Every (joshuarael@gmail.com)

How Anthropic Makes Claude More Reliable

Laura Entis / Context Window — 2026-06-18 12:00:00 -0400

Midjourney/Every illustration.

Living at the edge of AI is bittersweet. You can spend weeks building a workaround to a problem only for a frontier lab to swoop in and solve it for you in a more elegant, reliable way. Today, senior applied AI engineer Nityesh Agarwal explains how Anthropic’s dynamic workflows feature made his elaborate Claude setup look clumsy in retrospect, the Every team shares which corners of the AI frontier they’ve given themselves permission to ignore, and executive operations manager Jalaiyah Bolden walks through her step-by-step process for turning a Slack bot into a reliable coworker.

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Mini-Vibe Check: Dynamic Workflows

A closer look at how Claude Code coordinates multiple agents

When senior applied AI engineer Nityesh Agarwal built Every’s AI project manager Claudie, he spent days figuring out how to get around the model’s limited context window, or the cap on how much text an LLM can process at once—and the reason Claudie kept dropping key details. His solution: one coordinating agent that delegated tasks to fleets of subagents, which gathered data, made updates, and communicated with one another via local markdown files. The process was “a little bit hacky,” Nityesh says, but it worked.

If he were to build Claudie today, he could just use dynamic workflows, Anthropic’s feature for orchestrating large, multi-agent Claude Code tasks. Instead of deciding each step on the fly, Claude writes a reusable script that coordinates the work. It can assign tasks to many subagents and have them check each other’s work before reporting back the results.

Before dynamic workflows, trying to get Claude to reliably spawn reviewer agents was a persistent headache. Anxious about token spend, the model “would sometimes try to merge it all into one subagent,” Nityesh says, dragging down the quality of the results. Increasingly dramatic directives not to do this often went unheeded. Now, if you tell Claude you want three verifier subagents with dynamic workflows, Claude will write a script that generates three subagents every time.

Nityesh is grateful for the new feature, but watching weeks of work get negated by a single release was also disheartening. “I spent so many weeks building that other thing. Now it’s useless,” he says.

“But that’s the cost of being at the frontier,” he continues. “You need to be ahead of everybody else, and sometimes that means you need to throw away your past work.”

(Image courtesy of Anthropic.)

A dynamic workflows case study. For Spiral’s redesign, senior designer Daniel Rodrigues sent the writing app’s general manager Marcus Moretti a giant Figma file.

Marcus needed to convert the file into code. He did a pass in Claude Code, but the result had numerous errors. Before dynamic workflows, he would have flagged the mistakes in batches for Claude Code to fix—a repetitive, frustrating process.

Instead, Marcus asked Claude Code to set up a dynamic workflow that would review the Figma file section by section, extract all assets and design details, turn them into code, and check the results against the original file.

The Figma file had 11 sections, so Claude spun up 11 tasks, each with dedicated subagents. After running for a couple of hours, “it was not perfect,” Marcus says, but “it saved me a whole bunch of time.” Before dynamic workflows, each of the reviewer subagents would have been Marcus himself.

Try it yourself: For complex projects like a code migration, changing the programming language a product uses, or a major upgrade, dynamic workflows might be a good solution, Marcus says. To initiate the feature, you can simply type “workflow” in a Claude Code session, or include “ultracode” in the prompt.

Or test out Nityesh’s prompt for kicking off a dynamic workflow.

Permission to skip

Rapid-fire roundup edition

The pace of AI is unrelenting. Each week brings new model releases, benchmark results, and “paradigm shifts” that sometimes turn out to be incremental upgrades.

At Every, we do our very best to stay at the frontier—but for better and worse, we are human, which means we cannot run all night. Here, Every staffers share what they’ve given themselves permission to skip in order to, you know, sleep, touch grass, or run other AI experiments...

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The AI topics the Every team has given itself permission to ignore
How to turn a Slack bot into a reliable coworker
The internet’s reaction to Mistral joining the AI leaders at the G7 Summit

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Transcript: ‘Can GitHub Be for Everyone?’

Mike Taylor / AI & I — 2026-06-17 07:00:00 -0400

by Mike Taylor

in AI & I

The transcript of AI & I with Mike Taylor and GitHub COO Kyle Daigle is below. Watch on X or YouTube, or listen on Spotify or Apple Podcasts.

Timestamps

Introduction: 00:00:52
The agentic PR flood: 00:03:27
GitHub’s approach to helping open-source maintainers manage the surge: 00:04:33
What 14 billion commits means for code quality: 00:06:15
Moving from per-seat licensing to usage-based pricing: 00:08:03
Kyle’s dual role as GitHub COO and Microsoft’s chief marketing officer for developers: 00:09:45
Developer choice as competitive moat: 00:13:03
How to balance dogfooding your own tools with staying honest about the competition: 00:14:57
Hill climbing, frontier tuning, and solving the model-routing problem: 00:19:45
Kyle’s agentic communication hack: 00:24:45

Transcript

(00:00:52)

Mike Taylor

First thing I wanted to ask about—and we were touching on it yesterday—is that the demographics of your customer are changing, right? A lot of people who previously would never have used GitHub, or never used developer products before, are now using them. How has that changed the way you decide the product roadmap?

Kyle Daigle

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Loops for Non-coders

Laura Entis and Katie Parrott / Context Window — 2026-06-17 01:00:00 -0400

by Laura Entis and Katie Parrott

in Context Window

Midjourney/Every illustration.

AI can be exhilarating and destabilizing. Just when you think you have your setup figured out, a powerful new model drops—or, in the case of Anthropic’s Fable 5, gets abruptly disabled. Today, we explore this instability from multiple angles: Staff writer Katie Parrott maps the five stages of grief that accompanied the Fable ban and shares a practical playbook for the next time a model you depend on disappears, head of growth Austin Tedesco explains how loops are causing him to rethink his approach to working with AI, and GitHub chief operating officer Kyle Daigle tells AI & I guest host Mike Taylor how the company is responding to an agent-generated surge in commits.

‘AI & I’: Can GitHub be for everyone?

