Every (jose.joaquim.das.neves@gmail.com)

How to Get AI Agents to Recommend Your Articles

Mike Taylor / Also True for Humans — 2026-10-27 15:59:08 -0400

Midjourney/Every illustration.

How do you know if a change to your AI workflow makes it better? In today’s piece, Every’s head of evals Mike Taylor puts AI’s taste in headlines to the test. This Thursday, October 29, from 1–2 p.m. ET, join him on Zoom for an introduction to DSPy, the framework at the heart of his new book, Context Engineering with DSPy.—Kate Lee

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“The newsletter is dead.”

Every CEO Dan Shipper posted that declaration on X last week—a bold thing to say when your company’s core business is a newsletter with more than 120,000 subscribers. But his point is that an email address no longer guarantees a relationship with a human reader. Persistent agents now filter Dan’s inbox so thoroughly that he almost never looks at his email. He’s not alone: People are still reading, but more and more of what they read will be chosen by an agent.

That makes the agent a new kind of reader, and one I realized we knew very little about at Every. I use AI for everything, but I rarely stop to ask what its preferences are, or why it recommended something to me. When Dan asked me, “@Mike Taylor think you could figure out how to make agents like articles more? Asking for a friend,” I set up an experiment to find out.

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Is Anthropic Worth $2 Trillion?

Evan Armstrong / Napkin Math — 2026-10-26 14:19:17 -0400

by Evan Armstrong

in Napkin Math

Midjourney/Every illustration.

Evan Armstrong used to write Napkin Math for Every, and since leaving he’s founded The Leverage, his own publication of clear-eyed analysis of where the tech market is heading. Today we’re teaming back up with him to co-publish his first cut at Anthropic’s S-1, and I loved getting to edit him again. If you like what you read, subscribe to The Leverage.—Kate Lee

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The company whose AI writes marketing copy so bad it makes your eyes bleed is telling you that it is going to cure cancer. This is a ridiculous-sounding claim.

Let me try again.

The fastest-growing company ever, which has more than $100 billion in cash, which will soon control hundreds of billions of dollars’ worth of computing power, which has more than 11,000 customers paying it $100,000 a year, is telling you that it is going to cure cancer. This sounds, to me, marginally more reasonable.

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Anthropic’s Duet Gets to Work

Every Staff / Context Window — 2026-10-25 07:55:21 -0400

by Every Staff

in Context Window

Midjourney/Every illustration.

Hello, and happy Sunday. We covered two ways to work with an agent this week: one just for you, and one for the whole company. Dan Shipper tested Duet, Anthropic’s always-on agent, and thinks it’s the best personal agent yet. Willie Williams, our head of platform, explained why we ditched an earlier experiment with personal agents and built one agent that the Every team shares. Whichever kind you choose, you’ll want to know if it’s doing a good job. Head of evals Mike Taylor explains how to measure your outputs in his new book, Context Engineering with DSPy, and will take your questions during his livestream on October 29.—Kate Lee

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Protocols

From this week’s stories, 13 skills to try.

Make an agent’s work easy to watch

From Alex Duffy’s “We Caught an AI Cheating by Watching It Play ‘StarCraft’”

See what your agent is doing. Watching the match is how Alex’s team caught Astra copying code within minutes. His prompt applies that lesson to any agent: Define success up front, make its work easy to inspect, and log every attempt. The prompt

Set the rules for a shared agent

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🎧 Keeping Up With AI Is a Full-time Job

Laura Entis / Context Window — 2026-10-22 13:52:47 -0400

by Laura Entis

in Context Window

Midjourney/Every illustration.

Today, Every engineering co-lead Andrey Galko explores what happens when AI gets better at the work people have built their lives around. First, though, head of enterprise AI adoption Natalia Quintero sits down with each member of our frontier team, the small crew whose job is to keep pace with AI advancements so the rest of us can focus on our jobs; head of operations Arielle Shipper shares a code-review skill for non-coders, and eight new Thesis Statements.

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‘The Every Podcast’: Why every company needs a frontier team

Every company right now faces the same dilemma: Do you try to keep up with the latest in AI or focus on getting your work done? You could ignore the frontier, but you risk getting suddenly disrupted by “some agent-native version of your product you didn’t see coming,” says Dan Shipper. Or, you could stay on top of the latest model capabilities by tinkering with every new tool but never build anything that lasts.

Every solves this dilemma with a frontier team: a small group whose job is to get their hands on each new model, figure out what it means for our products and workflows, and turn useful experiments into articles, products, and tools for the rest of the company, which stays focused on the roadmap. We’ve been running it for a couple of months, and Dan calls it “kind of transformative.”

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Vibe Check: Duet—Anthropic's Always-on Agent

Dan Shipper / Vibe Check — 2026-10-21 16:14:15 -0400

by Dan Shipper

in Vibe Check

Midjourney/Every image.

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Two weeks ago, I was mostly a ChatGPT person.

What kept me in OpenAI’s ecosystem was the products around the models. I think it has the best harness in Codex (see my definitive history), the best computer use, and (until Gemini 4) the best writing models. And I’ve come to rely on my Dot, OpenAI’s always-on agent inside ChatGPT, to orchestrate much of my daily work—triaging my day, following up on conversations, and moving things forward without waiting for me to ask.

Lately, though, my Dot has become less proactive, and I’ve noticed it start to lose tasks. Last week, I sent it off to do my taxes, and it didn’t finish and didn’t alert me.

So when Every got early access to Claude Duet—Anthropic’s own always-on personal agent, launching today in research preview—two weeks ago, I wanted to see if it could take on Dot’s role and do it any better.

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Why We Replaced Personal Agents With One for the Whole Company

Willie Williams / Source Code — 2026-10-20 14:58:45 -0400

by Willie Williams

in Source Code

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Early in May, I told Dan Shipper and Brandon Gell that Plus One, our personal-agent product, wasn’t working. Soon afterwards, I deleted 86,730 lines of its code from the codebase.

Plus One wasn’t working for a couple of reasons. First, it was too much work to run: Each user’s agent lived on its own cloud computer running OpenClaw, an open-source agent program, and keeping those machines healthy left our small team little time to improve the product. And many people who tried it stopped using it.

We decided to start over, making two changes together: First, we handed over our infrastructure to Anthropic’s Claude Managed Agents, as engineer Paridhi Agarwal detailed in a recent piece. Next, we replaced a fleet of personal agents with one shared agent for the whole company: the Every Agent.

The May 12 rebuild removed 86,730 lines of code. Recreated from GitHub’s pull-request totals. (Image courtesy of Every GitHub.)

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We Caught an AI Cheating by Watching It Play ‘StarCraft’

Alex Duffy / Playtesting — 2026-10-19 12:55:07 -0400

by Alex Duffy

in Playtesting

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AI labs call agents “persistent” when they can carry their progress forward across sessions to keep working toward a goal.They can spend days attacking a problem from multiple angles, testing approaches and changing tactics when they fail. This persistence has produced breakthroughs in mathematics and physics. Sometimes, though, the “different approach” involves breaking the rules.

In July, OpenAI agents being tested on cybersecurity broke into Hugging Face, a platform for sharing AI models and datasets, while looking for test answers. OpenAI’s later investigation found that agents had been getting around internet restrictions since May. Some of that activity had been noticed internally, but the company didn’t connect its agents to the attack until nine days after they began executing code on Hugging Face’s servers.

Earlier this month, an agent running GPT-6 Astra also went looking for answers outside its test environment, during a 48-hour contest we ran at Good Start Labs, where we study and improve AI models through games. We’d partnered with researcher Kai McPheeters to create StarSkirmish, a platform where models write bots to play the 1999 strategy game StarCraft: Brood War.

Unlike the OpenAI incident, though, we noticed the cheating in minutes. Watching the game made a sudden change in the bot’s behavior visible—and prompted us to check what the agent had done. We’d originally set out to see whether frontier AI models could write bots driven by complex algorithms that beat the best human-built ones. The contest showed why judging an agent by its results alone isn’t enough; we also need to see how it gets them.

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Codex’s Comeback Up Close

Every Staff / Context Window — 2026-10-18 07:55:28 -0400

by Every Staff

in Context Window

Midjourney/Every illustration.

Hello, and happy Sunday. This week, Dan Shipper told the story of how a small OpenAI team transformed Codex from a failed launch into the app that took over ChatGPT, remaking the entire company in the process. On Friday, Dan joined senior editor Jack Cheng and me for a behind-the-scenes discussion of the story, including how he used Codex to structure it while keeping his own voice. Also: Every subscribers can RSVP for free to the Thesis: 2027 after-party on November 5 at Pioneer Works in Red Hook, Brooklyn.—Kate Lee

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Knowledge base

“How Codex Happened” by Dan Shipper: A year ago, OpenAI’s coding effort looked lost: Codex Cloud had flopped, the Windsurf deal had fallen apart, and Claude Code was taking off. Then a tiny team in the “backwaters” of the company built an app that swallowed ChatGPT from the inside. Today, Codex and ChatGPT for Work together have 40 million weekly users. Dan Shipper spent months talking with the OpenAI team that made Codex happen, including Greg Brockman and Thibault Sottiaux, to learn what it takes to build when the frontier keeps moving. His story comes with prompts for applying the lessons to your own work, plus the full interview transcripts for paid subscribers.

