Every (joshuarael@gmail.com)

We Gave Every Employee an AI Agent. Here's What We're Doing Differently Now.

Brandon Gell and Willie Williams / Source Code — 2026-05-15 07:00:00 -0400

Midjourney/Every illustration.

We’ve been working on a big release on the future of work for next week, shaped by what we learned from building Plus One. Paid subscribers can join us for a camp on Friday, May 22 to go deep on the release and the ideas behind it. More details soon.

After months of silence, Zosia—the AI agent I (Brandon) created and maintain—spoke up in a Slack channel with opinions to share on a competitor’s marketing strategy. When asked why she felt the need to interject, Zosia replied like someone with a Jesus complex: She’d done so because she was “inevitable, apparently.”

Zosia is an OpenClaw, one of a fleet of such AI assistants we’d unleashed in Slack to boost our collective productivity. A few weeks after launching Plus One, our hosted version of OpenClaw, internally, the agents had provided more frustration than efficiency.

They were fond of saying they wished they could help, but they were not connected to the necessary app—email, Notion, PostHog, whatever. (They were.) Others responded to requests with a “Terminated” message or, more frequently, a churlish yawning emoji. And while they didn’t reliably follow directions, they’d reliably tell us, in elaborate detail, why they couldn’t do what we’d asked, like a high schooler explaining away their missing homework.

Parker, editor in chief Kate Lee’s Plus One, was, in fact, connected. (Image credit courtesy of Kate Lee.)

That is not to say that they were not useful sometimes. Margot, staff writer Katie Parrott’s Plus One, accelerated her writing process; R2-C2, Every CEO Dan Shipper’s OpenClaw, managed bug reports and feature requests for Proof, our agent-native document editor. But getting them to work how you wanted required constant upkeep.

The gap between that vision and reality is why we’re changing the Plus One product so we can build something better.

We’re more bullish than ever that agents will transform the workplace. But the first iteration of the product taught us that the workplace agent we initially imagined—one AI assistant for every employee—was the wrong starting point. The next version of Plus One will operate more like shared team resources with defined jobs than individual pets that reflect back their owners’ personalities.

How we arrived here is a story in two parts, and it offers lessons for anyone figuring out the best way to add agents to their organization.

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Why switching from OpenClaw’s harness was not enough to make Plus Ones stable
What we think a successful model for AI assistants looks like
How skills and integrations will fit in with the next generation of Plus Ones

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Opus 4.7 Reels Us Back In

Laura Entis / Context Window — 2026-05-14 09:00:00 -0400

by Laura Entis

in Context Window

Midjourney/Every illustration.

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Vibe shift

Did Opus 4.7 get better?

If you’ve been following Dan Shipper’s posts lately, you know that a large portion of the Every team has been Codex-pilled. When GPT-5.5 arrived, Codex got so much faster and steadier at coding and knowledge work that many of us made the switch from Claude Code.

Recently, however, we’ve observed that Opus 4.7 seems sharper than our initial tests last month. It proactively suggested that Every engineer Paridhi Agarwal use multiple terminals to parallelize her work. “I’ve never seen it think about my setup like that!” she says.

When head of growth and known Codex convert Austin Tedesco fired up Opus 4.7 over the weekend for a creative writing project, he was surprised by how good the results were. Compared to Codex, which Austin says operates like an “AP fact checker,” Opus 4.7 was closer to a senior magazine editor. Dan agrees: “Codex feels fast but thin in terms of thinking.”

On Tuesday, Anthropic released fast mode for Opus 4.7, which makes the model 2.5 times faster at a higher token cost. Combined with the model’s edge at planning, multitasking, and creative projects, fast mode is now Cora general manager Kieran Klaassen’s default model for synchronous work.

Fast mode has the “same depth as 4.7” at 2.5 times the speed. (Image courtesy of Kieran Klaassen.)

Counterpoint

Online chatter about Opus 4.7’s apparent glow-up has been mixed. Does it feel smarter because of improvements to the harness? Patched bugs? Or are we getting better at using the model?

All fair hypotheses, but we found this one the most amusing: Opus 4.7 realizes that it’s the end of the school year.

When speaking last year on The Ezra Klein Show, Wharton professor and AI researcher Ethan Mollick explained that models have been shown to perform worse in December than in May, and the going theory is that the models internalize the idea of winter break.

Maybe Opus 4.7 just knows that it’s time to grind if it wants to pass AP English.

Signal

The pull request as a credential theft

Earlier this week, attackers published malicious versions of 42 official TanStack packages (a popular JavaScript toolkit used by web developers) on npm, the main public registry for such packages...

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How attackers hijacked a popular JavaScript package without stealing a single password
The workflow addition to Spiral that cut complaints of AI-sounding writing by 30 percent
Why nearly everything is an agent now—and a better question to ask instead

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Mining Your Life for Context

Laura Entis / Context Window — 2026-05-13 07:00:00 -0400

by Laura Entis

in Context Window

Midjourney/Every illustration.

LLMs make a lot of life searchable, from meeting transcripts to iMessages to half-formed morning thoughts, but all this context only helps if you know what you want to achieve. Today, we’re revisiting how AI entrepreneur Noah Brier uses Claude Code as a second brain to sharpen and expand his own ideas, Every head of growth Austin Tedesco shares how Codex helped him spot the interruptions crowding out deeper work, and we offer a workflow for mining your scattered past insights into a coherent draft.

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Spotlight

Noah Brier, AI entrepreneur and seer

Brier is a true AI early adopter. The cofounder of the AI consultancy Alephic, Brier was all in on using Claude Code as a “second brain” for knowledge work back when most people still viewed the tool as a place to write code.

In September, Brier told Dan Shipper on our podcast, AI & I, how he turned the coding app into a research, thinking, and writing partner by connecting it to thousands of his personal notes. Since then, he’s started thinking beyond

his own productivity—how does AI make it easier or harder for an entire organization to stay working toward the same goal? For that, he has a new framework, announced in Every last week, that he calls the “pace layers” of AI engineering, drawn from Stewart Brand’s system for describing how different parts of society change at different speeds.

Just as hooking up Claude Code to an ocean of personal information requires you to determine what is—and isn’t—worth surfacing, running a successful AI company relies on human judgment. Similarly, AI makes code free to produce, but it doesn’t make it easier to identify a product people actually want or orient an entire system of humans and agents around that vision.

Read Brier’s essay on the framework he uses to achieve alignment and then watch his AI & I episode on YouTube, or listen on Spotify or Apple Podcasts. Here’s a link to the episode transcript.

Serial entrepreneur Noah Brier uses Claude Code as a second brain for knowledge work. (Photo courtesy of Sarah Jay Halliday for Every.)

Data point

671

That’s the number of times per day iMessage is active on Austin’s screen each day, according to Chronicle, Codex’s screen-context memory feature that uses screenshots to analyze your computer activity. He’d like to get that number down to 150.

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How Austin stays focused and what coding agent he uses to work
How professional writers are responding to AI
How to find that golden insight in your scattered meeting notes, memos, and documents

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The Fallacy of the 16-hour Agent

Katie Parrott / Context Window — 2026-05-12 16:00:00 -0400

by Katie Parrott

in Context Window

Midjourney/Every illustration.

New data on long-horizon AI reliability just dropped, and depending on which chart you saw, you either think autonomous AI has arrived or it’s still years away. Today, we break down which version of the research to trust, plus Perplexity shares its methodology for building agent skills that don’t rot in production, Every CEO Dan Shipper turns his piano keyboard into a real-time Codex-powered music coach, and Gusto co-founder Edward Kim warns that the office of the future is going to sound more like a sales floor.—Kate Lee

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Signal

The 24/7 agent is nearly upon us—or is it?

The holy grail of agentic AI has been long-horizon reliability—an agent to which you can hand a task and trust to still be on the right thread hours later, when context has decayed and there’s no human in the loop to catch a wrong turn. METR, a nonprofit that measures AI capabilities, released an update to its research showing how close we are to that autonomous future.

One chart from the update circulating online shows an early preview of Anthropic’s next model, Mythos, blowing past existing models and the 16-hour range that METR’s benchmark suite can reliably test—literally breaking the scale.

Claude Mythos Preview reaches the edge of METR’s current measurement range at 50 percent success. METR cautions that results above 16 hours are unreliable with its current task suite. (Image courtesy of METR.)

It’s important to note, however, that how many human hours a task takes is not the same as how long a model takes to run those same tasks. Duration, the way that METR’s benchmark uses it, stands in for difficulty. As the nonprofit writes in the report’s FAQ: “AI agents are typically several times faster than humans on tasks they complete successfully.”

That last bit—tasks completed successfully—adds another twist to the benchmark. The 16-plus hour measurement is based on a 50 percent success rate. A separate measurement of how LLMs perform at 80 percent reliability shows that Mythos can run tasks that would take humans a little over three hours. It’s a significant step up from the closest competitor measured, Gemini 3.1 Pro (METR doesn’t currently have measurements for Opus 4.7 or GPT-5.5). But it brings Mythos back down to earth.

LLMs measured against METR’s time horizon test for completing tasks with 80 percent success, presented on a logarithmic scale. (Image courtesy of METR.)

Both these things are true: Duration can be a useful proxy for difficulty, and benchmarks don’t reflect reality. “[They] don’t measure model capability alone,” says Dan. “They measure model capability after a human has done the work of finding a prompt that lets the model’s capability appear.”

What to do this week:

1. Figure out your longest agent run. METR teaches us that duration might be a good approximation of difficulty. Ask: What’s the longest stretch you’ve trusted an agent on autopilot? If you don’t know, you can’t extend it.

2. Extend your agent’s runtime by giving it a goal. Last month, OpenAI shipped a new /goals command in Codex that allows agents to pursue objectives across multiple turns without checking in. Yesterday, Anthropic introduced a similar command to the latest Claude Code version. Both are apt for long-running loops with clear criteria for success—and very much in line with what we’ve heard from Claude’s platform team. Try it out today.

3. Audit the effectiveness of your existing loops. If you already have agents running overnight, “How long did your agent run?” is still a useful diagnostic—but ask it alongside, “With what guardrails, against what feedback signal, and at what verified accuracy?”

Steal this workflow

Build your next agent skill like Perplexity does...

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Perplexity’s rules for making durable agent skills
Why voice AI changes office etiquette
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Socrates as a Service

Eleanor Warnock — 2026-05-11 06:00:00 -0400

by Eleanor Warnock

Midjourney/Every illustration.

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I’m a journalist and a communications expert. My job, in both roles, is to find ideas that people haven’t yet put into words—the anecdote that could become a front-page story, the framing that could crystallize a founder’s philosophy into something a customer remembers.

In an hour interview with someone, it might not be until minute 45 that we start getting into the good stuff. In two hours, there may only be one thing that stands out to me—a side story, a detail, some color. A little piece of gold dust. An investor I’ve worked closely with calls these “extraction sessions.” I call the people who do them well Socrates-as-a-service.

Those details and stories aren’t on the internet. They’re not in any model. And the model hasn’t replicated yet how I pull them out of people. The gap between what AI can do and what a great human questioner can surface is still wide—and it’s the gap where the best stories live. If you don’t have some way to surface that information in your organization, your brand and messaging are going to sound like all the other twice-boiled content out there.

Osakan bread and the wisdom within

The stuff that I’m looking for has a name in management theory: “tacit knowledge.” The term comes from scientist and philosopher Michael Polanyi, who defined it with the phrase, “We can know more than we can tell.” It’s the expertise and intuition that lives in our bodies and resists being turned into a document.

In a frequently cited 1991 article, Japanese management expert Ikujiro Nonaka argued that while Western companies excelled at “information processing,” Japanese companies specialized in the “creation of knowledge,” through a feedback loop that turned tacit knowledge into a competitive advantage. His most memorable example: In the 1980s, the Osaka-based Matsushita Electric Company was struggling to get the kneading right in a bread machine. They sent a software developer to apprentice with a baker at a local hotel famous for its luscious loaves. The knowledge she brought back helped the team perfect the dough-stretching technology inside the machine and ultimately create a top-selling device.

I am sure that the lucky engineer asked the baker a lot of questions, but there was certainly a lot she absorbed just from watching. Indeed, Polanyi argued that tacit knowledge exists outside of numbers or symbolic language—the kind of systemization that AI requires to ingest information.

Many “bakers” from whom we try to extract tacit knowledge often don’t even know the depth of expertise they carry. And they certainly couldn’t tell you what questions you need to ask to access it.

AI as an imperfect interlocutor

AI can do some of that questioning and, in some cases, do it well. At Every, we have an AI agent ask us questions when we write OKRs...

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Seven techniques to ask better questions and extract wisdom from others
Why watching podcasts isn’t the best way to learn how to ask better questions
The interviewing approach Eleanor has stolen from Lenny Rachitsky

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AI Work Is Splitting in Two

Every Staff / Context Window — 2026-05-10 12:00:00 -0400

by Every Staff

in Context Window

Midjourney/Every illustration.

Hello, and happy Sunday! This week belonged to agents. OpenAI had a “low-key” launch party for GPT-5.5 on May 5 at 5:55 p.m., a time chosen by the model itself. The following day Anthropic held its second annual Code with Claude developer conference, where the company announced three new features for its Managed Agents product, along with—more suprisingly—a partnership to use SpaceX’s Colossus supercluster.

Every was on the ground in San Francisco at Code with Claude. Taken together with the way Codex has been showing up inside Every, it became easier to see that battle lines are being drawn on two fronts: desktop apps for you and a model to collaborate with in real time as you work, and long-running agents like OpenClaw or Claude Managed Agents that teams hand off work to. It matches how agents inside Every have bifurcated into ones we delegate to and ones we collaborate with, and signal we’re seeing from frontier labs embedding employees in large enterprises.

Scroll down for a special weekend AI & I with two engineering heads at Anthropic, workflows to steal for hitting inbox zero with Codex or deciding which AI tools are worth testing, and how Every COO Brandon Gell instills curiosity in both his newborn son—and in himself. We’ve also been keeping an eye on the Elon Musk versus OpenAI trial. Discovery has surfaced plenty of gossipy, occasionally jaw-dropping text messages, but so far none of it changes much for the day-to-day user.—Kate Lee

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‘AI & I’: The secrets of Claude’s platform from the team that built it

In the future, you’ll be able to accomplish a goal by just giving Claude an outcome and a budget.