Today we’re releasing a new episode of our podcast AI & I. Head of tech consulting Mike Taylor guest hosted this week and spoke to GitHub COO Kyle Daigle about how the company is responding now that everyone—and their army of agents—can ship code.

The volume is extreme: Last year, there were 1 billion commits on GitHub. This year, that figure will safely exceed 14 billion, Daigle says, which puts GitHub in an important but delicate position: It must help developers handle agent-generated code without dictating which pull requests communities should trust or merge.

Watch on X or YouTube, listen on Spotify or Apple Podcasts, or read the transcript. And for a behind-the-scenes look at the making of the podcast, check out Mike’s piece on his decision to ditch standard-issue prep in favor of building and mock interviewing an AI version of Daigle.

Here are the highlights:

The developer versus non-developer distinction is disappearing: GitHub has long taken an expansive view of who counts as a developer, but AI has blown up the definition entirely. Legal, finance, sales, and marketing professionals are using the GitHub Copilot app to build prototypes and apps. “A lot of the folks that the industry would call knowledge workers, or just non-developers by trade, are using these tools,” Daigle says.
Agents can write and review code, but humans decide what ships: GitHub has built agentic code review and merge tools to help developers handle the surge of pull requests, but people who run open-source projects should ultimately decide which outside submissions they merge. “We want to provide tools,” Daigle says, “but really leave them in control.”
Daigle runs a daily loop on himself: In AI, a loop is a cycle in which an agent does work, evaluates the result against a goal or standard, incorporates feedback, and repeats the process until the task is complete or the output improves. Daigle uses the same workflow to improve his communication style—each day, an agent reviews a rolling seven-day window of his emails and Slack messages, identifies patterns, provides constructive feedback, and checks whether he incorporated its advice.

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.

Inside Every

Loops, loops, loops

“I’m super loop-pilled,” says head of growth Austin Tedesco. He’s not alone. Loops—which have AI tackle a goal through iterative cycles of completing a section of the task, reviewing the results, incorporating the learnings, and generating the next step—have become a hot topic of discussion here at Every in recent days...

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How to use loops for non-coding work
How to determine which tasks you need frontier models for
How to prepare for another model to disappear

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We Built Our Own Agent-native Tool. It Overhauled How We Build Software.

Stella Garber — 2026-06-16 03:00:00 -0400

by Stella Garber

Midjourney/Every illustration.

Stella Garber cofounded Hoop, an AI agent to help subcriber brands cut churn, after years at Trello watching what makes software stick. When her team’s customer discovery calls became a mess of scattered notes and competing interpretations, they built an internal AI analysis tool from scratch using Every’s agent-native architecture philosophy. It reshaped how they build their actual product. Plus: While you’re waiting for Fable 5 to return, we’ve compiled 13 copy-ready prompts based on the Every team’s workflows. Use them to plan, build, research, verify, and hand off complex work that runs for hours.—Kate Lee

Get the Fable prompts

It was Monday morning, and my cofounder Brian was reading from our agent’s weekly analysis of customer discovery calls. “Subscription retention,” he said. “Five separate brands mentioned it as their top priority, and none of them trust existing AI tools to touch it.”

Just weeks ago, unearthing an insight like this would’ve been nearly impossible.

At my pre-product-market fit startup, we’d all been speaking with prospects and trying to figure out the positioning for our product, but keeping track of everything we learned was a mess across founders, platforms, and mediums. To share what we learned during our Monday meeting, Brian would read notes in Slack, collect transcripts from Granola, and try to make sense of it all in Claude Code.

We couldn’t afford to be that disorganized. We’d recently launched Hoop, an agent that helps subscription brands reduce churn, and we needed to learn as quickly as we could. So we had to talk to as many potential customers as possible, then rigorously document and score each call to separate polite interest from genuine demand. Everyone on our five-person team was putting in the effort, but each of us had our own process, our own tools, and our own interpretation of what happened on each customer call.

So my two cofounders and I—none of us with “engineer” in our title—built an internal tool to fix it. What we didn’t expect was that the tool would change how we built our actual product for customers, too.

‘I should build something for this’

Our Monday meetings were so unmethodical because the information from our client calls was in different places depending on who had taken the call and whether they had notes based on Granola or another transcription tool. We had no way to see patterns and draw conclusions about what our potential customers wanted.

Justin, my cofounder and the resident product expert, built the first version of a tool to bring those notes together in under 10 hours over a few days, fitting it in around his other priorities.

Here’s how it worked: You’d upload a Zoom transcript, the tool would run the transcript through four or five prompts, and you’d get a structured analysis scored against the PULL criteria—a framework developed at Harvard Business School to help early-stage startups find product-market fit. The tool would also pull together all the conversations with a given prospect into a summary, so you could see the full arc of a relationship instead of just a snapshot from one call. Rather than digging through notes and transcripts, the tool gave us a consolidated analysis week over week to help us see what was working and what wasn’t.

Justin set up the app using tools we hadn’t used before: Next.js framework with ShadCN components for the user interface, Supabase for the database that compiled all the notes, Claude’s API for the analysis.

For Justin, who had studied computer science but wasn’t writing much code anymore, it was an opportunity to dust off his skills and build his confidence with AI-native coding. He started by designing and building the visual interface because he is the kind of person who gets frustrated when software doesn’t look right, even if it functions. He made sure that the look and feel of the tool matched our brand, and got the components (buttons, labels, menus) looking clean before he went anywhere near the data.

Only then did he go straight to the data. He had to make sure that the tool’s analysis of the customer conversations was better than what people were already producing on their own with Claude. Otherwise, we would never convince the whole team to use the same tool. So he created a prompt that he tweaked after manually reviewing the output several times and relying on Anthropic’s prompting best practices for Claude.

Still too much friction

The first version of the tool generated high-quality analysis, but too many parts of the process were still manual. You had to download the call transcript from Zoom, upload it manually to the tool, fill in the customer name and call type, and wait several minutes while it was processed. Then you’d create a link and share the analysis in Slack.

The team could search the transcripts and analysis in the tool, but it didn’t return good results. For example, I searched for prospects who’d had bad experiences with AI customer support tools and got no results back, even though I knew a head of customer experience had spent five minutes talking about how embarrassed they were by their AI sending off-brand responses to customers. The tool could only match the exact words in my query, not the meaning behind them.