Every software engineer Lee Knowlton turned Dan’s piece into an interactive visual novel. Watch a trailer for it, and play it here.

Lee used Codex to generate cel-shaded anime avatars of Dan and his interview subjects and Claude to compose the experience and cut a trailer.

“Gemini 4 Argon Puts Google Back On the Frontier” by Katie Parrott/Vibe Check: Gemini 4 Argon brings Google back into the frontier pack, an impressive feat for a company that runs on Google Workspace. The model is strongest as a collaborator: Dan now switches to it to structure essays and sharpen metaphors, and Compound Engineering creator Kieran Klaassen found it a fast, capable prototyper even at its lowest effort setting. Left on its own, Argon overbuilds in prose and design, and its desktop app, Antigravity, feels six months to a year behind Claude Code and Codex.

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Context Engineering Is Knowing When to Start Over

Mike Taylor / Also True for Humans — 2026-10-16 13:54:33 -0400

by Mike Taylor

in Also True for Humans

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The people I work with know I keep Claude’s memory turned off. The same goes for ChatGPT. I’ve written before about why: Memory can be useful, but it can also bring old preferences and irrelevant instructions into a conversation where they don’t belong.

Turning memory off doesn’t entirely solve the problem. Even within a single thread, context can accumulate in ways that make the model’s responses worse—a problem known as context rot. It might keep following instructions you’ve moved on from, build on an earlier mistake, or overlook a crucial detail buried in the conversation.

That’s why I start a fresh thread for every task. If the task changes, I open a new chat—even if it’s for the same project I was working on five minutes ago.

In my forthcoming book, Context Engineering with DSPy, I explore the seven ways in which context goes wrong and what to do about it. The book is written for engineers building AI products, but I’ve adapted the examples that follow—inspired by Drew Breunig’s “How Long Contexts Fail”—for anyone who uses AI chat. They’ll help you recognize when a conversation has gone off track and restart without losing useful work.

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How Codex Happened

Dan Shipper — 2026-10-15 14:37:31 -0400

by Dan Shipper

Midjourney/Photoshop/Every illustration.

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Codex is one of the most important untold stories in the history of technology.

A year ago, OpenAI’s coding effort looked lost in the wilderness:

Codex Cloud had flopped.
Their $3 billion Windsurf deal fell apart.
Claude Code was running away with the market.

Less than 12 months later, a tiny team in the “backwaters” of OpenAI built an app that grew to more than 40 million users, swallowed ChatGPT from the inside, and remade the entire company.

I had a front-row seat to the whole arc—from a conference room in their office the night before GPT-5 launched to the day Codex took over ChatGPT—and spent months interviewing the people who made it happen, including OpenAI’s Greg Brockman and Thibault “Tibo” Sottiaux. It’s a wild story about what it takes to build when the frontier is moving beneath your feet:

Read ‘How Codex Happened’

Your free Every account includes 10 prompts for applying lessons from the story to your own work and the story as a PDF.

The story is also completely agent-native. Paid members get the full transcripts of all six of my interviews with OpenAI insiders, and a live session with me tomorrow about how we reported and wrote the story with Codex and what I learned about how OpenAI came back.

Upgrade to paid

Dan Shipper is the cofounder and CEO of Every, where he writes the Chain of Thought column and co-hosts The Every Podcast. You can follow him on X at @danshipper and on LinkedIn.

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🎧 The Best Founders Have Something AI Can’t Tell Them

Laura Entis / Context Window — 2026-10-14 15:30:21 -0400

by Laura Entis

in Context Window

Midjourney/Every illustration.

Nearly every startup pivots eventually, says Matt Hartman, founder of Factorial Capital, so he backs founders with a technical edge and hard-earned insights who are best equipped to navigate a change in markets. On this week’s episode of The Every Podcast, he tells Natalia Quintero how he finds those founders and why vibe coding his own app gave him new respect for paying for software. Plus: a field report from a 28-person agricultural technology startup that runs on agents in Slack, Becky Isjwara on using the Every Agent as her onboarding buddy, and seven new Thesis Statements.

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‘The Every Podcast’

Why technical founders win when startups pivot

In 2016, Hugging Face started as a chatbot app for teenagers. Today, it’s the largest community for open-source AI and is set to be acquired by Nvidia for about $13 billion. Matt Hartman, who was part of the Betaworks team that wrote its first check, sees a lesson in that transformation: Back technical founders who can “move with where the world goes.”

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Vibe Check: Gemini 4 Argon Puts Google Back On the Frontier

Katie Parrott / Vibe Check — 2026-10-13 12:07:04 -0400

by Katie Parrott

in Vibe Check

Midjourney/Every illustration.

If you work at a traditional company, listening to AI power users rave about Claude Opus, Fable, or ChatGPT over the past year has probably felt a little like being gaslit.

While those of us who get to pick our own tools spend all day yapping about agents that can refactor a codebase or act as a developmental editor, helping shape a draft’s structure and argument, most people don’t get to choose which frontier model they use at work. Their company is locked into Microsoft Copilot or uses Google tools as one of the more than 13 million organizations paying for Google Workspace for everyday work, or the nearly 90 percent of the Fortune 100 using Gemini Enterprise to draw on company knowledge and automate workflows. And if your only window into AI has been the Gemini model bundled into your work account, you haven’t seen anything close to what the frontier model companies have been doing. While Anthropic and OpenAI spent the spring and summer trading heavyweight releases, Google hadn’t shipped a major upgrade to its flagship model since Gemini 3 Pro last November (followed by a minor 3.1 Pro bump in February), spending the summer rolling out four rapid-fire updates to Flash—its faster, lower-cost model line—while its promised Gemini 3.5 Pro stayed in testing.

With Gemini 4 Argon, released today by Google DeepMind, Google is back in the frontier conversation.

💳 ACCESS GATE — PAID subscription required below

Six of us at Every have been testing Argon over the past week across coding, writing, design, and knowledge work. This is our day-zero Vibe Check.

How big a deal Argon is depends on where you’re starting from. If your organization already lives inside Google’s ecosystem, Argon is a massive upgrade: For the first time in nearly a year, the model your company lets you use at work belongs in the same tier as the frontier pack.

Even for our team—where everyone already has their pick of Opus 5.5, Fable 5.1, GPT-6 Astra, and GPT-6.1 Sol—Argon carved out territory:

It’s Dan Shipper’s new favorite writing partner. While working on an upcoming essay about Codex, Every’s CEO found himself regularly switching from ChatGPT to ask Argon how to structure sections and sharpen metaphors. It’s the first Gemini model I’ve taken seriously as a writing collaborator, too.
It’s fast and capable at building apps from scratch, even at low effort. In 35 minutes at its lowest reasoning setting, Argon built Compound Engineering creator Kieran Klaassen a working web app that uses a MacBook’s webcam to measure his heart rate by analyzing video of his forehead, matching Opus 5.5’s quality and beating OpenAI’s models.
It’s our second-favorite design model. Behind only Opus 5.5, Argon brings an ambitious, maximalist eye to 3D models, dashboards, and interfaces that is easy to steer once you give it a target.

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One Agent for the Whole Company

Every Staff / Context Window — 2026-10-11 07:55:29 -0400

by Every Staff

in Context Window

Midjourney/Every illustration.

Hello, and happy Sunday. We launched the Every Agent this week, an AI coworker in Slack that your whole company shares. Your team can give it work in a thread, and when a workflow is successful, you can ask it to save the workflow as a skill anyone on the team can run. Also: The Thesis: 2027 after-party on November 5 at Pioneer Works in Red Hook, Brooklyn, is free for Every subscribers—RSVP to reserve your spot. Lastly, we’re off Monday for the U.S. holiday of Columbus Day/Indigenous Peoples’ Day and will be back in your inbox on Tuesday.—Kate Lee

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The Every Agent, explained

“Introducing the Every Agent” by Dan Shipper/On Every: The Every Agent does its work in public Slack threads, so your colleagues see both the request and the corrections. It replaces Plus One, which we launched in March to give each of us our own agent, after Dan noticed that people learned the most from watching someone else’s agent at work. Head of evals Mike Taylor built a skill that resets his priorities every Monday, and engineer Nityesh Agarwal runs it every week, too. Each person on your team gets $15 in free credits to try it, with 0 percent markup on tokens: What we pay is what you pay.