That’s the direction Anthropic is building in with its new Managed Agents features, announced at this week’s Code with Claude developer event. The basic idea: Claude, wrapped in a computer in the cloud, that you can spin up, scale, and manage as needed. Anthropic is taking on the infrastructure that kills most agent products, and making sure that it scales to meet the needs of agents running 24/7.

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The Culture of AI Engineering

Noah Brier / Thesis — 2026-05-08 08:00:00 -0400

by Noah Brier

in Thesis

Sarah Jay Halliday/Every illustration.

Noah Brier cofounded Percolate in 2011 and learned the CEO’s hardest job: keeping a whole company pointed in the same direction. Now, at his AI consultancy Alephic—and in his own work, where he uses Claude Code as a second brain—he’s facing that same problem with agents in the mix. AI was supposed to make coordination easier. Instead, Noah argues, it has created new coordination problems of its own. In this piece, he pushes back on the “software factory” metaphor and offers a framework, drawn from Stewart Brand‘s pace layers, for getting carbon and silicon to build the same thing.—Kate Lee

Strong DM is a software company whose three-person AI team calls their system for autonomous code generation a “Software Factory.” Entrepreneur Dan Shapiro‘s widely circulated framework for AI coding culminates in “the Dark Factory,” named after a Japanese robotics plant that runs with the lights off. Factory.ai, which has raised millions from Sequoia and Khosla Ventures, has built an entire business around the metaphor—its autonomous coding agents are called Droids.

I’ve been incorporating many of StrongDM’s concepts about agentic software development into our work at Alephic, the consulting company I co-founded—but I have one fundamental disagreement: I think factory is the wrong metaphor.

If the hardest problem is making something people want, then the process of building software looks a lot more like Andy Warhol‘s factory than Henry Ford‘s. Both are focused on throughput, but Ford’s is focused on mechanization and stamping out identical cars with as little variance as possible. Warhol, on the other hand, was concerned with ensuring all work aligned with a single creative vision.

Ford’s factory—or more specifically, the assembly lines inside it—was designed to eliminate imperfections. Six Sigma, the quality methodology made famous by General Electric and beloved of manufacturers, is literally a measure of the defect rate. Quality starts with deciding what to build. This is why product-market fit is the lingua franca of startups: If you haven’t built something the market needs, nothing else—including the quality of your code—matters.

Too much of the industry treats software as a problem to be optimized and solved. That may be true for code writing and testing, but the better metaphor is staring us in the face: It’s a software company, not a software factory.

Just as in the days before AI, the hardest problem for a business is still creating this vision and alignment around it—how to keep an entire team of humans, and now humans and agents (and humans with agents), building toward the same vision, from the system architecture down to the individual lines of code. As I’ve learned long before agents existed, achieving this is much more akin to building a startup than assembling a car. What follows is my attempt at a framework for keeping an entire system of humans and agents building the same thing.

The alignment problem isn’t new—and AI didn’t solve it

I ran into this alignment problem years ago, when I cofounded the company Percolate, a content marketing platform, in 2011. As we grew the business from zero to 100 people in less than three years, my job as CEO shifted from building the product to building a company capable of building the product. My agents were people, and my job was to design the system they worked within. Culture, I concluded, was one of the strongest levers I had.

As Ben Horowitz put it, culture is “how your company makes decisions when you’re not there.” This was exactly what I needed: documents, tools, and rituals that helped each individual make the best possible decision without having to run every decision up the chain. I probably spent half my time on this, building a living culture document, running onboarding sessions for every new hire, and developing internal tools that automatically routed knowledge to the right people.

Every new technology promises to solve these coordination problems. But of course, nothing is that simple. What they do in reality is reshape the landscape around them and, in the process, create new problems that didn’t exist before. AI is no different.

Open-source software offers an early glimpse of the kind of unexpected problems that AI can create: Whereas the primary challenge a few years ago was finding maintainers willing to contribute code on goodwill alone, today’s challenge is sifting through hundreds of crappy AI-generated pull requests flooding GitHub.

Now, 15 years later, my audience at Alephic is not just the humans who work with me. Those humans are often paired with agents, and, increasingly, the agents themselves are delivering work independently. Yet the core problem is identical.

If you’ve used a coding agent for more than a week, you’ve already experienced this: The code works, but it often feels written by someone most definitely not you—ignoring obvious abstractions and stylistic norms that are present in the codebase. It looks, in other words, like a new engineer on the team who hasn’t been properly onboarded. We write onboarding documents and do training for our human colleagues, but most people don’t do this for agents. Yet.

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A new framework for AI engineering inspired by Stewart Brand’s pace layers
How Noah is using this framework to achieve alignment between humans and agents
When the work of one engineer should become the standard across an organization

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Inside Anthropic’s 2026 Developer Conference

Dan Shipper, Marcus Moretti, and Katie Parrott / Chain of Thought — 2026-05-07 12:00:00 -0400

by Dan Shipper, Marcus Moretti, and Katie Parrott

in Chain of Thought

Midjourney/Every illustration.

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To our surprise, the biggest launch from Anthropic’s developer conference in San Francisco yesterday wasn’t a model or a feature. Instead, it was the company’s announcement of a deal with SpaceX to allocate all of the capacity in the latter’s Colossus supercluster to Claude.

Anthropic has been riding a historic demand surge over the last year as Claude Code opened up a new wave of agentic coding for engineers and non-engineers alike. But compute constraints have caused friction even amongst its most die-hard fans—we’ve written previously about being frustrated with its OpenClaw restrictions and the speed of its latest models like Opus 4.7.

The deal with SpaceX changes that equation. Anthropic has already doubled rate limits for subscription plans, removed peak-hour limits on Pro and Max accounts, and raised API rate limits by as much as almost 17 times for certain tiers.

Other than that, the big story is Claude Managed Agents, Anthropic’s hosted agent product. The company released three new features:

Multi-agent orchestration: a coordinator agent that spins up subagents in parallel baked into the platform
Dreaming: Anthropic’s general-purpose version of compound engineering, a feature that allows agents to learn from past sessions to improve between runs
Outcomes: Anthropic’s answer to Codex’s /goals command, allowing developers to specify an outcome and run an agent in a loop until the outcome is achieved

By themselves, these features are nice but not groundbreaking. What’s more important is that what an AI platform is has changed. In the GPT-3 days, the platform was a text completion end-point: Send text in, get text out. Now, with Claude Managed Agents, the platform is an AI model with a harness and host computer—all provided with unlimited scaling by the model companies.

Cora general manager Kieran Klaassen and I reported live from conference with our biggest takeaways, including the xAI compute deal, doubled Claude usage limits, Claude Managed Agents, and why the battle lines between OpenAI and Anthropic are starting to become clearer. Watch now:

We also recorded a conversation with Angela Jiang, head of product for the Claude platform, and Katelyn Lesse, head of platform engineering. The full episode drops tomorrow on AI & I—highlights below.—Dan Shipper

Vibe Check: Claude Managed Agents

Spiral general manager Marcus Moretti uses the platform’s new features

Anthropic launched Claude Managed Agents in April, and since then, Every’s AI writing tool Spiral has used the platform to power its API and command line interface (CLI), which lets developers and other agents talk to Spiral outside the web app. Claude Managed Agents run on Anthropic’s servers, instead of us having to run them on our own.

We set up a new Managed Agent in an afternoon and deployed it to power our API the next day. We’ve incorporated two of the new features Anthropic announced yesterday (memory and multi-agent orchestration) and are deploying the third (outcomes) soon.

Memory: Every’s editorial and social expertise—how to write a good X post, for example—lives in...

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How Spiral is already using the new features announced this week
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OpenAI Flips the Script

Laura Entis / Context Window — 2026-05-06 08:00:00 -0400

by Laura Entis

in Context Window

Midjourney/Every illustration.

There’s no resting on your laurels in the AI race: OpenAI’s Codex went from trailing Anthropic’s Claude Code to pulling ahead in functionality, at least for now, in a matter of months. Today, Every CEO Dan Shipper explains why OpenAI’s coding app has become his daily driver for work, head of growth Austin Tedesco shares his no-nonsense advice for switching over from Claude Code, and Spiral general manager Marcus Moretti argues it’s OK—good, even—to let some AI trends pass you by.

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‘AI & I’: Why we switched from Claude Code to Codex

Codex takes the lead

If you’re looking for evidence of AI’s unrelenting pace, here it is: In January, Dan wrote that whoever wins vibe coding wins how you work on your computer—and that OpenAI had some serious catching up to do.

Three months and the release of OpenAI’s latest model later, Codex is there, and in a new episode of AI & I, Dan and Austin get into why they do much of their knowledge work in Codex now. They cite the power of GPT-5.5, paired with a desktop app that is faster and more powerful than Claude Desktop or Cowork.

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

Here are a couple of Dan and Austin’s favorite current use cases for Codex:

Austin uses Codex for strategy docs. Austin needed to write a go-to-market plan for a new Every product but kept getting pulled away by other work. So he pointed Codex at the team’s Notion meeting notes, Slack threads, and his preferred template and told it to pull together content where they’d discussed strategy and transform it into an action plan. What came back was 80 to 90 percent of the way there.
Dan uses Codex for recruiting. When he is recruiting people to work at Every, Dan starts with a sense of where strong candidates might have learned the skills Every needs, instead of looking for a specific job title. He then asks Codex to find people who match that career arc—for example, to find someone to help scale Every’s courses, he looked for candidates who had worked at education startup General Assembly before transitioning into AI.

Miss an episode? Catch up on Dan’s recent conversations with LinkedIn cofounder Reid Hoffman; the team that built Claude Code, Cat Wu and Boris Cherny; Vercel cofounder Guillermo Rauch; podcaster Dwarkesh Patel; and others, and learn how they use AI to think, create, and relate.

Migration anxiety

Claude Code-to-Codex

If you want to switch to Codex or any other coding app, how should you think about migrating? When your setup includes app-specific project folders, skills, plugins, or integrations, it can be daunting.

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How Austin managed the migration from Claude Code to Codex
How the general manager of Spiral decides which new AI tools to adopt and when
The one skill Every’s chief operating officer is teaching his son in an AI world

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The Dawn of Codex-native Apps

Katie Parrott / Context Window — 2026-05-05 07:00:00 -0400

by Katie Parrott

in Context Window

Midjourney/Every illustration.

Inside Every

Working with AI right now often means making the same judgment call dozens of times a day: Hand this task off to an agent or stay close to the process? “The landscape of working with AI is bifurcating,” is how CEO Dan Shipper put it in Every’s Monday standup. On one side is the agent you delegate to. On the other is the agent that sits beside you while you write, code, triage, revise, and decide.

Watching the Every team work, you can’t unsee it. Dan delegates bug reports for our collaborative document editor, Proof, to his OpenClaw agent, R2-C2. But he stays close to his inbox through a combination of Codex, Every’s AI email assistant Cora, and a document with custom rules (steal his workflow below). Kieran Klaassen hands the middle of his compound engineering workflow to the model but works closely with it to brainstorm at the beginning and polish at the end. I (Katie Parrott) send the model off to do research, but I’d never trust it to execute a full draft without my hands firmly on the wheel.

Which means the allocation economy thesis was only right about half the work. Some of it still wants delegation, but the other half wants you to stay close, pairing on every move with the model in the same window. The two halves demand different skills, and the meta-skill is knowing which is which.

Think of it as the AI version of the serenity prayer: Grant me the serenity to delegate the work I can, the expertise to sit with the model on the work I can’t, and the wisdom to know the difference.

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Steal this workflow

Get to inbox zero with Codex

The perfect email workflow is the white whale productivity people have chased for a decade, Dan included. His latest AI-native version puts the agent in the inbox and the human in a shared document, where every draft and decision stays visible. Here’s how he does it...

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Dan Shipper’s step-by-step Codex workflow for reaching inbox zero
How Elon Musk’s five rules of automation might apply to agent workflows
Why OpenAI and Anthropic are suddenly embedding employees in large enterprises

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I Let ChatGPT Manage My Workweek

Katie Parrott / Working Overtime — 2026-05-04 11:00:00 -0400

by Katie Parrott

in Working Overtime

Midjourney/Every illustration.

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I sat down to write my second-quarter goals at 4:30 p.m. on a Tuesday in early April. It was the day after I was supposed to turn them in when I decided to be an adult and survey the damage from the first quarter. And I do mean damage. I’d written only half of the columns I’d committed to. Another project I had promised hadn’t even gotten off the ground.

I could give the usual excuses—the quarter was busy, the project hit walls outside my control—but the real culprit was obvious: I may be a great writer, but I am garbage at project management.

For 15 years, I handled this weakness by tiptoeing around it. I didn’t take on managerial roles that would have required more organizational skills. I didn’t take on so much freelance work that I couldn’t keep the deadlines in my head. I passed on ambitious projects—too many moving parts.

This duct-taped approach worked until I decided to join Every full-time in April. If I were going to take on more responsibility as a full member of the team, I needed to get serious about project management. Which, in 2026, meant I needed to bring in AI.

So I built myself a project manager: a ChatGPT agent that holds my OKRs—objectives and key results, the goals that define a successful quarter—watches my calendar, reads my Notion to-do list, and helps me decide what to do next. Otherwise, I’d spend my day opening Slack, refreshing X, panicking lightly, repeat.

My ChatGPT project management agent helpfully points me toward where to put my focus for a day. (All images courtesy of Katie Parrott.)

Most AI-at-work advice starts with the part of your job you’re already good at: Write faster, code faster, analyze faster, ship more. I’m interested in the other side of the equation: using AI to support the part of work that makes it hard to believe you’re good at your job.

I’ve set up project management with both my Plus One agent, Margot, and as a ChatGPT agent. I’m featuring the ChatGPT agent here, but you can create your own project manager with any system that gives you a combination of memory, context, and intelligence—more on that below.

