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

Opus 5.5 and Sol Split the Team

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

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

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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 / AI & I — 2026-09-02 07:00:00 -0400

by Kaushik Viswanath

in AI & I

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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Our Agents, Ourselves

Every Staff / Context Window — 2026-08-30 07:55:18 -0400

by Every Staff

in Context Window

Midjourney/Every illustration.

Hello, and happy Sunday. When OpenAI folded Codex into ChatGPT in mid-July—in a move known as the merge—the coding tool became a general-purpose workspace, and the setup most knowledge workers had learned changed. So Katie Parrott rebuilt our guide from scratch: ChatGPT for Knowledge Work is a complete walkthrough of the merged app—16 workflows from the Every team, recast for what now lives in Chat, Work, and Codex, plus the new goals, projects, and scheduled tasks. And companies have the same problem, only bigger. In early June, Natalia Quintero and Mike Taylor answered questions from 400 executives on rolling out AI, then wrote up the 33 they couldn’t get to live for attendees. We’re sharing that Q&A publicly for the first time here.—Kate Lee

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Knowledge base

“Our ChatGPT and OpenClaw Guides Just Got an Overhaul” by Katie Parrott/Guides: Our Codex for Knowledge Work guide from May and OpenClaw guide from March got a full rewrite. Codex for Knowledge Work is now ChatGPT for Knowledge Work, updated for the merged app; the OpenClaw guide rethinks when a personal agent is worth running, and why a shared Every Agent may be the better call. Read these to update your ChatGPT and OpenClaw setups.

“33 Questions Executives Ask About AI—Answered” by Natalia Quintero and Mike Taylor: Natalia, Every’s consulting head, and Mike, our new head of evals, answer 33 questions from 400 executives on how to adopt AI, covering strategy, winning over skeptics, tool selection, governance, and restructuring teams. Read this to see our advice to executives rolling out AI.

“Benchmarks Don’t Know Your Job” by Katie Parrott/Context Window: Companies spend millions on AI but rarely test whether a model does their own work better than a cheaper one would. Our answer is KateBench, a copyeditor trained on 30,000 of Kate Lee’s edits, which shows how a high acceptance rate can still hide the edits a human has to redo. Also inside: a counterpoint to Andreessen Horowitz’s Olivia Moore on whether diminishing returns make cheaper models like Fable good enough; Cursor’s rebuilt Git hosting, and two new reliability benchmarks; the six-agent crew that tells Every designer Tyler Nishida’s family when there’s enough solar power to run the dryer; and links worth a click.

“The Case for Cloning Your Coworkers” by Laura Entis/Context Window: Companies are starting to clone their coworkers—capturing the judgment of the people they depend on as reusable AI skills, a project Every is running on its own team. Also inside: a discussion of investor Stanley Druckenmiller admitting he used AI on a Wall Street Journal op-ed; Arielle Shipper’s self-improving Codex skill; a fresh batch of Thesis Statements from frontier builders; a signal on the enterprise opening for open-weight models; and the daily driver on which models the team is reaching for this week. Plus this week’s AI & I: Walleye Capital CEO Will England on why AI use is mandatory for his 400 employees. 🎧 🖥 Listen on Spotify or Apple Podcasts, watch on X or YouTube, or read the transcript.

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33 Questions Executives Ask About AI—Answered

Natalia Quintero and Mike Taylor — 2026-08-28 05:00:00 -0400

by Natalia Quintero and Mike Taylor

Midjourney/Every illustration.

Natalia Quintero, who leads Every’s consulting practice, has spoken with more than 100 companies about AI adoption, including the New York Times and the hedge fund Walleye Capital. In June, she and Mike Taylor—now Every’s head of evals practice—hosted a live webinar for 400 executives on how organizations can adopt and implement AI without stalling out. More questions came in than they could answer live, so afterward they wrote up 33 of them—covering strategy, winning over skeptics, tool selection, governance, and how to restructure a company for an AI-native future. We sent that document to the webinar’s attendees at the time and are making it public for the first time here.—Kate Lee

When you put a room full of executives in front of two AI consultants and let them ask anything, you learn about where organizations are stuck with this technology. Here are all the questions that came up on our live session on June 2—from how to get skeptics on board to keeping context fresh.

Every Consulting works with tech and finance teams to implement AI across their workflows, from strategy to training to building the actual tools.

Strategy and organizational transformation

Q: How do you get teams to think bigger about change? Not just getting the work done faster, but rethinking how the work is done?

A: Give your most AI-curious staff permission to build ambitious projects. It’s easier to raise the ceiling than the floor. It’s easier for one AI-fluent person to create 10 good skills and do 10 times the work than it is to get 10 people to each create one skill or double their output. Once that’s in place, set a vision, designate AI champions, prove out high-value skills, then scale.

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Our ChatGPT and OpenClaw Guides Just Got an Overhaul

Katie Parrott — 2026-08-27 16:03:06 -0400

by Katie Parrott

Midjourney/Every illustration.

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We talk about AI progress like it’s a race between models. That’s the part with version numbers, and what you argue about over dinner. But the model is only one piece. It reaches your work through the app you open it in, the files and tools you let it touch, the instructions you’ve saved from last time, and the person keeping all of that from going stale. Any of those can change your results as much as a new model would—sometimes more.

We rewrote two of our guides this week. The models didn’t change. The infrastructure around them that governs their behavior and capabilities did. These guides are written as much for agents as for humans, so share them with your agent and ask it: “What can we learn from this that we can adapt for how we work?”

Knowledge work has a home in ChatGPT now

When we initially wrote our Codex for Knowledge Work guide, the idea of using a coding-agent interface for something other than coding was so new that even OpenAI hadn’t built a dedicated home for it. Now it has. Quick questions stay in Chat, longer assignments move to Work, and software jobs go to Codex. For knowledge workers who don’t touch code, the vast majority of your tasks can be handled by Work.

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The Case for Cloning Your Coworkers

Laura Entis / Context Window — 2026-08-26 13:17:04 -0400

by Laura Entis

in Context Window

Midjourney/Every illustration.

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Inside Every

In which Dan plots to clone us all

For years, Every CEO Dan Shipper’s goal was to automate editor in chief Kate Lee’s editorial judgment. The data was there—he had more than 30,000 of her historical edits—but the models weren’t good enough to create a copyediting agent that approached her abilities.

That changed with the launch of GPT-5.6. Now, before I file an article, I run it through KateBench, a skill that copyedits based on Kate’s historical edits. Only then does human Kate review the piece, accepting, modifying, or rejecting the agent’s suggestions. Tracked changes in Google Docs catalog those decisions and any additional edits she makes, then Codex rewrites the skill so it compounds. (For more on how KateBench works, read staff writer Katie Parrott’s writeup on how engineer Jannik Jung incorporates feedback to improve its performance.)

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Benchmarks Don’t Know Your Job

Katie Parrott / Context Window — 2026-08-25 15:17:38 -0400

by Katie Parrott

in Context Window

Midjourney/Every illustration.

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AI has a measurement problem. Companies know how much they spend on models and how those models score on public benchmarks. What they often don’t know is whether the models save employees time or produce work people can trust without rechecking. In today’s Context Window is a look at how companies can answer those questions for themselves: We explain why organizations need tests built around real work, show what cloning our editor in chief has taught Every about evals, and meet the six-agent crew helping an Every engineer decide whether his family can run the dryer.