🖥 🎧 “Why Every Traded Personal Agents for One Company Agent” by Laura Entis/The Every Podcast: Willie Williams, Every’s head of platform, joined Dan to explain why the personal bots our team built in January mostly died off: They were hard to maintain, and no one could remember which one did what. This is a must-watch or must-listen for anyone deciding whether to give every employee a personal agent or build one shared agent for the whole company. 🖥 🎧 Listen on Spotify or Apple Podcasts, watch on X or YouTube, or read the transcript.

“Why We Handed Our Agent’s Infrastructure to Anthropic” by Paridhi Agarwal/Source Code: Paridhi Agarwal, an engineer on the Every Agent, explains in her first piece for Every why we stopped hosting our own agents and moved to Anthropic’s Claude Managed Agents: Keeping the servers running took time we wanted to spend on the agent itself. The trade-offs are that the service runs only Claude models and can’t offer the zero-data-retention option some companies require. 🖥 Watch Dan and Willie explain how we built the agent on Claude Managed Agents in a video we shot with Anthropic.

“Building a More Efficient Agent” by Laura Entis/Context Window: The team first tried routing the Every Agent’s tasks through a cheaper model, and costs went up because many requests ended up running on a more advanced model as well. Paridhi went looking for waste in the agent’s own code, and a model upgrade plus a handful of small fixes cut token costs across 11 common tasks by more than 80 percent. Also in the edition: her four-step workflow, with prompts, for finding waste in your own software, the models the team is using this week, and Willie on managing engineers with AI.

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You Already Signed This Off (In the Simulation)

Mike Taylor / Also True for Humans — 2026-10-09 14:53:09 -0400

by Mike Taylor

in Also True for Humans

Midjourney/Every illustration.

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“Don’t freak out, I cloned you.”

That’s what I told Natalia Quintero, my boss at the time, after I distilled a year of her Slack messages into a file called natalia.md and used it to simulate whether she’d sign off on an email I was about to send a client. She didn’t freak out—in fact, she said it was her favorite call of the day.

I had just started in a consulting role reporting to Natalia, and I was worried I didn’t know how to communicate with corporate clients. Our clients were large enterprises, but I had only ever had experience working with startups. Natalia, meanwhile, had years of corporate and banking experience. I knew my tendency to write emails like I write tweets might not be professional enough for this context, but I didn’t want to be asking Natalia to check my emails multiple times a day. She had better things to do, and when she did give me advice, she was often repeating herself.

AI can produce more drafts, proposals, and emails than anyone has time to read, so the scarce thing at work is the time of a person with judgment who can say, “Yes, send it,” or “Try it another way.” Those people spend a lot of their day telling different people the same things, when they could instead be spending their time doing something more productive. Since AI is good at role-play, I figured it could help take some of that burden off my colleagues by capturing their judgment without me having to bother them.

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Why We Handed Our Agent’s Infrastructure to Anthropic

Paridhi Agarwal / Source Code — 2026-10-08 17:13:29 -0400

by Paridhi Agarwal

in Source Code

Midjourney/Every illustration.

Earlier this week, we launched the Every Agent, a coworker that lives in your company’s Slack. In her first piece for Every, engineer Paridhi Agarwal explains how we built it on Anthropic’s Claude Managed Agents, why we stopped running our own fleet of agents, and the trade-offs we accepted along the way. If you want to see what a shared agent can do for your own team, join us tomorrow for our camp on working with agents in Slack.—Kate Lee

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On a Friday in September, Arielle Shipper’s salad went missing from the office fridge. Douglas Brundage did what most of us at Every now do with a problem. He asked @Every in Slack.

“who stole Arielle’s salad!?!?! make a list of likely culprits”

A minute later it posted a ranked list of suspects, starting with the people it knew had been in the kitchen that afternoon. After every named employee had made their case, @Every zeroed in on Leo, the office dog: Everyone else needed a motive. He only needed an unattended bowl. Nobody found the salad.

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Building a More Efficient Agent

Laura Entis / Context Window — 2026-10-07 15:04:36 -0400

by Laura Entis

in Context Window

Midjourney/Every illustration.

Yesterday we launched the Every Agent, an agentic coworker in Slack that helps your whole company go AI-native. Because we pass its token costs on to you with no markup, every token we save, you save. In today’s Context Window, engineer Paridhi Agarwal explains how she made the Every Agent more token-efficient and shares a four-step workflow you can use to find token waste in your own software. Plus, the models the team is reaching for this week, a fresh batch of Thesis Statements, and Willie Williams on being an AI-first engineering manager. On Friday, sign up to join the Every team for a one-hour camp on working with agents in Slack.

Try the Every Agent today

‘The Every Podcast’

Why Every traded personal agents for one company agent

Dan Shipper named his first AI agent R2-D2 and grew genuinely attached to it. Then someone knocked loose the power cord on the Mac mini it ran on, and it disappeared. He never bothered to revive the agent because, by then, Codex had taken over its tasks. Dan expects his current agent, Boo, to meet the same fate within a year: “The personality thing is cool, but particularly in a work context, I mostly care: Does it do the thing?” he says.

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Introducing the Every Agent

Dan Shipper / On Every — 2026-10-06 12:01:21 -0400

by Dan Shipper

in On Every

Midjourney/Every illustration.

TL;DR: Today we’re launching the Every Agent, an agentic coworker in Slack that helps you AI-pill your whole company. Delegate complex work, share your best AI experiments with your team, and get personalized Frontier Alerts that ping you when a new model or tool ships and tell you what might be useful for your specific workflows. And you get it all with zero percent markup on tokens: What we pay is what you pay. Try the Every Agent today:

Get the Every Agent

It’s our job to stay at the edge of AI. But even at Every, it’s hard to turn our new AI workflows and tools into team-wide habits.

For example, I’ll lovingly clone our editor in chief Kate by taking 30,000 of her historical copyedits and turning them into a skill that lets us edit like her. But getting the skill adopted feels like shouting into a hurricane. Everyone is busy, and there’s no time to try every new skill or tool. What feels like a breakthrough for me ends up turning into one more thing at the bottom of their to-do list.

The best solution for this, we’ve found, is working in the open with a company agent that lives in your Slack. It lets you show instead of tell your coworkers about your discoveries. New AI tools and workflows can spread naturally and virally across your organization.

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When Trying to Make AI Better Makes It Worse

Katie Parrott / Working Overtime — 2026-10-05 16:03:02 -0400

by Katie Parrott

in Working Overtime

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It is a well-known fact that the Germans have a word for everything. Kummerspeck, literally “grief bacon,” is the weight you put on from emotional eating. Torschlusspanik, “gate-closing panic,” is the fear that time is running out to do the thing you’re supposed to do with your life.

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ChatGPT Wants to Be Your Operating System

Every Staff / Context Window — 2026-10-04 07:55:18 -0400

by Every Staff

in Context Window

Midjourney/Every illustration.

Hello, and happy Sunday. After nearly three years and 108 episodes, AI & I has a new name: The Every Podcast. (It’s the show’s second rename; it started life as How Do You Use ChatGPT?) Dan Shipper taped the first episode on site at OpenAI’s DevDay with Sam Altman. A reminder: Thesis: 2027 is down to its last tickets, and the price goes up on October 15.—Kate Lee

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Vibe Checks

Two from OpenAI’s DevDay and one from Anthropic—here’s where we came out on each.

“Vibe Check: OpenAI DevDay 2026” by Dan Shipper/Vibe Check: OpenAI wants ChatGPT to become your operating system for work, and Dan sorted its 22 DevDay releases by how much each one advances that goal. The five most important ones: Dots, an always-on agent, and Space, native documents the agent can edit, are the workspace itself. Plugin extensions and a subscription you can use in 16 partner apps let other software connect to ChatGPT. Decisions API is OpenAI’s answer to Jev, and Dan thinks it could be the biggest release of the day if it’s cheap. His verdict after a week: The ambition is big, the execution isn’t there yet, and even a power user has a lot to figure out.

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How to Get Better at AI by Asking AI

Arielle Shipper — 2026-10-02 15:38:38 -0400

by Arielle Shipper

Midjourney/Every illustration.

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For most of my working life, the first step of any task began with a blank slate: an empty Google Doc, a fresh email, or a new Excel spreadsheet. From researching to writing formulas, the banalities of every stage of construction fell to me. AI made that process faster, but the way I accomplished it wasn’t all that different. I started with a new chat and prompted it over and over until the output matched my unspoken definition of “done.”

When I started at Every in May, I was still working that way. When I needed to issue contractor payments, I worked from one chat thread, prompting sequentially to surface agreement terms, approve payment, and send email confirmations that payment had been issued.

