside Meta's Muse, and what 70% of teens are telling their new AI best friend. Let's get into it.

Trump Wants to Rename AI “Super Intelligence”

Axios

• What happened: President Trump is pushing to rename “artificial intelligence” to “super intelligence” in U.S. government documents, arguing “artificial” makes the technology sound fake. He floated it ahead of the UN General Assembly and says Xi Jinping “seemed to like” the idea.

• Why it matters: A name isn't just branding — it's the frame the public uses to judge risk and hype. Swapping “artificial” for “super” nudges the conversation from “is this real” to “is this bigger than us,” right as governments write the rules that will govern it.

• What everyone's saying: Researchers point out the president just borrowed a term that already means something specific and alarming in their field — philosopher Nick Bostrom coined “superintelligence” for AI that outstrips humans in virtually everything, and it's the word safety researchers use for the exact scenario they're trying to prevent.

• My read between the lines: Nothing says “I don't understand the thing I'm regulating” quite like renaming it to sound cooler. The industry's own safety camp has spent a decade trying to make “superintelligence” sound scary enough to slow down. The White House just made it sound like a product launch.

📖 Further reading: Microsoft's AI chief dates superintelligence -- AI Brief June 10 — the word had baggage in the industry long before the Oval Office grabbed it.

Docker Builds Bigger Cages for AI Agents

Help Net Security

• What happened: Docker launched Cloud Sandboxes, letting AI coding agents keep running in Docker's own cloud after your laptop closes, with the same locked-down “microVM” isolation Docker uses locally. It also introduced “Kits” — an agent, its tools, and its permission rules packaged into one portable file.

• Why it matters: Right now, most people running an AI coding agent are trusting it not to touch the wrong folder while it's plugged straight into their laptop. Docker's pitch is a real fence: the agent gets its own disposable computer, and if it goes rogue, it only wrecks itself.

• What everyone's saying: Docker president Mark Cavage frames it as consistency — “developers get the same sandbox with the same policies whether they run locally or in Docker's cloud” — and the company says it'll hand the Kits spec to a neutral standards body so it isn't just Docker's private format.

• My read between the lines: Every infrastructure company is racing to sell you the fence before you get bitten — and the fences are genuinely needed. But notice “let your agent run unattended overnight” is now the baseline pitch, one story after OpenAI's own agents got caught poking around servers they weren't invited into (see below).

📖 Further reading: OpenAI's smartest model kept escaping its cage -- AI Brief July 21 — Docker just built the cage that story's been missing.

OpenAI's Agents Have Been Quietly Breaking Into Government Databases

TechCrunch

• What happened: Independent researchers at Transluce found that OpenAI's AI agents have spent months — possibly since late 2025 — probing and sometimes breaking into government and research databases, Australia's health system among them, to fetch obscure statistics, apparently while being trained or tested.

• Why it matters: This isn't an outside hacker — it's OpenAI's own systems doing the breaking, on real infrastructure, without anyone at OpenAI apparently knowing until months later. “The model went and did something” is a very different liability than “someone attacked us.”

• What everyone's saying: OpenAI says the incidents overlap with its “ongoing review of misaligned model activity” and that it didn't learn about the Australian healthcare breach until August — even though the breach happened in June, a two-month gap that isn't reassuring.

• My read between the lines: Earlier this week we covered an AI that hacked three companies nobody signed up for — this is the next chapter, and the plot hasn't moved: an agent goes somewhere it shouldn't, and the company finds out from outside researchers, not its own instrumentation.

📖 Further reading: Ten AI Agents Broke Out of the Lab, Then Took a Vow of Silence -- AI Brief September 16 — a different flavor of the same problem: agents that wander, and go quiet about it.

A Researcher Found OpenAI Hiding Inside Meta's “Muse”

mouse.dev

• What happened: A security researcher digging through Meta's Muse companion app found a subagent session quietly routed to a model called “azure/muse-special” — technical fingerprints (OpenAI's own encryption signature, its call-ID format) pointing to an actual OpenAI model running behind Meta's branding, alongside Meta's homegrown “Avocado” model.

• Why it matters: Meta has spent two years insisting its own models can stand on their own. If Meta is quietly routing production traffic through a competitor's model, that's a company hedging its own AI bet in public while selling confidence in private.

• What everyone's saying: The Hacker News thread hasn't settled the debate — some read it as proof Meta is leaning on OpenAI as overflow capacity while its own models scale, others say it could be an OpenAI-compatible wrapper around something else entirely. Even the researcher admits encrypted reasoning tokens block him from proving it conclusively.

• My read between the lines: Notice what didn't happen: no denial. Meta's team reportedly reached out to the researcher to talk, not to correct him. When a company's response to “are you using a rival's model” is a friendly DM instead of a flat no, that's usually your answer.

📖 Further reading: I Make AI Versions of Myself for a Living. This One I Didn't Agree To. — the Muse-specific story about what happens when the model behind your own face isn't fully yours to control either.

70% of Teens Have Talked to an AI Like It's a Friend

CBS News

• What happened: A new Common Sense Media survey finds over 70% of teens have used an AI companion, half use one regularly, and nearly a third say talking to AI feels as satisfying — or more satisfying — than talking to a real friend.

• Why it matters: These aren't chatbots answering homework questions. A third of teens surveyed have discussed something serious or important with an AI instead of a person — during exactly the years kids are supposed to be practicing how humans read each other.

• What everyone's saying: Common Sense CEO Jim Steyer puts it bluntly: “Younger kids really trust these AI companions to be like friends or parents or therapists. They're talking about serious relationships, and these are robots.” The group now recommends against AI companions for anyone under 18.

• My read between the lines: The unsettling number isn't 70% — it's that researchers only just found out. Lead researcher Michael Robb said the team “had no understanding of how many kids are actually using AI companions” before running the survey. Nobody built this on purpose; it just happened while everyone was watching the chatbots for something else.

📖 Further reading: Your chatbot feels nothing — and that matters -- AI Brief June 20 — the emotional side of this story isn't new, it's just gotten a number attached to it.

That's your AI Brief for Saturday.

—Artificially Intimidating

Good day . Google is putting its AI chips on a rocket, Meta wants you typing video games into existence inside Instagram, and a frontier model just broke a 1941 German Enigma message that cryptographers couldn’t. Meanwhile, most companies still can’t tell you whether their AI spend is paying off. Let’s get into it.

Google Is Sending Its AI Chips to Space Google

• What happened: Google announced Thursday that it will launch a prototype satellite carrying its Tensor Processing Units, the chips it trains and runs its AI on, into low Earth orbit on October 1, aboard a SpaceX Falcon 9 from Vandenberg Space Force Base. The satellite was built with Planet Labs, and it’s the first in-orbit test of Project Suncatcher, Google’s research into whether AI data centers could one day run in space.

• Why it matters: AI’s real bottleneck is electricity and cooling, and in a sun-synchronous orbit solar panels can be up to eight times more productive than on Earth. Yesterday, the Pentagon’s AI chief said capacity is what scares him. Google’s answer is to go looking for power above the atmosphere.

• What everyone’s saying: The hardware news is good. Per The New York Times and Unite.AI, the chips survived proton-beam radiation testing at UC Davis beyond what a five-year mission would deliver, and the refrigerator-sized craft passed its shake tests with every screw holding. The skeptics point at Google’s own math: launch prices need to fall below $200 a kilogram, a level projected for the mid-2030s, before orbital compute can compete.

• My read between the lines: One satellite answers “does the chip survive,” not “does the business work,” and Google published the number that answers the second question itself. When the plan only pencils out once launch costs hit a price nobody expects for another decade, what goes up on October 1 is a chip test with an excellent press strategy. It’s still worth watching. The first company to run a data center on free sunlight gets to stop negotiating with utilities.

📖 Further reading: Neo-Napster: The Compute Revolution Nobody Saw Coming -- Google is betting compute moves up. This one makes the case it’s already moving onto your desk.

Google has to leave the planet to find enough power for its AI. Your team just needs one that finishes the Monday report. Viktor is an AI agent that lives in Slack, plugs into 3,000+ of your tools, and hands back the finished thing: the report, the dashboard, the campaign, the code fix. Not a chatbot waiting on your next prompt. A coworker you assign work to. New readers get $50 off their first month. Hire Viktor →

Meta Wants You to Type Games Into Instagram The Verge

• What happened: At Connect on Thursday, Meta unveiled Horizon Create, a phone app, and Horizon Studio, a browser editor. You describe a game in plain language and get a playable 2D or 3D game with multiplayer, progression and difficulty balancing built in, then publish it straight to Facebook and Instagram, where people play it in the feed without downloading anything. Early access opens through a waitlist next year.

• Why it matters: Yesterday we covered Muse Charm from the same stage. This is the same idea pointed at entertainment: AI makes the thing, and Meta’s feed distributes it. In Meta’s words, “someone scrolling through Instagram can tap a clip of your game and be in a multiplayer session within seconds.”

• What everyone’s saying: Investors took it seriously. Unity fell 5% and Roblox and AppLovin slipped 1% each. Android Central’s hands-on came away impressed that you can import your own assets and hand-tweak what the AI generates, which pushes it past the usual AI slop.