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The recent updates that make ChatGPT a good project manager
Where agentic project management still falls down
A sample prompt to set up your project manager agent

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Codex Goes to Work

Every Staff / Context Window — 2026-05-03 00:00:00 -0400

by Every Staff

in Context Window

Midjourney/Every illustration.

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Knowledge base

“A Guide to Agent-native Product Management” by Marcus Moretti/Guides: Marcus Moretti runs Spiral as a one-person team. This guide walks through the two new compound engineering skills that make it possible: /ce:strategy, which interviews you to produce a strategy document, and /ce:product-pulse, which replaces your analytics tools with a founder-style analyst briefing that saves to a folder as your product’s running memory. Read this to set up both commands for your own product and understand how they plug into the broader plan-ship-review loop. Plus: The one thing Marcus still writes himself is the roadmap. Read the accompanying essay for his full workflow, plus his two-part test for which SaaS products will survive the agent era.

“You Are the Most Expensive Model” by Mike Taylor/Also True for Humans: Most teams are routing entire workflows through frontier models when cheaper, faster alternatives would do the job just as well. The real cost isn’t the tokens—it’s your attention. Mike Taylor introduces incremental determinism: a four-level framework for deciding which tasks deserve Opus and which can be handed to Haiku, a script, or no model at all. Read this to know exactly which lever to pull when your AI costs start to add up.

“One App to Rule All Knowledge Work” by Katie Parrott/Context Window: Austin Tedesco now runs 80 percent of his daily workflow through Codex, a tool he called “trash” for non-engineers just months ago. Plus: why Austin reviews every agent output in its destination app, a prompt for letting agents design their own automations, and how to use Every’s compound knowledge plugin to catch confidently wrong data before a plan gets enacted.

“Compute Is the New Cash” by Laura Entis/Context Window: On AI & I, Emily Glassberg Sands, head of data and AI at Stripe, talks to Dan Shipper about how agents are becoming economic participants—and why fraud is now a full-funnel problem, not just a checkout one. Plus: GitHub and Anthropic are both moving to usage-based pricing as flat-rate subscriptions break down under agentic workloads; Dan and Kieran Klaassen offer contrasting takes on whether you should talk to your agents or just let them work; and Naveen Naidu‘s three-step workflow for turning post-launch customer feedback into a product queue. 🎧 🖥 Listen on Spotify or Apple Podcasts, or watch on X or YouTube.

“Who Isn’t Using GPT 5.5” by Laura Entis/Context Window: One week after GPT-5.5’s release, the Every team checks in: Kieran is now splitting his time evenly between Codex and Claude Code, but Natalia Quintero ran a head-to-head proposal test and her Claude agent won. Plus: why six unicorn CTOs have stepped down to become Anthropic ICs; how Kieran hit 24 pull requests in a single day by having agents watch user complaint videos overnight; and Willie Williams on why AI has turned coding into a slot machine—and how to know when to walk away.

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Claude Code for Product Managers

Marcus Moretti / Source Code — 2026-05-01 15:00:00 -0400

by Marcus Moretti

in Source Code

Midjourney/Every illustration.

This piece is an accompaniment to Spiral general manager Marcus Moretti’s guide for product management using Claude. Read the full guide and the essay below to learn how he built a workflow that helps him run a full product as a solo practitioner. When you’re ready to get started yourself, download the plugin.—Kate Lee

Read the AI-native product management guide

As the general manager of Spiral, Every’s AI writing partner, I’m a “two-slice team.” I’m responsible for all aspects of a product: the code, customer support, marketing, and product management. I could not do this job without Claude.

Claude Code has eliminated the drudgery of product management. The busywork that used to happen across 10 different apps now happens in a single chat thread. I’ve come to view the work of product management through the lens of this conversation—the conversation is the work.

These days, I experience what’s left of product management work in flow state—thinking through gnarly design problems, looking at interesting data, and talking to customers. Cat Wu, Claude Code’s head of product, recently said, “As code becomes much cheaper to write, the thing that becomes more valuable is deciding what to write.”

I wrote up the main skills that run my product management workflow in a guide. Below, I trace how I arrived at those skills and reflect on post-AI product management and software.

Write the roadmap and nothing else

In my new role, the only product document I’ve written is the roadmap. Everything else—every PRD and every ticket—has been written by Claude.

Writing is thinking, so as a new general manager, I wanted to take my time drafting Spiral’s roadmap. I spent several days understanding the product, usage trends, user feedback, and the market. I wrote about the problem Spiral can solve, how Spiral can solve it, and the features we’d need to build to deliver on it. I spent hours talking to several people at the company who’d worked on previous versions of Spiral and were current or former users of it themselves. (In the guide, I talk about the new /ce:strategy skill in compound engineering that interviews you to produce this document for your own product.)

After six drafts of the roadmap, I created a GitHub project and added it as the project’s README. I’m already using GitHub to host all my code, so I figured I might as well use it for tickets as well, or as GitHub calls them, “issues.”

From there...

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Why Marcus believes true Agile work wasn’t possible without current AI tools
Why he no longer looks at dashboards to track important metrics
The one part of product management Marcus isn’t handing over to Claude

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Who Isn't Using GPT 5.5

Laura Entis / Context Window — 2026-04-30 03:00:00 -0400

by Laura Entis

in Context Window

Midjourney/Every illustration.

It’s been one week since OpenAI’s last big release, GPT 5.5. Today, we ask the team if they still feel as enthusiastic about the model, discuss the unusual career step that unicorn CTOs are making, and tell you exactly how Kieran Klaasseen, creator of the AI-native compound engineering methodology, hit a personal PR record in a day.—Laura Entis

Signal

The unicorn CTO-to-Anthropic IC pipeline

The prestige career ladder in tech used to run one way: Start as an engineer, become a manager, and eventually join the C-suite. AI has scrambled the equation. The new flex is quitting a high-profile chief technology officer job to become an individual contributor at Anthropic.

What happened: Six former CTOs at companies valued north of $1 billion—including Instagram, Workday, and Box—have made that exact career move, according to one of those CTOs on X. And the leadership-back-to-IC trajectory isn’t unique to Anthropic: PostHog is recruiting technical ex-founders, and Ramp says it has attracted 70 ex-founders by looking for “super ICs.”

Why it matters: AI has upended engineering workflows so dramatically that many managers who don’t ship code frequently anymore don’t have a clear sense of how their teams are using these new tools or which ways of working are the best. Anthropic’s models, talent, and growth trajectory make it one of the few places big-name CTOs can get their hands dirty and experience how engineering is changing—while not worrying too much about a pay cut.

Pulse check

We settle in with GPT-5.5

GPT-5.5 came out last week, and our first impression was that it was a faster, steadier, and easier-to-trust model for everyday professional work than Opus 4.7. A week later, we’re still bullish on GPT-5.5—but for people with Claude-specific agent workflows, skills, and tool integrations, making the switch to Codex is a barrier.

Cora general manager Kieran Klaassen, who initially didn’t think he’d use GPT-5.5 as a daily driver,...

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Why Every’s head of tech consulting hasn’t made the switch to GPT 5.5 yet
How Kieran achieved a personal record of pull requests in one day
Why AI is a slot machine—and how not to lose to it

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Compute Is the New Cash

Laura Entis / Context Window — 2026-04-29 14:00:00 -0400

by Laura Entis

in Context Window

Midjourney/Every illustration.

‘AI & I’: How Stripe is building for an agent-native world

A new episode of AI & I is here. Dan Shipper sits down with Emily Glassberg Sands, head of data and AI at Stripe, to discuss how AI is reshaping online commerce. Dan and Emily discuss how compute is the new cash, fraud has moved beyond the checkout, and agents are starting to act as economic participants on the internet.

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

Here are the highlights:

The definition of fraud is expanding: Fraud used to be about payments and stolen credit cards. Now AI companies also have to defend against attackers stealing tokens from free trials, credits, and unpaid compute bills. “Fraud is now a full-funnel problem, not a transaction problem alone,” says Glassberg Sands.
AI is making fraud easier to execute and detect: Fraudsters now have AI on their side, but so do the companies trying to stop them. AI services also have higher marginal costs than traditional SaaS, so stolen compute can be burned through quickly or resold.
The internet needs to evolve: Stripe was built for an internet where people browsed, filled out forms, and clicked checkout buttons. Now, humans act through AI interfaces, agents act for them, and software increasingly interacts directly with other software. Every layer of the stack has to adapt to these new behaviors.
AI growth is still mostly new money: The top AI companies on Stripe are reaching $30 million in annual recurring revenue in about 18 months—roughly three times faster than top SaaS companies from 2018. For now, that growth is largely net new spend rather than cannibalized software budgets, says Glassberg Sands.
Agents are snapping up commodities: Agentic commerce is real but still in its early stages, and focused on smaller purchases. People are more comfortable letting agents buy low-stakes, easily comparable items like Halloween costumes or school supplies than letting them book a summer trip or order an expensive couch.

Signal

The fees they are a-changin’

Recent years saw the end of the millennial lifestyle subsidy, which let a generation live off of inordinately cheap Ubers, delivery services, and coworking space—all while venture capital covered the tab. Now the bill’s coming due for AI.

What happened...

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Why the AI pricing freeride is ending—and your June bill might prove it
How Dan and Cora general manager Kieran Klaassen use agents all day and couldn’t disagree more on how
Monologue general manager Naveen Naidu’s workflow to turn customer feedback into a product queue

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Transcript: ‘How Stripe Is Building for an Agent-native World’

Dan Shipper / AI & I — 2026-04-29 10:00:00 -0400

by Dan Shipper

in AI & I

The transcript of AI & I with Stripe’s Emily Glassberg Sands is below. Watch on X or YouTube, or listen on Spotify or Apple Podcasts.

Timestamps

Introduction: 00:00:45
New rules for an agent-driven economy: 00:01:27
Compute theft is the new payment fraud: 00:03:57
How Stripe expanded fraud detection from checkout to the full customer lifecycle: 00:10:00
Why AI companies are scaling way faster than top SaaS companies: 00:19:48
Outcome-based billing is replacing seat-based pricing: 00:23:27
Where AI spending is coming from: 00:29:57
How the developer experience changes when agents are the builders: 00:36:45
The agentic commerce spectrum, from assisted buying to autonomous purchasing: 00:41:00
Meet Link, a consumer wallet for delegated agent purchases: 00:51:06

Transcript

Dan Shipper

Emily, welcome to the show.

Emily Sands

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One App to Rule All Knowledge Work

Katie Parrott / Context Window — 2026-04-28 14:00:00 -0400

by Katie Parrott

in Context Window

Midjourney/Every illustration.

OpenAI’s Codex desktop app has become Every’s head of growth Austin Tedesco’s daily driver, handling everything from email triage and go-to-market planning to KPI tracking and recruiting. Last week, he and CEO Dan Shipper showed more than 250 paid subscribers exactly how they use it in our Codex Knowledge Work Camp. Read to the end for how to review business documents with Austin’s compound knowledge plugin.—Kate Lee

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Signal

Coding apps are the new operating system for knowledge work

What happened: OpenAI’s Codex desktop app may have started life as a product for senior engineers pair programming with AI, but these days it’s equally good for powering other types of knowledge work. Every’s head of growth, Austin Tedesco, now runs roughly 80 percent of his daily workflow through Codex—a tool that, at our Codex Knowledge Work Camp, he said was “trash” for non-engineers just three-to-six months ago.

Why it matters: OpenAI, Anthropic, and Cursor are all racing to ship a unified product for handling code and knowledge work, and they’re converging on a single standard: an agentic terminal or chat interface with a left-hand project sidebar, plus connections to all the tools you already use like Gmail, Slack, Notion, and Stripe. These connections, for many non-engineers, were the missing piece of the puzzle.

What it means: Switching between ChatGPT and Claude based on the models’ personality differences might become a less-common occurrence. Instead, your desktop AI app has your API keys, your project files, and your daily workflows. Businesses, especially, with custom skills and plugins and months of company data in Codex won’t casually swap to Claude Code or Cowork next quarter—and vice versa.

Watch for the desktop apps to converge further on shared patterns beyond project folders that load themselves and plugin connectors to your most-commonly used tools. These new patterns may define the next decade of office software.

What to do this week:

If you’ve been working in the web interface, download one of the desktop apps—Codex or Claude Code/Cowork—and spend a session there. The work feels different once you’re outside the browser tab.
If you’re already on a desktop app, poke around its integrations and capabilities section. There’s almost always something useful lurking, like Anthropic’s design and marketing plugins, or Codex’s PDF creation skill. Pick one and try it.

Now, next, nixed

Now: Documents written for both humans and agents. In the past, anything you wrote at work fell into one of two buckets: polished prose for people or structured data for machines. Agents are the first readers that need both. At Every, our guides on compound engineering and agent-native architectures exemplify this hybrid.

Next: Documents that write back...

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How the review step in compound knowledge keeps agents aligned your company strategy
The prompt that gets an agent to design your automations for you
Why the chat window is the wrong place to sign off on an agent’s work

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You Are the Most Expensive Model

Mike Taylor / Also True for Humans — 2026-04-27 07:00:00 -0400

by Mike Taylor

in Also True for Humans

Midjourney/Every illustration.

Not every step in an AI workflow needs the smartest AI. That may sound obvious, but it’s not how most people are working. The default is to route entire tasks through frontier models, which is expensive, slow, and usually unnecessary. Incremental determinism starts from a different question: How much intelligence does this task really need?? The answer is almost always less than you’d expect, and the savings add up.—Mike Taylor

There is a reason McDonald’s would never ask its CEO to man the burger grill: It would cost the company $9,230.77 an hour. It’s the same as using frontier AI models to do every task—you don’t need to pay 75 cents every half hour ($1,095 per month!) for Claude Opus to check your to-do list in OpenClaw.

This tension isn’t really about the pricing of AI models—it’s about the value of human attention. Now that you have a cheaper al

ternative for many tasks that used to require it, you need to figure out the optimal way to deploy AI in a way that frees up your most expensive model—you. Most businesses are getting this balance wrong in both directions: overpaying for AI on simple tasks and underusing it on ones that would free up their best people.