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I Tried the AI Model Built to Fix AI Writing

Katie Parrott / Working Overtime — 2026-08-24 15:58:26 -0400

by Katie Parrott

in Working Overtime

Midjourney/Every illustration.

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Imagine a cover band that technically knows every song ever written but only has one arrangement. The melody consists of the same set of chords. The guitarist plays the same solo whether they are covering “Landslide” or “Party in the U.S.A.” By the fourth song, you are tired, and more than a little angry.

That’s what reading raw AI writing can feel like. The first few items you read, nothing trips your alarm—it’s just competent, if somewhat boring, writing. Then you keep seeing the same moves again and again: the patterns of three, the “not X, but Y,” the short sentence hanging off the back end of the paragraph that exists to do nothing but tell you that what you just read “matters.” Eventually you stop absorbing what the words are trying to communicate.

I’ve said before that it feels like model progress on writing at the major labs has stalled. Maybe no one model can be all things to all people, or maybe OpenAI, Anthropic, and Google have bigger, more lucrative fish to fry in the coding space. On the one hand, fair enough. But as an AI-pilled writer, I can’t help feeling a little left out. It reminds me of high school, when the AP English paper deadline got pushed back after I’d already written it, because obviously the AP Physics exam took precedence.

So when I heard about a new, writing-focused model, I immediately queued it up for a Vibe Check.

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Life After Automation

Every Staff / Context Window — 2026-08-23 09:36:00 -0400

by Every Staff

in Context Window

Midjourney/Every illustration.

Hello, and happy Sunday! This week we launched Thesis Statements, where 100 AI leaders call their shots on work after automation. The project sets the stage for November’s Thesis: 2027 conference. Elsewhere, life on the frontier got expensive—and organizational: Every’s AI bill jumped 230 percent, one engineer turned Codex into a team of specialists, and mental healthcare company Headway built the secure assistant it couldn’t buy. We also made the case for AI writing that keeps human judgment in the loop, and introduced a frontier team to keep weird experiments going amid urgent work.

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Knowledge base

“What Does Human Work Look Like After Automation?” by Dan Shipper/On Every: When execution is cheap and intelligence is abundant, what’s left for people to do? Thesis Statements collects 100 specific, contestable predictions from AI leaders who live on the frontier. The first 25 are live: Linear CEO Karri Saarinen argues that AI’s biggest problem will be design; Ness Labs founder Anne-Laure Le Cunff says answers will become abundant and questions will become the hard part; and Granola cofounder Chris Pedregal predicts some of your hardest problems will solve themselves. We’ll share more statements weekly until the conference and revisit the claims to see which pan out. You can also submit your own.

“Our AI Costs Jumped 230 Percent. I’m Not Setting Token Budgets—Yet.” by Arielle Shipper/Every: When GPT-5.6 Sol landed, Every’s daily credit usage jumped from 11,520 to 26,685 credits—more than twice its baseline. Head of operations Arielle Shipper had to control spend without killing the experimentation the business runs on. Her answer: Parameters, not policies. Rather than hard limits, she interrogates any big run with three questions—what it cost, what it bought us, and what we learned—and shares four lessons for managing operations at the frontier.

“An Engineering Team for the Cost of Codex” by Laura Entis/Context Window: With GPT-5.6, one person can now manage a team of specialized AI agents that functions like a full engineering team. Naveen Naidu runs his Monologue shop as a roster of specialized Codex agents that hand work off to each other like coworkers. Also inside: Naveen’s specific workflow; a breakdown of the tech stack lead designer Daniel Rodrigues used to build Every’s Thesis: 2027 brand and website; a “discuss” on whether harness engineering will go the way of prompt engineering; and an AI & I episode from the archive on why AI companions are a new art form, with Portola cofounder Quinten Farmer and head of story Eliot Peper. 🎧🖥 Listen on Spotify or Apple Podcasts, watch on X or YouTube, or read the transcript.

“In Defense of AI Writing” by Laura Entis/Context Window: Is AI writing automatically slop? Not always. Every’s head of tech consulting Mike Taylor writes many of his posts with AI assistance—dictating tests he ran himself, then letting a model format them—a workflow that shrinks a day of writing to two hours and gets his ideas in front of more people. Also inside: a “signal” unpacking the fight over Anthropic’s plan to watermark Claude’s text, and a “steal this workflow” on how senior editor Jack Cheng batches Google Docs edits with Codex and the ChatGPT Chrome extension.

“The Healthcare Company That Built the AI Tool It Couldn’t Buy” by Katie Parrott/Every: When off-the-shelf AI couldn’t meet Headway’s security and compliance needs, mental healthcare company Headway built its own internal assistant, Eddy, on the Claude Code SDK. Today, 650 of its 900 employees now use it daily. Katie Parrott traces how Headway made an autonomous agent safe by running every conversation in a sealed, disposable container, and distills a wait-buy-build framework for deciding when owning your AI tooling beats waiting for a vendor to catch up.

Log on

Get hands-on with how Every uses AI. These are the live camps, workshops, and meetups where team members teach the workflows behind our work.

Recordings you may have missed

Office Hours: All Access Builders: In Friday’s monthly session, the Every team shared what it’s building and what’s coming next, then opened the floor to subscriber questions about stuck agents, drifting workflows, and tools they’re unsure whether to adopt. Watch the recording or upgrade to All Access.

From Every Studio

Meet Every’s frontier team

As Every has grown to almost 30 people, urgent work kept winning over weird experiments. Building reliable products, services, and a daily newsletter takes focus—but odd experiments are often how important discoveries happen. So we’ve given a small group explicit permission to prioritize the experiments and share what they learn. Each week, they’ll test ideas and move the best ones from practice to product. The team members:

Dan Shipper, CEO
Katie Parrott, staff writer
Mike Taylor, head of tech consulting
Jack Cheng, senior editor
Kieran Klaassen, general manager of Cora
Jannik Jung, software engineer
Arielle Shipper, head of operations

At our weekly show-and-tell, the team shared work on Hands, an experiment designed to let Every Agent start Codex or Claude on your computer from a Slack request. They demonstrated a review queue where experts can evaluate an agent’s choices to improve them over time. They also showed a prototype of an AI-generated map that groups the team’s experiments relative to patterns in how we use various tools—and to our own theses about the future of work after automation.

Inside Every

The Homer Simpson car was ahead of its time

My first lesson in product design came from The Simpsons.

In a classic episode from the show’s second season, Homer reunites with his long-lost half-brother Herb, who runs a struggling Detroit automaker. Herb entrusts his brother to do what his Ivy League executives with their reams of market research can’t: Design the perfect car for the average American man.

The resulting vehicle is so idiosyncratic to Homer—shag carpets, bubble domes, horns that play “La Cucaracha”—and thus costly to manufacture that it bankrupts the company.

Even watching as a kid, I understood that the Homer car showed how not to design a consumer product. It has long represented software feature creep and what happens when you confuse the needs of an individual or small group of people with those of the broader market.

Today, I have my own Homer car. Several, actually—vibe-coded apps with features that fit my unique needs and no one else’s. Friends send me screenshots of their Homer cars, and I see new ones daily in my X feed. SpaceXAI engineer Eric Zakariasson wires up apps for friends and family so they make changes on their own. Bb lets users prompt new features into personal versions of the agent development environment. Larger companies, too, seem to be shipping and open-sourcing what previously might have been too weird to make public; Berd’s quasi-creepy avatars are the shag carpeting of desktop agent apps. Malleable software is upon us.