And there was the classic adoption problem that we know all too well from our years at productivity tool Trello, where we’d previously worked. Justin’s tool was yet another place people had to remember to go, competing with Slack and Notion for our attention.

Going agent-native

Then we found the answer to our woes. Justin had been reading about agent-native architecture on Every. Instead of hard-coding a sequence of prompts that run in a fixed order, you give a model a set of tools and let it reason about how to use them. And instead of building a destination app that requires people to come to you, you bring the tool to where people already work, like Slack.

Justin gave Claude Code the link to the article and said that he wanted to build a system that aligned with those architecture principles. The agent needed two tools: one to upload and read a transcript, and one to add and edit a partner profile. With those in place, all users had to do was send a transcript to the app in Slack. The agent confirmed the partner name and call details, then uploaded the transcript, ran the analysis, created a summary page, and posted it to our user feedback channel.

Justin started checking everything he built against the agent-native architecture guidelines, not just the product-market fit tool. He’d go into planning mode with Claude Code, lay out a new feature, and send it alongside the Every article back to Claude Code and ask: “Where is this aligned, and where is it not?”

Sometimes he deviated from the guidelines when he didn’t think that users needed AI for a specific task. For example, the tool tracked LLM token usage and cost—useful information, but not something users needed to query. Exposing it to the agent would have only created confusion.

My turn in the codebase

I had a different problem. I needed to see the pipeline at a glance—who to follow up with, where each conversation stood—organized by people and stages, not just chronological call logs.

I opened Ghostty, a simple terminal app, copied the tool’s code so I could work on it locally on my laptop, and—hands a little shaky at the thought of directly editing code—fired up Claude Code.

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How a non-technical cofounder shipped an AI feature in a few hours
How and why their agent autonomously edited the database
The pricing insight buried in their call data

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I Interviewed an AI Version of GitHub’s COO—Then Spoke to the Real One

Mike Taylor / Also True for Humans — 2026-06-15 06:00:00 -0400

by Mike Taylor

in Also True for Humans

Midjourney/Every illustration.

I’ve attended many tech conferences as a participant and a speaker, but this year’s Microsoft Build, the company’s flagship developer event, was my first as a member of the press.

To quell the imposter syndrome, I tried an experiment before I sat down with GitHub chief operating officer Kyle Daigle, a true GitHub veteran who joined the company as a developer 13 years ago. I built a simulated version of Kyle—an AI persona distilled from his public writing, talks, and interviews—and asked the AI Kyle the same questions I planned to ask the real one.

I expected the output to be either eerily accurate or useless. It was neither—precisely what made it valuable.

Out of 12 questions, two responses were strong matches, four were partial matches, and six were material misses. To the simulation’s credit, when it lacked evidence, it said so instead of inventing something. Those holes were the most useful prep—they showed me what information wasn’t available on the public record, and therefore where I should spend my time in the live interview.

I’ve spent a lot of time talking to AI personas. My last startup, Ask Rally, was a virtual focus group tool. We found that AI is no substitute for the real thing, but in high-stakes scenarios, roleplay can help you get out of your own head, build confidence in your strategy, and avoid costly mistakes. We’re more predictable than we think, with some studies showing 85 percent accuracy in AI personas replicating real human responses.

What follows is the actual interview, with notes on what the simulation got right, what it missed, and where the comparison is interesting. We also went back to human Kyle—and his take surprised us more than the AI answers.

1. Expanding the definition of a developer

Mike: The demographics of the customer are changing. A lot of people who may never have used GitHub or developer products before are now using them. How has that changed the way you decide the product roadmap?

Kyle Daigle: GitHub has always had an expansive view of what a developer is. I started as a developer before I would have called myself a dev. I was writing code for myself, and I did not go to school for computer science. I was going to art school and wrote code to pay for art school.

That journey is important: I can create tools with a team and deliver them to people who want to build an app for themselves, their family, a startup, or a business. GitHub has serious developer tools used by the largest businesses, but when I look at something like the GitHub Copilot app, I see both developers running multiple projects and agent sessions and people on our legal or finance teams using it. Customers tell us the same thing. People the industry might call knowledge workers, or non-developers by trade, are using these tools to build little apps or assets.

Our focus is still very much on developers, but we want to make it easier for people to try writing code. There should always be an on-ramp into creating software, including through tools like the GitHub Copilot app.

Simulation note: A partial match. The AI Kyle correctly predicted the real Kyle’s thesis: AI is expanding who gets to build software—and even offered a framework to test this that the real Kyle plausibly could have mentioned: “The design test I keep coming back to is ‘no net new behavior.’ New capabilities should fit into the places where software work already happens.” But it couldn’t produce the art-school story or the legal-and-finance-teams example that made the real answer compelling.

2. Helping maintainers handle a flood of pull requests

Mike: How do you help developers deal with the burden of all the extra pull requests? Open source maintainers I talk to are drowning. What needs to happen to help them?

Kyle Daigle: For developers generally, we are building tools like Copilot code review. It is now agentic, so it finds more novel vulnerabilities, and you can comment and have the agent implement a change. Code review is an overlooked way to get pull requests into a state where they are much easier to review.

Agentic merge is another example. A pull request can be almost ready, but there are still manual steps to finish processing it. Instead, I can define what GitHub Copilot is allowed to do and tell it to merge the pull request, wait for CI, and wait for policies.

Open source has a unique set of needs because maintainers do not control who sends changes. We are focused on giving maintainers more control: whether they want to accept pull requests, who they want to accept them from, and how much work a contributor needs to do to demonstrate that a contribution will be meaningful. Every community is choosing a slightly different approach. GitHub wants to provide the building blocks and leave maintainers in control. If a standard practice emerges, we can cement a system around it, but we do not want to impose one first.

Simulation note: The AI got the governing principle right and the substance wrong. Its best line—“The system should give maintainers explicit rules and guardrails, not just a larger inbox”—is something the real Kyle could have said. But it named zero products. Copilot code review, agentic merge, contributor acceptance controls: all invisible to a persona built from public material, because they weren’t publicized until the event.