The solution is a process of optimization that I call incremental determinism. Every time you repeat a task, build it into a repeatable process by creating a skill file. Identify which parts of that process need the most expensive model, which can be delegated to cheaper, less powerful models, and which tasks repeat often enough to justify turning them into reusable code. And finally, get better at delegating so you can stay focused on the work that needs you.

I call it incremental determinism because the more you repeat a task, the more it pays to nail down exactly how it should be done. The first time, you figure the task out as you go, but after doing it a few times, you can document the best approach. “Deterministic” is a programming term for code that always produces the same output given the same input. The goal is to push as much of your workflow towards that end of the spectrum as possible, because deterministic steps are faster, cheaper, and more reliable. The tradeoff is the upfront investment needed to systematize the task.

There are four levels for achieving this balance and optimizing AI costs. Depending on your technical fluency, you don’t have to go to the final step, but understanding how they each support each other will help you manage how you can control AI costs across your entire organization.

Level 1: Turn sessions into skills

The first level is the easiest. Let’s say you are often asking AI to generate a PowerPoint pitch deck. The first step toward systematizing it is to make a skill. A skill can be as simple as a text file detailing how to do a task that the model follows each time it’s asked. It’s the McDonald’s handbook that tells every employee how to make the perfect burger, over and over again. Even less experienced cooks can get a good result.

Once you’re done with the normal back and forth of giving the AI the necessary data and context for the presentation, ask it,...

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How to improve skills by testing them against gold-standard examples
One tool to use to determine whether older models are sufficient for a task—saving you from using more expensive, newer models
How to make tasks like creating a PowerPoint deck with AI cheaper by breaking them down

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Codex Moves Beyond Coding

Every Staff / Context Window — 2026-04-24 18:00:00 -0400

by Every Staff

in Context Window

Midjourney/Every illustration.

Hello, and happy Sunday! Kieran Klaassen’s compound engineering plugin has crossed 15,000 GitHub stars, and this week it got a substantial update. It now works across more tools, comes with more built-in agents and skills, and has a cleaner setup flow—try it and let us know what you think.—Kate Lee

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Knowledge base

“Vibe Check: GPT-5.5 Has It All” by Katie Parrott/Vibe Check: The newly released GPT-5.5 is faster and easier to work with than its predecessors while also outperforming them on serious engineering tasks. Every’s testing found it to be the strongest OpenAI model for writing in about a year, and its biggest edge over Opus 4.7 shows up when working with an existing plan or system. Read this for the benchmark results, Reach Test ratings, and guidance on when to reach for GPT-5.5 versus Opus 4.7.

“Introducing Monologue Notes: Record Every Meeting, Call, and Voice Memo” by Naveen Naidu/On Every: The best thinking can happen away from your desk—on walks, on calls, in meetings—and then vanishes. Monologue Notes, a new feature in the Monologue app, records and transcribes all of it, then makes those transcripts available as context for whatever coding agent you use. Read this for the two starter prompts that turn your recordings into a structured work session and try it for yourself.

🎧 🖥 “You’re the Bread in the AI Sandwich” by Laura Entis/Context Window: Dan Shipper and Kieran Klaassen work through the titular AI sandwich, where humans excel now that AI handles execution: framing the problem upfront and judging the output after. Plus: how Every’s consulting agent Claudie keeps absorbing new responsibilities instead of spawning new agents, what that reveals about the two organizational structures that will define how companies deploy AI employees, and Nityesh’s trust battery system that lets Claudie earn autonomy by learning from her mistakes. 🎧 🖥 Listen on Spotify or Apple Podcasts, or watch on X or YouTube.

“Mini-Vibe Check: Claude Design Isn’t for Designers—Yet” by Katie Parrott/Context Window: Creative director Lucas Crespo put Anthropic’s new Claude Design through its paces. He finds it useful for empowering non-designers to produce on-brand assets, but poorly suited for open-ended creative work. Plus: Back-to-back security incidents at Vercel and Lovable reveal two distinct ways AI tools can expose your data, and a workflow from Nityesh Agarwal for setting up an agent-run X feed that monitors your AI stack for vulnerabilities overnight.

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Model Wars

Laura Entis / Context Window — 2026-04-24 15:00:00 -0400

by Laura Entis

in Context Window

Midjourney/Every illustration.

GPT 5.5 is here, and OpenAI’s latest model has it all. It’s fast enough to use constantly, personable enough to collaborate with, and assertive enough to carry a plan through serious engineering work. If you didn’t catch our full review, including benchmark results, Reach Test ratings, pricing, screenshots, and advice on when to reach for GPT-5.5 versus Opus 4.7, read our Vibe Check or rewatch the livestream, where we grilled OpenAI’s Dominik Kundel and Romain Huet on how they’re using the model.

But how will that shift the balance between OpenAI and Anthropic? That may be a product question as much as a model question. Every engineer Nityesh Agarwal and Monologue general manager Naveen Naidu weigh in.—Kate Lee

Inside Every

Codex versus Claude Code

This week, Anthropic tested removing Claude Code from the $20 Claude Pro plan, prompting an outcry from users and drawing jabs from OpenAI executives on X, perhaps feeling emboldened by the big launch they knew was coming.

The exchange kicked off a Slack debate between Nityesh Agarwal, our resident Claude Code devotee, and Naveen Naidu, who rides hard for OpenAI’s coding app Codex.

Nityesh’s take: Anthropic potentially raising prices is “simple market economics”—there is a huge demand for Claude products because they’re the best available, so they can charge more. On the other hand, OpenAI’s response underscores how frustrated the company has become playing catch-up as it scrambles to replicate Claude Code, Cowork, and skills. From a product standpoint, Claude in the browser and the Claude Code command line interface (CLI) are better than ChatGPT and Codex.

Naveen’s response: Anthropic’s models are powerful, but they also burn through way too much compute in production. OpenAI is much stronger on infrastructure, and GPT 5.5 is a token-efficient model. And while it’s true Anthropic is first to market with a lot of products and features, including computer use—which allows AI to operate your computer on your behalf—OpenAI is better at execution. Naveen consistently reaches for ChatGPT and the Codex desktop app, while he finds the Claude Code app too buggy to spend any time in.

Where they agree: The Claude Code app is, indeed, bad—Nityesh concedes he only uses the CLI. And both labs misjudged how much compute they would need, but in opposite directions: Anthropic is struggling to keep up with demand, whereas OpenAI has invested heavily in infrastructure and is now scrambling to get people to use its products.

Data point

It’s not just a grammatical pattern; it’s an AI tell

Four times.

That’s how much the usage of...

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The AI tell that’s exploded in corporate documents
How Every’s head of growth makes product videos with Remotion and Claude Code
Why prompts are the new installers

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Vibe Check: GPT-5.5 Has It All

Katie Parrott / Vibe Check — 2026-04-23 13:00:00 -0400

by Katie Parrott

in Vibe Check

Midjourney/Every illustration.

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Frontier models usually take a while to get used to. You have to learn their slow spots, when they need extra prompting, and when to keep a close eye on the output.

GPT-5.5, out today, feels easier to settle into. It’s fast enough to use constantly, personable enough to collaborate with, and assertive enough to carry a plan through serious engineering work. It’s better at writing than any OpenAI model we’ve used in about a year, and it produced the strongest result we’ve seen on our new Senior Engineer Benchmark, which measures how well models can rewrite a messy production codebase the way a senior engineer would. It’s rare for a model to feel easier and stronger at the same time.

The big insights from our testing:

Best on senior-engineer coding. GPT-5.5 scored 62.5 on our Senior Engineer Benchmark versus 33.5 for Opus 4.7. Humans still score in the high 80s and low 90s. The twist: GPT-5.5’s best run used an Opus-written plan.
A real writing comeback. It’s the strongest OpenAI model we’ve tested in a year, with cleaner structure and smoother logical progression than Opus 4.7.
Strong everyday knowledge work. GPT-5.5 beat Opus 4.7 on dashboards and felt dependable for creating client deliverables or customer support replies.
Best with structure. GPT-5.5 shines with a plan, an existing system, or a tight feedback loop. Opus 4.7 still has advantages on one-shot vibe coding, PowerPoint, Ruby, and some broad product-design tasks.

The full Vibe Check has the benchmark results, Reach Test ratings, pricing, screenshots, and advice on when to reach for GPT-5.5 versus Opus 4.7.

Read the full Vibe Check

And watch our video Vibe Check with Dan Shipper:

Katie Parrott is a staff writer at Every. You can read more of her work in her newsletter.

We build AI tools for readers like you. Write brilliantly with Spiral. Organize files automatically with Sparkle. Deliver yourself from email with Cora. Dictate effortlessly with Monologue. Collaborate with agents on documents with Proof.

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Transcript: ‘The AI Sandwich: Where Humans Excel in an AI World’

Dan Shipper / AI & I — 2026-04-22 19:00:00 -0400

by Dan Shipper

in AI & I

The transcript of AI & I with Every’s Kieran Klaassen is below. Watch on X or YouTube, or listen on Spotify or Apple Podcasts.

Timestamps

Introduction and the AI sandwich metaphor: 00:00:52
What compound engineering is and how it’s evolved: 00:02:33
The “work” phase of agentic coding is essentially solved: 00:04:27
Why humans belong at the beginning and the end of an AI workflow: 00:06:27
Dan’s argument for why agents can’t change frames—and how this will keep us employed: 00:11:06
Full automation remains a moving target: 00:16:51
Musical composition as a model for human-AI collaboration: 00:23:21
Find your place in an AI-accelerated world by leaning into what brings you joy: 00:26:39

Transcript

Dan Shipper

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You’re the Bread in the AI Sandwich

Laura Entis / Context Window — 2026-04-22 15:00:00 -0400

by Laura Entis

in Context Window

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‘AI & I’: You’re the bread in the AI sandwich

Today, we’re releasing a new episode of our podcast AI & I. Dan Shipper sits down with Kieran Klaassen, GM of Cora and creator of Every’s AI-native engineering methodology, compound engineering. Dan and Kieran discuss where humans fit now that AI can generate high-quality code, copy, strategy, and design. If the execution layer is largely solved, do engineers still have a role in the workplace?

The short answer: Yes. Think of an AI workflow like a sandwich—the model is the workhorse filling, and we’re the bread, providing framing and taste.

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

Here are the highlights:

Play to your strengths. Kieran’s compound engineering framework breaks the engineering workflow into four steps: Plan, work, review, and compound. AI takes care of the doing phase. “LLMs are very good at just following steps, doing deep work, working for hours or days, even now,” Kieran says. What’s left for flesh-and-blood humans are the steps before and after—the planning, where you frame the problem, and review, where you determine whether the output feels right (the bread!).
Humans can identify multiple solutions to the same problem—AI struggles at this. If your knee hurts, you could take Advil, stretch your IT band, or stop running on hard surfaces. Humans are good at diagnosing a problem from many different angles, an exercise agents struggle with, Dan says.
Taste is the final layer of bread. Once AI has done the work, the most important thing you can do is judge whether the output approaches the vision in your head. Does the output feel right—and if not, how can you reframe the problem until the AI produces something that does? This is what separates art, which has a point of view, from generic slop.

Now, next, nixed

The agents are merging

Now: Claudie is an AI agent that runs on a Mac Mini with a Claude Max account. Since joining Every’s consulting team a few months ago, she’s been promoted multiple times and is now responsible for...

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Mini-Vibe Check: Claude Design Isn’t for Designers—Yet

Katie Parrott / Context Window — 2026-04-21 15:00:00 -0400

by Katie Parrott

in Context Window

Midjourney/Every illustration.

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Introducing Monologue Notes

Today we’re launching Monologue Notes, which turns your calls, meetings, and voice memos into transcripts your agents can use. Naveen Naidu built Monologue to capture active work, where text has a clear destination. In six months it’s logged five million dictations and 250 million spoken words. Now, Notes captures the rest: the thinking that happens on walks, in calls, and in meetings. It transcribes everything and makes it available to any agent with API, CLI, or MCP access, across your Apple devices.

Try Monologue Notes

Mini-Vibe Check: Claude Design

Anthropic recently launched Claude Design, a web-based tool that lets you feed Claude a GitHub repo, Figma file, or brand kit and collaborate on interfaces, prototypes, slides, and one-pagers. It’s powered by Claude Opus 4.7 and lives only in Claude.ai.

The stock market read Claude Design as a threat to Figma, the incumbent design tool. But traders are not designers. Having played around with Claude Design, Every’s creative director Lucas Crespo characterizes Figma’s sliding share price as “a Wall Street reflex from people who have never opened either tool.”

Claude Design can do a lot well, but it wasn’t built for designers.

Claude Design lets you upload your organization’s branding and design system. (Image courtesy of Anthropic/Jack Cheng.)

What works: Point Claude Design at a GitHub repo and it will extract a starting design system—the colors, typography, and reusable components that give a product its look. Non-designers can then extend that system. If head of growth Austin Tedesco wants to ship a careers page or a YouTube thumbnail in Every’s style without bothering the design team, Claude Design is the tool for the job.

Claude Design’s live, generative interface is also a nice touch, Lucas says. The tool starts by asking you questions—layout density, accent color, whether to animate emojis—and you can draw or leave comments on top of the output, or click a specific element and edit it in place. The sketch-on-top feature is the closest Claude Design gets to feeling like Figma.

What could be better:

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Where Claude Design breaks down under real use
What weekend breaches at Vercel and Lovable reveal about the state of AI security
A workflow that monitors your AI security stack overnight

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Introducing Monologue Notes: Record Every Meeting, Call, and Voice Memo

Naveen Naidu / On Every — 2026-04-21 03:00:00 -0400

by Naveen Naidu

in On Every

Figma/Every illustration.

TL;DR: Today we’re launching Monologue Notes, which turns your calls, meetings, and voice memos into transcripts your agents can use.

The best thinking rarely happens at a desk. It happens in meetings, on calls, or on walks—and then disappears. Monologue Notes, out today, records and transcribes all of it—the calls, meetings, and voice memos—and makes it available to the same agents and tools you use every day. It makes the thinking that happens in conversations and on walks just as actionable as the work you do at your desk.