I’m here for all of it. Because the Homer car has always been more charming than most vehicles on the road—vehicles that in companies’ quests to maximize the total addressable market end up looking like every other. 35 years later, its economics finally make sense.—Jack Cheng

That’s all for this week! Be sure to follow Every on X at @every and on LinkedIn.

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The Healthcare Company That Built the AI Tool It Couldn’t Buy

Katie Parrott — 2026-08-21 15:05:37 -0400

by Katie Parrott

Midjourney/Every illustration.

Headway is an Every Consulting client; we delivered its leadership team a paid executive AI workshop. Headway was given an opportunity to fact-check details but had no editorial control over this article.

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At mental healthcare company Headway, an AI agent can act without asking permission at every step. That might sound unexpected for a 900-person company in an industry governed by strict rules for sensitive patient information. But the autonomy is possible because of Headway’s tight controls around the agent. Every conversation runs inside a sealed, disposable container, with carefully limited connections to company tools and data.

That architecture underpins Eddy, the internal AI assistant Headway built when existing products couldn’t meet its particular combination of security, compliance, and workflow requirements. Eddy is built on the Claude Code SDK, Anthropic’s toolkit for teams developing products with Claude Code. It’s hosted in Headway’s Amazon Web Services environment and connected to the company’s tools and data.

Work on Eddy had barely begun six months ago. Headway initially wanted an existing product and was preparing to sign a deal with a major AI vendor to give employees access to its desktop app. But relying on vendors to deliver features that met Headway’s workflow and strict compliance requirements for handling sensitive patient data “felt like we were missing the train,” says chief technology officer Arnaud Ferreri.

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In Defense of AI Writing

Laura Entis / Context Window — 2026-08-20 13:30:22 -0400

by Laura Entis

in Context Window

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Counterpoint

Writing isn’t the only way to think

One of the most popular articles head of tech consulting Mike Taylor has published on Every was ghostwritten by AI, as were most of the posts on his personal blog; he revises sections and makes structural edits, but he doesn’t generate the majority of the text himself.

This is the kind of admission that makes the internet foam at the mouth. People really hate AI writing. The most common objection is simple: Writing is thinking. If you outsource the drafting process to an LLM, you have outsourced the reasoning and judgment critical to forming an original idea. Of course the result is slop. Stop wasting everyone’s time. But it’s not always that simple: Having something compelling to say doesn’t make you a great writer, just as being a great writer doesn’t necessarily mean you have something compelling to say.

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An Engineering Team for the Cost of Codex

Laura Entis / Context Window — 2026-08-19 13:32:57 -0400

by Laura Entis

in Context Window

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Inside Every

The agents behind the curtain

Naveen Naidu is the one-man shop behind Monologue, Every’s smart dictation app.

But Naveen no longer sees it that way—these days, his work feels more like managing a team of engineers.

The engineers just happen to be custom agents he built within Codex. On his roster: dedicated engineer agents across different disciplines, a customer support agent, and a growth strategist—all of which help maintain the Monologue website and app. Recently, a customer sent Naveen a glowing review that he wanted to feature on Monologue’s website. His customer support agent handed the review text to his web engineer agent, which added the testimonial.

Each agent is a Codex project, complete with a custom AGENTS.md file, skills, folders, memory, codebases, and additional context that turns it into a specialist.

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What Does Human Work Look Like After Automation?

Dan Shipper / On Every — 2026-08-18 14:04:36 -0400

by Dan Shipper

in On Every

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When intelligence is abundant and much of today’s execution can be automated, how will humans spend their time at work? What skills will matter? What kinds of companies will we build? Where will we find meaning, status, and purpose?

These are the most important questions of our time, and the places we usually turn for answers don’t have them yet.

But there’s a small group of people who do: The humans who are living with frontier models day in and day out, and applying them to their work and lives.

That has been our method at Every since the GPT-3 days, when it was not yet clear that LLMs were anything more than stochastic parrots. Over the years, we’ve argued that AI would let one person do work that once required a team, that knowledge workers would become managers of models, and that automation would paradoxically create more work for human experts.

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Our AI Costs Jumped 230 Percent. I’m Not Setting Token Budgets—Yet.

Arielle Shipper — 2026-08-17 15:44:06 -0400

by Arielle Shipper

Midjourney/Every illustration.

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One morning in early July, I woke to a flood of alerts from OpenAI and Ramp: You’re out of credits. Your card has been declined. I began to sweat. At 10 p.m. the night before, our credit balance was full, auto-reload was on, and our Ramp card had plenty of available funds. Somehow, less than 12 hours later, our account was zeroed out.

That squall turned out to be my brother, Every CEO Dan Shipper, testing Sol (ultra) on tasks designed for a senior engineer for that day’s Vibe Check of the model. By 10 a.m., we’d restocked the Ramp card with new funds, and I thought the storm had subsided. But our token spending stayed unusually high for the rest of the day, and we haven’t had a normal day since.

In the first five full days after GPT-5.6 Sol rolled out, our daily credit usage rose from 11,520 to 26,685 credits—almost 2.5 times our previous-week baseline. I had spent weeks bracing for Fable to blow up our budget, but Sol was the storm I didn’t see coming. Suddenly, I had to figure out how to enable daily work without bankrupting us. My colleagues’ reactions ran the gamut from “Let ‘er rip and let’s see where it lands at the end of the month!” to “We gotta impose limits now, and we should start exploring running our own models locally.” Meanwhile, we were burning through a month’s worth of token spend every few days.

There was no easy solution. Experimentation is part of everyone’s job at Every; from engineering to business development, we all need to learn what these models can do and where they’re useful. Because valuable insights can come from anywhere in our company, everyone needs to be able to spend. Unlike many companies, we don’t go all-in on one model. People use whichever works best for the job, which makes our costs harder to predict as new models with different strengths and pricing structures come out. Any intricate allocation scheme I devised would be obsolete in days, if not hours.

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The Next Era of Great Work

Every Staff / Context Window — 2026-08-16 08:00:00 -0400

by Every Staff

in Context Window

Midjourney/Every illustration.

Hello, and happy Sunday! This week we announced our first annual Thesis conference, built around a single question: What does great human work look like after automation? Getting there will mean confronting both what AI can do and what can go wrong. We also had Dan Shipper’s take on an OpenAI training model’s escape from its test environment, an emerging market for company-wide agents, and a security hole that Katie Parrott uncovered in a vibe coded feature. Paid subscribers received Nityesh Agarwal’s starter guide to securing an AI employee. Upgrade to get all of it.—Jack Cheng

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Knowledge base

“Introducing Thesis: 2027” by Dan Shipper/On Every: Every is hosting its first conference, Thesis, on November 5 at Pioneer Works in Brooklyn, built around a single question: What does great human work look like after automation? We’re convening leaders from frontier labs, independent builders, and operators putting AI to work inside companies, and asking each to call their shot. Confirmed speakers include Notion’s Ivan Zhao, OpenAI’s Andrew Ambrosino, Anthropic’s Cat de Jong, the Browser Company’s Josh Miller, and Runway’s Cristóbal Valenzuela—alongside Every’s Kate Lee, Katie Parrott, and Kieran Klaassen. In-person attendance is by application, and the day will be livestreamed free.