3. Growth in agent-generated activity

Mike: You have a front-row seat to this new agent economy. You said publicly that you have had more pull requests submitted in a month than in all of last year. How are those stats exploding?

Kyle Daigle: We are seeing much more activity on GitHub. Last October at GitHub Universe, we shared that there had been one billion commits on GitHub for the full year. We are on track for 14 billion if growth is linear this year, which it will not be. In March, 17 million pull requests were created by agents alone.

There is much more code being created. Sometimes people dismiss it as slop: code pushed up that nobody cares about. That is not really true. We are leaving the super-early-adoption stage. We are not at the peak, but we are climbing the hill and learning what we can build when it is not just Kyle building, but Kyle plus one, two, or N agents using my skills, resources, and context.

We are investing heavily in preparing for the next wave of growth because this does not seem to be growing and then plateauing. No matter where people build or what tools they use, the code ends up on GitHub for sharing and collaboration. We need to support everyone’s agent moment, not just GitHub Copilot.

Simulation note: Miss. The AI recited last year’s public numbers—it can only re-serve the stats you already have.

4. Business models for always-on agents

Mike: How does the business model change?...

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The questions where the AI Kyle and the human Kyle most diverged
How GitHub’s business model could evolve in a world of agents
The agent loop that Daigle has set up with email and Slack

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Fable, Disabled

Every Staff / Context Window — 2026-06-14 08:00:00 -0400

by Every Staff

in Context Window

Midjourney/Every illustration.

On Friday night, the U.S. government banned Anthropic’s distribution of Fable 5 and Mythos 5 to non-U.S. nationals. In response, Anthropic disabled Fable for all customers. As of this writing, the situation is ongoing.

It remains to be seen how it will play out, but I can already see the difference in my AI usage. Here’s a graph comparing my Claude and Codex usage before and after the ban (the “event” below):

Image courtesy of Dan Shipper.

Before the ban, I was split about evenly between Claude and Codex. After (and after a period where I was using neither because I was sleeping), I switched almost entirely to Codex.

My guess is that this ban is not going to last very long. It seems to rest on a misunderstanding between the government and Anthropic about which kinds of guardrail bypasses are fixable and what counts as an adequate solution. Anthropic believes the jailbreak identified by the government is narrow rather than universal—it surfaces only minor vulnerabilities that other public models are already susceptible to. The government apparently believes otherwise. Because both sides are highly incentivized to work this out, I’d bet that the ban is revoked after a few days—and demand for the newly returned Fable skyrockets.

However, this kind of move is extremely disruptive and distracting for people working at Anthropic. The only comparable scenario I can remember is Sam Altman’s firing, which was resolved relatively quickly. Even though Altman was reinstated, I do think the chaos disrupted the company’s momentum for months afterward.

We’ll keep a close eye on whether the same is true here.—Dan Shipper

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The Moral of Fable

Dan Shipper / Chain of Thought — 2026-06-12 15:00:00 -0400

by Dan Shipper

in Chain of Thought

Midjourney/Every illustration.

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As any child who’s heard Aesop knows, the point of a fable is its moral. We see the consequences of falsely crying wolf. We learn why slow and steady wins the hare race.

So what is the moral of Claude Fable 5, Anthropic’s newest model, which this week we called the best coding model in the world?

For engineers, the case is easy to make. For many knowledge workers, though, Fable might feel incremental. You may have one-shotted an impressive demo or two, but you’re probably not using it for your day-to-day work. Why would you? It costs twice as much and the results aren’t that much better.

But there is a certain class of developers who are feeling Fable’s full force. These are people like Cora general manager Kieran Klaassen, who are suddenly churning through his backlog of bug fixes and feature requests in hours instead of days. “This is my favorite model ever,” he told me.

What’s the difference between Kieran and everyone else? The difference between Kieran and most people using Fable isn’t simply that he’s a developer but that he’s at Level 7 or 8 on our scale of AI use: He delegates whole projects, lets agents work asynchronously, reviews the results, and feeds what he learns into the next run. In other words he writes—dare I say it, loops—not prompts.

For now, this might make Fable seem like a tool for developers. But in AI, developer workflows have a habit of spreading to the rest of knowledge work. Claude Code started as a developer tool, and now the same methodology is being used for everything from slide decks to spreadsheets inside of Cowork and Codex.

If you’re not feeling Fable’s force, that’s probably because you haven’t yet started to treat your work like gardening ...

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The gardening metaphor that explains who gets the most out of frontier AI
How Fable helps usher in the era of the individual
How the technology gap between the cutting edge and everyone else raises real questions

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AI Everywhere, All at Once

Laura Entis / Context Window — 2026-06-11 10:00:00 -0400

by Laura Entis

in Context Window

Midjourney/Every illustration.

Anthropic’s Mythos-level Fable 5 is here, which means we’re experimenting with how to get the most of the super-capable, token-hungry model. Today, four Every team members share their approaches, plus we package eight Fable workflows into prompts you can test out for yourself. Elsewhere, Monologue general manager Naveen Naidu reports from the ground at Apple’s developer conference on why Siri is—wait for it—finally good, and head of platform Willie Williams argues the one thing even the most powerful LLMs can’t do is vibe.

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Inside Every

Fable 5 versus everything else

Anytime there’s a major new model release, there’s pressure to reconsider your AI setup. Or, if you’ve just come out of a meditation retreat, maybe your entire life.

Should you swap out your preferred model for the newest arrival? Is the new model sufficiently better to make the switch if you don’t like the harness?

Fable 5 has thrown the Every team into a new round of existential questioning. It’s an obvious first choice for certain projects—those that are large, complex, and

delegable—and an arguably worse, too-expensive fit for others.

After a week of testing the model, most of us at Every have settled into a two-prong approach: Fire up Fable for ambitious assignments, let it do its thing, and reach for your favored coding agent for smaller-scale, iterative tasks.

Head of growth Austin Tedesco’s breakdown: Fable 5 demands “a very different way of approaching knowledge work,” one that requires fine-tuning exactly what outcomes you want from the model, what information it needs to execute, and trusting it enough to sit back and let it cook.