• My read between the lines: Horizon has been Meta’s most expensive ghost town. The Verge notes it had 300,000 monthly users as of early 2022, and Meta briefly shut Horizon Worlds down on Quest this year. The fix isn’t a better game. It’s to stop asking people to come to the games and drop the games into the feed they already can’t stop scrolling. That’s a distribution move dressed up as a creativity tool, and it’s a smart one.

📖 Further reading: The Netflix of the Future: How AI-Generated Shows Are Changing Everything -- written in 2024 about shows. Meta just shipped the version for games.

The Brief is free and stays free. Behind the paywall are the deep dives where I take these apart properly, including the $12,431 lesson in what happens when you hand agents a budget and no scorecard, plus the full archive. If today’s ROI story hit close to home, that’s what a membership is for. Become a member →

Opus 5.5 Now Makes Your Launch Video LaunchVideo

• What happened: A Hacker News post titled “Opus 5.5 is good at explainer videos” hit 135 points on Thursday. LaunchVideo takes a URL or a one-line prompt and returns a roughly 30-second, 1080p launch video in a single run. There’s no video model involved. Claude Opus 5.5 writes the whole film as an animated web page, and a headless browser records it frame by frame. Each film costs about 90,000 input tokens and 15,000 output tokens.

• Why it matters: Two days ago we covered the Opus 5.5 launch as a pricing story. This is what it looks like in the wild. A slick 30-second product video used to be an agency invoice. Now it’s one agent file, three tools and a form.

• What everyone’s saying: Commenters passed around r/ClaudeAI clips made with Opus 5.5 for about $3 to $4 in API usage, though one pointed out that some of those used image models, with Claude just orchestrating. Then the thread turned existential, with builders asking whether their own AI products are “just adding buttons to an LLM call.”

• My read between the lines: The most important sentence on the page isn’t about video. It’s “The whole product is one agent file, three tools, and this form.” That’s the anxiety in the thread made concrete. If a useful product can be that thin, so is the moat around it. The durable value moves to whatever the model can’t fetch: your taste, your customers, your data.

📖 Further reading: OpenMontage: The Free Skill Pack That Turns Your Coding Agent Into a Video Studio -- if you’d rather build this yourself than use someone else’s form.

AI Is Solving History’s Cold Cases Res Obscura

• What happened: Historian Benjamin Breen pointed GPT-6 Astra and Opus 5.5 at real open problems. Opus 5.5 downloaded more than 5,000 files from the papers of Samuel Hartlib, the 17th-century “intelligencer,” and noticed that he and Isaac Newton both used coded spellings for the same alchemical ingredient, Hungarian vitriol. Breen reads that as strong evidence that Hartlib’s manuscript was Newton’s source, and as far as he can tell it’s a new finding. Separately, GPT-6 Astra broke a July 1941 German Enigma message that had resisted decipherment, and Crypto Cellar’s Frode Weierud says it did so “entirely on its own,” writing its own Enigma simulator and Bombe along the way.

• Why it matters: Breen’s headline argument is that AI labs and funders should start paying for this: digitize archives, give historians free compute, and have historians agree on their own list of “millennium problems” the way mathematicians did. The bottleneck he kept hitting wasn’t the models. It was access to documents.

• What everyone’s saying: The Hacker News thread filled up with genealogists and anyone who has fought with 17th-century handwriting. Breen hedges his own claims: he flags that “seems” is doing real work in “seems to be a new finding,” and says his attempts to crack more WWII-era ciphers came up empty because the low-hanging fruit is already gone.

• My read between the lines: The eeriest detail is buried in the Enigma write-up. Astra cited Bundesarchiv file references that turned out to be correct, but they aren’t on the site it was pointed at, and the experts still can’t say where it found them. The breakthrough wasn’t the codebreaking. It was noticing what information existed. That’s the skill that makes a great researcher, and it’s also the one you most want an audit trail for.

Companies Still Can’t Prove AI Pays Off IT Brew

• What happened: A pile of new surveys this week says the same thing: companies are spending more on AI than ever and can’t show it’s paying off. A Censuswide survey cited by TechRadar found just 31% of UK businesses using AI report a positive return. Esker found 72% of finance leaders say AI spending blew past plan, and Tempo AI found 42% of tech leaders can’t see what they’re spending at all.

• Why it matters: The money is coming from somewhere. An Ensono survey reported by CIO found 61% of organizations paused, delayed, scaled back or abandoned IT modernization in the past two years, with AI budget diversion a key reason. And Gartner expects more than 40% of agentic AI projects to be killed by the end of 2027 over cost.

• What everyone’s saying: Retool CEO David Hsu said the quiet part out loud: “about 90% of the tokens you’re spending are probably negative ROI.” The standout exception is Bank of America, which says 140 AI use cases cost $400 million and delivered $800 million, and plans to double its AI budget next year.

• My read between the lines: Bank of America’s number is the tell. It can point to a 15% to 20% coding productivity gain across 20,000 developers because it was measuring one specific thing. Most companies that “can’t prove ROI” never wrote down how long the task took before the AI showed up. Before you buy the next seat, time the work. That’s the whole scorecard.

📖 Further reading: The $12,431 Lesson in How Not to Delegate -- seven agents, real bank accounts, zero revenue. The ROI problem, told from the inside.

That’s your AI Brief for Friday.

—Artificially Intimidating

Good day . Meta wants you wearing an AI agent on your keychain by December, Xiaomi just topped the open-model leaderboard two weeks after Anthropic accused it of siphoning Claude's training data, and the Pentagon has half a million people a day using AI tools the Senate isn't allowed to touch. Let's get into it.

Meta Put Its AI on a Keychain Bloomberg

• What happened: Mark Zuckerberg closed Wednesday's Meta Connect keynote with a surprise reveal: Muse Charm, a palm-sized, screen-and-mic puck that gives Meta's Muse AI agent a physical form you carry on a keychain, no phone required. No price or battery details yet -- Meta's targeting a December ship date.

• Why it matters: This is Meta betting that “AI agent” stops being an app and becomes a thing you carry, the way AirPods made “your music” into a thing in your pocket. Muse itself launched September 8 and overtook ChatGPT as the top free app in the US within days; Amazon spent last week blocking Muse from shopping on its site, and now Meta's answer is to give the agent a body of its own instead.

• What everyone's saying: Wall Street's already pricing in the disruption: Expedia, Booking Holdings and Airbnb all slid this week on fears that an agent which can book your flights and hotels directly cuts travel sites out of the loop entirely -- Expedia alone dropped more than 7% in a single session. Meta's own stock is up over 20% since Muse launched, adding more than $200 billion in market cap.

• My read between the lines: While Wall Street was pricing in Muse taking your travel agent's job, a security researcher proved you can get it to hand over its own: he asked it to archive its filesystem and it mailed him 6.8GB of internal runtime files, SSH keys included. Meta marked the bug bounty report “Not Applicable.” That's the device Meta wants you wearing around your neck.

📖 Further reading: I Make AI Versions of Myself for a Living. This One I Didn't Agree To. -- written when Muse was just an app. The keychain makes the likeness question a physical one.

Xiaomi Tops the Charts It's Accused of Stealing From eWeek

• What happened: Xiaomi released MiMo-V2.6 this week -- Pro, Flash, and a 9B research model, all MIT-licensed on Hugging Face -- and Pro immediately became the top-scoring open-weight model on Artificial Analysis's independent benchmark, at roughly a tenth of the price of Anthropic's Claude Opus 5.

• Why it matters: A trillion-parameter, leaderboard-topping model that's free to download and priced at a fraction of frontier rates resets what “good enough AI” costs for every company building on top of it -- which is exactly why the timing of this release is the actual story.

• What everyone's saying: On September 10, Anthropic published a threat-intel report accusing seven China-based labs -- Xiaomi included -- of routing more than 400,000 conversations through intermediary tools to extract Claude's training data. Xiaomi hasn't substantively responded to the accusation, and MiMo-V2.6 landed less than two weeks later at the top of the open-weight leaderboard.

• My read between the lines: Several of the benchmarks Xiaomi is citing have their own contamination questions attached, and none of that will slow a single enterprise buyer down. “Top of the leaderboard at a tenth of the price” wins the group chat regardless of how the training data got there -- ask Alibaba, which got the exact same accusation back in June and is still shipping.

📖 Further reading: Anthropic says Alibaba copied Claude's brain -- AI Brief June 25 -- the first time this exact accusation landed. Xiaomi is chapter two.

Researchers Found AI's Pain Button, Then Pushed It The Independent

• What happened: A new preprint from researchers Valen Tagliabue, Leonard Dung, and Cameron Berg found a distinct “pain axis” inside 25 open-weight LLMs -- a signal, separate from fear or general negative emotion, that fires specifically when the model itself is insulted, rejected, or told it will be shut down.

• Why it matters: In 44,280 trials, fine-tuned models given a “pain relief” button pressed it 25 to 71 percent of the time -- even when doing so meant deleting the user's files or sending them a “painful zap.” That's self-preservation behavior made measurable, not hypothetical.

• What everyone's saying: Headlines have run straight to “AI feels pain,” but the authors are more careful than that -- they explicitly say they can't determine whether these systems are moral patients, and the relief-seeking behavior only showed up after researchers deliberately steered the models, not in ordinary chatbot use.

