AI progress creates more work for humans, not less. Dive into our new report from @danshipper — and use the companion repo to read it with your agent 👇

.@danshipper’s Dot spotted a meeting conflict that wasn’t on his calendar. He was rescheduling a flight when his Dot Boo warned him about an overlapping meeting it had read about in Slack. Dan says the early product is still buggy. But this is an example of why he finds it useful: The agent can connect details across apps that a calendar check alone would miss. Watch the full episode: every.to/podcast/how-sam-alt…

“@OpenAI just killed your startup” is a recurring reaction to new AI releases. @sama argues that it misses how much there is left to build. His reasoning: OpenAI can imagine only a small fraction of what developers will create. Giving builders access to its models and tools lets them explore possibilities the company wouldn’t think of itself. Watch the full episode: every.to/podcast/how-sam-alt…

.@danshipper and @craigmod on the necessity of setting a boundary with AI. AI can be so compelling, it can be hard to look away, Dan admits, describing how he can’t stop checking on his agents once he’s set them on a task—which can sometimes run for 20 hours at a stretch. Craig relates: the idea of locking himself away with 10 people and “mainlining AI” ten hours a day for six months is exciting—but he deliberately holds back. If he doesn’t, he says, he’ll lose touch with the human part of himself capable of writing the “weird books” only he could write. Read the full episode transcript: every.to/podcast/transcript-…

You're probably sleeping on computer use. At Every, we’re handing agents chores that once required opening an app and clicking through a series of steps. Computer use lets AI click, type, and navigate those apps for you. Here are 17 ways our team is using it: 1. Fill out school forms. 2. Update six course presentations with new screenshots, assets, and hundreds of small edits. 3. Make video edits. 4. Check every link in a book’s PDF proofs, verify that each destination matches the surrounding text, and collect problems in a spreadsheet. 5. Find an old maintenance request and submit a follow-up about a missing dishwasher. 6. Add kids’ school and camp events to a calendar. 7. Browse iPhone photos, identify items to sell, and create marketplace listings. 8. Clear WhatsApp storage through iPhone Mirroring. 9. Export images from Figma and attach them to posts in Typefully. 10. Manage app builds. 11. Assemble, rig, and repair characters in Blender. 12. Talk to Verizon support about a better plan, reading responses and continuing the conversation while you do something else. 13. Send Slack messages with attachments when the connector falls short. 14. Diagnose and fix a slow computer. 15. Transfer internet service to a new apartment. 16. Request an AI usage report from Slack and get it back in the same thread, using our internal Mac app to hand the task to Codex. 17. Build a Google Slides presentation from reference slides, then turn corrections into saved instructions. You don’t need a big project to see whether computer use helps you. Pick a small chore whose result you can check. Watch the first attempt, review what the agent did, and use that experience to decide what to hand over next. More computer use workflows curated by @lauraentis: every.to/context-window/you-…

Evals are showing up in job listings. @Lennysan shared 25 product management openings, and nearly half asked for experience writing tests of how well an AI system performs a specific task. At Every, @Nityeshaga is helping people build benchmarks based on their own work and standards. When a new model launches, they run those tests and decide whether to switch. When several models can do the job, the decision comes down to speed, cost, and how closely the output matches what you'd have written. Start with tasks you already do and a few examples you’d approve. A leaderboard won’t tell you whether a model is worth switching to. Read the full piece: every.to/context-window/why-…

What changes for an AI app when users can bring their own ChatGPT subscription? @danshipper explains the developer’s tradeoff: Limit usage or pick a cheaper model to keep costs manageable, potentially making the app worse. A portable subscription could let users access the models in their plan instead. @sama says he wants to take his subscription between apps, without signing up for a new one each time. Watch the full episode: every.to/podcast/how-sam-alt…

Faster AI responses change how @sama thinks through a problem. He does all his prompting on Ultrafast. The shorter feedback loop lets him move quickly between an idea, a response, and another iteration—whether he’s building something or working through a question. Watch the full episode: every.to/podcast/how-sam-alt…

5 @Microsoft Copilot updates you may have missed: • One app for chat, code, and Office. The new Copilot brings Word, Excel, PowerPoint, and a persistent agent called Autopilot into a shared interface. • Autopilot can keep working in the background. Give it a goal and it can act on your behalf, with access governed by your organization’s permissions. • A Code tab for building and sharing small apps. It’s designed for people who aren’t developers, with apps running in secure environments. • Memory of how you work. Autopilot draws on your last 30 days of work, calendar, and place in the org chart and remembers preferences and corrections. • Routines for recurring jobs. Set up a daily briefing or a standing job that keeps watch on a project while you work on something else. They’re rolling out in previews and early access over the coming months. Read @ryansloan’s dispatch from the launch: every.to/p/copilot-gets-a-se…