So far, Austin’s reserved Fable 5 for “rocket launcher” projects that can run for four-plus hours, like building an NBA front office simulation game, or researching and executing growth experiments overnight. With the model, he typically uses compound engineering’s LFG flow, which has the agent brainstorm, plan, work, review, and repeat.

The Codex app remains his daily driver. Austin has a setup where, when a meeting ends, Codex retrieves the action items, decides whether it can handle any of them on its own, and, if so, starts a new thread to do the work. He also uses Codex with the Spiral MCP for drafting Every’s social copy, internal strategy documents, and most same-day tasks.

Austin’s current setup. (Image courtesy of Austin Tedesco.)

Cora general manager Kieran Klaassen’s breakdown: The way Kieran likes to work—an “AI sandwich” in which he sets the task, the machine executes, and he reviews the results—is the ideal setup for Fable 5. His process hasn’t changed, but Fable 5’s superior abilities on complex, multi-step assignments means the setup works a lot better than it used to.

Fable 5 has become Kieran’s default for the middle of the sandwich. For the “bread” stages, he usually works in Cursor, where he brainstorms and polishes. And for smaller independent tasks he can assign to an agent and review later, he uses Codex CLI, Claude Code CLI, or Cursor managed agents.

What made the Kieran cut. (Image courtesy of Kieran Klaassen.)

Head of platform Willie Williams’s breakdown: Willie is still working out his setup. Fable crushes other models on Every’s Senior Engineer benchmark, but it’s too slow and token-hungry to be a good collaborator. “Do I take the downside of a slightly less capable model, knowing that when we go to the iteration portion of the relationship, it’s more enjoyable to iterate with?”

For now, the Codex app is still where he does most of his daily work. He has spent a lot of time building his setup inside the app: “I can have one thread talk to another thread that talks to another thread—it makes for a nice workflow where I always know what’s going on.”

He plans to test Fable 5’s limits with tasks he’d give a senior engineer, such as reviewing a full codebase alongside a long list of product tickets and looking for an elegant fix that could solve several complaints at once.

Head of tech consulting Mike Taylor’s breakdown: The second there is a superior model, Mike reorganizes his workflow around it. Mike plans to put Fable 5 through its paces with tasks built around ambitious loops, such as having it write a technical book section by section from a table of contents, checking each section against editorial guidelines before continuing. “I will still use Codex, but mostly out of obligation that I should try all the different things,” he says. “If I weren’t working at a company where we need to have an opinion on these things, and thus need to try everything, I would probably just be using Fable.” (An AI early adopter, Mike is happy to shell out for access to the best new models—he already pays for his own Claude Max plan for personal projects.)

One giant caveat: Mike discovered, and alerted the rest of the consulting team, that Fable cannot be used for work done on behalf of the team’s clients. Consulting work often includes confidential information, and Fable’s model environment may retain context beyond a specific task, violating existing NDAs.

A Fable prompt starter pack

We put eight of our best Fable workflows into a copy-ready prompt library, including:

Four prompts inspired by Anthropic Labs head Mike Krieger
Four workflows tested by the Every team
The full transcript from Dan Shipper’s interview with Mike with insider Fable tips
Easy downloads to share with your agent

Get the full prompt library

To learn more, join us tomorrow at 12 ET for our Fable 5 Camp.

Signal

An Apple AI comeback?

For years, Apple has been...

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The demo that made one of Every’s engineers change his mind about Apple
The design tool that Every’s team uses to make high-quality custom graphics for articles
Why LLMs still can’t do vibes

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How to Get the Most Out of Fable 5

Laura Entis / Context Window — 2026-06-10 17:00:00 -0400

by Laura Entis

in Context Window

Midjourney/Every illustration.

We’re hosting two live camps for paid Every members to put the latest frontier tools to work: Fable 5 Camp this Friday, June 12, followed by a rescheduled Codex for Power Users Camp on Friday, June 26. If you already registered for this Friday’s camp, your seat is saved for the Fable deep dive, and you can RSVP for the Codex Camp.

‘AI & I’: Fable 5 upends how we build

Today, we’re releasing a new episode of our podcast AI & I. Dan Shipper sits down with Mike Krieger, the cofounder of Instagram and head of Anthropic Labs, to discuss what it feels like to build with Fable 5, a model powerful enough that it’s forcing him to rethink the very definition of productivity, engineering, and creative agency.

As someone who built one of the most popular consumer apps in the pre-GPT era and has had access to Fable 5 for months, Krieger has a rare vantage point on what the radical compression of the product development arc means for builders.

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

Here are the highlights:

More work is happening overnight. Fable 5 is the first model capable enough that you can hand it a complex task, walk away, and trust it will be completed by morning. When it hits an obstacle—a remote service goes down, say, or a tool stops working—it writes a workaround and forges ahead. That resilience has changed the daily rhythm of Krieger’s work: He now ends his workday by briefing the model on what needs to get done while he sleeps, rather than sitting down to do it himself.
The gap between what’s in your head and what exists in the world is closing. Given access to Fable 5 and a set of internal MCPs, an Anthropic recruiter described the experience as, “The first time in my life where I feel like the thing that’s in my head and the thing that exists in the world are right next to each other. I can just do it.” This is the most meaningful thing about the new model class, Krieger says—it allows non-engineers to create the exact products they need to get more done.
Software engineering is dead. Long live software engineering. Engineers now spend less time writing code and more time setting direction, reviewing what their AI agents have built, and making judgment calls when something breaks in production. The divide between product managers and engineers has blurred. “There is a feeling of loss, I think, in some of the better engineers that I talk to, as well as the feeling of, ‘Oh my God, but I can do insane amounts of work now at the same time.’ We’re holding both ideas in our heads at once,” Krieger says.
All eyes are on verification. If we can delegate more to the model, it becomes more important to check what it has built works in practice. Krieger’s approach combines regression testing on known workflows, visual checks—including giving the model video captures of its own work so it can catch animation glitches screenshots would miss—and mock backends for anything too complex to test live. When a bug arrives via Slack, Fable 5 makes the fix, posts the pull request, then follows up hours later.