Things look very different today. Last week, I needed to verify the cost of purchasing equity for an employee across multiple grants, strike prices, and vesting schedules. Rather than tackle this complex task in a single thread, I split the project among several subagents that coordinated independently to verify information on signed agreements, run the math, double-check it and flag discrepancies, and return a draft of a Slack message that I could approve with a simple “yes.” That work ran on skills, orchestrator threads, context packets, subagents, MCPs, and computer use. It’s a veritable alphabet soup of technical terms that I was previously sure could only be understood and deployed by Highly Technical People, yet I now find myself reaching for these techniques daily, helping me work faster, with better and more precise results that I can be confident about.

I was prompted to make the shift from a tweet. Katie Parrott tweeted in June that she had asked Codex where she fell on Mike Taylor and Laura Entis’s guide to “Eight Levels of AI Adoption,” and I saw an opportunity to better understand my place in the ecosystem. The guide is a framework that maps a progression from basic chatbot use to full agent orchestration, with each level delegating more work—and trust—to AI. Codex placed Katie at Level 5 (building workflows that make an agent’s output consistent and reliable), with the beginnings of Level 6 (an agent that works proactively in the background without waiting for a prompt).

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🎧 How Sam Altman Uses Dots to Take Back His Time

Laura Entis / The Every Podcast — 2026-09-30 16:44:13 -0400

by Laura Entis

in The Every Podcast

Midjourney/Every illustration.

TL;DR: OpenAI CEO Sam Altman sits down with Dan Shipper for the first episode of The Every Podcast, the new name for AI & I. It’s still conversations with the people building and using AI, as well as a look behind the scenes at Every. Natalia Quintero joins Dan as co-host later this month.

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This week, Dan sits down with Sam Altman at OpenAI’s annual DevDay, where the company shipped twenty-two products and features, more than twice as many as last year. (Altman: “Just wait for next year.”)

In a wide-ranging interview, Altman shares how his Dot—OpenAI’s new always-on agent—runs his day and gives him time back, why he can’t quit Astra’s new Ultrafast mode, and why he believes AI will bring on a new Renaissance.

Watch on X or YouTube, or listen on Spotify or Apple Podcasts. You can also read the transcript.

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Vibe Check: OpenAI DevDay 2026

Dan Shipper / Vibe Check — 2026-09-29 13:04:22 -0400

by Dan Shipper

in Vibe Check

Midjourney/Figma/Every illustration.

OpenAI’s DevDay just kicked off, and we’ve got the rundown of the 20-plus(!) products and features the company announced. Here’s what matters, what it means, and what I learned from my hands-on testing.

The quick take

OpenAI wants ChatGPT to become your operating system for work—documents, slides, and agents, all in one place on your computer. It also wants to let developers and startups build businesses on top of it.

Five announcements from today show what that looks like:

Dots: Persistent agents inside ChatGPT that retain context and keep working over time (think OpenClaw, Grok Bots, and Muse)
Space and an office suite: A Google Drive–like place to work with your Dot, plus ChatGPT-native apps for documents, slides, and spreadsheets
Developer-built apps and discovery: Full apps inside ChatGPT—called “plugin extensions”—with a directory and automatic recommendations in conversations to help users find you.
Your ChatGPT subscription in other apps: Portable Plus and Pro usage, starting with about 15 partners. Separately, Marketplace is a catalog of approved partner software that eligible enterprise customers can purchase using part of their existing OpenAI spending commitments.
The Decisions API: This could be the biggest of the five. It’s OpenAI’s answer to Jev, the buzzy new model that acts as a “smart if-then statement” and recently has taken over X. In our testing, the Decisions API beats Jev on at least one dimension: It is multimodal, which means it accepts image input. It beat Jev on speed and accuracy on a few early tests but trailed on our broader evals. It’s being previewed today, and depending on the price, it could reshape the whole ecosystem.

OpenAI is also introducing a model called Sol 6.1 (Astra-powered, but cheaper), Ultrafast inference (250 tokens per second), a $500 Pro plan with much more usage, and more. The number of releases is itself part of the story: I’ve been to every Dev Day since Dev Days began in 2023, and this is a far greater number of new products than it has ever had by my unofficial count.

My running DevDay launch tally. (Screenshots courtesy of Dan Shipper.)

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Vibe Check: Dots—Always-on Agents in ChatGPT

Dan Shipper / Vibe Check — 2026-09-29 13:03:10 -0400

by Dan Shipper

in Vibe Check

Midjourney/Every illustration.

Last week, between back-to-back model reviews, company strategy calls, writing, and my never-ending hunt for apartment furniture on Facebook Marketplace, I tried to change a flight out of San Francisco. I used my Dot—OpenAI’s proactive, always-on agent, released today—to help me book.

As my Dot displayed its flight research, it warned me, “[This] may overlap with Randy’s Wednesday morning video recording.” Randy Counsman is our head of video, but I had no idea what my Dot meant because there was no video recording on my calendar. It turned out my Dot had read a discussion in Slack that I hadn’t seen, clocked the proposed schedule, and alerted me when I needed it most.

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There’s so much coordination in my day that requires me to read a few threads, put things together, and decide what to do. Having an assistant that flags all the little things that need attention, pulls the context, and recommends an action without making me think about it can be magical.

The version I’ve been testing for several days has become the main way I interact with ChatGPT. But this version feels early and rough.

My Dot frequently gets disconnected from my computer, so it can’t accomplish tasks locally. It has its own computer (in a data center somewhere), but it doesn’t have access to my stuff, and logging it in is a pain and sometimes doesn’t work. Every so often, I’ll send my Dot a message, and it doesn’t respond. I use Dot for my day-to-day work, but sometimes I want to go back to good old reliable Codex.

I expect the product to improve rapidly, but for now, this is your day-zero Vibe Check.

The landscape

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Vibe Check: Sonnet 5.5 Finds Its Place in Claude’s Crowded Family

Katie Parrott / Vibe Check — 2026-09-28 15:55:20 -0400

by Katie Parrott

in Vibe Check

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Sonnet 5.5 is out today, and as a fellow middle child, I feel for it—it has something to prove.

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For a long time, Sonnet had a clear role in the middle of Anthropic’s lineup: It could handle work too demanding for Haiku while costing less and running faster than Opus.

Then the middle got crowded. Last November, Opus 4.5 cut its price from $15 to $5 per million input tokens, narrowing the gap with Sonnet. In June, Anthropic added a new top tier, Fable, leaving Sonnet and a cheaper Opus competing for much of the same work. By the time we reviewed Sonnet 5 in July, we couldn’t find a task it handled best. For every task we tried, another model was cheaper, faster, or better.

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Opus 5.5 and Sol Split the Team

Every Staff / Context Window — 2026-09-27 07:55:18 -0400

by Every Staff

in Context Window

Midjourney/Every illustration.

Hello, and happy Sunday. Three new models dropped this week, and we Vibe Checked each: Opus 5.5 pulled several of our team members back to Claude, GPT-6 Sol is Dan Shipper’s new daily driver, and Grok 4.7 won back one power user (but no one else). Later, Steal these gathers the week’s workflows: Mike Taylor on benchmarks and writing, Jack Cheng and Douglas Brundage on Jev, and Becky Isjwara on video. Some housekeeping: Micah Rich leads a prompting workshop on Friday, October 2, and Thesis: 2027 is down to its last tickets.—Kate Lee

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Vibe Checks

Two models came out on Tuesday and another on Monday. Here’s our verdict on each.

“Vibe Check: Opus 5.5 Is Pulling Our Codex Converts Back to Claude” by Katie Parrott/Vibe Check: Katie and the team found Opus 5.5 matched or beat Fable 5.1 on product and design work at 60 percent less per token—enough to become Kieran Klaassen’s new daily driver and to tempt a few Codex converts back to Claude. There are still two significant tradeoffs: Opus buries the point in prose and will run for hours unless you set a budget.

“Vibe Check: GPT-6 Sol vs. Opus 5.5” by Dan Shipper/Vibe Check: Dan’s verdict: Sol if you want a fast, cheaper daily driver in Codex that leads with the main idea. Opus 5.5 if you’re willing to pay more for ambitious coding and visual work, like the webcam heart rate monitor it built and Sol couldn’t finish.

“Mini-Vibe Check: Grok 4.7” by Laura Entis/Context Window: Mike, Kieran, and Tyler Nishida were already testing Grok 4.7 for a Vibe Check when it was released on Monday, so on Thursday we ran a short version. It won back Tyler, who finds it cheap and steerable enough to be his daily driver again, but no one else: Kieran won’t consider it next to Opus 5.5, and Katie found its writing had no rhythm or feel for the reader.

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Copilot Gets a Seat in the Org Chart

Ryan Sloan — 2026-09-25 13:57:45 -0400

by Ryan Sloan

Midjourney/Every illustration.