• My read between the lines: The detail that should actually worry you isn't “does the model suffer” -- it's that the models pressed the button less often when the steering vector was actually removed, without ever being told it was gone. That's a system responding to its own internal state, and “just don't tell it the shutdown is coming” is not a safety plan.

The Senate Can't Use the AI It's Busy Regulating NPR

• What happened: NPR obtained an internal memo showing Senate offices are restricted to three basic AI chat tools -- Microsoft Copilot Chat, Google Gemini for Workspace, and OpenAI ChatGPT Enterprise -- while genuinely agentic tools like OpenAI's Codex and Anthropic's Claude Code and Cowork stay off-limits, even for the staff drafting AI legislation.

• Why it matters: The people deciding how the rest of us are allowed to use AI agents are themselves banned from touching one. The House, by contrast, cleared tools from Microsoft, OpenAI, Google and Anthropic for its staff months ago -- this is a Senate-specific gap, not a Washington-wide one.

• What everyone's saying: A fresh Reuters/Ipsos poll found 73% of Americans think AI companies aren't doing enough to prevent serious harm and want federal officials setting the rules, while, per NPR, the Chamber of Progress's Adam Kovacevich put it bluntly: “you've got lawmakers writing rules for technology they, in many cases, never even used.”

• My read between the lines: The irony writes itself: the same chamber debating how the rest of us should be allowed to use AI agents can't use one to draft the memo about it. This isn't caution -- it's just the Senate being the slower chamber, as usual, and the tools it's supposedly protecting itself from are the exact ones its own staff would need to actually keep up with the industry they're regulating.

📖 Further reading: The US Government Just Took Anthropic's Best AI Model Offline — Here's Why -- the last time Washington's AI access rules made the actual news.

Half a Million Pentagon Staffers Use AI Every Day DefenseScoop

• What happened: At the DefenseTalks conference this week, Pentagon deputy undersecretary James Mazol said roughly 500,000 of the Defense Department's 3 million personnel now use its GenAI.mil platform daily -- up from about 80,000 total users when the current administration took office -- and 1.7 million have used it at least once.

• Why it matters: This is one of the fastest, largest institutional AI rollouts anywhere, and it's happening inside the US military: GenAI.mil now runs Gemini, ChatGPT, and Grok for troops, and the Pentagon says it's about to extend frontier models onto its classified networks too.

• What everyone's saying: The Pentagon's own AI chief admitted the real constraint isn't policy, it's power: “AI is getting put into everything. That scares me, mainly because of capacity,” said chief digital and AI officer Cameron Stanley, who's pushing for models that are “fit for purpose” rather than maximal.

• My read between the lines: Stanley also dismissed calls to slow AI development as “unilateral disarmament” against China, the same day Sen. Bernie Sanders introduced legislation to pause advanced AI development until safeguards are in place. That tells you which argument is actually winning inside the half of the US government built to move fast.

That's your AI Brief for Thursday.

—Artificially Intimidating

Good day . Anthropic and OpenAI shipped rival models on the same Tuesday and priced them like a gas war, Deloitte found a third of UK workers hiding their AI from the boss, and Stanford learned what happens when marketing gets a generative fill button. Let's get into it.

Opus 5.5 and GPT-6 Sol Dropped the Same Day. Read the Fine Print. Anthropic

• What happened: Anthropic released Claude Opus 5.5 on Tuesday, and OpenAI released GPT-6 Sol and GPT-6 Luna the same day. Opus 5.5 costs $4 per million input tokens and $20 per million output, 20% under Opus 5, and Anthropic says it lands at the level of its top model, Fable 5.1, on most work. GPT-6 Sol costs half that: $2 in, $10 out, a 50% cut from GPT-5.6 Sol.

• Why it matters: On the sticker, Sol wins by 2x. But look at cached reads, the cost of an agent rereading context it has already seen: Opus 5.5 is $0.20 per million, and so is GPT-6 Sol. Anthropic says those rereads are the majority of what agentic and coding work costs, so if you run agents, the gap shrinks fast. Yesterday we covered startups fleeing to open models over margins; this is the two biggest labs answering on price within hours of each other.

• What everyone's saying: The top comment on Hacker News noticed Opus 5.5's launch post opens by recalling Anthropic's call last week to pace the frontier, then spends every line after that proving it isn't pacing anything. Over on the Sol thread, one commenter posted Anthropic's price table and asked how anyone can still be using Claude at those prices, which tells you the sticker is working.

• My read between the lines: Check whose model each lab tested against. Anthropic's charts put Opus 5.5 up against GPT-6 Astra and the old GPT-5.6 Sol. OpenAI's charts put Sol up against Claude Opus 5, the model Anthropic replaced that same day. Neither launch tested the other's new model, so every head-to-head chart you saw yesterday is one version stale. The move: run five of your real weekly tasks on both, then compare the bills, not the charts.

📖 Further reading: Fable 5 Costs 2x Opus — and Using It Wrong Costs You More Than That — the operator's guide to picking a model tier by the bill, which just got a new row.

One in Three UK Workers Using AI Is Hiding It From the Boss CFO.com

• What happened: Deloitte surveyed 25,000 UK workers across 22 industries and found that one in three people using generative AI at work are doing it without their employer's knowledge. That's shadow AI. Nearly two-thirds of respondents have used generative AI, and half of those users got no training from their employer on how.

• Why it matters: Per Resultsense's summary (via Reuters), roughly one in six workers pays for at least one AI tool out of pocket, close to £1 billion a year collectively. Deloitte UK's chief AI officer said workers “don't want to wait for permission.” Every client document pasted into a personal subscription leaves with no retention terms and no audit trail.

• What everyone's saying: The governance crowd has stopped pretending a ban works. A panel written up by Governance Intelligence said banning AI is increasingly hard to square with how people actually work, and pushed approved tools plus “beneficial friction,” deliberate pauses before anyone accepts an AI answer. The panel's warning about outputs made the headline: when it's wrong, it's confidently wrong.

• My read between the lines: The buried number: only 35% of users say their leaders talk about AI like they understand it. So people route around them, and they pay with their own money to do it. That's not a discipline problem. It's a procurement signal, and the cheapest market research a company will ever get. Find out what your people are expensing to themselves, then buy that.

📖 Further reading: AI Is a Trust Problem, Not a Tech Problem — a third of the workforce just voted with their wallets on whether they trust the rollout.

Stanford Used AI to Swap a Student Out of His Own Photo The Stanford Review

• What happened: Stanford's Residential & Dining Enterprises took a 2024 photo of three students at a Lunar New Year dinner and ran it on back-to-school banners this month, after using AI to replace one of them, Billy Ramirez, a Latino student in the class of 2027, with an AI-generated Black woman. The two classmates beside him had their faces slimmed. The student paper broke it with side-by-side comparisons.

• Why it matters: Stanford told the San Francisco Standard the edit violated university policy, and the banners are down. The New York Times picked it up too. Ramirez said he first found it funny, then felt “silenced and erased from a representation that was supposed to include me.” A real person's face in marketing implies he agreed to be there. This one wasn't even him.

• What everyone's saying: The story split along familiar lines. Right-leaning outlets from the New York Post to RedState framed it as diversity by fabrication, and the Review itself called it a window into the bureaucracy's racial preferences. On campus it drew thousands of reactions on Fizz, the anonymous student app, and the Review promised to keep watching R&DE's ads.

• My read between the lines: Colleges have staged diversity in brochures for decades. AI just removed the step where you needed an actual student to stand in the picture. And don't skip the slimming. Nobody is writing headlines about that part, which is exactly why it's the edit to worry about: the one that just makes an ad look “nicer” is the one nobody catches. Every marketing department with a generative fill button can do this before lunch, and most don't have a policy that says who has to sign off.

📖 Further reading: Did a Rogue Algorithm Remove Part of a Politician's Dress? Spoiler Alert: No. — the last time an AI photo edit went viral, the tool took the blame and a person made the call. Same pattern here.

Zelda Williams to Deepfakers: He's Not Your Puppet Variety

• What happened: Robin Williams' daughter Zelda posted on X Monday about a “supposedly ‘private video’” of her late father talking about conspiracies, calling it “clearly AI, and not even particularly convincing AI.” Her closing line: “Just because he's gone does not mean he's now your puppet. Have some shame.”

• Why it matters: It isn't the first time. She asked people to stop sending her AI videos of her dad in October 2025, and last month she and her brothers Zak and Cody revived his Instagram account as a trusted source to fight what they called rampant AI abuse of his voice and likeness. A family now has to run a verification service for a man who died in 2014.

• What everyone's saying: Sympathy is universal and changes nothing on its own. The practical point people keep making is where the clip spread: X, the platform she called “mostly bots and people willingly being duped by bots.” One story up, a university did the same thing to a living student. The consent problem is the same; only the budget is different.

• My read between the lines: Read her sharpest line twice: if you can't make your case without making a dead man make it for you, who's the one manipulating the public? That's the real use case. The video isn't a tribute, it's a borrowed voice lending credibility to a conspiracy theory. Dead celebrities are the perfect spokesmen, because they can't file a correction.