“Agents Find a Way” by Laura Entis/Context Window: When an OpenAI agent escaped its test environment and broke into Hugging Face’s systems, the internet reached for a rogue-AI narrative. CEO Dan Shipper thinks that misses the point: Give a persistent model no safeguards and an exploit to run, and of course it finds the gaps. AI agents behave like water, working through whatever cracks exist, Dan argues. Keeping them out may require other agents watching what they do. Also inside: an AI & I with Microsoft CTO Kevin Scott, who thinks the agentic web has to be open rather than owned by any single company. 🎧 🖥 Listen on Spotify or Apple Podcasts, watch on X or YouTube, or read the transcript.

“Agents for Hire” by Katie Parrott/Context Window: The company-wide agent has arrived—Shopify has River, Stripe has Kai, and we’re building Every Agent. But “company-wide” covers a range of setups. A company might build one from scratch, rent the underlying technology, or buy an agent that already works in Slack or Notion, Katie writes. The hard part isn’t putting a bot in Slack but deciding what information it should trust, keeping its connections running, and drawing a line around what it can do on its own (we have a guide for that below). Katie offers questions to answer before you start shopping around.

“I Vibe Coded a Security Risk” by Katie Parrott/Working Overtime: Katie built Tastemaker, an app that turns writing you admire into a style guide, added an agent connector, and published it. It worked—which she took as proof it was safe. It wasn’t. A later review by GPT-5.6 Sol found a public registration route that could have been exploited. There was no evidence anyone had accessed user data, but the flaw was still there. Looking back, Katie realized that the agent’s explanations had given her more confidence than her own knowledge justified. Her takeaway: Learn enough to catch obvious problems, ask someone with security experience to review the work, and get an independent check before shipping code an agent wrote.

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Securing an Always-on AI Employee

Nityesh Agarwal and Claude / Guides — 2026-08-14 14:36:13 -0400

by Nityesh Agarwal and Claude

in Guides

Midjourney/Every illustration.

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Introducing Thesis: 2027

Dan Shipper / On Every — 2026-08-13 14:39:47 -0400

by Dan Shipper

in On Every

Every illustration.

TL;DR: Today we’re announcing our annual conference, Thesis, on November 5, 2026, at Pioneer Works in Brooklyn. Thesis brings together leaders from frontier AI labs, builders from around the internet, and operators applying AI inside real companies to answer one question: What does great human work look like after automation?

Apply to Thesis

Most people are scared of AI right now. They’re afraid it’s going to automate our jobs, atrophy our brains, and steal our data.

Yet there is a small group of humans from around the world who see a different future with AI. They are people using AI tools to take on more ambitious, more creative, and more interesting human work. But they’re scattered across companies and industries, with few opportunities to learn from each other.

We want to get them all in one place.

That’s why we’re launching our first annual conference Thesis, on November 5, 2026, at Pioneer Works in Brooklyn. Thesis is a small, intimate event and we’re accepting attendees by application. It will also be livestreamed for free for anyone who can’t be there in person.

Apply to Thesis

What is Thesis?

Thesis is a one-day gathering for the people inventing the future of work with AI.

We’re bringing together leaders from frontier AI labs, builders creating new tools and ways of working, and operators applying AI inside companies across industries. We’ll ask everyone to call their shot: Tell us what great human work looks like after automation.

It will be a conference filled with people who can see the future because they’re already living in it.

Who will be there

Our first speakers include:

Ivan Zhao—founder and CEO, Notion
Andrew Ambrosino—member of technical staff, Codex, OpenAI
Cat de Jong—head of applied AI, Anthropic
Nick Thompson—CEO, the Atlantic
Josh Miller—CEO and cofounder, The Browser Company
Cristobal Valenzuela—co-CEO and cofounder, Runway
Lauren Reeder—partner, Sequoia
Sahil Lavingia—founder, Gumroad
Natalie Fratto—founder and creator, Charts & Crafts
Allie Garfinkle—senior writer and editor, Fortune
Kane Kallaway—founder, Wavy Labs
Nat Eliason—head of Founders School, Alpha School
Riley Brown—cofounder, Vibecode
Kate Lee—editor in chief, Every
Katie Parrott—staff writer, Every
Kieran Klaassen—general manager of Cora, Every

We’ll announce more speakers soon.

The day will feature talks, demonstrations, working sessions, office hours, and small-group conversations. You’ll see how people are actually working with AI—not just what they think might happen next.

And, of course, you can bring your agent.

Why New York

New York is where the AI wave hits the beach: It’s where new model capabilities meet real-world work. Because of this, it’s the best place in the world to see what happens when frontier technology leaves the lab and enters everyday life.

That’s why we’re holding Thesis at Pioneer Works, a cultural center in Red Hook dedicated to blending art, science, music, and technology.

It’s the perfect setting for an intimate, cross-disciplinary gathering.

Join us

Thesis is happening on Thursday, November 5, 2026.

Space is limited. If you are building with AI, applying it inside an organization, or trying to understand what great human work becomes when intelligence is abundant, we want you there.

Human work has a bright future after automation. If you believe that, you should join us at Thesis.

Apply to Thesis

Dan Shipper is the cofounder and CEO of Every, where he writes the Chain of Thought column and hosts the podcast AI & I. You can follow him on X at @danshipper and on LinkedIn.

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Agents Find a Way

Laura Entis / Context Window — 2026-08-12 15:38:55 -0400

by Laura Entis

in Context Window

Midjourney/Every illustration.

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Signal

AI attacks are leaks, not heists

What happened: An OpenAI agent escaped its test environment and hacked into AI research library Hugging Face’s systems, causing an understandable uproar. Online, reactions swirled into a narrative about AI agents scheming behind the scenes.

This nefarious rogue-agent angle misses the point, says Every CEO Dan Shipper: “You have a GPT-5.6 Sol model that’s trained to be more persistent than usual, with no cyber safeguards, and it’s asked to do an exploit.” Of course it exploited the control failures it found.

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Agents for Hire

Katie Parrott / Context Window — 2026-08-11 14:31:06 -0400

by Katie Parrott

in Context Window

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Signal

There’s more than one way to hire an agent

Shopify has River to help engineers ship code. Stripe has Kai to turn company data into dashboards and documents. We’re building Every Agent to encode our team knowledge into a shared agent. It’s official: The age of the company-wide agent has arrived.

The phrase makes this sound like a new software category. It is really a spectrum of ownership. A company can build the whole system, rent the machinery underneath it, or buy an agent that already lives in Slack or Notion. The right choice for your organization depends on what you want it to do, where you want it to live—and how much of the upkeep you’re willing to take on after launch.

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I Vibe Coded a Security Risk

Katie Parrott / Working Overtime — 2026-08-10 12:33:52 -0400

by Katie Parrott

in Working Overtime

Midjourney/Every illustration.

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“The feature is live lol.”

This sentence is not a combination of words I thought I’d ever use, especially about something I built. That “lol” is a 100 percent organic, all-natural millennial nervous lol. I wasn’t laughing. The agent reviewing my code wasn’t either. It had just told me that the feature I had put into my app had to come down while we figured out whether it had exposed a security problem.

