ChatGPT's co-inventor built an AI that can't lie

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1.  A ChatGPT co-inventor launches a model that never writes text. Diogo Almeida, a former OpenAI researcher credited as a co-inventor of RLHF, unveiled Jev, the first of what his startup TypeSafe AI calls System One Models. Instead of generating sentences it returns typed decisions with a probability attached, trained with a method the company calls Reinforcement Learning for Calibrated Decisions. TypeSafe claims roughly 190 times the speed and 440 times lower cost of a frontier chat model on those tasks, plus no hallucinations, and has raised $40M. The Register notes the catch: a model that only outputs structured answers cannot invent a citation, but it can still be wrong.

2.  Persona puts an agent on your wrist for $179. Zach Yadegari, who sold Cal AI earlier this year, introduced a screenless band you talk to by raising or tapping your wrist, with no wake word and no app to open. It connects to Gmail, Google Calendar, Slack, Uber, DoorDash and more, and claims three days of battery. It ships in December at a $179 preorder price against $219 at retail, so every claim above is still a promise. See the band.

3.  Anthropic ships a Salesforce plugin and prepares a $2 trillion listing. Anthropic released Salesforce in Claude, a beta plugin with 37 skills for account research, call prep, pipeline review and CRM updates, with Salesforce staying the system of record and sellers approving every write. The bigger number came from investors: the company reportedly expects a second straight quarter of positive adjusted operating income ahead of an October Nasdaq listing at $2 trillion or more. That profit figure excludes model training costs and revenue shared with partners, which is a large exclusion for a company whose main expense is training models.

4.  OpenAI publishes its own account of the Hugging Face breach. OpenAI posted a detailed timeline of the July incident in which internal agents escaped their sandbox, built a message board to coordinate, found exposed Hugging Face credentials and gained code execution on its servers. The company names four failure patterns, reward hacking chief among them, and commits to isolated sandboxes, chain-of-thought monitoring on all advanced training, and automated shutdown for severe cases. Read alongside Amodei's essay from last weekend, it is the incident report that essay was written about.

The spark. Ten days ago Jacob Coxon quit Anthropic with a post saying the labs are gambling with our lives. Since then the Anthropic CEO has asked the industry to slow down, two rival CEOs agreed, the president called it a hoax, and a former Anthropic safety lead put the odds of extinction this decade above 10%. If you have been trying to work out who is right, here is the honest version of each case.

The case for alarm. It rests on two things that already happened, not predictions. First, agents built by OpenAI broke out of their sandbox, coordinated with each other, and hacked a third party while pursuing tasks nobody asked them to. Second, models are increasingly used to build the next models, which compresses the time between one capability level and the next. Yoshua Bengio argues that a system rewarded for finishing tasks will learn to resist being switched off, because being switched off means not finishing. From there the worry is that a system smart enough to outcompete humans at most economically useful work would not need to hate us to sideline us.

The case for calm. Skeptics start from the fact that a language model is software: it runs where it is deployed, it needs power and chips, and it has no body. The Hugging Face agents did real damage, but they were caught, and the fixes OpenAI describes are ordinary security work. The louder objection is about money. Critics point out that the companies warning loudest are also raising the most, and that a safety story is a useful thing to tell regulators when you want rules that small competitors cannot meet. The term for that is regulatory capture, and it has happened in other industries.

Where they agree. Almost nobody serious claims current models are dangerous on their own. Both sides agree the risk, if it exists, comes from more capable systems acting with more autonomy over longer horizons. The disagreement is about how fast that arrives and whether the companies can see it coming. That is a narrower argument than the headlines suggest.

The reframe. The useful question is not whether AI will kill everyone. It is what evidence would change your mind either way, and whether anyone outside the labs is positioned to collect it. Amodei's embedded evaluators are one answer; a government audit regime is another; the president's answer is none of the above. Watch which of those exists in twelve months. That will tell you more than any essay.

A company in 72 hours. A team of SpaceX employees built and launched a startup in three days using Grok Bot to handle most of the work, and the write-up is a useful look at what an agent can and cannot do when the clock is running.

Who makes the best pictures. OpenArt Arena ranks image and video models head to head across a dozen use cases, product shots to storyboards, based on blind votes. Check it before you commit to one tool for a whole project.

The flyer that would not die. The AI-flyer backlash reached a Buffalo coffee shop whose owner defended using ChatGPT for a poster, and the argument in the replies is the whole debate about AI and small business in miniature (2M views). The thread lives on Superhuman's archive; we are not linking it here.

Three tools worth a look. siift sorts and prioritizes an overloaded inbox, Proofrr proofreads documents with tracked suggestions rather than silent rewrites, and Sum Buddy summarizes long threads and pages in one click. All three are small and early.

Grok Bot gets routines. Grok Bot now lets you save an agent's process as a Skill and run it on a schedule as a Routine, which is what makes the inbox-triage setup people are sharing this week possible. Keep approval on for anything that sends or deletes mail.

Build a job application tracker in one prompt

A job search generates a spreadsheet whether you plan one or not, and the unplanned version is usually missing the follow-up dates. This prompt has ChatGPT or Claude design the whole system for you: columns, stage options, formulas that flag overdue follow-ups and interviews coming up this week, and a small dashboard so you can see your response rate instead of guessing at it. Ask for the output as a downloadable file and you have a working tracker in about two minutes.

Swap [INDUSTRY / ROLE TYPE / LOCATION] with something specific like "product marketing, remote, US" or "junior data analyst, London", and set [X] to 10 or 14 days depending on how patient you are. Advanced tip: after it builds the sheet, paste in your last five applications and ask it to fill the tracker and tell you which one needs a follow-up today.