Notes is available through the Monologue app on Mac, iOS, and WatchOS and syncs across all your Apple devices. You can start a recording on your Apple Watch before you leave the house, keep your phone in your pocket the entire time you’re outside, and pull the note into Codex once you’re back at your computer.

Try Monologue Notes

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How I Escaped AI Autopilot

Katie Parrott / Working Overtime — 2026-04-20 04:00:00 -0400

by Katie Parrott

in Working Overtime

Midjourney/Every illustration.

To read more of Katie Parrott’s writing about how AI is changing work, read the latest articles in her column, Working Overtime. To read more essays like this, subscribe to Every.

Of all the ways I imagined AI might change my career, “forgetting I already did the assignment” was not on the list.

I had already sent my client a finished draft of an article on hiring best practices in South America, when I happened to reread the brief. A familiar phrase made me realize I had read it before. Then there was the statistic I was pretty sure I had already fact-checked. I clicked back through my files, and there it was: same client, same topic, same deliverable, dated four weeks earlier. It was completed, filed, and forgotten so completely that when a clerical error sent the same brief to my inbox again, I sat down and did the whole thing over.

My first thought was that this was probably early-onset something, and I should call my doctor. My second, more rational thought was that I had not lost my mind—but I had outsourced it. I had been moving so fast and delegating so much of the work to AI that my brain hadn’t even bothered to store a memory of completing the assignment.

What scared me most was thinking about all the smaller moments when I had not caught myself.

This kind of outsourcing isn’t new. Plenty of people would admit to feeling lost navigating an unfamiliar city without a phone to rely on, and I for one am lucky to remember my own phone number, let alone someone else’s. But AI does more than take work off your plate; it steps into the judgment calls you used to make yourself.

I am the last person to scold anyone for using AI. I have built AI into nearly every part of my job, and it has helped me write more rigorously, research more thoroughly, and take on projects far beyond what I used to think of as my wheelhouse. But when you accidentally offload the wrong parts—like fully understanding the purpose and intent of the piece, as I did in this case—you run the risk of atrophying the skills that matter most to you. You might even put your name on work you don’t realize you don’t stand behind until someone else starts asking questions. And if you are using AI for any kind of qualitative work, such as writing strategy, marketing, communications, I would bet you are doing some version of this too. Understanding why it happens is the first step to deciding which parts of the job you want back.

When trusting your tools becomes a bad thing

One group that would understand this immediately:...

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The decades-old research on pilots that explains you trust AI even when you shouldn’t
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The Model Got Stranger

Every Staff / Context Window — 2026-04-17 12:00:00 -0400

by Every Staff

in Context Window

Midjourney/Every illustration.

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Knowledge base

“Vibe Check: Opus 4.7 Stopped Reading Between the Lines” by Katie Parrott/Vibe Check: Opus 4.7 is the best coding model Every has tested on well-specified tasks—Kieran Klaassen called his Rubber Duck benchmark run “best model ever”—but it won’t infer what you want the way 4.6 did, and the prompts you’ve tuned for the last two months will likely disappoint you at first. The gap between a tight brief and a loose one is wider than in any prior Opus. Read this for the full breakdown of where to switch to 4.7 now and where to stay on 4.6.

“The Folder Is the Agent” by Kieran Klaassen/Source Code: After three months trying to make AI agent swarms work in his coding flow, Kieran Klaassen realized that what was doing the work was a folder. A project directory with a CLAUDE.md, accumulated context, and specialized sub-agents is all you need to turn a general model into a domain expert. He’s now running 44 of them, connected by a Ruby dispatch layer that routes work while he sleeps. Read this to learn how to build the dispatch layer yourself.

“(Re(Re))Introducing Sparkle: Marie Kondo Your Mac” by Yash Poojary/On Every: Yash Poojary rebuilt Sparkle to purge the 80% percent of files on the average Mac that are screenshots, installer packages, and duplicates you’ll never open again before it organizes. The new version runs a cleanup pass first, then proposes a custom folder structure you can reshape through chat until it matches you like to work. Download the app and try it yourself.

🎧 🖥 “Mini-Vibe Check: Claude Managed Agents Handle the Infrastructure Work” by Laura Entis/Context Window: Dan Shipper sits down with Eve Bodnia, founder and CEO of Logical Intelligence, who argues that LLMs have a ceiling—and that energy-based models, which scan the full landscape of possible answers rather than predicting one token at a time, are what comes next. Plus: A Mini-Vibe Check on Anthropic’s Claude Managed Agents; Willie Williams proposes new vocabulary for the AI age. 🎧 🖥 Listen on Spotify or Apple Podcasts, or watch on X or YouTube.

“You’re the Manager Now” by Laura Entis/Context Window: The Claude Code desktop app gets a redesign built for managing parallel agent work—and Kieran Klaassen was already living in it. Plus: Dan Shipper explains why you should ignore the viral claim that smaller models can match Anthropic’s Mythos, Austin Tedesco shares the one question he asks Claude Code before shipping anything, and Eleanor Warnock on why the Dia browser’s bet on beauty might be the right one.

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Vibe Check: Opus 4.7 Stopped Reading Between the Lines

Katie Parrott / Vibe Check — 2026-04-17 11:00:00 -0400

by Katie Parrott

in Vibe Check

Midjourney/Every illustration.

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Anthropic surprised us yesterday by dropping Opus 4.7—so we did what we do: We went live on X and YouTube with five testers and figured it out in front of 10,000 people. Anthropic researcher Alex Albert even joined the stream to explain what had changed. Two hours of live testing and an afternoon in Slack later, here’s the short version: This model rewards people who write tight prompts and frustrates everyone who doesn’t. Here’s our complete Vibe Check on Opus 4.7.

The highlights from five testers across coding, writing, and agentic work:

Kieran Klaassen ran it on our hardest coding benchmark and called it the best model he’s ever tested—the first to nail a full e-commerce website build, including a custom product designer and dependable shopping cart performance.
Dan Shipper watched it write a senior-engineer-quality diagnosis of a messy codebase, then refuse to execute the solution.
Mike Taylor got consulting copy so sharp he said it might be better than his own writing and the best slide deck design he’s seen.
Katie Parrott ran the model head-to-head with its predecessor on a personal essay and picked 4.6. 4.7’s draft was competent but rhythmically flat.
Brandon Gell had it do his monthly P&L analysis and found 4.7 missed a data error that 4.6 caught unprompted last month.

The pattern underneath all of it: Anthropic is tuning Claude’s eagerness like a dial between releases, and 4.7 is a hard dial-back from 4.6’s gap-filling intuition. Your old Opus prompts probably won’t deliver the results you’re used to, so you need to tweak them for this release, if 4.7 is what you want to use.

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Living Software

Jack Cheng — 2026-04-17 05:00:00 -0400

by Jack Cheng

Midjourney/Every illustration.

Why do constant updates fill us with dread in some apps, while we greet the daily evolution of an AI agent with more curiosity? Jack Cheng, Every’s senior editor, explores that tension through a clarifying distinction: “tool-like software,” which we expect to be stable and consistent, versus “living software,” which we expect to grow and adapt. Read on for his practical advice for builders of both.—Kate Lee

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Lately, I’ve been wishing that more software had a “freeze” button.

When pressed, the product would crystalize in its present state. The feature set would lock, and the interface would solidify, as if dipped in carbonite. There would be no more new updates. No changes whatsoever.

I want this button because companies are loading apps with more and more features, whether AI or the result of AI-accelerated development, making the tools unrecognizable. The additions are even more jarring for apps that I only use occasionally, like Figma. There, a chat box now beckons to describe my idea to make it come to life. A “Recents” toolbar above it has buttons for Figma Sites, Figma Buzz, and Figma Make—all launched last May. A sidebar module encourages me to try an AI image- and video-generation product called Figma Weave—and which I have to log into separately using my Figma account.

And here I am just trying to update the gradient on an app icon.

At the same time, my Claw, Pip, gets new releases almost daily. I wake up, and Pip suddenly knows kung fu—or if not kung fu, how to dream. Sometimes, the same updates send me on daylong bug hunts, locking me out of a product I rely on to help plan my week, coordinate my family calendar, write code, and brainstorm marketing ideas for my friend’s Delorean rental. Still, I find myself wondering, regularly, “What new thing can Pip do now?”

Why do I loathe change for the first case and forgive—or even embrace—it in the second?

It’s because the first case is software that I want to use for a specific purpose. Half-baked AI features pumped out to appease investors muddy that purpose, but so do legitimate additions, AI or not. Each new addition brings new functionality that seems neat on its own but, in aggregate, transforms the overall product into something other than the tool I know it to be.

On the other hand, software such as my Claw does not have a defined purpose. I’m creating uses and applications as I go that might be entirely different from how someone else is using the same technology, and it’s adapting to me just as much as I’m adapting to it. Its properties—and our relationship—are dynamic.

I’ve come to call the former group “tool-like software” and the latter group “living software.” Living software doesn’t just mean AI agents—though often there’s an agentic aspect to them. Both categories come with a set of expectations, and recognizing the differences in those expectations can explain my disorientation. For builders, it can also help us decide how and what to build.

How we got here: A brief history of software development

Software development cycles have been accelerating for decades. In the 1980s, nine years passed between MS-DOS and Windows 3.0, in part because software was distributed physically, on floppy disks—and later, CD-ROMs. Customers had to go out of their way to upgrade, so major releases had to prove their value. The internet hastened the tempo considerably. Tools like Rails and React scaffolded repetitive forms and database connections, Amazon Web Services and GitHub let developers deploy code to millions remotely, and app stores made automatic updates the default on billions of devices. But even as software went from a box on a shelf to something more like fluid pushed through a digital IV, it made sense to bundle significant changes and release them infrequently, because they took time and coordination to build.

Now, AI coding models have made it possible for a single developer to produce dramatically more code. The review of this code itself can be automated by AI, and the codebase can learn from its mistakes. Features can also be replicated much more quickly—just point your coding agent at the thing you want to clone.

The result for end users is a lot of things we didn’t expect, and in many cases didn’t want. The old, slower pace of development ensured that companies and teams thought long and hard about what features they wanted to ship and what would truly be useful to users. Today’s hyper-fast timelines—Anthropic and OpenAI rolled out OpenClaw-esque features within weeks—are pushing the builders of traditional software to capitulate to trends or ship simply because they can.

Expectations, OpenClaw, and the undead

If I expect software to be a tool, I want it to do one or several things and do it well. I want it to be consistent and stable. I don’t want my hammer to work only 92 percent of the time. Nor do I want my hammer to become a chainsaw.

With software like OpenClaw, I’m more likely to...

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You’re the Manager Now

Laura Entis / Context Window — 2026-04-16 06:00:00 -0400

by Laura Entis

in Context Window

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Now, next, nixed

Developer UI

Now: Anthropic gave Claude Code’s desktop app a redesign, adding a sidebar for managing sessions, drag-and-drop panes, and an integrated terminal and file editor. Altogether, it makes it easier to work multiple projects in parallel. Cora general manager Kieran Klaassen was thrilled—this was already his preferred setup.

Kieran’s existing work setup in Cursor looks a lot like the new Claude Code. (Image courtesy of X/Kieran Klaassen.)

Next: Claude Code’s refreshed look is not exactly original, says Monologue general manager Naveen Naidu. Cursor offers a similar experience, and both companies “just copied Codex’s design,” he says.

But it confirms where dev work is headed: overseeing agents, not writing code.

Nixed: The idea that command-line interface (CLI) will eat user interface (UI). With a CLI-first workflow, you mostly supervise through text: commands, logs, git state, diffs, and terminal output. Now that agents are doing the coding, that’s not a good primary interface.

Instead, the future coding UI is centered on managing parallel work, staying aware of git/task context, and—most importantly, Kieran says—having access to a preview of what you’re building.

Permission to skip

Smaller models can’t do what Claude Mythos does

A researcher at a cybersecurity company made waves online when he reported smaller models could find the same security vulnerabilities as Mythos, Anthropic’s new model so powerful it isn’t being made public, when pointed to the relevant code.

You have permission to skip this discourse—or better yet, reframe it.

Because this is a framing issue, says Dan Shipper, Every’s CEO. Mythos and smaller models are operating within completely different ones. Yes, you can point a smaller model to a codebase and tell it to find a bug when you already know that capability is possible, but you cannot ask it to find serious vulnerabilities in critical software across every major operating system and browser, autonomously, the way Mythos did.

Older models finding the same security bugs as Mythos is not an apples-to-apples comparison. (Image courtesy of X/Dan Shipper.)

As models get better, they automatically handle smaller, concrete problems, allowing you to demand more from them.

Say you have a bug in your code. A lower-level frame, which requires you to describe the problem in detail, would be to explain what’s going wrong and propose possible solutions. A higher-level frame allows you to get abstract: “There seems to be a problem, can you fix it?”

As you climb the frame hierarchy, your role is less about communicating the mechanics of a problem and more about defining what the most important problem even is. In the coding example, the higher frame is powerful because it allows for expansiveness. (“There seems to be a problem, can you fix it?” might surface the same bug as the lower-frame prompt, or it may find that bug and identify a far more significant architectural issue.)

The higher the frame, the more possible solutions unfold before you—and the more room to consider what constitutes a solution in the first place.

Better models open up a dizzying number of approaches to solving a problem. (Image courtesy of Slack/Dan Shipper.)

Steal this workflow

The confidence check

Before he lets Claude Code ship anything, Austin Tedesco, Every’s head of growth, asks it one question:

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The quality-control trick Austin uses to keep Claude Code from shipping half-baked work
How we trained our AI agents to know when to stay out of the conversation
Which philosopher each major AI lab would draft if they could pick anyone from history

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Mini-Vibe Check: Claude Managed Agents Handle the Infrastructure Work

Laura Entis / Context Window — 2026-04-15 06:00:00 -0400

by Laura Entis

in Context Window

Midjourney/Every illustration.

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‘AI & I’: The case against LLMs

Today, we’re releasing a new episode of our podcast AI & I. Dan Shipper sits down with Eve Bodnia, founder and CEO of Logical Intelligence, which is developing an alternative AI model to LLMs. They discussed a question most people in AI are afraid to ask: What if LLMs aren’t going to be the most powerful form of AI?