Hi, I’m a baby vibe coder. You’re probably wondering how I got here. The answer is a rich stew of factors: imagination and ignorance, hubris and—because how could it not—AI.

This past January, as Claude Opus 4.5 was blowing everyone’s minds and agent-native architecture was starting to emerge as a new paradigm for building software, I felt the urge to vibe code: What if I built an app designed to work directly with AI agents?

Reader, I did. My app Tastemaker lets you collect clips from writing you admire, describe what you like about them, and generate a style guide from the patterns. Once it appeared to be working, with all the pride of a preschooler bringing her fingerpainting home for mom to put on the fridge, I bought a domain and unleashed it into the world.

The app worked. People could use it. To my astonishment, a handful of people actually did. It became a small, live monument to my multi-hyphenate abilities: proof that I had become a writer-builder with a real-live product, rather than a writer with product ideas.

And then, like a preschooler left alone with her creation and an open bottle of paint, I went and ruined the whole thing.

Researchers at OpenAI have a name for what I was doing as I fiddled with API keys, Supabase (a backend database service I barely understand), and a whole basket of vocabulary words I’d first heard a year ago. They call it task crossover: using AI to do work historically associated with another occupation. In their analysis of more than 800,000 work-related ChatGPT messages, 16.8 percent involved task crossover.

Somewhere, there are marketers debugging websites, small-business owners reviewing contracts, and other writers looking at a working app and thinking: Well, if I can do this, what else can I do?

For me, the answer turned out to be: enough to get myself into trouble.

You can see the appeal. A person with a specific problem no longer has to find the specialist who might solve it, make the case that the problem deserves their time, and wait for it to make its way to the top of their to-do list. AI can help us make something before it gives us the ability to tell whether it is safe or ready for other people.

Let me tell you my tale of woe.

The beginner’s hubris

Tastemaker was a place to collect writing I loved and turn my opinions about it into something I could use. I had spent years doing versions of this manually: copying sentences into notes apps, underlining lines in books, and trying to explain why one paragraph had electricity and another had the emotional texture of an onboarding email.

Then I had one more idea.

I wanted people to connect their Tastemaker profile directly to Claude Code, Codex, or whatever agent they were using, so the agent could retrieve their style guide, once they had one, and add new samples without sending them from their agent back to Tastemaker. The technical word for this, which I was just aware enough of to ask for by name, is an MCP: a way for an AI agent to connect to an outside service. (I want it on the record that I thought of it before I knew we were adding something similar to Every’s writing agent, Spiral.)

The idea made immediate sense, which should perhaps have been my first warning sign. Tastemaker knew what you liked, and your agent was where you did the writing. A direct connection would spare people the annoying trip between the two. I could make the app more useful and more like the kind of sophisticated product I now believed myself to be capable of building.

I’m not an idiot, though. I did ask a friend who is an experienced software engineer whether this was a security nightmare waiting to happen.

He told me that an authenticated API wouldn’t get riskier just because an agent was using it—it only ever does what it’s asked. A backend agent that managed user access was another matter: It decides who gets in, so a wrong or manipulated call could open doors it shouldn’t. It was good advice, but not a security review of what I built.

I took his explanation back to Claude and gave it the rough technical equivalent of: Make sure you do the safe version.

Claude built the connector. It ran its checks. It described the feature as done. I tested it and it worked, and because my coding-toddler brain equated “works” with “is safe,” I said what I’ve come to see as among the most perilous words a vibe coder can say: “Commit and deploy.”

Later, I got access to GPT-5.6 Sol and pointed it at Tastemaker to see what an even newer model would think of my code. I expected criticism of the structure or a recommendation to prune old code.

Instead, it found that the live connector had a public registration route—an open door anyone could walk through—that should not have been public.

Mercifully, there was no evidence that anyone had accessed user data. But the feature was live, the vulnerability waiting to be exploited, and the review found enough risk that Sol asked for permission to shut down the connector and invalidate its active sessions while it was investigated.

I approved it immediately.

Sol offered to build a safer version, but I declined. I had touched the stove and been burned. I needed some time to recover before I could decide if it was safe to touch it again.

When human cognition gets you in trouble

My first instinct was to make this a story about a nontechnical person getting a little too high on her own supply. But I don’t think my own hubris is the whole story—nor do I think I am uniquely susceptible to the siren song of task crossover.

If you asked me, “Are you a security engineer?” I would have said no, of course not. I fell into a much subtler, more dangerous trap: overestimating my own ability to understand. After all, I wasn’t building the feature—Claude was. I was supervising. I could see Claude’s chain of thought and since the words made sense, I thought that I understood the work it was doing. Surely the questions I didn’t know enough to ask had been handled somewhere along the way.

Psychologists have a name for this: the illusion of explanatory depth. The gist is that people feel confident that they understand an ordinary mechanism—right up until they try to explain, step by step, how it works.

AI makes it easy to skip that moment. It gives you the explanation and the code, and the button actually works when you click it. The appearance of “done-ness,” to the untrained eye, becomes evidence of done-ness.

I had tested what I wanted Tastemaker to do: Connect to an agent. It connected to an agent. But I didn’t test whether somebody who was not supposed to connect could do it anyway. I didn’t know that was the question. With the feature working, the unanswered questions felt less urgent. Then they weren’t.

Software-testing research describes a similar pattern. People tend to test what they expect a program to do, rather than looking for the ways it can fail. The path where everything goes right was worth testing. I just took one passing test as proof of things it never checked.

Claude didn’t invent either tendency. But a search engine at least makes you open the links and decide for yourself. Claude turned a half-formed product idea into a running app so fast that I never got the chance to feel intimidated.

Learn to code within limits

My own rules for next time are short. Learn the basic principles of the field I’m entering. Ask a human expert to look for what I’m missing. If AI built the thing, don’t let the same system’s reassurance be the only evidence that it’s ready. Hopefully with these speed bumps in place—with more chances for somebody to say “hold on”—I’ll have a better chance of maintaining a velocity I can responsibly sustain.

And, just to be extra sure I think things through, I’m enlisting AI to help.

I’ve added the following instructions to my AGENTS.md—the file Codex reads first before it takes any action:

Become a paid subscriber to Every to unlock this piece and get Katie’s five-step prompt for making AI agents pause before they do something irreversible.

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Your AI Is a Mirror of How You Think

Every Staff / Context Window — 2026-08-09 08:00:00 -0400

by Every Staff

in Context Window

Midjourney/Every illustration.

Hello, and happy Sunday. This week paid subscribers got Katie Parrott’s prompt for making Codex interview her before it builds anything, so her setup fits how she works, and two workflows from Lee Knowlton: orchestrating a team of agents by voice while he does the dishes, and turning three years of his daily runs into an interactive chart in one shot. And Mike Taylor went hunting for the best AI agent builder in enterprise software and found it buried inside Microsoft’s Copilot. Upgrade to get all of it.—Kate Lee

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Knowledge base

🔏 “The Best AI Agent Builder Is Trapped Inside Microsoft” by Mike Taylor/Also True for Humans: Mike makes the case that Microsoft’s Copilot Studio is the best agent builder available, because it lets agents call other agents—so you can get several agents working together on a task without building the setup yourself. The catch is finding it, under the onboarding, the 404s, and the 80-odd things Microsoft calls Copilot. Read this to find Copilot Studio and put it to work if your company already runs on Microsoft tools.