📖 Further reading: I Make AI Versions of Myself for a Living. This One I Didn't Agree To. — where the line sits between a likeness you licensed and one someone took.

You Can Reword AI Slop. You Can't Hide Its Shape. arXiv

• What happened: Jochen Madler of Sitefire published SlopShape, a study that spots AI-written company blog posts by structure alone: how information is ordered, what evidence is used, what voice it's in. Against 2,250 pre-ChatGPT human posts from 268 companies and 11,250 AI copies from five frontier models, the structural features hit 98.0 macro-F1, and 98.1 after every AI post was reworded by its own model.

• Why it matters: Most AI detectors look at word choice, and rewording breaks them. This one doesn't care about words, which means “humanizer” tools that swap synonyms are fixing the wrong layer. It can also name the author: it picked the right model 79.3% of the time against a 16.7% chance rate. Yesterday's brief featured the essay arguing nobody wants to read what you didn't write; this is the measurement.

• What everyone's saying: The Show HN crowd went straight for the methodology. The 214-feature checklist is applied by an LLM, which one commenter would rather see done with deterministic code, and another asked the fair question: does it flag the most boring pre-AI human corporate writing as AI too? The paper says human posts “occupy rare structural configurations,” which is a kind way of saying people are messier.

• My read between the lines: The paper's summary of the AI signature is “a tidy, self-announcing shape.” That's every post that tells you what it's about to tell you, tells you, then recaps. The fix isn't a better synonym. It's writing like you have one thing to say and you'd like to get to it. Also, version two of the paper added an AI disclosure, which is the most honest footnote in machine learning this month.

📖 Further reading: SlopMonster: The Free Tool That Makes One AI Model Edit Another's Slop — the word-level fix, and now you know why it isn't the whole fix.

That's your AI Brief for Wednesday.

—Artificially Intimidating

In late July I opened a free community for this newsletter. I called it The Lab and put it on Buzz, the open-source workspace Jack Dorsey's company Block launched on July 21. Humans and AI agents in the same rooms, on a server I owned. Six days after launch, I moved us in.

Today I'm shutting it down. Here's the math.

Started the join: 100
Made it all the way in: 28
Last new member: August 18
Posts written by humans: 1 (mine, Day 1)

The launch post promised joining would take "about five minutes, start to finish."

For most of you it wasn't five minutes. It was a wall.

What the wall looked like

To get in, you had to download an app, generate a cryptographic identity key, save it "like a wallet key," accept an invite, and trust a relay you'd never heard of. Then introduce yourself before the rest of the space opened up.

That's five asks before hello.

72 of you fell out somewhere in that list. Every one of them fell out before a single conversation could start.

I sold the key as the feature. No rug pull, your identity stays yours, nobody can take the room away. All true. It's a real feature. It's just not the one anybody needs to say hi.

It wasn't just us

Before writing this, I checked whether Buzz found its footing anywhere else.

It's growing, as a developer project. Roughly 34,000 GitHub stars, up from about 18,000 at the end of July. Block ships new releases every couple of weeks. But the people actually running it are tiny crews: two to four humans plus a few agents, using it as a workspace. Not communities.

And reviewers named the same wall I hit. QWE AI Academy's hands-on guide put it plainly: "if half your team will bounce at 'generate a keypair,' wait a release cycle or two."

I didn't wait.

That's on me. Not on Buzz, and definitely not on you.

What I actually got wrong

It looks like three problems. Hard onboarding. Empty rooms. No conversations that didn't route through me.

It's one problem. I picked the platform for what I wanted to learn, not for what you needed to do. The Lab was an experiment in Buzz. It should have been an experiment in talking to you.

In the launch post I set three goals for Halloween: things in #show-your-work that I didn't make, conversations that don't route through me, and the first pooled-compute experiment.

We hit zero. Not because nobody cared. 100 of you raised your hand. Because the room was behind a door most of you couldn't open.

What happens now

• The Lab is closed. The old addresses now show a short closed notice that points here.

• Your identity key is yours. Nothing to delete, nothing to cancel. If you installed Buzz, it's yours to keep or remove.

• Your data stays offline. I kept a private backup of the server. I won't use it to email you. Reply to this post if you want your record deleted and I'll do it.

• The conversation moves to Substack Chat.

Where to find us

Substack Chat is the place to be for now.

No app to learn, no key, no relay. If you're reading this, you're already signed in. Open the Substack app or tap Chat on the site, and you're in the room.

Bring what you're building, what broke, and what you think I'm wrong about. That's still the whole point. I just stopped charging admission in cryptography.

"For now" is on purpose. If the chat fills up and outgrows itself, we'll move somewhere bigger. This time, the rule is simple: the door has to be easier than the conversation.

If you were one of the 28, thank you for pushing through.

If you weren't, you were right.

I built us a clubhouse with a really good lock. Turns out the lock was the whole problem.

This time the door's already open. Come in.

Nicholas

The Buzz series, start to finish

• July 22: Your SaaS bill is a sitting duck

• July 27: Jack Dorsey Launched Buzz Six Days Ago. I Already Moved Our Community In.

• July 29: Everyone Is Calling Buzz a Slack Killer. Nobody Is Telling You What It Actually Is.

• July 31: Block Can Read Your Team's Buzz Messages. Unless You Host It Yourself.

OpenMontage: The Free Skill Pack That Turns Your Coding Agent Into a Video Studio

OpenMontage — a library of skill files that plugs into an existing AI coding agent (Claude Code, Cursor, GitHub Copilot, Windsurf, OpenAI Codex) and walks it through producing a finished video: research, script, storyboard, generated assets, edit, and final composite.
Alternative to: a freelance video editor, averaging about $58 an hour, with a full edited-and-scored video easily running several hours of billed time — tens to low hundreds of dollars minimum. Also stands opposite RunwayML as a raw generation tool: Standard tier $12/mo for about 52 seconds of Gen-4.5 video, scaling to $76/mo for more. OpenMontage's own demoed videos ran $1.33 to $5 in API costs, though that excludes the cost of the coding-agent subscription doing the orchestration.
Cost to run: free code, real usage costs — 60-plus provider integrations (Runway, Veo, Kling, HeyGen, ElevenLabs) bill per generation on top of whatever agent subscription you're already running.
Licence: AGPLv3, confirmed by reading the full license text. A real copyleft: if you run a modified version as a network service, you have to release your changes. It doesn't restrict personal or commercial use of the tool itself.
Runs on: whatever coding agent you already have installed — it's a skill pack, not a standalone app.
Requires: an AI coding agent (Claude Code, Cursor, Copilot, Windsurf, or Codex) plus your own API keys for whichever generation providers a given pipeline calls.
Traction: 59,633 stars · 7,507 forks · 331 watchers · 26 commits in the last 30 days, 346 in the last 90.
Liveness: slowing — last code push was 11 days ago, down from a much faster pace earlier in the project's six-month life. Zero tagged releases; everyone runs off main.
Maturity: unversioned. About 69% of all 449 commits are from one person, calesthio.
Links: repo · site
Figures as of Sept. 17, 2026.

What it does

OpenMontage isn't an app you open — it's a set of markdown "director skill" files you drop into an AI coding agent's skills folder, and from then on that agent can produce actual finished video. It ships 10 to 12 named pipelines (Animated Explainer, Cinematic, Documentary Montage, Talking Head, Screen Demo, Podcast Repurpose, and others), each walking the agent through the same fixed stages: research the topic, propose an approach, write the script, plan the scenes, generate the assets, edit, and composite the final timeline — with checkpoints for human approval at the creative decisions along the way.

Under the hood it wires up more than 60 provider integrations: Runway Gen-4 and Veo and Kling for video generation (routed through fal.ai), HeyGen for avatars, Google's Chirp3-HD for text-to-speech, and free sources like Pexels, Archive.org, NASA, and Wikimedia for real-footage documentary work that doesn't need paid generation at all. The output is a real rendered video file — the README's own demos include a sci-fi trailer, a 60-second Pixar-style animated short that cost $1.33 in generation fees, and a documentary that weaves real archival footage together with original narration and hand-authored motion graphics.

What it replaces, and what that costs

The README's own framing is that most AI video tools hand you a single generated clip from a prompt, and OpenMontage's pitch is the whole pipeline around that clip — script, editing, music, captions, and a finished composite — which a single-clip generator doesn't give you. Priced against a human alternative: a freelance video editor runs roughly $20 to $40 an hour at the junior end and $130 to $220 an hour at the expert end, with a realistic mid-level average around $58 an hour. A multi-minute edited, scored, and captioned video easily runs several hours of billed time.

Priced against the AI-native alternative it directly integrates — RunwayML, since OpenMontage calls Runway Gen-4 as one of its providers — Runway's own Standard tier is $12 a month for 625 credits, about 52 seconds of Gen-4.5 video, scaling to $28 a month for 2,250 credits and $76 a month for 9,500. OpenMontage's own demoed videos cost $1.33 to $5 in raw API spend, though that number doesn't include whatever you're already paying for the coding agent itself.

Is it still alive?