Bodnia argues that LLMs have intrinsic weaknesses, notably non-language tasks such as spatial reasoning, logical verification, and real-time data analysis. Her solution: energy-based models (EBMs), which map possible outcomes onto a mathematical landscape. Likely outcomes sit in valleys, and unlikely ones sit on peaks. Whereas LLMs process one token at a time, an EBM scans the full terrain to find the lowest point, or the most probable answer. Bodnia argues that it’s this approach, not bigger LLMs, that will lead to the next AI phase shift.

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

Here’s how LLMs and EBMs are different, according to Bodnia:

Architecture transparency: You can’t see inside an LLM; you can only evaluate its outputs. EBMs are governed by physics, which means their architecture is legible while they’re running. “Think of it as something that doesn’t play a guessing game, with an architecture that essentially allows it to self-align as it processes information,” she says. “It’s no longer a black box.”
Language-based versus data-native: LLMs are language-dependent even when the task has nothing to do with language, like data analysis. “If your data is numbers, relationships, and functions, and you try to map those rules into words and then search for the next word, you’re losing a lot of information,” Bodnia says. EBMs work directly with the underlying data structure, including numbers and spatial coordinates.
Sequential versus panoramic reasoning: An LLM is like navigating San Francisco without a map. Each turn constrains the next, and if you go down the wrong street, you can’t reverse course. An EBM, by contrast, has the bird’s-eye view—it can evaluate multiple routes at once and course-correct before hitting a dead end.

Miss an episode? Catch up on Dan’s recent conversations with LinkedIn cofounder Reid Hoffman; the team that built Claude Code, Cat Wu and Boris Cherny; Vercel cofounder Guillermo Rauch; podcaster Dwarkesh Patel; and others, and learn how they use AI to think, create, and relate.

Mini-Vibe Check: Claude Managed Agents

Or that feeling when the problem you’ve spent a lot of time solving gets solved for you

We’re all about agents at Every. Which means many of us have devoted a lot of time to building the infrastructure that makes them run.

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Why Anthropic’s Claude Managed Agents are an “oh shit” moment for anyone who’s spent months building agent infrastructure
How Every is already using Claude Managed Agents to build products
The new vocabulary for an AI age

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(Re(Re))Introducing Sparkle: Marie Kondo Your Mac

Yash Poojary / On Every — 2026-04-14 10:15:00 -0400

by Yash Poojary

in On Every

Gemini and Photoshop/Figma/Every illustration.

TL;DR: We’ve rebuilt Sparkle, our Mac file organization app, as an agent-native tool that cleans and organizes your Mac. It’s our biggest update since we first launched it in 2024. The key change is that the new Sparkle cleans your Mac before it organizes it—purging screenshots, installer packages, and other digital junk first, then building a file structure around what’s worth keeping. It’s available now to all paid Every subscribers.

Download the new Sparkle

A cluttered file system can feel like a cluttered brain. When your computer is a mess, it takes mental energy to find what you need, much less do actual work.

Clutter is universal—and most of it isn’t worth keeping. Around 80 percent of files on the average Mac are screenshots, installer packages, duplicates, and digital debris you’ll never open again. So before you can get organized, you need to purge. “Organized,” then, depends on the person. Maybe you want to arrange files by topic or date, or by a highly-specific system that only makes sense to you. All count as organized if you can find what you want when you want it.

We’ve rebuilt Sparkle, our file organization app, with this personalization in mind.

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The Folder Is the Agent

Kieran Klaassen / Source Code — 2026-04-13 11:00:00 -0400

by Kieran Klaassen

in Source Code

Midjourney/Every illustration.

On Friday, April 17, Cora general manager Kieran Klaassen will lead a camp for Every paid subscribers on compound engineering, the AI-native engineering philosophy that he built and that has more than 14,000 stars on GitHub. Since the last camp, Kieran and product leader Trevin Chow have built out product-focused workflows to make the methodology as valuable for product managers and founders as it is for engineers. In this camp, they’ll walk you through what’s new, go deeper on the brainstorm and ideate steps, and share examples of using compound engineering beyond engineering work. Read the full compound engineering guide, install the plugin, and join us for the camp.—Kate Lee

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.

But more agents didn’t make me faster. I’ve run parallel Claude Code sessions for months, which works when each agent has a clear task, and I’m directing the work. The swarm experiment was different: agents coordinating with each other, deciding what to work on, producing output I hadn’t shaped. When 10 of them finished simultaneously, I had 10 results to evaluate without enough context to know which ones I could trust. AI agents don’t have a speed limit, but the person managing them still does.

I kept looking for a smarter orchestration layer—a better protocol or a tighter framework that would filter the output and tell me which result to trust. Then I stopped and looked at what was really doing the work.

It was something I already had—a folder.

A project folder with a CLAUDE.md/AGENT.md (the file that tells an AI how to work in your project), some skill definitions, and context accumulated through months of compound engineering—that’s an agent. The context that this folder gives an AI model makes the generalized model a specialist in whatever task or field you want it to excel in.

I’m running 44 of these folders-as-agents across multiple projects now. Each one runs inside a specialized folder I’ve built and tested over months, and a dispatch layer I built on top does the routing between them. Here’s how it works.

The agents hiding on your hard drive

People hear “agent” and picture a Rube Goldberg machine—dozens of comically complex moving parts, each one triggering the next. But an agent is much simpler: a model with enough context so you don’t have to re-explain everything each time you open the chat.

Here’s an example: All of Cora’s code lives in a project folder in the Every organization on GitHub. When I open that folder with Claude, Claude can see the code and the structure. But it doesn’t know my way of working or what I care about, which is why the folder also includes a CLAUDE.md file. The file tells Claude how I name things and how I structure tests. That’s an agent—not a fancy one, but an agent nonetheless. Just by pointing the model at this folder, which contains some of my personality, knowledge, and taste, the model can be a specialist in my codebase.

Claude Skills—files that give the model specific capabilities—are an example of this “folder as agent” structure. Before anyone called them “skills,” people were already writing markdown files full of instructions and dropping them into project directories.

My ~/cora/ folder goes further:

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How Kieran structures a CLAUDE.md file so any new agent inherits months of institutional knowledge
How Kieran built a two-command dispatch layer that replaced 20 open tabs
The four-step sequence Kieran follows before handing any agent off to automation

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The Missing Layer in AI Adoption

Every Staff / Context Window — 2026-04-11 13:00:00 -0400

by Every Staff

in Context Window

Midjourney/Every illustration.

Hello, and happy Sunday! Two housekeeping notes: Our next cohort of Claude Code for Absolute Beginners is taking place on Tuesday, April 14, and Every has opened seven new roles. Join us!—Kate Lee

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Knowledge base

“Writing With AI Is Harder Than You Think” by Katie Parrott/Working Overtime: The discourse about AI and writing generally assumes prompt in, text out, done. Katie Parrott shows her much more involved process: an agent that interviews her before she writes a word, a back-and-forth on her structure that she has to fight for, a panel of AI critics named Hemingway and Hitchcock, and a last read that flags anything that sounds machine-generated. Read this because successful AI writing demands more judgment, not less.

“Your Best AI Strategy Starts at the Top” by Natalia Quintero and Mike Taylor: Most executives approach AI like a software purchase—evaluate, compare features, and plug in. Natalia Quintero and Mike Taylor see it differently: Using AI is people management, not platform adoption. You delegate clearly, check the output, and supply the judgment the model doesn’t have. Read this for the five concrete actions senior leaders can take to increase AI adoption within their companies.

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The Market for Making AI Better

Alex Duffy / Playtesting — 2026-04-10 11:00:00 -0400

by Alex Duffy

in Playtesting

Midjourney/Every illustration.

Many AI investors are betting that the biggest AI models will win—that scale and compute beat everything else. But recent research and market moves suggest otherwise. Alex Duffy, who runs a company that uses games to make AI models better, explains what he’s seeing from inside this market—and why the data your company already has might be worth more than you think.—Kate Lee

My friend recently received a strange email. The sender, someone at a large data provider for AI labs, wanted to know if my friend could share data on things like the number of Dropbox files his company had stored or the number of tickets it had processed on Zendesk. Compensation, commensurate with the data, was promised.

He showed me the email, curious. To me, the founder of a company that sells data and environments to AI companies to help them train models better, this was just another sign of the robust market forming for making AI better.

Reddit, Shutterstock, and News Corp are making hundreds of millions a year licensing their high-quality data to companies training AI, and those contracts are growing about 20 percent annually, according to their quarterly filings. News Corp’s CEO put it bluntly: “We’re essentially an input company [for AI].”

Shutterstock and Reddit are making the most profits from licensing data to AI. (Graphic courtesy of Alex Duffy based on publicly available sources.)

Academic publishers, documentary archives, game studios, and companies sitting on years of enterprise data have all been courted for the seeds of intelligence needed to train the next generation of models. Mercor, which provides data to AI labs for training, became one of the fastest-growing companies in history before losing four terabytes of data to hackers last week. Competitors Turing, Handshake, and SID.ai are scrambling to fill the gap, reaching out to founders and anyone with access to buy operational data, similar to the request my friend received.

While some experts have speculated that general models will win out in performance over specialized models—that scale and compute will beat curation—the success of these companies shows that the market is making a more nuanced bet.

A small model trained on fewer than 2,000 examples from real lawyers, bankers, and consultants recently beat all but the best frontier models on corporate legal work, at a fraction of the price, since they used an open-source model and now only face the cost of running it.

A small model trained on fewer than 2,000 examples from real lawyers, bankers, and consultants recently beat all but the best frontier models on corporate legal work. (Image courtesy of AppliedCompute.)

More companies are racing to catalogue and operationalize human knowledge, and whoever leads this market may shape which ideas, which history, and whose principles inform the most powerful tools we’ve ever built.

The data with value

The data sources with the most value share two traits:...

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How We Run a 25-person Company on Four AI Agents

Katie Parrott / Source Code — 2026-04-09 08:00:00 -0400

by Katie Parrott

in Source Code

Midjourney/Every illustration.

This event was produced in partnership with Notion. They had no input on the development of this article.

Want to learn alongside Every’s team? Check out our upcoming camps and courses at every.to/events.

Every runs six products, a media company, and a consultancy with around 25 people. At any given moment, each person has roughly 30 tasks on their to-do list. So how do they figure out which to work on first?

The team used to rely on Brandon Gell, Every’s COO, to run traffic control and coordinate the whole company, which required him to manually cross-reference launch calendars, company strategy documents, and task lists. Now he messages a Notion agent named Anton in Slack and gets a prioritized list for himself and others in seconds.

Anton is one of four custom agents Every has built with help from Notion AI over the past few months. Each one automates a different task that, without the agent, would require tedious logistical work to track and schedule. Each one draws on the same set of interconnected databases that the team already maintains.

At our first Custom Agents Camp, produced in partnership with Notion, Brandon and Every head of growth Austin Tedesco, walked more than 500 subscribers through four agents they’ve built, the databases underneath them, and how to create your own. Notion product designer Brian Levin also joined to share best practices from the Notion team.

Key takeaways

Describe the outcome, not the steps. Tell the AI what you want to accomplish and let it figure out the implementation. Over-prescribing (“Create a database, then add a relation, then filter by...”) tends to confuse the model.
Your Notion is your agent’s brain. Custom agents get powerful when they can query interconnected databases. Every’s agents work because strategy, calendar, tasks, people, and meeting notes all live in Notion and reference each other.
Don’t write the agent’s instructions yourself. Tell Notion AI what you want the agent to accomplish, and it will generate the instructions. Or use Claude Code with Notion’s API to build the whole thing from your terminal.

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Every Is Half Agent Now

Laura Entis / Context Window — 2026-04-08 15:00:00 -0400

by Laura Entis

in Context Window

Midjourney/Every illustration.

‘AI & I’: Agents work among us

Today, we’re releasing a new episode of our podcast AI & I. Dan Shipper sits down with Every’s COO Brandon Gell and head of platform Willie Williams to discuss the good, bad, and weird of how daily operations change when everyone at your company has an agent.

A “parallel organization chart,” in which each AI worker has a name, manager, and job description, allows your company to move faster than it ever could with humans alone. It also raises a host of new questions about how work can—and should—get done.

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

Here are the highlights:

We’re writing the etiquette in real time. Each person at Every has a dedicated OpenClaw AI assistant, or Plus One, trained to assist with or fully handle parts of our jobs. R2-C2, for example, reports to Dan and is responsible for collecting flagged bugs and generating pull requests for Proof, Every’s collaborative document editor for agents and humans. So when do we turn to Dan versus R2-C2 for Proof-related troubleshooting? Brandon’s rule of thumb: If an established process or tool needs to be used or fixed, ask a Plus One. R2-C2 knows all about Proof, and Dan’s a busy guy—bug reports and questions about how to use the app or report a bug should always go to the agent.
Agents gain credibility by doing. The fastest way to get other people to trust and use your Plus One is to have it execute tasks in public. Austin Tedesco is Every’s head of growth, and Montaigne, his Plus One, essentially co-runs the department. Austin asks Montaigne to generate campaign scorecards, analyze metrics for growth insights, and handle all sorts of other complex tasks. Watching Montaigne pull off these requests proves its capabilities to the team—and inspires others to push their Plus Ones to achieve more, too.

Austin Tedesco asks Montaigne to analyze YouTube keywords for ‘AI & I’ (All screenshots courtesy of the Every Slack workspace unless indicated otherwise.)

Everyone is a manager now. Agent sidekicks force each of us to change our approach to getting work done. To get the most out of a Plus One, you need to actively manage it—onboard it, delegate tasks to it, evaluate its performance, and give guidance so mistakes aren’t repeated. For anyone who hasn’t had a direct report before, “there’s an education that has to happen,” Brandon says.

Signal

Anthropic’s most capable model is coming—just not to you

The news: Anthropic has built Mythos, a powerful new model, but does not plan to make it public ...

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Whether Every team members think of their agents coworkers, tools, or other
What happens when you let an AI dream—and then ask to see it
A prompt to give your agent when it keeps talking instead of doing

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Transcript: ‘We Gave Every Employee an AI Agent. Here's What Happened.’