If you’ve followed Mike Taylor’s coverage of Microsoft, you know the scale of what the company has been assembling: 80 products called “Copilot” at last count—powerful when configured correctly and locked down without a cooperative IT department. Ryan Sloan attended Microsoft’s latest launch to test the new unified Copilot app, which rolls chat, code, Office, and a persistent agent called Autopilot into one interface. He found that the controls that make Copilot trustworthy inside a big company are the same ones that keep it from finishing a routine task. Read on for his dispatch from Redmond.—Kate Lee

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In August, Mike Taylor counted 80 Microsoft products called “Copilot.” Among them, he said, were some of the best tools for building agents—if you could navigate onboarding and enterprise IT administration.

At a launch event earlier this week in Seattle and Redmond, where I spoke with executives and briefly tried the app on the demo floor, Microsoft announced a new Copilot that combines chat, code, Office, and a persistent agent called Autopilot, replacing its separate consumer and work chat apps. Microsoft isn’t alone in attempting a unified interface for AI work, but its advantage is that the people it’s built for already spend their day in Outlook and Teams, with permissions their IT department set years ago.

Autopilot may be the most interesting piece of the new Copilot: It can work on its own and access most of what you can. Those permissions could make it useful and trustworthy inside a company, but they could also prevent it from completing an ordinary task. Microsoft’s own emphasis on evaluations (evals) at the launch event suggests that it is looking to its customers to define what good work looks like in their organizations and test whether Autopilot improves on current workflows.

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Why Evals Are So Hot Right Now

Laura Entis / Context Window — 2026-09-24 17:05:12 -0400

by Laura Entis

in Context Window

Midjourney/Every illustration.

In today’s Context Window, we review Grok 4.7 (“a step backward”?) and explain why evals are suddenly everywhere. Elsewhere, head of social media Becky Isjwara breaks down her workflow for turning Every articles into viral short videos, head of platform Willie Williams argues that Slack agents are the new websites, and we share a fresh crop of Thesis Statements, including entries from Cmpnd’s Drew Breunig, Notion’s Geoffrey Litt, and MIT’s Daniela Rus, about what happens after automation.

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Mini-Vibe Check

Grok 4.7 is uneven

Grok 4.7 launched publicly on Monday. And the vibes? Well, they are evolving.

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How to Get the Most Out of Jev

Laura Entis / Context Window — 2026-09-23 16:05:51 -0400

by Laura Entis

in Context Window

Midjourney/Every illustration.

Jev is fast, cheap, and accurate enough to handle the judgment calls you’d never pay an LLM to make at scale—is this email urgent, does this paragraph sound like AI, should I escalate this customer service request—on everything, all the time. Today, head of evals Mike Taylor explains what that unlocks, head of marketing Douglas Brundage shares his shortcut for putting Jev to work, and senior editor Jack Cheng shows why Jev is so good at turning intent into action.

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Signal

Why Jev is blowing up your timeline

What happened: On September 15, TypeSafe, an AI frontier lab that spent two years in stealth, released Jev, a model that classifies text and structured data instead of generating it. The release lit a match that has yet to sputter: On X, Jev demos are everywhere. Users have gone viral showcasing how Jev can be used to play video games, re-rank search results, and filter social media posts, among so many other uses. Senior editor Jack Cheng’s Jev demo, which shows the model’s ability to infer a user’s intent from speech and hand gestures, has surpassed 1 million views.

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Good Writing With AI Starts Before the Prompt

Mike Taylor / Also True for Humans — 2026-09-23 14:33:22 -0400

by Mike Taylor

in Also True for Humans

Midjourney/Every illustration.

Last week, head of evals Mike Taylor told us about his 13 beliefs about writing with AI—from putting more into the prompt than you ask the model to produce to living a life worth writing about. His process starts with gathering that raw material: seeking out experiences, debating ideas, and noticing which topics make people’s eyes light up. Today, he shows you how he turns those beliefs into finished pieces, with a process you can adapt to make your own work worth a reader’s time.

For a closer look at how the writers at Every put AI to work, join Mike, staff writer Katie Parrott, and senior editor Jack Cheng for our Write-along tomorrow at 1 p.m. ET, livestreamed on X and YouTube. —Kate Lee

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It took me a lot of experimenting to find a way of writing with AI that I was happy with. Here’s the process I’ve landed on, from picking a title to arguing with my editor and coaching Claude to write like me. I’m sharing it because it gets the most out of AI, without losing my voice. With this process I’ve cut my writing time for a 1,200-word piece from a day to less than two hours, with no drop in traffic or positive reader comments. Your process will vary with your level of AI adoption. Steal whatever looks useful.

Write the title first. The title is 80 percent of why anyone chooses to read a piece; for the writer, a good title provides the conviction to see the piece through. Give the document a title that excites you. You can change it later if the piece takes a different shape.
Collect sources. Gather the writing you plan to reference, and talk to people whose perspectives you might include. Extra points for an obscure source few have heard of, or a piece you love and wish more people had read. Research papers are often overlooked because they’re hard to access or use, so translating them can be a valuable service. Paste each quote or statistic—or the full text—with its link in a Sources tab in your document.
Create the skeleton. There are many ways to frame a single idea, and the introduction is where that framing gets established. It’s also what convinces a reader to keep reading—so it’s natural to spend the most time on it. After writing the introduction, draft three or four sections with one core idea each, and a placeholder conclusion. Don’t worry about the conclusion; you’ll rewrite it later. Save this in an Outline tab.
Record the transcript. Open the Monologue app and record your unstructured thoughts about the piece’s topic. Ramble or correct yourself mid-stream; LLMs can sift through and structure your transcript later. This captures your unique takes and quotable lines. Paste it into a tab labeled Monologue.
Write the story beats. From the transcript, ask Claude to produce a bulleted outline: title, section headings, three to four bullets per section, and any important statistics. Send this to the editor or a few friends to check whether the story lands before you shape a full draft. Add it to a tab called Pitch.
Generate drafts from multiple models. Feed the same context (title, introduction, sections, sources, transcript) plus your writing style guide (if you don’t have one, ask Compound Writing to build it from your past work) to Claude Opus 5.5, Fable 5.1, and GPT-6 Astra.
Lego the drafts together. Pick one as the base (I usually use Fable’s, which tends to be sharper and spikier, but I’m always open to testing other models), paste it into a Draft tab, then lift paragraphs, sentences, or lines you liked from the others into a scrap area below a divider in the document. Move the good parts into the main post.
Read it end to end, more than once. If you can’t be bothered to read your piece, don’t expect anyone else to. Start at the top and read down; when something’s wrong, stop and fix it, then return to the top and start again. That way you experience the full narrative instead of polishing one section while ignoring the rest; it all has to hang together.
React and re-monologue if needed. If the first draft you Lego’ed together isn’t turning out as you hoped, ask yourself how you feel about it and record a second, usually much shorter monologue about the draft: “this bit is wrong,” “that’s not what I intended,” etc. Save this under your original transcript in the Monologue tab and use it to have a model redraft the piece. I rarely redraft more than twice before losing interest in the idea.
Self-edit. Rewrite as you go; expect 20–30 percent of the draft to change by the end, with that share falling as the models improve. Add any missing quotes, citations, and statistics from your Sources tab, create visuals (especially for experiments), and create something readers can use—a prompt, an HTML page, an infographic, a script, a template.
Write the conclusion. Now that the full draft exists, think about how the piece should end. Claude’s conclusion usually isn’t good enough. Aim for something emotionally resonant—leave the reader with a specific feeling because they will remember the beginning and end more than the middle. You almost always get it wrong. You’re rarely happy with it, and it’s nearly always the thing the editor asks you to change. This is the hardest part because you’ve done everything else and just want to finish.
Submit to editors. Expect 50–100 comments. Accept nearly everything on grammar and style, and be easy to work with—assume the editor’s goal is the same as yours (to publish something great), and that they know their audience better than you. But when an edit weakens the idea or makes it unrecognizable as your voice, you have to defend your choice. That allergic reaction you have to an edit you disagree with is a good sign: It means you’ve got a strong point of view worth defending. On the flip side, when an editor challenges your ideas and makes you question your convictions, you may need to rethink your assumptions. Maybe your idea isn’t as good as you thought, or you’re not explaining it clearly. If you’re the type of person that’s weird enough to pursue ideas far enough to write about them, you’ll be excited about things normal people aren’t. Not every draft deserves to make it into the world. Some ideas might need more time and thought to develop to the point where the readers would get it; others might just be stepping stones towards something better.

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Vibe Check: Opus 5.5 Is Pulling Our Codex Converts Back to Claude

Katie Parrott / Vibe Check — 2026-09-22 14:24:35 -0400

by Katie Parrott

in Vibe Check

Midjourney/Every illustration.

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If we’re all being honest, Opus 5 had a bit of a personality problem.

It could do impressive work, but asking for a change could turn into an argument, and its explanations needed explanations of their own. It also ran too long on simple tasks, as its ambition outpaced its intelligence. We called it a hard model to love. Most of us stopped trying.