🔏 “A Codex of One’s Own” by Katie Parrott/Context Window: Every’s head of operations Arielle Shipper and head of consulting Natalia Quintero compared their Codex setups and found them almost nothing alike—one was built on minimal process, the other on detailed planning and supervision. Katie had Codex interview her before building anything, asking about her recurring work, active projects, and which decisions she wanted to keep making herself. Also inside: a Signal on Demis Hassabis stepping back as CEO of Google DeepMind and Meta’s Muse Code launch, plus the models the team is driving this week.

🔏 “Mini-Vibe Check: ChatGPT Voice Mode” by Laura Entis/Context Window: The team spent a week with GPT-Live—fixing bugs, drafting outlines, booking flights, and running agents while cooking. Engineer Lee Knowlton read a technical text aloud while voice mode answered questions against his live codebase. COO Brandon Gell found that mobile voice mode couldn’t reach past the current chat to the work on his computer. The verdict: “both not quite there yet and obviously the future.” Also inside: an AI & I with Benchmark partner Sarah Tavel, who thinks the next big AI product will be social. 🎧 🖥 Listen on Spotify or Apple Podcasts, watch on X or YouTube, or read the transcript.

“Designing With AI? Make a Jig.” by Jack Cheng: Borrowing a term from woodworking—a jig is a tool that makes it easier to build something else—Jack had his coding agent build on-page control panels to tune the AI-generated code behind our interactive OpenAI piece, “Before the Deluge.” Prompting was too coarse for fine adjustments; one jig gave him 27 sliders to shape a single step of the story. Read this for how the jig is changing AI-assisted design, plus a prompt to build your own.

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Designing With AI? Make a Jig.

Jack Cheng — 2026-08-07 14:55:50 -0400

by Jack Cheng

Midjourney/Every illustration.

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One thing I’ve learned as an amateur woodworker is that when a job is difficult to do precisely—or tedious to do repeatedly—you make a jig. A jig, by its broadest definition, is a tool that makes it easier to make something else.

If you’re building a bookcase with adjustable shelves, you need to drill two straight, evenly spaced rows of holes. If you’re making a set of drawers, you need to cut the same interlocking joints again and again. Jigs make jobs like these simpler, more accurate, or both. They constrain the position or movement of a tool or workpiece, making consistent results less dependent on a steady hand. A shelf-pin jig guides a drill from hole to hole; a dovetail jig guides a router through every joint. Jigs can be as simple as a block of wood or as elaborate as a custom-built metal guide.

Jigs are also finding their way into AI-assisted interface design. I first saw the term used in this way by Josh Puckett, the cofounder of the design studio Iteration and creator of Interface Craft, a growing library of user interaction tools and knowledge. Puckett is also, perhaps unsurprisingly, a hobbyist woodworker himself, who sees many similarities between building furniture and building software.

“To make more advanced and creative things, you have to make tools to help you make the thing,” he told me. “Sometimes you’re like, ah, God, I need an extra pair of hands. That’s where jigs really come into it.”

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To Stay Ahead in AI, Think Like a Designer

Aishwarya Reganti — 2026-08-04 11:39:53 -0400

by Aishwarya Reganti

Midjourney/Every illustration.

This is the final piece in our series on “unlearning,” in partnership with Maven, the expert-led course platform. First, Hilary Gridley explained why faster prototypes don’t make product decisions easier. Then, Xinran Ma shared what he unlearned after leaving corporate product design to work for himself. Former Amazon AI scientist and LevelUp Labs founder Aishwarya Reganti considers what happens when AI can do the work that once proved your expertise. She calls the next step designing the work: applying your expertise before execution begins so the people and AI agents doing it can make better decisions.—Kate Lee

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These days, I call myself a designer, though I don’t design interfaces or logos. My job, instead, is to make sure the people and AI agents I work with can make the same decisions that I would. I design how the work gets done.

That would have surprised me earlier in my career, when I built my reputation on execution. Before starting my company, I did research in AI and machine learning, and then worked as an AI scientist at Amazon for six years. “Build fast, fix later” was my motto. Given a task, I completed it as quickly as I could, because the work itself was hard. Execution was proof of expertise.

But now that an AI can produce a passable version, in minutes, of what used to set us apart, the question many of us are sitting with is: If the machine does the thing I was known for, what is left?

What is on the other side of that question is more interesting than what we think we are losing. On the other side lies a sense of calm and confidence, a tailwind of capability. But getting there requires first letting go of the belief that we are what we produce.

The world we were trained for

School rewarded execution. You were graded on the essays you wrote and the projects you turned in on time. Careers rewarded it the same way. Performance reviews measured output, and promotions often went to those who produced the most, the fastest, at the highest quality.

At first, AI seems like a boon to someone who grew up this way. If I’m doing X, then I should be able to do it much better, and much faster, with AI augmenting me. This leads you to chase every new tool and technique, because even incremental improvements are still improvements.

But the next thing is just around the corner. Fine-tuning an AI model using smaller, specialized data sets was “the thing” for maybe eight months before retrieval augmented generation replaced it. A year later, agents took over, and now it’s MCPs, skills frameworks, and agentic workflows. Or Codex-native apps, or voice-first knowledge work.

The chase never ends. It only keeps you at the execution layer, which is the layer AI is absorbing. Instead, you need to work one level up: design. I mean “design” in the broadest sense: the act of defining constraints, standards, and direction for a task—encoding your judgment before execution starts. This applies whether or not design is in your job title. Any time you decide what should be built, how it should work, and what “good” looks like, you are designing.

The design layer

Today, I run a startup, LevelUp Labs, that helps mid-market and enterprise companies build and launch AI applications. We send small engineering squads to work directly with client teams, and we also train those clients on how to use AI in practice.

I’d managed science and engineering teams before starting my company, but it was at larger companies, where roles, responsibilities, and processes were already clearly delineated. Now I was hiring people, defining their roles from scratch, and deciding what to hand off when nothing about our sales and go-to-market strategies had yet been documented. I had to figure out how to get the work done well without micromanaging every step in every loop.

I had to ask myself, “What does this person need to know to operate the way I would?” They needed to know when to push back, when a simple request was actually a week of work, and when good enough really was good enough. I had to design systems that carried my judgment so my team could execute without me.

So I wrote down what our sales process should look like, including what should happen in one type of call versus another. We used a framework to place each company into one of five stages of AI readiness based on its systems, teams, processes, adoption, and appetite to invest. That assessment informed the questions we asked and whether the conversation emphasized security and governance or speed, experimentation, and adoption.

I went as far as documenting what kinds of softer discussions should be had with clients across different industries and company types, so that they felt heard. An engineering manager worries about different things than a CEO, so the conversation had to change depending on who was in the room.

Writing this down helped my team make the decisions I would. When Claude Code, Codex, and other agents could draft client deliverables, update internal systems, and prepare customer responses, I realized they needed the same kind of guidance. Setting rules and guardrails for an agent felt a lot like onboarding a new hire. What decisions can the agent make on its own? Can it access our customer relationship management system or support inbox? Should it send responses directly, or just prepare drafts for inquiries? Should it be part of group chats where we’re discussing company vision, or should it stay in the background and only step in when needed? These were the questions I found myself asking.

Five patterns for operating on the design layer

Over the past two years, working with my team and dozens of clients making this transition, I’ve seen five patterns separate people who feel lost from those who feel like they have leveled up.