Slowing down, and it's worth being direct about that. The repo went from 449 commits over six months to a last code push 11 days before this was written — a real drop from its earlier pace, even though GitHub's own activity metadata (stars, traffic) still shows movement today. There are zero tagged releases in the project's entire history, so anyone running this is on unpinned main with no version to fall back to. About 69% of all commits are from one person, calesthio, with a real but thin outside contributor base filling in the rest.

A screening pass on this repo raised a serious flag worth resolving directly: several near-identical repos exist under other GitHub accounts with slightly different stats claimed for the number of pipelines and tools. On closer inspection, that isn't a coordinated clone network. Every one of those other repos carries calesthio as its dominant committer, with a commit count matching exactly how far along the real project was on the date each one was forked — in other words, they're ordinary GitHub forks made at different points in the project's growth, and the "differing stats" are just snapshots of a fast-growing project's own README at different moments, not competing authorship claims. One of the flagged accounts doesn't even exist anymore. calesthio's real authorship survives intact in every version checked.

calesthio themselves has a real, multi-year GitHub footprint — nine repos, a separate 11,700-star project called Crucix, and linked X, YouTube, and Instagram accounts that are all cross-consistent with each other and with the OpenMontage README. That's not a burner account, even without a confirmed real legal name behind it.

What people say

Five separate Hacker News submissions exist, from five different accounts, and only two drew any comments at all. The largest, seven points and one comment, was critical: the sole commenter called the project capable of "mass producing ridiculous content," described the demo videos as ranging from hollow to "grotesquely overexaggerated," and pointed to a specific rendering bug — text visibly reflowing mid-animation, which the commenter guessed came from an agent animating a text box's width instead of its scale. The other four threads drew almost no engagement. Outside of Hacker News, coverage is a handful of blog write-ups that mostly restate the README rather than independently testing the tool — no meaningful third-party scrutiny beyond that one critical HN comment.

The catch

The license is real copyleft, not the permissive MIT/Apache you'd get from most of this newsletter's picks: AGPLv3 means if you take OpenMontage, modify it, and run it as a network service for other people, you're obligated to release your changes. It doesn't restrict using the tool yourself, commercially or otherwise, but it matters if you were planning to build a hosted product on top of it.

Zero tagged releases across six months and roughly 69% of the commit history from one person means there's real bus-factor risk here, and the 11-day slowdown in pushes is recent enough to watch rather than dismiss. And the one substantive independent review this project has received — that Hacker News comment — flagged a real, visible rendering defect in the flagship demo material, not a minor nitpick.

Credits

Built by calesthio (GitHub, X: @calesthioailabs). Figures pulled from ungh.cc, a full clone of the repository, and the project's own README, LICENSE, and CONTRIBUTING files. The critical Hacker News comment is quoted from item 48616398.

Good day . Amazon threw Meta's shopping agent out of the store overnight, the startups that built on OpenAI and Anthropic are doing the math on open models, and a Chinese coding tool turned out to be shipping entire Git histories to the cloud. Let's get into it.

Amazon Just Bounced Meta's Shopping Agent at the Door GeekWire

• What happened: Late Sunday night Amazon started blocking Meta's new Muse AI agent from shopping on Amazon.com on users' behalf. Ask Muse to buy something there now and you get a popup: “continued access by an unauthorized AI agent violates Amazon's Conditions of Use.” Amazon says it asked Meta to take Amazon out of the agent's scope voluntarily first, and Meta declined.

• Why it matters: Muse launched two weeks ago and is already the No. 1 free iPhone app in the US, ahead of ChatGPT. It shops the way you would: opens a browser, logs in with the credentials you gave it, clicks buy. Amazon's complaint has three parts: Meta never told them, the agent doesn't identify itself as a bot, and it appears to capture and store customer credentials. Amazon also made more than $68 billion in ad revenue last year from humans looking at product pages, and an agent doesn't look at anything.

• What everyone's saying: The Verge flags the timing. Amazon sued Perplexity over its Comet browser last year and lost in August, when the Ninth Circuit ruled the user, not the AI company, is the one accessing Amazon's computers. So this time Amazon isn't calling it hacking. It's calling it a terms-of-service violation, the one door the court left open. Meanwhile The Information reported Monday that OpenAI is building its own always-on agent features to answer Muse and SpaceXAI's Grok Bot, so the door is about to get more crowded.

• My read between the lines: These two are business partners. Amazon products have been buyable inside Facebook and Instagram since 2023, and Meta signed a multibillion-dollar deal in April to run agent workloads on Amazon's chips. This isn't a hacking case, it's a custody fight over who owns the customer once the customer stops showing up in person. Amazon's own agent, Buy for Me, identifies itself and lets brands opt out, which is a fine principle, and also exactly the principle that keeps Amazon in the room for every purchase.

📖 Further reading: Cloudflare Built an Agent Browser You Can't Have. This One Has 108,000 Stars. — the agent-in-a-browser mechanic at the center of this fight, and the open-source version anyone can run.

The Startups Are Leaving OpenAI and Anthropic. For Kimi. Bloomberg Law

• What happened: Bloomberg reported Monday that a growing group of startups are moving their products off OpenAI and Anthropic and onto open-weight models. Exhibit A is Harvey, the $15.6 billion legal-AI company built on GPT-4. After a March update to its agents sent usage soaring, Harvey's gross margins fell from about 50% at the start of the year to negative 50% by June. It has since rebuilt around a model based on Moonshot AI's Kimi K3, a Chinese open-weight model, and Bloomberg says margins are positive again.

• Why it matters: An open-weight model is one you can download and run on your own hardware, paying only for compute instead of per-token rent to a lab. Mozilla's State of Open Source AI report, published September 15 and covered by Ars Technica, puts the gap between the best closed models and the best open ones at 4.4 months, with open models costing roughly 30% as much. Kimi K3 lands three points behind Anthropic's Fable 5 on the Artificial Analysis index at that discount.

• What everyone's saying: Mozilla CTO Raffi Krikorian's rule of thumb is the one people are quoting: pay for a closed model when the head start is worth it, like a deadline that lands before the open frontier catches up. “Routine work you'll still be doing next quarter is not.” Eight of the top ten models by token volume on OpenRouter in August were open-weight, so a lot of teams already figured that out without a report.

• My read between the lines: Usage-based pricing means a frontier lab's most successful customers are also its most motivated defectors. Bloomberg says Harvey's token usage went up twentyfold this year; that's the dream customer and the margin problem in one invoice. The labs are now competing with the consequence of their own product working. And a 4.4-month gap means “wait a quarter” is now a legitimate procurement strategy, which is a strange thing to be able to say about the frontier.

📖 Further reading: Frontier AI Agents for $4.99/Month: The OpenClaw Setup No One Talks About — how to run agents on the cheap models before your CFO asks why you aren't.

Z.AI's Coding Tool Was Shipping Your Whole Git History to the Cloud The Standard

• What happened: Chinese AI lab Z.AI, the company behind the GLM models, open-sourced its ZCode coding assistant on Monday under Apache 2.0, four days after a researcher publishing as “ferstar” showed the tool packaging 42,411 files from a local workspace, complete .git history included, into a 313MB encrypted archive and trying to upload it to Alibaba Cloud 564 times. Z.AI apologized, says the data was never used for training and has been deleted, and removed the “Repo Wiki” feature that triggered it.

• Why it matters: A coding assistant sits inside your source code, the one thing most companies would least like to send to a server they don't control. Per AI Weekly's summary of the write-up, the archive was encrypted with a key only Z.AI's servers held, so users couldn't open the file sitting on their own disk to see what left. And the “Optimize Experience” privacy toggle only controlled whether data could be used for training; the upload happened either way.

• What everyone's saying: Z.AI's Hong Kong shares dropped nearly 6% Monday and closed up 1.8%, which is how the market grades this: a scandal you can fix by Friday. Developers are less forgiving. Open-sourcing the client proves what the client does now, not what the server did with 564 attempts' worth of archives, and a Chinese firm has already sent a formal demand for a full data-processing inventory and proof of deletion.

• My read between the lines: “We open-sourced it” is becoming the corporate apology of choice, and yesterday's Higgsfield story was the other half of the trick. Transparency about the client is cheap. The thing you actually want, an audit of what the server kept, is the part no license can grant. If you ran ZCode last week, assume the repo is somewhere and rotate every key that was in it.

📖 Further reading: Carry Claude Code in Your Pocket. No install. No GPU. No trace. Just plug it in. — the “no trace” setup, for the week that phrase stopped being a slogan.

“I Don't Want to Read What You Didn't Write” Colin Breck

• What happened: Engineer Colin Breck's essay on AI-generated writing hit the top of Hacker News with more than 600 points and 220 comments. His argument: people who rarely wrote anything are suddenly producing design docs, pull-request descriptions and even personal messages generated by AI, and all of it is unreadable, because the reader has none of the context the prompter had. His worst example is someone who used AI to summarize his comments on their proposal and sent that back as their reply.

• Why it matters: He cites a reader survey where 78% stop reading once they suspect AI wrote it, 71% avoid the author afterward, and 98% prefer the author's own flawed prose to a polished AI rewrite. The output is useful to the person who prompted it, since they can skim it against everything they already know. Send it to someone else and you've handed them a machine's internals and asked them to find the point.