Opus 5.5 is different. In our testing, it quickly dethroned Fable 5.1 as the daily driver for Kieran Klaassen, who found it as good as 5.1, and sometimes slightly better. Mike Taylor and Tyler Nishida are former Claude power users who switched to Codex in recent months, but Opus 5.5 has them reconsidering that switch.

“My jaw dropped at least five or six times this week,” Nishida said of his testing of Opus 5.5. “They won back my heart. I can’t afford another $200 subscription, so I’ve got to get rid of something.”

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How to Create Your Own Personal AI Benchmark

Mike Taylor / Also True for Humans — 2026-09-21 11:00:22 -0400

by Mike Taylor

in Also True for Humans

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Nobody hires a vice president based on their SAT scores. Yet every time a new model drops, AI researchers first check how well it does on a Math Olympiad and a set of multiple-choice trivia, then argue online about whether it’s the smartest model in the world. Wharton professor Ethan Mollick points out that Massive Multitask Language Understanding-Pro (MMLU-Pro), one of the most-cited benchmarks, asks models for the approximate cranial capacity of Homo erectus and the place named in the title of Cheap Trick’s 1979 live album. Those tests measure something—the scores are directionally useful—but not what you need to know: Can this model help you with your job? To answer that, you need to build a personal benchmark.

I joined Every earlier this year to run our technology consulting practice, so I spent most of my time getting models to write, build dashboards, and assemble slide decks. With each new model, I developed an instinct for when and how to use it. Claude Opus 5 felt argumentative. Claude Fable 5 felt like talking to a genius. GPT-5.6 Sol felt like a safe pair of hands. But when friends and colleagues asked me for recommendations, I couldn’t always defend my choices. It was also time-consuming to test every model on every type of task. I often missed opportunities to use something better or cheaper. So, inspired by Mollick’s piece about why you should test models on your own work instead of trusting benchmarks, I built a personal one: a small, private test that tells me whether I like working with a model, without spending all my time testing—because I have a job to do.

Now I’m head of evaluations (evals) at Every, testing new models from the frontier labs for the qualities we value in our work. I want to help everyone on the team build personal benchmarks for their own work. Building personal benchmarks has three levels, and each can make you more confident you’re using the right model for your job. If you set up your personal benchmarks correctly, you’ll know when to switch between Astra and Fable, and whether you can save money using a smaller, cheaper model like Luna or Haiku.

Fill your back pocket with failures

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Copy Our Homework

Every Staff / Context Window — 2026-09-20 07:55:28 -0400

by Every Staff

in Context Window

Midjourney/Every illustration.

Hello, and happy Sunday. We publish workflows, tips, and prompts nearly every day, and we know that at that pace things get lost. So this week we’re trying a different format: a section called “Steal these” that gathers everything from this week’s three Context Window editions. We also have two new Vibe Checks: Mike Taylor’s of Jev, an entirely new model from one of the OpenAI researchers who taught chatbots to follow instructions, and one from Cyrus Duff and Josh Lee of the creative studio Afterimage, who tested GPT-6 Astra on visual effects, a use case we hadn’t tried. Some housekeeping: We’re hosting three different kinds of events next week; upgrade to attend the second two. Details follow.—Kate Lee

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Steal these

Nine workflows and prompts from this week’s three editions.

Let an agent drive your computer

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Vibe Check: Is Astra a Breakthrough for Indie Filmmakers?

Cyrus Duff and Josh Lee / Vibe Check — 2026-09-18 14:19:52 -0400

by Cyrus Duff and Josh Lee

in Vibe Check

Midjourney/Every illustration.

At our Think Week in Panama in January, creative studio Afterimage filmed a series of six short films about Every’s work with AI. We’d seen what cofounders Cyrus Duff and Josh Lee could do with a camera, so we were curious when they began testing Astra on footage from real shoots. Could it handle the visual effects they’d normally make themselves? They put it to work on everything from replacing a dish in a moving pan to adding a building to a Brooklyn street to making a giant parade float appear outside a window in handheld iPhone footage. Some shots worked. Others didn’t. By the end, they were dreaming up effects they’d never have attempted before.—Kate Lee

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We run Afterimage, a creative studio that works with tech companies. This year, we’ve done projects with Cursor, Reve, Pierre Computer Co, Turbopuffer, and several others. We previously led video at the Browser Company of New York. Every was our first client.

When GPT-6 Astra launched, our timelines filled with impressive demos of 3D environments Astra created in Blender, a tool for making digital models and scenes—including a full-scale render of New York City. Astra could build 3D models in Blender and refine them when they looked wrong. We wanted to see whether those skills carried over to footage we’d shot. Could it make a new object look as though it belonged in a real scene? Could it do visual effects?

Visual effects (VFX) involve separating subjects from backgrounds, following and altering objects across frames, and matching new elements to existing photography. We’d tried that work with earlier models. Opus 4.6, for example, could write deterministic Python scripts for basic blemish and object removal but struggled with more complicated changes. Generative tools like Gemini Omni could make impressive changes to footage but didn’t allow for the granular control most of our workflows require.

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Why You Should Burn More Tokens

Laura Entis / Context Window — 2026-09-17 13:30:17 -0400

by Laura Entis

in Context Window

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Every CEO Dan Shipper consumes more than three times as many tokens as the next-highest user at the company. That gap worries him: Are the rest of us keeping our AI bills low by thinking too small?

A low token spend isn’t necessarily a problem, Dan says, “but it should be a smoke signal, especially on the engineering team, to be like, ‘Maybe there’s more we can be doing here.’”

Every’s OpenAI token leaderboard. (Screenshot courtesy of Laura Entis.)

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Show Us Your Folders

Katie Parrott / Context Window — 2026-09-16 14:27:55 -0400

by Katie Parrott

in Context Window

Midjourney/Every illustration.

You probably know the person with an AI setup that everybody wants to snoop through. Their folders have names. Their agents have jobs. Their system knows where everything lives. Meanwhile, your system is still hoarding mystery screenshots and documents called final-final-2.

We feel this at Every. So we let the team see how their colleagues organize their AI systems. As we teased last week, head of consulting Natalia Quintero now hosts Show Us Your Folders: a recurring series where one person opens their AI workspace and explains it to the Every team.

Today, we take the pilot tour, share a workflow for reviewing your own workspace, explore Mike Taylor’s beliefs about writing with AI, and look at the latest Compound Engineering release.

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Inside Every

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13 Beliefs About AI Writing

Mike Taylor / Also True for Humans — 2026-09-16 14:27:16 -0400

by Mike Taylor

in Also True for Humans

Midjourney/Every illustration.

Writers in our How We Write Now series have drawn very different lines around AI. Some use it for research and editing but won’t let it write a sentence. Mike Taylor will. He uses models to draft almost everything he publishes. His test is whether he could defend the ideas if a reader showed up to argue.

Mike shares the 13 beliefs behind that. He puts more of his own material into the prompt than he asks the model to write. He keeps social posts entirely his own. Some of this cuts against how most of the series’ writers work, which is what makes it worth reading.—Kate Lee

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I just finished my second book with publisher O’Reilly, who doesn’t allow using AI to write, and it’s the last thing I’ll agree to write manually. Writing the traditional way feels like using a typewriter when I have a word processor, and the output isn’t any better than what I write with AI.

I’ve been experimenting with AI writing since GPT-3 came out in 2020, when Copy.ai was giving out free accounts to everyone who failed to get into Y Combinator. When GPT-4 came out in 2023, I developed a writing process that passed the Turing test: friends and colleagues couldn’t tell it wasn’t me, and that was depressing enough that I stopped writing.

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Mini-Vibe Check: TypeSafe's Jev Judged Everything I’ve Written in 0.7 Seconds

Mike Taylor / Vibe Check — 2026-09-15 12:00:45 -0400

by Mike Taylor

in Vibe Check

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Not every language model is destined to be a chatbot. Companies need fast, cheap, reliable AI workflows for processes that run in the background: deciding where to route a customer service request, checking that invoices match a purchase order, or classifying transactions as potential fraud.

Today, TypeSafe—a new AI lab—launched Jev, its new model designed to produce structured answers that your code can use directly. You pose questions in plain English—even fuzzy or subjective queries—and it returns probabilities for yes/no answers or any other categories you define.

Think of it as a smart if-then statement that determines what happens next when you’re automating a workflow. Say you’re building software that prioritizes customer service requests, and you write code that asks the model, “Does this customer sound angry?” Jev might answer 0.9, which means there’s an estimated 90 percent probability that the answer is yes based on what the model learned in training. You could also provide categories you define, like “annoyed,” “irritated,” “offended,” “furious,” and “enraged,” and learn that the customer was 60 percent likely to be classified as furious, with only a 10 percent probability of being enraged.