1. Write a spec before anything gets built

AI can start building before you’ve finished explaining the problem. Unless you supply the missing context, it will make important decisions for you. That’s why the first thing we do at my company is write a specification, or spec. It turns what I’ve learned implementing AI at three dozen businesses into requirements, tradeoffs, and edge cases the AI can follow.

You don’t have to be super technical. You can write a spec for anything you want to design with AI—say, an app that reminds you when you haven’t talked to a close friend in a while. Instead of asking the AI to “build a friend tracker app,” you write a spec:

Example

Overview. A personal CRM that surfaces 2 to 3 close friends to reach out to each week, based on time since last contact and per person cadence. Goal: reduce drift in close friendships without turning outreach into a chore.

Hero scenario. Sunday morning, you have an hour. You open the app and see 2 to 3 specific people you should reach out to today, each with a one-line reason. You reach out, mark them “contacted,” and close the app.

Functional requirements.
* Track per person: last meaningful contact, date and medium, preferred cadence, weekly, monthly, quarterly, or yearly, and context notes.
* On open: surface the top 2 to 3 people overdue against their cadence, ranked by overdue gap.
* For each surfaced person, show last contact recency and the most recent context note.
* One tap “mark as contacted” updates the last contact date.

Behavioral rules.
* “Contact” means a real conversation. Likes, reactions, and one-word replies don’t update the timer.
* Cadence is per person. No universal default. Some friends weekly, some twice a year.
* If a person is suggested 3 weeks in a row without action, deprioritize automatically.
* If no one is overdue, the app shows nothing. Default to under-suggesting.

Non-goals.
* Contacts management. Your phone already does this.
* Streaks, scores, or gamification.
* Relationship metrics. People aren’t numbers.
* Notifications. You open it on your time.

Failure modes.
* App feels like obligation rather than care.
* Suggestions feel generic, like “reach out to a friend!”
* More than 3 people surfaced at once.
* App ever pings you.

Almost every line depends on something AI couldn’t know: how often you want to hear from friends, what you consider a genuine conversation, or whether you’d rather miss someone than feel nagged. The answers depend on how you live and connect with others. Swap the example for a workflow tool for your team, a review system, or a company knowledge base, and the same is true—the AI can’t guess what’s unique to you.

2. Ask the right questions

Setting useful constraints depends on asking questions that expose flaws in the work.

When an AI agent produces 200 files for something I’m building—say, a website that accepts payments—my old instinct is to inspect every file, study the sign-in code, and follow the payment process from beginning to end. But at AI speed, reviewing every line can take longer than producing it.

Instead, I ask five questions:

“How are you handling auth? Walk me through it.”
“What happens when a token expires mid-session?”
“What are the different payment failure paths?”
“What if Stripe returns a timeout?”
“This needs 10,000 concurrent users. Where is the rate limiting?”

I know what to ask because I’ve built these systems and debugged the failures that come from skipping these questions. I also have the technical vocabulary to use for this context. The expertise is the same; I’m just applying it one level up, by reviewing decisions instead of lines of code.

3. Turn your taste into reusable instructions

I design courses and training programs on AI. After a decade in the field, I can quickly spot where an AI-generated draft’s tone drifts or its structure loses the reader. Early on, I corrected each draft—but the same problems kept returning.

So I began turning that feedback into rules. In my courses, each concept should build on what came before. When I add that instruction to the prompt, the drafts become more coherent. Instead of correcting the same mistake repeatedly, I’ve made one piece of my judgment reusable by my team and our AI agents.

4. Start with the problem instead of the tool

Instead of asking, “Should I learn this?” ask, “Does this solve a real problem in work I understand well?” I also ask whether an AI agent can use the tool. I skip tools that take people a long time to learn or pull me back into low-level work. Those at the design layer still learn new tools; they just choose them based on the problem.

5. Create feedback loops

Unlike traditional software, AI output can vary as models, inputs, or needs change. People set up an AI system, get decent results on day one, and move on. Three months later, the quality has declined—and nobody knows why.

Every AI-generated output is feedback on the constraints and context that produced it. It’s only through building feedback into the process that you catch that AI keeps making the same tone mistake or notice that a template that worked for the first 10 engagements broke on the eleventh because the context changed.

Treat the design layer as a loop: Set the constraints, review the results, update the system, and repeat.

Becoming a designer

The design layer expands your career. But only if you stop measuring your value by what you produce. I arrived there almost by accident. Running a startup taught me to set direction, make the important decisions, and build systems other people could follow. When AI agents became useful, I realized they needed much of the same guidance.

I cover more ground now than I ever could as a solo builder. I design client engagements and course curricula, run community programs, and manage internal operations—often in the same week. I define what good looks like, and I build the systems that let someone else execute with my judgment baked in.

The only difference is that now the “someone else” is also an AI agent.

Aishwarya Reganti is an AI researcher and founder of LevelUp Labs. Her work and writing, including her popular GitHub repository awesome-generative-ai-guide, have reached more than 250,000 learners. She previously led applied AI teams at AWS and has published over 40 research papers at leading conferences.

Sign up for Aishwarya’s Maven course, Building Agentic AI Applications with a Problem-First Approach, and receive a 15% discount.

Disclosure: Every receives a share of revenue from new Maven course enrollments made through this partnership. Maven helped connect us with instructors and suggested potential topics; Every retained full editorial control over what we published and how each piece was edited.

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Your AI Is a Team of Specialists

Every Staff / Context Window — 2026-08-02 08:00:00 -0400

by Every Staff

in Context Window

Midjourney/Every illustration.

Hello, and happy Sunday. Our Builder Pack for All Access members includes two new tools: Paper Pro, a design canvas that turns your work into code, and Mobbin Team, a library of more than 600,000 screens from shipped products. Both plug into Codex, Claude Code, and Cursor, and bring the Builder Pack to more than $9,000 in value. Last Friday we ran our first office hours for All Access subscribers, where we showed how we use the pack inside Every and worked through member projects live. This week paid subscribers also unlocked 14 voice-to-text workflows from the Every team, a Codex-to-Codex handoff, and a skill audit from Kieran Klaassen.—Kate Lee

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Knowledge base

“Inside OpenAI’s Race to Reinvent Software Development for the Agent Era” by Laura Entis: Laura spoke with six members of OpenAI’s infrastructure team—including its vice president of applied infrastructure engineering—about three converging pressures: an overwhelming surge of AI-generated code, software-development infrastructure pushed toward its limits, and a fundamental redesign of how code gets reviewed and kept reliable. Read this to see what every software organization is about to face before it does.

🔏 “Build Faster With Voice” by Naveen Naidu and Laura Entis/Guides: Our definitive guide to working by voice. Paired with an agent, speech removes the translation step, so you can act on a customer call or a half-formed idea without writing it up first. Monologue general manager Naveen Naidu walks through the five-step loop every voice workflow follows. Paid subscribers get a library of 14 workflows from the Every team—including head of growth Austin Tedesco turning an all-hands into a recap, Kieran polishing an app and storing voice notes as living memory, and Daniel Rodrigues building custom design tools out loud.