How the Every team is using Fable 5

The easiest way to be disappointed by Fable 5 is to use it as if it were GPT-5.5 or Opus 4.8, smart models that require specific instructions and careful prompting for the best results.

Instead, Fable 5 feels like working with a capable coworker—at least that’s Every’s consensus after a week of testing.

“It feels like you have an engineer on your team that you just gave a problem to, and they’ll figure it out,” says Cora general manager Kieran Klaassen.

That means, to get the most out of Anthropic’s first Mythos-class model available to the public, you have to think like a manager: Equip the model with context, goals, and a way to verify the work, then step aside. It may even stumble on a solution you hadn’t considered.

Not every task deserves this treatment. Smart colleagues don’t come cheap, and neither does Fable 5. Here’s how to get the most out of this powerful new model and some of the workflows the team is using.

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

What kinds of tasks are best for Fable 5
Why Every’s head of growth was unsatisfied with his first Fable 5 use—and how he changed his prompt to get a better output
How Kieran Klaassen is using Fable 5 to help him make smarter fixes faster to our AI-native email application, Cora
Plus: Prompts to copy all of their Fable 5 workflows

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My Editor Caught Me Sounding Like AI. Now AI Catches Me First.

Katie Parrott / Working Overtime — 2026-06-08 18:00:00 -0400

by Katie Parrott

in Working Overtime

Midjourney/Every illustration.

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Before a recent one-on-one with Kate Lee, Every’s editor in chief, I opened our shared document and found a list of my own writing fails staring back at me. My drafts had picked up too many of the AI tells that both I—and you—know how to spot from across the room: the symmetrical sentence structures, the little rhetorical throat-clears, the phrases that sound profound on first pass but on closer inspection contain nothing but air, and those pesky sets of three.

The worst part was that I should know better. I am the person at Every who writes about writing with AI while using AI to write about writing with AI. I have custom agents, style guides, editorial workflows, and an apparently bottomless appetite for turning every lesson into a system. And still, I had let the machine’s smoothness pass for my own judgment enough times that my editors felt the need to intervene.

After the meeting, I did what I generally do when I learn something new, embarrassing or otherwise: I baked it into documentation for my agents. I opened the notes, pulled out the patterns Kate had flagged, and listed them in a new skill called /guardrails, which turns any agent I write with into an exacting editorial specialist that keeps me honest.

I’ll never be completely done with /guardrails, or any of the review skills like it that I’ve built, because my human tics and tendencies will move around like a squirmy toddler. But I’d rather make new mistakes than keep repeating the old ones. Review skills are the mechanism by which I do that. They’re another form of editor, one that can catch a draft’s more annoying weak spots before they become a human editor’s problem.

The start of my guardrails skill, where I’ve compiled all the particular ways that content I submit can fall below par. (All images courtesy of Katie Parrott.)

Writing with AI tends to be portrayed as a bargain: The machine does more, so the human does less. But in my experience—a microcosm of Every CEO Dan Shipper’s argument in “After Automation”—it changes what the human does instead of reducing the workload. I have to be clear about defining my standards so a model can understand them. That creates more work—but it helps me understand them better myself.

Setting up reviews like /guardrails takes time, attention, and a certain comfort with a tool like Codex or Claude Code. But once the reviewers are in place and working, I can spend more of my time pushing the draft from good to great. My drafts are now much cleaner and my own preferences are less of a mystery to myself, because I’ve had to think and talk about them enough that they’ve worn new grooves into my brain.

I’m going to show you a few of the reviewers I rely on and what goes into them (I’ll share a set on Every’s GitHub along with this piece). But it should serve as an example, not a blueprint; the special sauce of this process comes from setting and enforcing your own collection of style requirements.

Skills rule everything around me

In the beginning of any good guardrail system, there are skills.

At the mechanical level, a skill is a Markdown file with instructions inside it. Practically, it’s a way of packaging judgment. When I invoke the guardrails skill, I am asking the model to read a draft through a set of lenses: Look for AI tells, vague claims, hedges, limp openings, and all the little ways a zombie draft can pass as finished without a pulse.

I’ve become fanatical about naming conventions. After all, skill names have to be sticky enough that you remember them when you need them—although this gets less true with every model release, as AI becomes better at deciding which tools it needs to do the job. Still, “assess narrative momentum” sounds like a task someone puts in a project management tool shortly before everyone involved loses the will to live. Instead of clinical descriptors, I’ve given my more editorial skills their own personas: Sorkin is a reviewer with a job. He wants to keep the piece walking and talking, not mired in unnecessary specifics. Similarly, Mom wants to know where a reader who’s not as AI-pilled as I am might get lost. Asshole wants to attack the weakest version of the argument, which is annoying because sometimes the weakest version of the argument is the one I wrote.

Each of these reviewers asks a different question. Together, they give me a way to pressure-test a draft before I hand it to a human editor whose attention I would prefer is spent on problems only a human editor can solve. Our brains belong on the piece’s angle, claim, storytelling, and audience fit. You know, the fun stuff, with some stakes attached.

Running the guardrail gauntlet

Here’s an image to give you a sense of what a typical final review looks like before I hand a piece to an editor:

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How Katie uses skills to review her draft before sending to an editor
The exact skill Katie uses to catch bloat in a draft
How Katie uses a committee of reviewers to catch different aspects of style, from tension to humor
Plus: Download Katie’s draft checker kit from GitHub, including all of her skills

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AI Is Ready. Organizations Aren’t.

Every Staff — 2026-06-06 15:00:00 -0400

by Every Staff

Midjourney/Every illustration.

Hello, and happy Sunday! This week the consulting team published two practical guides. Mike Taylor built on engineer Steve Yegge’s viral post to map the eight levels of AI adoption—with sample prompts and signals for when to move up—and Natalia Quintero (who’s talked to leadership teams at hundreds of organizations) laid out a foolproof five-step process for executives rolling out AI across their companies. Covering Microsoft Build, Mike argued that enterprise adoption lags the news cycle—a gap he sees up close with the enterprise clients he advises. He also made a counterargument to Dan Shipper’s essay about the future of work, “After Automation.”