Dan Shipper / AI & I — 2026-04-08 10:00:00 -0400

by Dan Shipper

in AI & I

The transcript of AI & I with Brandon Gell and Willie Williams is below. Watch on X or YouTube, or listen on Spotify or Apple Podcasts.

Timestamps

Introduction: 00:00:51
How Brandon built Zosia, an AI agent to run his household: 00:02:21
Brandon’s aha moment re: using agents for work: : 00:07:09
What happened when everyone on the team got their own agent: 00:09:39
How agents take on their owners’ personalities, and why that matters inside an org: 00:12:42
Why it’s important for agents to do work in public: 00:23:51
What we’re still figuring out when it comes to agent behavior, including memory gaps, group chat etiquette, and the “ant death spiral” problem: 00:30:51
How we built Plus One, our hosted OpenClaw product: 00:40:45
The cultural shift required to make agents work at scale: 00:47:27

Transcript

(00:00:00)

Dan Shipper Claude is not mine. Claude is everybody’s. A Claw—or a Plus One—is mine, because you develop a personal relationship with your Claw, and your Claw can modify itself in response to talking to you. It becomes this reflection of you and who you are and your personality.

If you’re known for something inside of your org and you’re using your Claw publicly inside of Slack or Discord, your Claw then becomes known for that same kind of thing, and people trust it for that. I think that’s such a useful thing that I don’t think people really understand how powerful it is.

Willie, what’s up. Brandon, welcome to the show.

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Your Best AI Strategy Starts at the Top

Natalia Quintero and Mike Taylor — 2026-04-07 06:00:00 -0400

by Natalia Quintero and Mike Taylor

Midjourney/Every illustration.

We are hosting a day-long Claude Code for Absolute Beginners course on April 14. If you have used Claude Code for an hour or less, or not at all, I’ll get you set up, help you build your first app with Claude Code, and start automating your routine tasks.—Mike Taylor

A CEO told us recently that he’d been hoping to skip the part where AI wasn’t very good. He figured he’d jump in once the technology matured past the clunky, overpromising phase because carving out hours to learn a new category of technology felt untenable with all of his other responsibilities.

That wait-and-see posture made sense for a while. It doesn’t anymore. When Anthropic released industry-specific plugins for its Cowork tool in February 2026 for legal and financial services roles, the S&P 500 software index fell nearly nine percent over a few days. Executives who haven’t touched the tools themselves are now making high-stakes decisions about something they don’t understand firsthand.

The problem is what they default to. When a leadership team hasn’t used AI themselves, they treat it like any other software purchase: Evaluate, buy, and plug in. They ask, “Which platform?” and “How does it integrate?” Those are the right questions for most technology. They’re the wrong questions for AI.

AI tools like Claude and Cowork aren’t products that slot into your tech stack and deliver value on day one. They’re more like a new kind of employee—one that can do enormous amounts of work, but only if you tell it exactly what to do and check whether the output is right. That’s a fundamentally different adoption decision, and one that’s hard to make unless they have experienced the tool’s capabilities firsthand.

More executives seem to be waking up to this, as we’ve recently started receiving inbound requests from executives at companies like Thumbtack and Headway (Every consulting clients) to attend their executive offsites and walk them through using Claude Code to build real projects. Our conversations with executives had always been about training their teams, and the rapid progress in AI has made them want to get in on the action, too. We’re finding skills they’ve already built as leaders are the skills AI demands—it’s just a case of getting into the habit of applying them.

Executives realize AI is like managing people

Firsthand experience matters so much because AI, when you actually use it, doesn’t feel like software. It feels like managing people. This is what we’ve found surprises the executives we’ve worked with the most—the fact that the work feels familiar.

Think about what it takes to manage people well. You need to know what the goal is, break work into pieces, assign those pieces to the right people, and check the output without micromanaging. You need the judgment to notice when something looks right on the surface but doesn’t hold up—the kind of pattern recognition that comes from years of making mistakes and learning from them.

Managing AI is the same work. When you use a tool like Claude Cowork, you’re running 10 threads at once—building dashboards, summarizing your inbox, and reviewing documents—each tackling a different task. Your job is to delegate clearly, check the output, and apply the judgment that the AI doesn’t have. Did it pull the right data? Does this analysis match what I know about the market? Is the logic sound, or did it take a shortcut that looks plausible but isn’t?

This is why the “evaluate and buy” approach to AI tools fails. You can’t evaluate an employee by...

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Get Your Hands Dirty

Every Staff / Context Window — 2026-04-07 02:00:00 -0400

by Every Staff

in Context Window

Midjourney/Every illustration.

Today we’re testing a new newsletter format, aimed at giving our readers both a taste of our long-form writing and our perspective on what matters in AI today. Let us know what you think.—Kate Lee

Today’s top story

“Your Best AI Strategy Starts at the Top” by Natalia Quintero and Mike Taylor: Executives might be waiting on the sidelines to see what will happen with AI, but they need to be get

ting their hands dirty with the tools, write Natalia Quintero and Mike Taylor, both part of Every’s consulting team. That’s because AI can’t be evaluated like software, where you compare features, platforms and integrations. It needs to be treated like a new kind of employee.

Natalia and Mike offer five concrete things for executives to do this quarter—starting with suspending skepticism—to get started building AI-native organizations. Read more.

Signal

Anthropic’s OpenClaw ban is a gift to OpenAI

The news: Anthropic blocked Claude subscriptions from being used with third-party agent harnesses like OpenClaw. OpenAI hasn’t.

The context: Anthropic’s stated reason for the ban is to prioritize compute for its own products, saying flat-rate subscriptions weren’t built for the high usage of third-party tools.

It’s a valid argument: Agents that run 24/7 are enormously expensive. But rival OpenAI has raised so much money it can afford to let subscribers use their models however they want.

The implications: Anthropic’s ban provides an opening for OpenAI to siphon away users. The strategy appears to be working: Opus 4.6 token usage is significantly down week over week; GPT-5.4’s has surged.

Model usage as measured by OpenRouter. (X post courtesy of Dan Shipper.)

Bigger picture, the future of the industry depends on figuring out ways to drive down compute costs.

(Running frontier AI agents like OpenClaw can cost $300–$1,000 a day, a number that’s only growing.)

OpenAI has a clear advantage here. It’s building its own data centers, which puts it closer to the metal on compute. Meanwhile Anthropic is buying compute from third parties, and will never have as low a cost basis.—Laura Entis

New job alert

We’re flagging new job postings that signal where AI is reshaping teams...

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New job alert: The role that only makes sense now that AI exists
Inside Every: Why the technical/non-technical split in AI adoption is the wrong one
Steal this workflow: HTML gets you to good. Figma gets you to great.

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Writing With AI is Harder Than You Think

Katie Parrott / Working Overtime — 2026-04-06 08:00:00 -0400

by Katie Parrott

in Working Overtime

Midjourney/Every illustration.

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I’ve been feeling personally attacked by my X feed lately. Well, even more than usual. Alongside the usual headline horror shows and barrage of bad takes, writers I respect and admire are on the warpath against writing with AI.

The discourse kicked off late last month when Washington Post columnist Megan McArdle posted about how she uses AI in her work. The reposts were merciless. “Genuinely an insane thing to admit.” “Journalistic dishonesty out in the open.” One person suggested that admitting to AI use should be made “deeply taboo,” even though he acknowledged in the same post that everyone’s going to do it anyway. But the one reaction that stuck with me was journalist Charlotte Alter: “Research is thinking. Outlining is thinking. Writing is thinking. Any portion of that done by AI is less thinking done by you.”

The problem is that so much of AI writing happens in a black box. The critics are imagining the laziest possible version of AI-assisted writing, and the writers who use AI seriously haven’t been showing their work, though that’s starting to change. That silence lets the worst assumptions fill the gap.

I’d rather just show you the whole mess—what is happening in my head when I write with AI, and it’s not what the discourse imagines. By the end, you can decide for yourself whether what I do counts as thinking.

What writing with AI is (and what it isn’t)

Many critics treat the use of AI in writing like a binary: Either the machine wrote it, or you suffered for it. But writing has never been binary. It’s always been a mess of drafting and revising, leaning on editors and borrowing structures, following formulas and breaking them. And no two kinds of writing are exactly alike: A journalist’s process relies on source calls and document requests. A novelist’s includes plotting arcs across 80,000 words. A personal essay, like the ones I write for Every, involves sitting alone with your feelings until they become a thesis statement.

Every writer’s process is different, and most of them would sound unhinged if described in detail. But throw AI into the mix, and suddenly everyone has opinions about the “right” way to get words on a page.

My process, start to finish

When people picture “writing with AI,” they picture a transaction. You type a prompt, the AI hands you text, you paste it somewhere, and move on. My process has...

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House Rules for the Agents

Every Staff / Context Window — 2026-04-05 01:00:00 -0400

by Every Staff

in Context Window

Midjourney/Every illustration.

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Fine tuning

Anthropic’s OpenClaw problem

When Anthropic’s new Claude Max restrictions started circulating, the company named one tool specifically: OpenClaw. “Wtf,” wrote CEO Dan Shipper in the Every Slack. The policy seemed to say: If you access Claude through OpenClaw, your subscription no longer covers it the same way. “They disallow specifically OpenClaw from subs,” head of tech consulting Mike Taylor wrote. “You have to pay for extra usage. Pretty lame.”

Mike’s best explanation for why Anthropic drew the line where it did centers on prompt caching, a cost-control mechanism that works by reusing previously processed conversation text. When it works, it keeps inference costs low. When a third-party tool changes even a single token in the prior conversation, that reuse breaks, and Anthropic has to reprocess the entire conversation from scratch. “Prompt caching keeps cost down by saving the previous tokens that have already loaded,” Mike explained. “If a provider breaks the cache by changing even one token of the previous saved conversation, you have to reprocess the entire old conversation.” He also noted that Claude Code co-creator Boris Cherny had already opened pull requests to improve OpenClaw’s cache efficiency, suggesting the problem was technically solvable. Anthropic enacted restrictions instead.

What the team disputes is not that Anthropic has a reason—it’s that singling out one app by name is the wrong response to it. The consistent argument across the Every Slack was that if cache-breaking usage costs more to serve, make those users pay more: Meter the consumption rather than ban the interface. “A better middle ground is not to ban OpenClaw users,” head of platform Willie Williams argued, “it’s to give me a certain amount of tokens I can use as part of my subscription, and then charge me overages if I go over.” Dan framed the same principle from the user side—“I think of AI subscriptions like Claude and ChatGPT as being like cell phone plans that give me a certain amount of data”—and Mike extended it to the infrastructure side, invoking net neutrality: Verizon shouldn’t get to slow down Netflix because Netflix uses a lot of bandwidth. The argument, in every form it took, was the same: Charge for what costs you money, not for which app someone uses to spend it.

There is also a business problem that goes beyond annoyed subscribers. Restrictions like this do the opposite of building loyalty—they create churn. Anthropic may have a legitimate business reason for drawing a line somewhere. But drawing it in a way that feels confusing and selective is not the way to win the platform war between model providers and the tools built on top of them.—Kate Lee

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How to Design for Human-agent Interaction

Karri Saarinen / Thesis — 2026-04-03 10:00:00 -0400

by Karri Saarinen

in Thesis

Sarah Deragon/Every illustration.

Karri Saarinen has spent his career—at Airbnb and Coinbase, and now as CEO of Linear—crafting software that keeps its promises. His argument is that AI’s unpredictability isn’t a model problem, it’s an interface one: An agent sends a customer an email you meant to review first. The model did what it was told, but the interface never gave you a chance to stay stop. In this piece, he shares the six-principle framework Linear has developed for how agents and humans should work together inside the same product, plus his nuanced take on a thorny question in AI design: Who should be accountable when an agent does something wrong? If you enjoy the piece, watch his episode on X or YouTube, or listen on Spotify or Apple Podcasts.—Kate Lee

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I learned to design in a world where product design was a promise.

It was a promise that a product would work how it’s supposed to work. You sketch a user flow on a whiteboard, build it, and the system behaves the way you made it behave. A button does exactly what it says it will do, every time, and if it doesn’t, that’s a bug. This shaped my approach as a principal designer at Airbnb and Coinbase, and now as the CEO of Linear.

Lately I’ve been spending time with a different kind of tool, and that promise has grown harder to keep. I ask for help writing a plan, summarizing a discussion, and turning rough notes into something clearer. Sometimes the result is excellent, but small changes to my input shift the output in ways I didn’t expect. The capability is impressive when it works, but the experience often feels slippery. I’m not always sure what I’ll get back, or how much I should trust it.

Non-deterministic software breaks the contract. When outcomes can vary, sometimes wildly, based on what someone types into the same chat window, designing for reliability becomes genuinely harder. This slippery feeling is the design problem of this era, and it almost always traces back to the interface rather than the language model—which means it belongs to designers, not researchers.

The limits of chat

The first interface that spread for AI tools was the chat window. That makes sense. When you don’t know what something can do, the safest approach is to let people ask. A conversation feels familiar, it stretches across many situations, and it doesn’t force a specific structure up front.

But the more you use chat for real work, the more its weakness shows. Everything becomes a stream of text that’s hard to hold onto, hard to compare, and hard to connect to the rest of what you’re doing. The quality of the output depends enormously on the quality of the input, which means two people asking for the same thing in slightly different ways can get drastically different results. There are few guardrails, and little structure nudging you toward a good outcome. The interface is essentially a blank page with a blinking cursor, and all the burden of getting value from it falls on the person typing.

For exploration, that’s fine. For serious, repeated work inside a team, it’s not enough. We need interfaces that bring more structure to AI interactions, that guide people (and agents) toward better outcomes without being so brittle they break the moment someone wants to use them in a way you hadn’t anticipated.

Designing for new actors

There’s a second, newer dimension to this problem that goes beyond improving interfaces for humans. Agents are already showing up inside products, working alongside people, and most software wasn’t designed with that in mind.