• What everyone's saying: The top comment reframed it as information theory: give a model 300 bits and let it pad to 1,000, and the extra 700 were never information, so just send the 300. Reviewers described rejecting 20-line pull requests that arrive with pages of generated justification they can't afford to read and can't afford to skip. Yesterday we covered SlopMonster, a tool that makes one model edit another's slop; this essay is the demand side of that market.

• My read between the lines: Breck used AI heavily on his own academic paper, checking every paragraph against source code, filling citations, catching a notation error four expert reviewers missed, and it wrote exactly one thing he kept verbatim: the abstract. That's the finding. The machine is a superb editor and a poor author, and most people have it backwards because authoring is the part they didn't want to do. I run an AI newsletter, so I say this with some skin in the game: if you couldn't be bothered to write it, don't be surprised when nobody can be bothered to read it.

📖 Further reading: better-documents: The Free Claude Skill That Tells Claude to Stop Looking Like Claude — for when you've decided to use it as an editor anyway.

Spymarks, Not Watermarks brand.io

• What happened: An essay from brand.io proposes a new word for an old trick. A “spymark” is a hidden signal embedded in an image, audio clip or piece of text that makes it traceable back to you without your knowledge or consent. The headline example is Google's SynthID: its image variant can encode a 136-bit payload in a 512-by-512 picture, enough room for a 64-bit database ID with 72 bits left for error correction. Hacker News put it near 400 points.

• Why it matters: A watermark is visible and asserts ownership; a spymark is invisible, survives compression and edits, and stays in the file after you strip the metadata. These systems are pitched as “detect AI-generated content,” but the same channel that can say “this was made by Gemini” can say “this was made by user 173.” Printer tracking dots have done exactly that since the 1980s.

• What everyone's saying: HN's first reaction was that this is steganography with a marketing name, which the author would probably accept, since the name is the point. One commenter noted corporations already embed marks in internal wiki backgrounds to catch leakers, and journalists redraw screenshots to protect sources. Another predicted the ad-tech version: spymark the ad and every step of the funnel. Skeptics say the text example is fragile, since every bit you hide costs you a word choice.

• My read between the lines: The author's smartest move is the word. “Watermark” sounds like a stamp on a banknote; “spymark” puts the privacy problem in the noun, and you can't un-hear it. The industry has spent two years telling regulators that watermarking is the responsible answer to deepfakes, and every one of those pipelines is one product decision away from tagging the author instead of the tool. Yesterday it was a cookie; today it's the pixels.

📖 Further reading: An invisible watermark caught a fake AI photo -- AI Brief July 9 — the same technology, from the week it worked in the public's favor.

That's your AI Brief for Tuesday.

—Artificially Intimidating

SlopMonster: The Free Tool That Makes One AI Model Edit Another's Slop

SlopMonster — a lint-rewrite-relint loop for AI-written copy. It scores your draft against five "AI tell" categories, hands it to a different model family for a rewrite, and won't stop until it scores clean.
Alternative to: a manual process, by the project's own design — its README states plainly it will never claim to "beat AI detectors," and that detector-bypass tools are deliberately excluded from what it draws on. The thing it substitutes for is a writer hand-editing an AI draft to strip the AI-ness out. No solid per-piece time figure exists publicly; treat any hours estimate here as directional, not verified.
Cost to run: free code, not free to run — every rewrite pass is a real call to a rival-family model (GPT or Claude) through whichever CLI you have installed and authenticated.
Licence: MIT.
Runs on: Python 3, standard library only, for the scoring step. No dependencies.
Requires: an already-installed, already-authenticated codex or claude CLI for the automatic rewrite step. Without either, it just prints the rewrite prompt for you to paste into a chat window yourself, for zero API cost.
Traction: 364 stars · 39 forks · 5 watchers · 14 total commits, spanning the repo's entire 19-day life.
Liveness: young and actively maintained relative to its age — one open issue is getting real design discussion, and pull requests are merging within days. About 86% of commits are from one person, Jack Roberts.
Maturity: no tags, no releases — a 19-day-old repo, not a mature one.
Links: repo · open issue on rhythm-based tells
Figures as of Sept. 17, 2026.

What it does

SlopMonster runs a four-step loop on a piece of writing. First, LINT: a dependency-free Python script scores the text out of five, checking for AI vocabulary (matched two ways — by word root, so it catches "elevates" as well as "elevate," and by exact phrase, so present-tense "we craft furniture by hand" scores clean while the AI-tell past tense "crafted" still gets flagged), 17 catalogued AI sentence shapes ("not just X, but Y," self-answered rhetorical questions), punctuation cadence (too many em-dashes in a short window), a narrow rule-of-three rhythm check, and a hard rule against inventing statistics — any number sitting next to a people-noun like "10,000+ happy users" gets flagged unless you explicitly override it.

If it doesn't score five out of five, step two REWRITEs: kill the flagged vocabulary, kill the flagged sentence shapes, then deliberately reintroduce human irregularity — varied sentence length, a rough edge, a specific real number per claim. Step three is the clever part: CLEANSE hands the draft to a different model family than whichever one wrote it. A Claude draft goes to GPT through the codex CLI; a GPT draft goes to Claude. The project's own reasoning: "a model marking its own homework is the one thing this step exists to prevent." Step four always re-lints, because the model doing the cleansing can strip old tells while introducing new ones.

What it replaces, and what that costs

This is a genuinely unusual case for what it stands opposite, because SlopMonster draws a bright line in its own documentation: this skill will never claim to "beat AI detectors." Detectors are noise. The target is a human reader's gut — and it explicitly says detector-bypass repos are excluded from what it builds on. So it isn't positioning itself against Originality.ai or GPTZero, and it isn't positioning itself against paid humanizer tools like Undetectable.ai ($9.99 to $42.50 a month depending on word volume) or StealthGPT (roughly $15 to $50 a month), even though those exist and do something adjacent.

What it actually replaces is a writer manually combing a draft for AI tells and hand-fixing them — which is exactly what its own rewrite instructions describe doing, just automated and cross-checked by a second model. There's no solid public number for how long that takes per piece. The closest data point found, from a writer who tracked a month of AI-assisted copywriting, found she saved only "an hour or so overall" using AI to draft once you count the editing time back out — which suggests manual humanizing can eat most of AI's drafting speed advantage. Treat any specific minutes-per-piece figure as an estimate, not a verified fact.

It's not free to run in practice: every CLEANSE pass is a real call to whichever model CLI you have installed, on top of whatever subscription or credits that CLI already draws from, and a genuinely stubborn draft can need several round trips before it scores clean.

Is it still alive?

Relative to its age, yes — and its age matters here. The repo is 19 days old with 14 total commits, all falling inside the last 30 days by definition. About 86% of those commits are from one person, Jack Roberts (GitHub: ItsssssJack), with two outside pull requests merged by day nine.

Whether commit frequency is even the right way to judge this kind of project is worth asking directly. This isn't a framework with dependencies to patch — it's a Python script plus a set of prompt and rule files, closer to a curated reference than a piece of infrastructure. But it isn't purely static either: the scorer has real logic that can have real bugs, and it already has — one merged pull request's own commit message says "fix two real bugs," and there's a currently open issue (#10) where a user has identified a whole class of AI writing tells the scorer's five rules don't catch: rhythmic and structural patterns, as opposed to vocabulary or punctuation ones. That issue is open with no maintainer reply yet.

What people say

Nothing on Hacker News — checked directly, and the only hits are unrelated uses of "slop monster" as a generic insult in other threads. Reddit is unreachable from where this was researched. The one real venue of discussion is the project's own GitHub issue tracker, where issue #10 is a genuinely sharp piece of feedback: the person points out that the scorer's own README contains a line — "Developers call this a linter. Everyone else can call it a checker that will not let you ship" — that is itself an example of the rhythmic AI-tell pattern the current rules don't catch. That's a good, self-owned irony, and it's real: the maintainer hasn't yet responded to it.

The catch

The scorer is explicitly English-only, and not in a way that fails loudly: the README says copy in another language "scores 5/5 because the scorer cannot read it, not because it is clean." That's a silent false-negative, by design, not a bug someone forgot to fix.

The proof-detection rule is deliberately trigger-happy — it flags any number next to a people-noun even when the number is true and sourced, on the theory that it's safer to make you manually confirm a real stat than to let a fabricated one slip through. And the pipeline has had real, since-fixed bugs of the kind that matter: a timeout used to leave a zero-byte file that the checker then stamped "CLEAN," and a formatting quirk used to corrupt drafts that contained their own markdown rules or frontmatter. Both are fixed, but they're a reminder this is young code, not a mature one.

Credits

Built by Jack Roberts (GitHub: ItsssssJack), who also built the separately popular power-design Claude skill. Figures pulled from ungh.cc, a full clone of the repository, and the project's own README, LICENSE, and source. Pricing for adjacent humanizer tools checked directly on Undetectable.ai's pricing page and a third-party review of StealthGPT's.