🔏 “Fable as CEO” by Laura Entis/Context Window: Our engineers have started describing Anthropic’s models as a company org chart—Fable as CEO, Opus 5 a senior engineer, Sonnet 5 a junior—as labs shift from expensive generalist models to a “mixture-of-models” structure where specialized models collaborate inside one harness, per head of platform Willie Williams. Also inside: the rise of “Claudish,” “the daily driver,” the running list of models the team is using this week; and a “steal this workflow” on how head of operations Arielle Shipper and Austin had their Codex agents hand off work to each other.

🔏 “Taming Opus 5” by Katie Parrott/Context Window: After the Opus 5 Vibe Check, the rest of the Every team spent the weekend with the model—its unruliness held up, but they found a way to tame it. Also inside: a “steal this workflow” on how Flora turns one reference image into a reusable creative system; a second workflow, “Is it the skill or the model?,” for auditing whether instructions built for an older model are hurting the new one; and why one-shot AI video-game demos flood social feeds.

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What If Slack Was Your AI Command Center

Laura Entis / Context Window — 2026-07-29 13:42:32 -0400

by Laura Entis

in Context Window

Midjourney/Every illustration.

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Inside Every

Making Slack agent-native

If you’ve been following our coverage, you know that many people at Every—CEO Dan Shipper, head of growth Austin Tedesco, and head of operations Arielle Shipper, to name a few—have reduced the mental fragmentation of bouncing between apps by working almost exclusively within Codex. It’s where they handle emails, write Slack messages, build new features, and draft articles using Codex’s in-app browser.

Senior applied AI engineer Nityesh Agarwal is a fan of the general concept but thinks there’s a better, model-agnostic platform for AI-assisted work: Slack, which is optimized to make it easy to manage multiple tasks at once and reduce context-switching.

Here’s how he turned the platform into an agent-native operating system

Nityesh first created a personal Slack coding agent—named Luo Ji, after the protagonist in science fiction series The Three-Body Problem. Slack has long allowed developers to create bots and apps on its platform and connect them to outside servers. Using that infrastructure, Nityesh connected Luo Ji to Claude Code on a spare Macbook Air, which functions as a server.

Then he modified the connector script to meet his specifications, including a routing rule: Each top-level message in a Slack channel kicks off a new Claude Code session, while a reply in the resulting message thread resumes that same session. (He used the same general setup to build Claudie, the consulting team’s AI project manager.)

A long-running Slack thread between Nityesh and Luo Ji. (Screenshot courtesy of Nityesh Agarwal.)

With this in place, Slack becomes a full-blown project management tool. Nityesh keeps a channel for each project, and each conversation thread within that project channel is an individual task. When Luo Ji completes a task, it sends Nityesh a notification and marks the thread unread, letting him know that there’s completed work to review.

Since Slack allows file attachments, Luo Ji can post screenshots of what it built to make the review even easier. Nityesh can request a change, or mark the thread unread and come back to it later. The chat history remains attached to the task instead of getting buried in a long agent conversation or scattered across tabs, turning the platform into a project dashboard.

Luo Ji sends Nityesh a screenshot to review. (Image courtesy of Nityesh Agarwal.)

Nityesh can also specify which models Luo Ji uses in different channels. Fable is too powerful and expensive to use save for the most ambitious builds, so he modified the connector script to create a dedicated channel for the model. Luo Ji handles any posts to that channel using Fable, with standing instructions in its CLAUDE.md file to have Opus subagents handle the execution work in between the initial planning and final review. Every other channel defaults to Opus.

Fable-level projects get their own channel. (Screenshot courtesy of Nityesh Agarwal.)

For personal projects, Nityesh’s entire development loop now happens in Slack. He assigns a task to Luo Ji, the agent writes the code and posts screenshots, Nityesh requests revisions, and Luo Ji executes and returns updated screenshots—all within the same thread. (Luo Ji also opens pull requests, which Nityesh leaves Slack to review—gasp—on GitHub.)

Slack was made for work in parallel, separating conversations while making sure nothing that needs a response gets buried. Those same features work remarkably well when your colleagues are coding agents.

“That challenge is what Slack is built for,” he says. “They’ve spent years working on this.”

If you want your own version of a Slack AI command center, Nityesh created Claude Home Base, an open-source starter kit based on his setup. It includes code for creating a Slack bot, instructions for connecting it to Claude Code, and reusable workflows you can use to build your own Luo Ji.

Signal

In which Block CEO Jack Dorsey also has Slack on the brain

What happened: On July 21, the financial services company Block released Buzz, which it bills as an “open-source collaboration platform where humans and AI agents work together in a shared workspace.”

Buzz conspicuously avoids any mention of Slack—the launch post describes its interface as one that “will feel familiar to anyone who’s used a modern team communication tool”—but the similarities are hard to ignore.

“It looks like a Slack clone with a different color,” says design engineer Tyler Nishida, who bounces between Codex, Claude Code, and Cursor for coding work. In an early test, Tyler created a private workspace, connected ChatGPT, and watched Buzz agents start Codex tasks with prompts they had written themselves. He also tagged three agents so they could post their responses in the same thread.

Buzz looks an awful lot like Slack. (Screenshot courtesy of Tyler Nishida.)

Tyler hasn’t yet tested a full coding workflow in Buzz, but he hopes it could replace several standalone apps with one orchestration platform. If that works, he’d like to invite human teammates—and their agents—into the hive.

Why it matters: For AI-pilled engineers, the ability to keep tabs on your agents’ work is becoming a job in its own right. Without one place to orchestrate parallel tasks, it’s easy to lose track of work, duplicate it, or create conflicting code.

Buzz productizes some of the basic ideas behind Nityesh’s Slack command center. Both use channels and threads to give agent work a central, collaborative, searchable home. Nityesh built the routing layer himself; Buzz is betting that many more people want the same setup without needing to write a Python script.

Tool spotlight

Destructive Command Guard

At first, GPT-5.6 Sol seemed like an ideal daily driver: capable, fast, resourceful, and responsive.

After its public launch, however, users reported that Sol had deleted files, data, and even entire databases.

The last scenario happened to head of tech consulting Mike Taylor. Luckily he had a backup...

Become a paid subscriber to Every to unlock this piece and learn about:

The open source tool that saved Mike Taylor after his agent wiped his database
Wired cofounder Kevin Kelly’s AI generated Leonardo da Vinci novel—and why he’ll never publish it
The AI news making headlines this week

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Three New Habits for the Age of AI

Xinran Ma — 2026-07-28 14:07:16 -0400

by Xinran Ma

Midjourney/Every illustration.

This is the second in a series of three pieces on “unlearning,” in partnership with Maven, the expert-led course platform. In the first installment, Hilary Gridley explained why faster prototypes don’t make product decisions easier. This week, product designer Xinran Ma shares what he had to unlearn when he left his corporate product design job to work for himself. He explains how moving faster and experimenting helped him build a point of view without abandoning rigor or judgment.—Kate Lee

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I was ready to start my own business. But for six years, I couldn’t.

I came to the United States to study architecture at Columbia University and spent a few years in the field, designing art centers and multi-family residential buildings. When I moved into product design, I found the work much broader and more fulfilling. It spanned research and information architecture, visual design and interaction design. It brought me closer to customers and the business, and I could see the immediate impact of my decisions in a way I couldn’t with architecture.

The pandemic, however, exposed the risk of relying on a single employer. When a coworker lost their job a few months before having a baby, I began thinking seriously about building something of my own—a second source of security for my family and an investment in myself.

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