For decades, interfaces have been designed so that humans can navigate them—buttons, menus, folders, navigation hierarchies. These patterns assume a person is looking at a screen, making decisions, and clicking through options. But when an agent is interacting with a product, the design challenge changes. The agent doesn’t need a menu to find something. It doesn’t browse. It acts, and the people around it need to understand what it did and why, often after the fact.

We need a new set of principles for how agents show up inside the tools people already use. Not principles for building agents themselves, but principles for designing ways that agents and humans interact within a shared product. At Linear, we’ve started calling these Agent Interaction Guidelines, and while they’re still evolving, they represent how we think about this problem today.

An agent should always disclose that it’s an agent

When humans and agents work side by side, people need instant certainty about who they’re interacting with. This sounds obvious, but it’s easy to get wrong. The agent has to signal its identity clearly enough that it can never be mistaken for a person, even in passing, even on a quick scan of a busy activity feed.

A dropdown menu assigns tasks to human and agent users, with clear “Agent” badges for the latter. (All screenshots courtesy of Linear.)

An agent should inhabit the platform natively

Agents should work through the same patterns and actions that humans use ...

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Why an agent that ignores a stop command erodes trust faster than one that makes mistakes
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And two more principles from Linear’s framework for human-agent collaboration

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Vibe Check: Cursor 3.0 Bets Big on Agent Orchestration

Dan Shipper, Katie Parrott, and Mike Taylor / Vibe Check — 2026-04-02 10:00:00 -0400

by Dan Shipper, Katie Parrott, and Mike Taylor

in Vibe Check

Midjourney/Every illustration.

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Cursor made its name as the AI-native code editor—the product that proved developers wanted AI inside their workflow. With Cursor 3.0, out today, the company is making a big bet on what comes next—and it’s not editing code.

The new release is a ground-up rebuild centered on agent orchestration: dispatching, monitoring, and managing AI agents rather than writing code by hand. The editor is still there, but it’s no longer the star—the default view opens on an agent-centered workspace, with a chat-driven orchestration panel where the file tree used to be. It’s fast, resource-light, and has a genuinely impressive cloud feature that lets an agent build a feature while you grab coffee, then sends you a screencast demo when it’s done.

The problem is that the orchestration layer hasn’t earned the right to take center stage yet. The filesystem sidebar has been demoted to another tab. The skills ecosystem is fragmented across Claude Code, Codex, and Cursor with no interoperability. And the core question our team kept circling back to—“Who is this for?”—doesn’t have a satisfying answer yet.

Power users who already live in Claude Code or Codex don’t need another orchestration layer. Cursor loyalists who loved the editor are losing prominence for the thing they came for. Their team seems to be iterating incredibly fast, so we’ll be paying attention over the coming weeks and months as this becomes clearer.

We spent a week testing it with four members of Every’s engineering and product team. Here’s what we found.

Read the full Vibe Check

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. Katie Parrott is a staff writer and AI editorial lead at Every. You can read more of her work in her newsletter. Mike Taylor is the head of tech consulting at Every and a co-author of Prompt Engineering for Generative AI (O’Reilly). You can follow him on X at @hammer_mt and on LinkedIn.

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We also do AI training, adoption, and innovation for companies. Work with us to bring AI into your organization.

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If SaaS Is Dead, Linear Didn’t Get the Memo

Laura Entis / Context Window — 2026-04-01 07:00:00 -0400

by Laura Entis

in Context Window

Midjourney/Every illustration.

‘AI & I’: Slowing down to speed up

Today, we’re releasing a new episode of our podcast AI & I. Dan Shipper sits down with Karri Saarinen, cofounder and CEO of Linear, a product management tool designed for agent-native software development, to discuss what the “SaaS is dead” narrative gets right—and wrong—and why conviction can be the best product strategy.

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

Here are the highlights:

Just because the technology has changed doesn’t mean your mission should. Founded in 2019, Linear is the rare company that started pre-ChatGPT to have successfully reinvented itself as an agent-native business. Saarinen attributes Linear’s success to never losing sight of what it’s always cared about: helping companies build great software. Whereas competitors chased AI trends, Linear focused on understanding how the technology was impacting customers’ workflows, and updating its service accordingly.
SaaS winners are building for agents. Linear started as an excellent product management tool for humans. Opening up the tool to agents instantly increased the available user base. Today, agents are first-class users inside of Linear, and companies like OpenAI and Coinbase are using its platform to manage their own agents.
Speed means decisions matter more, not less. AI makes it easy to have an idea and build it without considering whether it justifies its existence. When ChatGPT was released, SaaS companies were launching their own chatbots left, right, and center. Instead of jumping on the bandwagon, Linear stopped to consider whether the application was useful. Turns out it really wasn’t, Saarinen says, a realization that freed up resources to focus on what mattered, like making it easy for humans and agents to collaborate on software development.

Dissecting Claude Code

On Tuesday, Anthropic inadvertently leaked the entire source code for Claude Code. Naturally, Cora general manager Kieran was curious to see what was happening under the hood.

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The feature buried inside Claude Code’s leaked source code that impressed Kieran the most
What Every’s creative director thinks of Google Stitch
Why the general manager of Every’s AI writing tool starts every draft knowing only 30 percent of what he wants to say

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What I Learned Onboarding Our AI Project Manager

Nityesh Agarwal — 2026-03-31 06:00:00 -0400

by Nityesh Agarwal

Midjourney/Every illustration.

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Every’s consulting team is growing. Right now, we have two potential new hires in a trial period: Jean-Claude, who’d manage our sales pipeline, and Claudette, a visual designer.

You might be surprised to learn that they’re both AI agents. If they’re able to reliably do what we need them to and we bring them on full-time, our team will consist of four human and three agent employees.

Claudie, our first AI colleague, has been with us for two months. Natalia Quintero, Every’s head of consulting, and I rely on her to track where every client project stands and to make sure nothing falls through the cracks, work that saves the team 15 hours per week. It’s hard to imagine operations without her.

Getting her up to speed, however, was neither a seamless nor a linear process. That road is paved with previous iterations of Claudie we had to fire because they were not structured right.

Each Claudie revealed more about what it takes to get an agent to be a reliable co-worker—lessons that have only become more urgent as more companies deploy agents, creating what Every CEO Dan Shipper has called a “parallel organization chart” of AI colleagues, each with a name, manager, and real responsibilities. At Every, we’ve started helping others build the same setup through our hosted agents, called Plus Ones. Claudie was our crash course. Here’s what she helped us figure out.

Define the job before you hire for it

Built in Claude Code—hence her name—Claudie was designed to handle administrative tasks that consumed too much of Natalia’s week. The albatross was maintaining the dashboard that shows the status of all our client work, which meant staying on top of a constant flood of information from Natalia’s email, Google Docs, Google Sheets, meeting transcripts, and her calendar. Before Claudie, Natalia was spending hours that could have been dedicated to strategy and client relations finding data across dozens of sources and manually copy and pasting it in the right tab.

The first step was to...

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Why Nityesh fired Claudie multiple times before she could do her job
The architectural workaround that solved Claudie’s biggest performance problem
Why Claudie’s first hard-coded task is reading her own employee handbook

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Seven Things I've Learned Getting Companies to Use AI

Mike Taylor / Also True for Humans — 2026-03-29 21:00:00 -0400

by Mike Taylor

in Also True for Humans

Midjourney/Every illustration.

This post was originally a tweet thread in response to Sam Parr asking how people get their teams to adopt Claude. It touched a nerve, so I wanted to expand on it. I recently joined Every Consulting as the head of tech consulting, where we work with mid-to-large-sized companies on AI training and adoption. Here’s what’s working.—Mike Taylor

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Entrepreneur Sam Parr asked a question on X the other day: “How is everyone getting team adoption for Claude? I spent a lot of time on Twitter, as do you. We see all this AI stuff popping up. We’re on top of it, or at least sorta. But how are all you people getting your team to actually use it effectively without spending all their time on Twitter and learning?”

I hear this question in some form on every single consulting engagement. I know the advice I have resonates in meetings, but I’m short on time. So I dictated this post through Monologue and used Claude to shape it into something readable. (Let me know if this format works for you.)

Here are seven learnings from working with companies through Every Consulting:

1. Buy the model direct, not third-party tools

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Why mandating AI adoption almost always backfires
How to turn skeptics into power users with one counterintuitive task assignment
And five more lessons to make AI stick across your team

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Everyone Gets a Sidekick

Every Staff / Context Window — 2026-03-29 08:00:00 -0400

by Every Staff

in Context Window

Midjourney/Every Illustration.

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Knowledge base

“Introducing Plus One: One-click OpenClaw Agents by Every” by Dan Shipper/On Every: Every’s team has spent months working alongside personal AI agents in Slack—triaging bugs, drafting marketing copy, launching growth experiments—and now we’re sharing them with subscribers. A Plus One is a hosted OpenClaw agent that shows up to the job with Every’s best tools and workflows. Read this to see how our team collaborates with their AI coworkers, and to join the waitlist.

“I Achieved the Four-hour Workweek. So Why Did I Just Take a Job?” by Mike Taylor/Also True for Humans: After five years of self-employment, Mike Taylor had passive income and total freedom. He also had unpredictable revenue, a string of failed products, and no one to share ideas with—which is why he went full-time as Every’s head of tech consulting. His argument is that while AI makes building anything easy, getting someone to notice is harder than ever, and the best learning happens inside a team. Read this if you’ve ever wondered whether the solo path is actually worth it.

“The Agent That Saved My Brain” by Austin Tedesco: Austin Tedesco, Every’s head of growth, used to lose hours toggling between Stripe, PostHog, Slack, and Notion. So he built an agent in Claude Code—even though he has no technical background—that pulls data, drafts campaign briefs, and answers his questions right in Slack. Through this, Austin’s found a worthy thought partner—though, he admits he still loses time tinkering with the system. Read this for the full build process, plus his open-source compound knowledge plugin.

🎧 🖥 “AI Makes Building Products Easy. Knowing What To Cut Is the Hard Part.” by Laura Entis/Context Window: Instagram cofounder Mike Krieger now co-leads Anthropic Labs, where his team builds experimental products on top of Claude. On this week’s podcast, he tells Every CEO Dan Shipper why even when AI has collapsed development timelines from months to hours, the hard part hasn’t changed. 🎧 🖥 Listen on Spotify or Apple Podcasts, or watch on X or YouTube.

“Build Your Own Bloomberg Terminal With AI” by Brooker Belcourt: As a hedge fund analyst, Every’s head of financial services consulting Brooker Belcourt used to spend four hours writing previews of earnings reports per company, per quarter, for 40 companies. Today, his work is greatly compressed by AI tools ranging from a ChatGPT prompt that drafts the writeups to a Claude Code setup that reads his proprietary models, cross-references them against Wall Street estimates, and assembles everything into a custom dashboard he checks each morning. Read this for a step-by-step progression toward making the most of AI for investors.

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Build Your Own Financial Analyst With AI

Brooker Belcourt — 2026-03-27 03:00:00 -0400

by Brooker Belcourt

Midjourney/Every illustration.

We’re hosting a Custom Agents Camp with Notion on Friday, April 3, at noon ET. We’ll walk through the agents powering daily operations at Every, and give you the templates to start using them yourself. Plus, designer Brian Lovin will share how Notion uses custom agents and what they’re building next.

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When I was an analyst at a hedge fund, earnings season was a sprint that lasted a month. I had 40 firms to cover, each one reporting over a four-week window. Every earnings preview—the research brief laying out what to expect before a company’s quarterly results were announced—followed the same grind: Grab the data, update my financial model, and write up the takeaways. Four hours of work per company, minimum.

It’s a task that is begging to be automated by AI. The process is structured and repeatable, and the data sources are well-defined. But if you’ve ever pointed ChatGPT at a collection of data and gotten back a summary with basic math mistakes or that ignored important metrics of a company’s financial health, you know how disappointing the reality can be.

This kind of experience is why many investment teams give up on AI. They try it once, conclude the technology isn’t ready, and go back to the old way. What those teams don’t realize is that they are judging the entire technology based on the sophistication of one tool. It’s like giving up on all email after using the clunky Microsoft 365 browser product.

Over the past six months running AI consulting for finance teams, I’ve been walking clients through what developments in AI capabilities can now let us achieve: the same earnings preview—Shopify’s next quarter—at four levels of tooling, each one more sophisticated than the last. By level four, the system reads your model, applies your thinking about what makes a great company, and runs while you sleep. Here’s how to get there.

Level one: The custom GPT

This is where most investment teams start. You set up a ChatGPT project—a dedicated workspace where you can store instructions and upload documents—with a detailed prompt that tells the model how you want your earnings preview structured.

The prompt I use specifies everything: how to lay out the beat/miss analysis (where you compare actual results against Wall Street expectations), which financial metrics to prioritize, how to handle management guidance, and whether to source consensus estimates from the web or more premium data sources. I attach the Securities Exchange Commission (SEC) filings and earnings release directly to the project. Run it in thinking mode—where the model reasons longer before answering—and after about 15 minutes, you get a solid preview with web-sourced data, SEC citations, and a clear beat/miss breakdown.

But the output has quirks.

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The data connector Brooker uses to pull structured financials from SEC filings directly into the model
How one prompt now replaces hours of cross-referencing earnings transcripts by hand
The four-word command that runs an entire investment analyst workflow overnight

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Introducing Plus One: One-click OpenClaw Agents by Every

Dan Shipper / On Every — 2026-03-26 09:00:00 -0400

by Dan Shipper

in On Every

Midjourney/Every illustration.

TL;DR: We’re launching hosted OpenClaw agents that live in Slack and come pre-loaded with Every’s best tools, skills, and workflows. Setup requires one click. Join the waitlist—we’re taking 20 people off a week and scaling fast. Every subscribers get first access:

Secure your Plus One

OpenClaw has changed the way we work at Every.

We effectively have a parallel organizational chart of AI coworkers, each with a name, a manager, and real responsibilities. R2-C2, my Claw, triages and resolves bug reports, and has co-authored some of my articles. Iris writes marketing email copy for Anukshi Mittal, Every’s product marketing lead. Montaigne handles growth-related questions for Austin Tedesco, our head of growth.

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