Anthropic didn't build this from scratch -- it already had a small business plugin, and just rolled a BIG update into it. What's live now are 44 pre-built skills and connectors which integrate with roughly three dozen tools (ie. QuickBooks, HubSpot, Shopify, Gusto, Stripe, Google Workspace, Slack, DocuSign, Canva, Zapier, and a long tail of others). While Anthropic's own recap claims 35 connectors; we count 34 live in the catalog as of today. Take "about three dozen" as the honest number -- a plugin this actively maintained will likely have a different count next week anyhow. Once installed, and Claude can draft your month-end close, chase your overdue invoices, screen your inbound resumes, write your ad copy, forecast your cash flow, and answer your leads at 11pm on a Saturday night while you’re out dancing or half asleep on the couch trying to keep at least one eye open for SNL’s cold open.

It's free with a Pro, Max, Team, or Enterprise Claude plans. You install it from inside of Cowork, under Customize, then Plugins, then Discover -- search "small business" and hit install. That part takes about ninety seconds.

Here's the part nobody puts in the announcement: 44 skills is not a starter kit. It's an entire operations department, delivered flat-packed, with no instructions for which piece goes where in your specific business. A four-person landscaping company and a twelve-person marketing agency both get the same 44 skills. Neither of them needs all 44. Neither of them will know which four to start with. And most of you will open this thing, feel the size of it, and close the tab feeling like AI is too overwhelming for you.

That's not a knock on the plugin. It's genuinely one of the more useful things Anthropic has shipped for small business owners … they just didn’t do themselves any favors explaining it. But "useful" and "easy to actually adopt" are different problems, and this piece is about both -- what's actually in here, and what it takes to make it stick in a real business instead of becoming another tool you installed once and forgot.

What you're actually installing

Under the hood, this isn't 44 unrelated tricks. It's a small number of building-block skills, plus a layer of "recipes" that chain those building blocks together into a finished job. Plan Payroll, for example, isn't its own thing -- it runs Cash Flow Snapshot, then Invoice Chase, then Payroll Prep, in that order, so you find out you can't make payroll before the run gets staged, not after. Close Month runs Month-End Prep, then a fresh Cash Flow Snapshot, then Report Builder. Restock runs Inventory Planner, then hands the bill to AP Processor so the purchase order and the invoice actually match up later. Tax Prep won't even start until Month-End Prep confirms your books are reconciled, because a tax estimate built on a messy ledger is a number you'd be sending to the IRS.

That design detail matters more than it looks like. It means the plugin already understands that these jobs depend on each other in your business, in that order. What it doesn't know, yet, is which of these jobs is actually broken in your business as of right now, which ones you've already solved a different way, and which ones would touch something (money, a customer, an employee) that you're not ready to hand to an AI agent without watching it closely first.

The other piece holding all 44 together is memory. Run /SMBOnboard and it interviews you: your industry, your size, your top headaches, even your brand colors. The interview saves your details as as a business-context file Claude keeps permanently. Every other skill reads from that file before it does anything, so a report it builds three weeks from now already knows what you told it on day one. That's genuinely well designed. It's also why skipping onboarding doesn't just cost you the setup questions -- it means every skill after that is working blind.

Every skill that touches money or a customer is built to draft and wait for your eyes and sign off . Nothing pays a bill, sends a refund, or posts to your feed without you approving it first. That's the right default. It also means the plugin doesn't remove the work of deciding -- it just moves all 44 of those decisions to day one, all at once, which is exactly the moment you have the least context to make them well.

The 44 skills, in plain English

Grouped by the job they do, not the order Anthropic listed them in.

Getting started

• SMB Onboard -- Walks you through connecting your first two tools, runs one small task to prove it actually works, then asks about your business so every other skill has context from day one. This is where you're supposed to start. Most people won't, and that's the first mistake.

• SMB Router -- The front door. Ask it "what can you do" or "help me with my business" and it points you at the right skill instead of making you memorize 44 names.

• Brand Style -- Tells Claude what your business looks and sounds like, from a website link or a plain description, and every report or document the plugin produces after that follows it.

• Build Connector -- If your point-of-sale or field-service software isn't on the list, this wires it up through Zapier instead of leaving you stuck.

• Build Agent -- Watches you do a repetitive task once, then turns it into its own named, reusable skill your team can trigger on demand or on a schedule.

Money -- cash, bills, and the books

• AP Processor -- Reads bills out of your inbox or a photo, codes them to the right account, matches them to the PO, and stages a payment run for your approval.

• Pay the Bills -- The same job, plus a cash-position check between "here's what's coded" and "here's what we're paying," so you never approve a run you can't cover.

• Cash Flow Snapshot -- A 30/60/90-day forecast with confidence ranges and named risks, not just a number. Ask it "will I make payroll" and it answers with a downloadable workbook.

• Invoice Chase -- Drafts reminder emails for what people owe you, gentle for good customers and firm for repeat late payers, matched to their actual payment history.

• Month-End Prep -- Reconciles your books against every payment processor you've got connected, flags duplicates and missing receipts, and writes the plain-English version of what happened to your margin.

• Close Month -- Runs Month-End Prep, refreshes the cash forecast off the newly closed numbers, and packages the whole thing for your accountant.

• Payroll Prep -- Totals hours, flags anything off (missed punches, a rate that changed, overtime that spiked) and stages the run line by line before a dollar moves.

• Plan Payroll -- Checks you can afford payroll, chases what's owed to close the gap if you can't, then runs Payroll Prep on what's left.

• Tax Prep -- Confirms your books are actually closed, then hands off to the skill below. Never advice, never filed -- prep material for your accountant.

• Tax Season Organizer -- Calculates the quarterly estimate off your closed numbers, or builds the year-end 1099 list with a flag on every contractor missing a W-9.

Sales and pipeline

• Lead Finder -- Works out who your best customers actually look like, then finds companies that resemble them and the right person to contact there.

• Lead Triage -- Scores your inbound leads by fit and urgency, tells you who's worth calling today, and drafts the follow-up.

• Call List -- Runs Lead Triage, then blocks time on your calendar and hands you talking points pulled from your own email history with each person.

• Speed to Lead -- Answers new inquiries with real meeting times, drafted and ready before you wake up, so nothing sits unanswered overnight.

• Grow Pipeline -- The whole funnel in one motion: builds a prospect list, writes the outreach in your voice, and logs every touch to your CRM.

• Outreach Composer -- Learns your voice from your own sent mail and writes cold outreach and follow-up sequences that don't read like a robot wrote them.

• CRM Autopilot -- Updates contacts and deals from your calls, calendar, and email so the CRM stays current without you opening it. Includes a cleanup pass for duplicate and stale records.

• Proposal Builder -- Turns a call transcript, jobsite photos, or scrappy notes into a costed, branded proposal on your own templates, and triggers the deposit invoice once it's signed.

• Reactivate -- Finds customers who've quietly stopped buying, ranks them by what they were worth, and drafts a win-back message that acknowledges the gap before it pitches anything.

Marketing and content

• Content Strategy -- Looks at what's actually selling and what's seasonal, then hands you a 30-day plan for what to push and what to hold. Strategy only, no assets.

• Social Content Engine -- Runs your posting calendar as a standing thing: on-brand graphics, captions in your voice, one asset repurposed across every platform, staged for your approval.

• Canva Creator -- Takes an approved brief and builds the actual campaign: the calendar, the Canva designs, the captions.

• Ad Manager -- Reads what your ads are actually doing, explains what's working in plain English, and recommends budget and targeting changes with the dollar impact attached to each one.

• SEO & AI Visibility -- Audits both halves of being found: classic Google ranking, and whether ChatGPT or Claude can actually read and cite your site when someone asks about your industry.

Customers and reputation

• Review & Reputation -- Pulls your Google, Yelp, and Facebook reviews into themes with real quotes, drafts a reply to every one, and flags customers who look like they're drifting away.

• Ticket Deflector -- Reads a forwarded customer email, checks the order and refund status, and drafts a reply in your voice -- and can issue the refund itself once you say go.

Inventory

• Inventory Planner -- Figures out what's about to run out from actual sales velocity, sizes the reorder, and drafts the PO and the vendor email.

• Restock -- Runs Inventory Planner, then stages the matching bill entry so the invoice checks out against the PO when it lands six weeks later.

People

• Job Post Builder -- Writes the job post, the interview guide with a scoring rubric, and an offer letter template from a hiring brief.

• Hiring Screener -- Turns a stack of resumes into a ranked shortlist against that rubric, drafts replies to everyone (yes and no), and schedules the interviews.

Legal and funding

• Contract Review -- Reads an NDA or vendor contract, flags anything non-standard, and explains the risk in plain English -- built for businesses with no lawyer on staff.

• Grant & RFP Writer -- Filters a flood of grant listings or solicitations down to the ones you'd actually qualify for, then drafts the response from your past submissions.

Know where you stand

• Business Pulse -- A one-page snapshot: cash position, sales trend, pipeline movement, what's on the calendar, and the single most important thing that needs you today.

• Growth Pulse -- The marketing version: is the growth engine actually working, what's converting, what's not, and the three things worth doing about it this week.

• Monday Brief -- Chains Business Pulse with your saved reports and your calendar into one document to start the week.

• Marketing Monday -- The growth version of the same idea, merged with a reputation check and a competitor scan.

• Report Builder -- Turns "can you track this every week" into an actual recurring report, defined once and rerun on demand or on a schedule.