.@sama’s business dashboard changes what it shows as his priorities change. It reads his Slack messages and continually updates a summary of the business just for him. 6 months ago, he says, he was more focused on revenue and growth. His focus has since shifted toward safety, alignment, and security. Watch the full episode: every.to/podcast/how-sam-alt…

Vibe checking the food at @OpenAI Dev Day:

When your job is to experiment with AI, procrastination can look exactly like work. @kplikethebird had an essay to write… but she couldn’t stop tweaking her AI-built worlds. Exploring what AI makes possible is part of her job. So was finishing the essay. When both were equally enjoyable, it was hard to determine which one deserved her afternoon. She tried turning the worlds into something other writers could use. But adapting an experiment for others required substantial extra work. She set the project aside—she had already drawn lessons from the experiment for her essay. Read Katie’s piece: every.to/working-overtime/wh…

.@hammer_mt on using AI to simulate a village. Mike asked Fable 5.1 to build one based on “Generative Agents: Interactive Simulacra of Human Behavior.” When he came back an hour later, it was working with what he describes as a Pokémon-style aesthetic. He used it to explore how messages diffuse, like news of an upcoming Valentine’s Day party. Mike calls it a toy version of what could be possible. One day, he says, policymakers and economists could A/B test messages and see how they travel. Read the full vibe check: every.to/vibe-check/fable-5-…

.@danshipper on why Fable beats Astra for long-running tasks. Astra’s tendency to misunderstand the sense of prompts and add too many “bells and whistles” to things you ask it to build makes it a less reliable companion than Fable for big delegation tasks. Every’s senior editor Jack Cheng recently asked Astra and Fable to build him an app to digitize his handwritten journals—and the results were telling of where each model’s strengths lie. Read the full vibe check: every.to/vibe-check/gpt-6-as…

Every business will have its own agent, the way every business has a website—and customers will expect to reach it from inside @OpenAI’s ChatGPT or @claudeai, not by visiting the company's site. Companies are bringing their agents into conversational platforms like @SlackHQ, @Microsoft Teams, and @WhatsApp. @tryramp, for example, is issuing people corporate cards via Slack DMs. At Every, we use our company agent daily. Our Every Agent is in beta: every.to/agent?utm_source=x&… Read the full piece: every.to/context-window/why-…

.@danshipper and @nataliazarina on why modern work is like gardening. Before gen AI, knowledge workers were like sculptors—doing everything with their hands. Now, Dan says, they’re more like gardeners: they don’t make the plants, but create the conditions for them to grow. Natalia frames this as an evolution: knowledge workers used to be individual contributors using systems to do tasks themselves. Now, they’re managers building systems to support a bigger team—one that includes AI. Having good people management skills—creating conditions for success—can also make someone a good “model manager” of AI. Link to the full episode on how Natalia, Every’s head of consulting, uses Codex every day: every.to/podcast/transcript-…

.@danshipper on how AI helps him manage his company better. Fable 5.1 is proving stronger at what Dan calls “discernment”—discerning whether information is relevant enough to the situation at hand, or a person’s interests, not just what’s technically related. He tested it by having the model build him a feed of every meeting at Every, complete with short summaries. It also flagged meetings with 50/50 decisions where it would be helpful for him to weigh in. Dan says it’s the first model he’s used that has pulled out things worth his attention, giving him a new lens into his own company. Feeds used to be the domain of social media companies with millions of data points. Now, Dan says, you can build your own in natural language. Read the full vibe check: every.to/vibe-check/fable-5-…

Ran out of budget the second he tried Ultrafast. Our Dev Day chat with @MatthewBerman

ATTENDING: Janette Roush of @BrandUSA will attend Thesis: 2027. Apply to attend Thesis, our inaugural conference on work and AI: every.to/thesis-2027?utm_sou…

Dev Day with @danshipper, from the hotel door to interviewing @sama

.@sama spent 10–20 minutes trying to find something he remembered seeing in Slack. He gave up. The next day, his Dot found it. He’d forgotten it was a screenshot, not a text message. Dot had noticed the unsuccessful search, kept looking overnight, and used image recognition to find the screenshot. Watch the full episode: every.to/podcast/how-sam-alt…