Spiral 4.0 shipped this week: Every’s writing tool can now draft in your voice from inside any agent, with a price cut to match. Elsewhere, Figma’s Matt Colyer makes the case that the SaaSpocalypse is overblown, designer Daniel Rodrigues shares a two-tool image generators workflow, Monologue general manager Naveen Naidu has a system for making coding agents more efficient with custom local skills, and the team names its most annoying model output.—Kate Lee

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Knowledge base

“The Eight Levels of AI Adoption” by Mike Taylor and Laura Entis/Guides: A framework mapping every stage of AI adoption, from Level 1 (a chatbot you ask and it answers) to Level 8 (an orchestrator agent that runs a team of sub-agents), with example prompts and guidance on when to move up. A higher level isn’t automatically better—the right level for a task depends on how much you trust the AI to run without intervention and how costly a mistake would be. A companion essay lets you figure out which level you’re on. Read this for where you stand today and what it takes to move up a level.

“An Executive’s Guide to Implementing AI” by Natalia Quintero/Guides: AI adoption isn’t being held back by the models—it’s the organization. Natalia Quintero, head of Every Consulting, gives executives who’ve bought the tools but aren’t seeing returns a five-step framework, laid out as a 60-day plan—with a companion essay that previews it. Read this for the five steps and how to run them.

“How Microsoft Is Building for a World of Metered Intelligence” by Mike Taylor/Also True for Humans: Reporting from Microsoft Build, Mike Taylor argues that Microsoft is the first big company to design for a world where intelligence is metered and the era of subsidized AI subscriptions is ending. Its response includes automatic model routing, a laptop that runs AI locally, and cheaper, smaller models. Read this for a ground-level look at AI’s post-subsidy era.

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How Microsoft Is Building for a World of Metered Intelligence

Mike Taylor / Also True for Humans — 2026-06-05 13:00:00 -0400

by Mike Taylor

in Also True for Humans

Midjourney/Every illustration.

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As I rode in my Uber to Microsoft’s annual Build conference on Monday, I fondly recalled a time when you could get anywhere in San Francisco for $5. Those days are long gone. Venture capitalists lost their appetite to supply unlimited funding in a viciously competitive market, and Uber needed to show a path to profitability ahead of its 2019 IPO.

There are signs that the “$5 Uber era” of LLMs is over now, too. AI labs are subsidizing subscriptions to the tune of thousands of dollars, which can’t continue forever. This year Anthropic, OpenAI, and SpaceXAI are all going public—and like Uber seven years ago, they’ll need to take a hard look at their books. On June 1, the eve of the event, Microsoft sparked outrage by switching to token-based billing on GitHub Copilot. Some users said their bills jumped from $39 to over $3,000 per month.

Rather than backtracking on billing, Microsoft used the conference stage in California to make the case for using AI more pragmatically in the face of rising costs. I came away from the event thinking that Microsoft is the first company to get real about a world where intelligence is available on tap, but constrained by how many coins you can put in the meter. Here is what the company’s vision looks like in practice, and what it might tell us about how we’ll be paying for and pricing AI in the future.

Intelligence on and off the meter: A product approach

In his opening speech, CEO Satya Nadella addressed pricing concerns head-on. He promised “unmetered intelligence to every desk and every home,” an AI-era update to Bill Gates’s vision of “a computer on every desk.”

Microsoft CEO Satya Nadella promised “unmetered intelligence to every desk and every home.” (All images courtesy of Mike Taylor.)

The most tangible way to experience that vision is with the RTX Spark, a new laptop Microsoft designed for AI workloads with Nvidia. The device is able to run a medium-sized 128-billion-parameter model locally (frontier models are in the trillions of parameters) so developers can get a lot of work done without paying a penny for tokens. Microsoft is taking advantage of the fact that the leading open-source models like Kimi-K2.6, which have a trillion parameters, are too big to fit on most laptops, and is betting that budget-conscious coders might not mind being a year or two behind the frontier and use a smaller model. The device will be released in the fall.

The RTX Spark laptop follows earlier feature announcements that show that Microsoft wants to decrease switching costs for customers by being the place where you can use any model, agent, or harness. The laptop has a rebuilt smart terminal app that allows you to run any coding agent harness and has adopted popular terminal commands from the Mac ecosystem to make the shift easier for developers.

Even the GitHub Copilot Desktop app, also released at the conference, makes it easy to switch providers between OpenAI-built, Anthropic-built, and local open-source models running on your device.

When questioned about the affordability of agentic coding, Mario Rodriguez, GitHub’s chief product officer, cited the automatic model routing feature in GitHub Copilot, which can delegate less complicated tasks to cheaper models. In my interview with Kyle Daigle, GitHub’s chief operating officer, he lamented that developers tend to choose “the model of the day, or week, or hour,” even when the task doesn’t merit that kind of power. A person probably will not manually switch to a cheaper model for that final step, “but the tools could.” I’ve also long argued that not every task needs to be done by a frontier model.

I get the feeling the team built this model router feature for themselves after facing the same problem everyone else is right now—Microsoft itself has been cancelling Claude Code licenses to reduce costs.

Features like automatic model routing show that Microsoft understands how runaway costs hurt enterprises that need tighter control over spending. The AI labs won’t let large companies buy highly subsidized individual “Max” plans, so big companies end up paying full freight on every token they burn. One that wasn’t properly monitoring usage is rumored to have spent an eye-watering half a billion dollars on Claude tokens in a single month.

That wasn’t the only news that day: Microsoft’s research lab, led by Mustafa Suleyman, released a set of new (cheaper) smaller models spanning image, voice, transcription, coding, and reasoning.

Tackling costs through model optimization

But when you don’t use the latest models to save cost, there’s a higher risk of making a costly mistake. One answer was a phrase I heard over 100 times at the one-day event:...

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What Satya Nadella called “greatest IP” that a company could have
Why Mike believes Microsoft built a specific feature for GitHub Copilot
Which famous AI builder made a surprise appearance at the event

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Why We’ll Still Be Employed When AI Can Do Everything

Laura Entis / Context Window — 2026-06-04 14:00:00 -0400

by Laura Entis

in Context Window

Midjourney/Every illustration.