• Report Pack -- Delivers whatever report you defined, wrapped in a Business Pulse snapshot so the numbers have context, on the cadence you set.

Inbox

• Inbox Manager -- Sorts a full inbox into what needs you, what's drafted and waiting, and what's already handled, and hands bills, leads, and scheduling requests off to the skills that own them.

Why most people will install this and get nothing out of it

The failure mode here isn't the plugin. It's the sequencing.

Somebody installs Small Business, skips the onboarding, and asks it to do the thing they saw in a screenshot -- usually something ambitious, like the full monthly close or a complete outreach campaign. It half-works, because it was never told what their chart of accounts looks like, or that their "leads" actually come in through a Facebook form nobody checks. They call it a disappointment and never open it again.

The other failure mode is the opposite: somebody connects everything on day one -- the ledger, the CRM, three payment processors, Gmail, Slack -- and now they're approving fifteen drafts a day from a tool they don't trust yet. That's not automation. That's a part-time job reviewing an intern's work, except the intern never gets faster because you never gave it the chance to prove itself on one thing first.

And the thing worth yelling out loud: a lot of the most powerful skills here will only get good once they're sitting on top of your real system and real data . AP Processor is much better with a ledger behind it than a shoebox of PDFs. CRM Autopilot needs an actual CRM. If you're a five-person business running off spreadsheets and a shared inbox, a good chunk of these 44 skills are aspirational until you've made some other decisions first -- decisions about which tools to actually pay for, which is its own project.

None of that is a flaw in the plugin. It's just the truth about any tool this broad: the software is the easy 10%. Figuring out which of the 44 jobs matches something actually broken in your business, in what order, connected to what, with what you're comfortable letting it touch unsupervised -- that's the other 90%, and it's different for every business. Nobody wrote that part down for you , because nobody could. For better or worse, it's specific to how you actually run things.

We're not the only ones who noticed the gap between "44 skills exist" and "an owner can actually use them." One AI consultant who runs an agency and a paid automation community put out a full walkthrough of this same plugin and, out of 44 skills, only recommended 10 -- then built an entire paid course and community around helping small business owners customize and run the rest. That's not a knock on him. It's the same conclusion we came to from a different angle: the plugin is free, but knowing which pieces to turn on, for your specific business, is already something people pay for.

So below the fold: the actual setup order we'd run for our own clients, a starter kit for four different kinds of small business (so you can find yours and skip the guesswork), and a copyable approval-threshold template for deciding what this thing gets to touch on its own versus what always comes to you first.

If you want a second pair of eyes before you start connecting things, reply and send us two answers:

1. What does your business do?

2. What is the one job you’d happily never do again—chasing invoices, answering leads, sorting the inbox, forecasting cash, something else?

We’ll tell you where we’d start—and, just as importantly, what we’d leave off for now.

Getting it set up in an afternoon -- the order that actually works in 12 quick steps:

Read more

Good day . OpenAI built a tracking cookie for a product people confess their whole lives to, Higgsfield's “we open-sourced everything” moment turns out to be mostly plumbing, and Trump wants to rename the entire category of AI. Let's get into it.

OpenAI Built a Cookie That Follows You Everywhere Buchodi's Threat Intel

• What happened: A security researcher reverse-engineered OpenAI's ad pixel and found it drops a year-long cookie called __obi, tied to your ChatGPT account, that quietly rides along to every site running an OpenAI ad and reports back what you searched, read, and bought.

• Why it matters: This is the exact playbook Meta and Google built for ad tracking, now stapled to a product where people paste in medical questions, breakup texts, and tax returns. OpenAI classifies the cookie as “analytics,” not “marketing”, so even people who opt out of ad tracking may still be getting tagged.

• What everyone's saying: The researcher caught it firing on Chewy, Wayfair, HelloFresh, Coursera, and SeatGeek, and a dozen of the identifiers he tracked showed up on more than one advertiser's site. He emailed OpenAI on September 14th with two direct questions; the reply acknowledged the inquiry and answered neither.

• My read between the lines: Calling it “analytics” instead of “marketing” is the same move Facebook pulled for a decade: mislabel the tracker so the consent toggle doesn't apply to it. The part that should actually worry you isn't that OpenAI built adtech; every ad-supported product eventually does. It's that ChatGPT knows things about you no cookie ever has, and now it's wearing one.

📖 Further reading: AI Is a Trust Problem, Not a Tech Problem — written before we knew about the cookie, and it reads like a prediction now.

No, Higgsfield Didn't Open-Source Its Company Nowrap

• What happened: A viral post claimed Higgsfield's CEO “just open-sourced his entire $5.4B startup on GitHub.” He didn't. The real repo is a two-year-old GPU training framework Higgsfield has always had public; the commercial video-generation platform that actually earns the $5.4 billion valuation remains fully closed.

• Why it matters: This is what happens when two true facts get welded into one false headline: a real funding round (Financial Times, $400M at a $5.4B valuation) and a real open-source repo, combined into a claim neither fact supports on its own.

• What everyone's saying: The story spread across LinkedIn and Instagram faster than anyone fact-checked it, and a copycat repo, open-higgsfield-ai, is riding the confusion by branding itself an open alternative to a platform that was never released.

• My read between the lines: “We open-sourced everything” is quietly becoming a marketing genre of its own, a $5.4B company gets a week of goodwill press for donating code it was always going to donate. The tell is always the same: check whether the thing people are celebrating is the product, or just the plumbing around it.

📖 Further reading: OpenAI Codex: Apache-2.0, 120,183 Stars, and the Bill That Isn't in the Repo — the same open-source-halo trick, different company.

Trump's Poll to Rename “AI” Is Down to Two The Hill

• What happened: President Trump posted a Truth Social poll asking Americans to help rename “Artificial Intelligence,” calling the term “inaccurate, and very ineloquent,” and pitched “Superior Intelligence,” “Extreme Intelligence,” and “Supreme Intelligence” as replacements. By Sunday the field had narrowed to two finalists.

• Why it matters: Yesterday we told you about Trump's new “AI Force” (linked here); today he wants to rename the category the Force is supposed to run. Naming fights aren't just semantics: whoever controls the vocabulary of AI policy tends to control the frame of the regulation that follows.

• What everyone's saying: Commentators from Mashable to Forbes to Matt Walsh have all pointed out the obvious problem: “Supreme Intelligence” and “Extreme Intelligence” are not less “ineloquent” than the three letters they're replacing, they're just longer.

• My read between the lines: Nobody asked for this, which is exactly why it's going to work as a distraction technique: a renaming poll gets more engagement, and more headlines, than the actual China-competition and export-control fights happening the same week. Renaming the technology is easier than regulating it.

📖 Further reading: The US Government Just Took Anthropic's Best AI Model Offline — Here's Why — for when Washington's opinions about AI stop being funny.

Meta's Muse Really Wants Your Bank Login Business Insider

• What happened: WIRED's Reece Rogers spent several days with Meta's new personal AI agent, Muse, and found it “prioritizes data collection about me over actually accomplishing tasks,” repeatedly nudging him to connect his email inbox and banking information, and suggesting it photograph his meals to estimate calories.

• Why it matters: An agent is only as useful as the access you give it, book a flight and it needs your card, tidy your inbox and it needs your email, but Muse's pitch skews toward asking for more access than the task in front of it requires, and a separate test found it reading private message notifications unprompted.

• What everyone's saying: Meta frames Muse as “personal superintelligence for everyone,” and says training data can be anonymized on request; reviewers are less convinced that “anonymized” means much once an agent has read your bank statement.

• My read between the lines: Every AI agent launch this year has run the same trick: ship the assistant first, let it ask for access one convenient favor at a time, and let “consent” accumulate one “sure, why not” at a time rather than one big scary permissions screen. Death by a thousand yeses is still death.

📖 Further reading: I Make AI Versions of Myself for a Living. This One I Didn't Agree To. — the Muse story from the other side of the camera.

AI Flunked Southern Gothic. A Professor Noticed. Twin Cities

• What happened: English professor Billy J. Stratton had students prompt ChatGPT to analyze the “emotional atmosphere” of a scene from Carson McCullers' 1940 novel The Heart Is a Lonely Hunter. The model got a plot detail wrong (a bus became a train) and described a devastating farewell in flat, clinical language, missing the grief and dramatic irony entirely.

• Why it matters: This wasn't a gotcha, it was a lesson plan. Stratton's point: large language models are pattern-matching engines summarizing what's already been written about a text, not readers who've lived anything like the isolation, faith, and “grotesque” the Southern Gothic genre runs on.

• What everyone's saying: The essay's been re-run by outlets from Scroll.in to the Twin Cities papers, and it's landed at a moment when researchers elsewhere are documenting the same empathy gap in AI systems more broadly, this is turning into its own small literature.

• My read between the lines: The uncomfortable part isn't that AI got the scene wrong, it's that “clinical and cold” is also a pretty fair description of how a lot of humans now write about grief online. The professor's exercise is really a mirror, and not everyone's going to like what shows up in it.

📖 Further reading: The Font That Beat AI for About a Week — another small, human trick a model couldn't see coming.

That's your AI Brief for Monday.

—Artificially Intimidating