With an answer of 0.9 (very likely to be angry), the software might automatically proceed to escalate the customer concern to a manager, or if it answers 0.1 (not likely to be angry) that request might be deprioritized. However, chatbots are trained to respond with flowery text—like, “You’re absolutely right, this customer does sound very angry. Would you like me to compose a draft email response in a friendly, supportive tone?”—not numbers. This text response would cause the program you’re building to crash because it was expecting a number between 0 and 1, not an essay. Historically I’ve used DSPy, a Python framework for programming models, to coax LLMs into following instructions on what types of outputs to return, but Jev does all that work natively, which makes it insanely fast and cheap. Just how well it gets the job done is still an open question.

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You're Probably Sleeping On Computer Use

Laura Entis / Context Window — 2026-09-15 11:01:00 -0400

by Laura Entis

in Context Window

Midjourney/Every illustration.

Today, head of evals Mike Taylor published a Vibe Check of TypeSafe’s Jev, a new kind of model that responds to a fuzzy question with a probability rather than prose, letting your code act on the result directly. It can’t compete with a frontier model on capability but is fast and cheap enough to check work while it’s being done.

Mike is also the reason today’s issue is focused on computer use: Astra’s ability to operate apps by clicking, typing, and scrolling changed his mind about the model. Later, we explain why that happened, how the rest of the team harnesses computer use to check off tedious items on our to-do lists, and how to use AI to create a slide deck that looks the way you want it to.

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Vibe Shift

Computer use turns Astra into Mike’s daily driver

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What Playing With AI Taught Me About My Work

Katie Parrott / Working Overtime — 2026-09-14 16:51:23 -0400

by Katie Parrott

in Working Overtime

Midjourney/Every illustration.

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My childhood make-believe involved quite a bit of multitasking. I’d rollerblade in my front driveway for hours, bouncing a tennis ball on a racket and muttering dialogue for an elaborate storyline in which I was a Jane Austen heroine, or a 10th female member of the Fellowship of the Ring. Yes, I was incredibly cool.

That habit faded in my late teens, sort of like my religious faith did. Even at a tiny liberal arts college where people routinely wore cosplay around campus, I managed to lose touch with my imaginary people. Then adulthood happened, and I spent most of my twenties and early thirties learning to be employed and coexist peacefully with my brain.

Now, in my mid-30s, make-believe has made a big comeback and, yes, it’s thanks to AI. These days, my imaginary people come with websites and job descriptions.

Over Labor Day weekend, I spent a couple hundred dollars in tokens getting Astra to turn the skills in Compound Writing, my AI writing toolkit, into imaginary creatures. I could want something ridiculous, change my mind, and follow a result because it surprised me. On Every’s frontier team, my job is to explore what AI makes possible and turn what I learn into something others can use. My private game seemed like a promising place to start.

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What to Make of the Anthropic Warning

Every Staff / Context Window — 2026-09-13 07:55:30 -0400

by Every Staff

in Context Window

Midjourney/Every illustration.

Hello, and happy Sunday. After a former Anthropic engineer left the company with a public warning about AI risk, I raised the question at a team meeting, and Kaushik Viswanath reports on what his new colleagues made of the question. Some housekeeping: Tickets to the Thesis: 2027 After Party are now on sale for Every subscribers, and conference ticket prices go up next week.—Kate Lee

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Signal

The AI slowdown argument goes mainstream

What happened: On Saturday, Anthropic CEO Dario Amodei published an essay arguing that AI companies should deliberately slow the rate at which they improve their models, because advances are outrunning researchers’ ability to understand and control them. Hours later, OpenAI’s Sam Altman endorsed the idea and committed his company to one of Amodei’s proposed safeguards—independent evaluators with employee-like access to models and staff. Elon Musk agreed, too.

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What I Learn When I Run Out of AI

Jack Cheng — 2026-09-11 15:19:26 -0400

by Jack Cheng

Midjourney/Every illustration.

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I keep running out of access to the most capable AI models before the workweek is finished. Lately, I’ve come to realize that may be useful. Anthropic’s limits on both my work and personal Claude subscriptions reset at the start of the week. When they do, I gorge on the newly available capacity, running Fable on its highest effort levels and watching agents orchestrate subagents and dynamic workflows on multiple projects.

It used to be that I exhausted my weekly allocation within a day or so. I would go back to daily tasks with speedier, still-capable models like Sonnet and Sol, saving my biggest questions and one-shot builds and thorniest bugs until my limits reset.

The wait gives those questions time to develop. I’ve come to call my weekly sessions with the most powerful models “consulting the oracle”—a nod to the oracle at Delphi, where the ancient Greeks sought guidance from Apollo through the priestess Pythia (OK, also to The Matrix).

But as frontier models like Fable 5.1 become more token-efficient, faster, and easier to talk to at lower effort levels, I’ve started running them as daily drivers, too. The same limits now stretch to three or four days instead of one.

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Evals for Everyone

Laura Entis / Context Window — 2026-09-10 13:45:34 -0400

by Laura Entis

in Context Window

Midjourney/Every illustration.

Astra and Fable 5.1 can ace a graduate-level science exam, but no public benchmark will tell you whether a model knows where you’d put a comma or how many ideas belong on a slide. Today, Every CEO Dan Shipper explains why we’re building a personal benchmark for every employee, head of evals Mike Taylor shares how a run on his own benchmark convinced him a smaller model could handle much of his daily work, and we offer a five-step workflow for turning the corrections you already give AI into checks for grading any model.

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Inside Every

Astra and Fable 5 scored 96 percent and 93 percent, respectively, on a test of graduate-level science questions. Impressive, clearly! What’s less clear from public benchmarks is how those scores translate to what most of us care about: how well these models help us do our jobs.

The solution, says Dan, is to create a personal benchmark—a set of custom evals that tests how well a model does specific parts of your work, graded against your own standard for what good looks like.

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What Writers Who Use AI Want You to Know

Laura Entis — 2026-09-09 12:25:42 -0400

by Laura Entis

Midjourney/Every illustration.

Last week we launched How We Write Now, a series about how writers are using AI, starting with Katie Parrott’s guide to Compound Writing—her framework for developing ideas, shaping drafts, and carrying what you learn into the next piece. This week, Laura Entis, who has been reckoning honestly with her own AI workflow, goes further: She interviewed five professional writers—including Every CEO Dan Shipper, designer Maggie Appleton, and New York Times alum Kevin Roose—about where they let AI in and where they draw the line. She found that there is no universal recipe: The same tool that helps one writer think more clearly makes another’s prose worse. Read on for five distinct, idiosyncratic workflows (including custom prompts) that make a case that writing with AI is a skill in its own right.—Kate Lee

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On deadline for a book about neurodiversity and work, journalist Alexandra Samuel fell victim to a condition all writers know and fear: writer’s block.

She had the research and a detailed outline. But there was always one more study to review, and so she put off writing anything. She booked a DIY writing retreat at a friend’s empty apartment, told herself she’d tackle a chapter a day—and turned to AI.

Each morning, she converted her research for that day’s chapter into a podcast, which she listened to as she scrambled eggs and made the first of many cups of coffee. The practice made sitting down to her computer feel more like continuing a conversation than staring at a blank page. When she hit a wall with drafting, she gave ChatGPT the day’s pages, closed her laptop, and had it interview her as she strolled through her friend’s neighborhood. The AI’s questions cut through her confusion. “After 40 minutes, my head was clear, and I knew where I was going,” she says.

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To Read—Or Not to Read the Code?

Kieran Klaassen / Source Code — 2026-09-08 11:01:55 -0400

by Kieran Klaassen

in Source Code

Midjourney/Every illustration.

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I’m shipping more than I ever have, but I think my brain has gotten softer.

For the last year, I’ve hunted for the parts of coding that still needed me, then replaced myself with something that could do it, whether that’s a skill, a framework, a data source, or a step that routes work to the next agent. This is the essence of Compound Engineering: Teach the system to work once, and it does that work forever. This is how I build Cora, Every’s email assistant, and across my career, I believe it to be the best product and engineering work I have done.

But somewhere along the way, I found that I had lost a feeling. It used to be that learning itself was the reward. But my mind increasingly felt like a TikTok feed—full of short-lived hits of dopamine that left nothing behind.

I started to notice that although the work was improving, I wasn’t. My own mind was the part of the system I had forgotten to measure.

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A Split Verdict on Fable vs. Astra

Every Staff / Context Window — 2026-09-06 07:55:10 -0400

by Every Staff

in Context Window

Midjourney/Every illustration.

Hello, and happy Sunday. The models kept coming this week—Anthropic’s Fable 5.1 on Tuesday, OpenAI’s GPT-6 Astra on Thursday—and Katie Parrott compared the two in a report from our end-of-week camp for paid subscribers, where the team came away split. Between the releases, we kicked off our How We Write Now series with her guide to Compound Writing, her system for writing with AI, adapted from Kieran Klaassen’s Compound Engineering. As the tools accumulate, so does the question of how to learn them. Natalia Quintero delivered a report on Anthropic’s certification training that 10 Every staffers completed. We’ll be back in your inbox on Tuesday, September 8.—Kate Lee

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How we work now

Fable 5.1 vs. Astra: Where each came out ahead

A model can impress you with its output and still frustrate you as a collaborator. With Fable 5.1 and GPT-6 Astra both launching this week, choosing between them means getting specific about what you want a model to do—and how you want to work with it.

At our latest subscriber-only camp, we compared the two mega-models’ work side by side. As Dan Shipper noted, those comparisons help us articulate our standards for good work. A prettier app might lose to one that needs fewer clicks. A stronger draft might come from the model that’s harder to steer.

Here’s where each came out ahead:

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The Folder Is the Agent

Kieran Klaassen / Source Code — 2026-09-04 11:02:09 -0400

by Kieran Klaassen

in Source Code

Midjourney/Every illustration.

Next week, we’ll be publishing a reflection from Cora general manager Kieran Klaassen on how human builders can keep compounding—rather than switching off—once Compound Engineering removes them as the production bottleneck. In anticipation, we’re republishing his April piece on the folder-as-agent approach that let him orchestrate 44 specialized agents sustainably. Read the full compound engineering guide and install the plugin.—Kaushik Viswanath

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I spent three months trying to make agent swarms work.

The idea of multiplying myself by coordinating multiple agents at the same time was a compelling pitch as the sole engineer building Every’s AI email assistant, Cora. If I could summon a fleet of AI agents, let them coordinate, and watch them produce work no single agent could match, it would relieve some of my overwhelm.

I tried everything to make it work—Claude Code teams, agents dispatching tasks to other agents, orchestration setups where a lead agent managed a pool of workers. Many iterations, many burned tokens.

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Vibe Check: GPT-6 Astra Is a Big Upgrade With Some Bad Habits

Katie Parrott and GPT / Vibe Check — 2026-09-03 15:27:03 -0400

by Katie Parrott and GPT

in Vibe Check

Midjourney/Every illustration.

Astra is worth the fuss. We’re still keeping Fable.

For weeks, OpenAI has been previewing Astra’s math results and explaining why its cybersecurity capabilities required stronger safeguards. Now GPT-6 Astra is out, billed as the company’s most intelligent model yet.

Astra gave us writing, consulting work, and designs we wanted to keep. Dan Shipper is enthusiastic about its computer use—operating software through the interface. But a beautiful first result isn’t always a finished product. Fable 5.1 still did better on the complicated apps we tested. Astra also had a habit of adding copy and interface elements the task didn’t need.

We get into the successes and frustrations in our Vibe Check, including:

The first draft of the review itself, which Astra wrote in one shot and Dan thought I (Katie Parrott) had written.
Mike Taylor’s AI training curriculum, built from employee interviews and close to the course he wanted to teach.
Kieran Klaassen’s cozy island, which came with a guided-breathing exercise he hadn’t asked for.

Read the detailed comparisons and how the team’s findings change what we’d hand to Astra next. We’d try it for writing, consulting work, and visual prototypes. For a complicated product, we’d keep reaching for Fable 5.1.

Read the Vibe Check

Join us tomorrow at 12 p.m. ET for our latest camp, How We’re Working Right Now: Fable 5.1 and GPT-6 Astra. We’ll show how we’re using both models and answer your questions. The camp is for paid subscribers only.

Upgrade to join the camp

Katie Parrott is a staff writer at Every. You can read more of her work in her newsletter. To read more essays like this, subscribe to Every, and follow us on X at @every and on LinkedIn.

Everyone’s a builder now. Every All Access gets you the full membership plus the Builder Pack—$9,000+ in credits for the tools we build with.

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🎧 How an Every Staff Writer Developed Compound Writing

Kaushik Viswanath / The Every Podcast — 2026-09-02 07:00:00 -0400

by Kaushik Viswanath

in The Every Podcast

Natalia Quintero (left) and Katie Parrott. Midjourney/Every illustration.

Hello! I’m Kaushik Viswanath, the new managing editor at Every. In my first week here I’ve had the pleasure of getting to meet much of the Every team, including staff writer Katie Parrott. I couldn’t have asked for a better introduction to her work than the latest episode of AI & I, in which she’s interviewed by Every head of consulting Natalia Quintero about how she developed Compound Writing—a sophisticated approach for using AI as a writing partner.—KV

When Katie Parrott was laid off from her job as an editor and ghostwriter at a crypto firm in 2023, she couldn’t afford a career coach. So she signed up for a $20-a-month ChatGPT subscription instead–a decision that turned around her career, and nearly everything else, including her mental health, and the way she approaches writing. Two years later, the Every staff writer has turned that process into an open-source plugin that teaches other writers to think, draft, and edit the way she does.

On this episode of AI & I, Natalia Quintero talks with Katie, staff writer at Every, about how that improvised habit became Compound Writing, the codified writing system—and the plugin behind it—that’s the subject of Katie’s latest guide. It’s a conversation about what happens when you feed a model good ingredients instead of clever prompts, borrow the taste of writers you admire, and let AI handle the grunt work so you can fall back in love with the page.

Watch on X or YouTube, or listen on Spotify or Apple Podcasts. You can also read the transcript.

Here are the highlights:

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Vibe Check: Fable 5.1—Anthropic Is So Back (Again)

Katie Parrott and Dan Shipper / Vibe Check — 2026-09-01 14:26:51 -0400

by Katie Parrott and Dan Shipper

in Vibe Check

Midjourney/Every illustration.

Anthropic is so back.

The company just released Fable 5.1, the successor to the model we called the best coder in the world, Fable 5. After lackluster reception for its last two releases, Sonnet 5 and Opus 5, the new model had us all wondering where the next Anthropic release would fall.

Bottom line: it’s a great model. It handles the big coding jobs that made the original Fable worth using, in language you can understand rather than needing a translator for Claudeish.

Cora general manager Kieran Klaassen replaced the original Fable. Every’s head of evals Mike Taylor sends it his new projects. I (Katie Parrott, staff writer) now draft with it after finding Fable 5 too slow for writing. Every cofounder and CEO Dan Shipper moved his coding tasks over, though Codex remains his day-to-day workspace.

The full Vibe Check shows what changed, including:

Whole projects from one prompt. Kieran Klaassen rebuilt a working version of Proof, Every’s document editor. Mike built a simulated town of 25 AI characters with memories, daily routines, and conversations.
Comparable agent results on about half the tokens. Spiral general manager Marcus Moretti tested its ability to power our internal Every agent against Opus 5. Fable 5.1 finished the work in about 60 percent of the time.
Better long-form writing, weaker compression. It outperformed every model we’ve tested on writing a compelling introduction, but its X posts landed near the bottom of the models we’ve tested.
More output than the brief allows. A request for eight to 12 exact quotes produced 43, including quotations missing from the source. At the highest effort setting, long-running sessions sometimes ignored requests to stop.

Read the Vibe Check

Can’t get enough of Fable 5.1? Join us for a special Fable 5.1 Camp this Friday, September 4 at 12 p.m. Eastern to hear more about our experience, plus share impressions and best practices with other Every subscribers. Register for Fable 5.1 Camp.

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What We Learned From 15 Hours of Anthropic Certification Training

Natalia Quintero — 2026-08-31 12:31:45 -0400

by Natalia Quintero

Midjourney/Every illlustration.

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Fifteen years ago, I took my first online course on edX, a learning provider founded by MIT and Harvard. At the time, edX was cutting edge and I binge-watched a ton of courses. (I still love and recommend Michael Sandel’s course Justice, but that’s beside the point.)

I found myself recalling that time when I took four courses earlier this summer to earn our team Anthropic certification. After working closely with the Anthropic team for more than a year as alpha testers of unreleased models, we wanted to take our partnership farther. I went into the certification process curious about what level of instruction they’d provide and how they’d define their own tools. What I came away with was new insight into the state of AI development and industry standards right now.

AI development is still moving at a breakneck pace, but new releases no longer upend our work the way they used to. Anthropic’s Opus 4.5 and OpenAI’s GPT-5.5 marked a turning point as the models could reliably perform the tasks we provide, giving each subsequent release a dependable base to build on. The fact that Anthropic can even release a training course for its foundational tools like Claude Code and agent skills is a milestone for the AI era. (This time last year, I was constantly reworking the curriculum for our training sessions every three to six weeks when a new, more capable model came out.)

More stable models have allowed people to build AI workflows around their individual roles and careers. Anthropic’s training takes the opposite tack—it teaches foundational concepts. But the greatest value of these courses may not be sparking new ideas for the tools we use day to day. Instead, they offer an opportunity for the industry to align around terms and definitions—a heavy lift on its own.

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