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Launch

Spiral 4.0

Today we’re launching Spiral 4.0, which writes drafts in your voice from idea to line edit. Spiral has a new MCP alongside the existing CLI and API, so any agent or workflow can write in your voice too. For teams, we’ve expanded workspaces, which let you share styles, prompts, knowledge—and now chats and drafts. Finally, Spiral has a new pricing model: We’ve switched from session limits to token limits, so costs match your actual usage rather than how many times you opened a new chat. A vast majority of users will end up paying less: Personal plans now start at $15 a month—down from $25—and team plans are $25 per user, down from $35.

Try Spiral 4.0

Signal

Enterprise AI product roadmaps are hard

Microsoft is moving fast. Three months after OpenClaw came out in November 2025, Microsoft CEO Satya Nadella described it as a “virus”-like security risk. By May, the company’s “Project Lobster” was internally testing “ClawPilot,” an OpenClaw-based desktop environment. This week at the Microsoft Build conference, the company released Scout, a personal agent for work built on OpenClaw. For a company employing 100,000 engineers, this is blindingly fast. Unfortunately, it may already be too late.

The Google Trends graph for the term “openclaw” shows search interest spiked in January and began its descent soon after. (Screenshot courtesy of Mike Taylor.)

OpenClaw search traffic spiked in early January, after everyone had a chance to experiment with Opus 4.5 over the holidays. The sharp rise in interest died down almost as quickly as it took off, helped along in early April by Anthropic ending support for subsidized Max plan usage—thereby forcing everyone to scramble to get OpenClaw working on cheaper models.

This doesn’t mean OpenClaw is dead; the open-source project saw a recent uptick in download and is still under active development, with millions of dollars of patronage from OpenAI, which hired its creator Peter Steinberger. AI agents as a category aren’t dead, either, as traffic has moved to other agents like Hermes, Google has just rolled out Gemini Spark (first announced last month at its I/O developer conference), and Claude and Codex have both adopted more agentic features inspired by OpenClaw.

That said, it must be tough to manage enterprise AI product roadmaps these days. You do everything right, watch the latest trends, pivot your focus to supporting new tools and making them secure in enterprise environments. You move mountains to explain to stakeholders why this is a good idea. You plan the keynote of your big conference, which has to be scheduled months in advance. Then a month after the internal beta (just three months since the tool went viral), you’re already behind the news cycle. Everyone has moved onto the next shiny thing. You go back to the drawing board and think “maybe next time, we’ll just announce it on X.”—Mike Taylor

Log on

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

Upcoming camp

Compound Engineering Camp: On June 5, Cora general manager Kieran Klaassen and Trevin Chow host a one-hour walkthrough of compound engineering, the AI-native development workflow Every uses to ship products. Learn more and register.
Codex Camp: Our Power User Guide: On June 12, Dan Shipper and the Every team host a two-hour live walkthrough of the Codex power-user guide—setup, workflows, and Codex-native app development. Learn more and register.

Steal this workflow

Make your agent more efficient with custom skills

These days, Monologue’s general manager Naveen Naidu spends most of his time in the Codex app with Fin—formerly Intercom, a customer support platform—open in the coding agent’s in-app browser. Working from a repository-local project, he has Codex investigate the customer issue displayed in the browser, create a bug report in Linear, link the Intercom ticket to the Linear issue, and draft a reply to the customer with information about the bug report—all without having to leave the app.

Fin has an MCP with 13 common actions, like searching conversations or reading and writing messages. Naveen’s workflow required a more specific one: Turn the active Fin conversation into a markdown file the coding agent could read.

Here’s Naveen’s workflow for creating a more focused setup ...

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Naveen’s workflow for making his agent more token-efficient
A counterpoint to “After Automation” from inside Every
The AI conversational tics that irk Every team members

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Spiral 4.0 Goes Agent-native

Marcus Moretti / On Every — 2026-06-04 10:00:00 -0400

by Marcus Moretti

in On Every

Figma/Midjourney/Every illustration.

TL;DR: Spiral v4 just shipped with four major updates: a style engine that generates writing indistinguishable from your own 87 percent of the time, agent-native access via MCP, CLI, and API, team workspaces for writing in a shared voice, and a $10 price drop, bringing personal plans to start at $15 a month. Spiral will continue to be free for paid Every subscribers along with access to all our tools and content.

Try Spiral 4.0

Today we’re announcing a number of updates to Spiral, the writing partner for you and your agent. Spiral is built by writers for writers, to help you from idea to line edit, matching your writing style throughout.

The highlights:

With stylometry (or the study of writing styles), Spiral now sounds more like you. We’ve built a new Style Engine from the ground up, so Spiral computes your writing fingerprint and picks relevant samples for new drafts.
Use Spiral wherever you do work. With a new MCP, plus our existing CLI and API, Spiral can step in if you’re underwhelmed by your agent’s writing output, or need good writing in any workflow.
For teams, use Spiral to speak with one voice. Team workspaces let you share styles, prompts, knowledge, and now chats and drafts.
And finally, we’ve given Spiral a new coat of paint and logo, designed by Daniel Rodrigues. The primary brand font is now Edgar, from Frere-Jones Type.

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Opus 4.8 Is Smart Enough to Get in Your Way

Laura Entis / Context Window — 2026-06-03 18:00:00 -0400

by Laura Entis

in Context Window

Today, we update our Opus 4.8 Vibe Check with a Pulse Check featuring perspectives from more team members, Dan Shipper sits down with Figma’s Matt Colyer to unpack why AI hasn’t killed professional design services, and Every senior designer Daniel Rodrigues shares the two-tool AI workflow he uses to get precise, visually stunning results.

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‘AI & I’: The limits of chat-based design

In a new episode of our podcast, AI & I, Dan talks with Matt Colyer, Figma’s director of product management for developers, about the limits of chat-based AI agents for design and why the rise of vibe-coded everything is, despite what you might have heard, a boon for the company.

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

Here are the highlights:

The “SaaSpocalypse” narrative has it backwards. AI agents turn anyone into a vibe coder, kicking off investor panic that traditional software-as-a-service (SaaS) companies like Figma would cease to justify their cost. Colyer isn’t worried: AI has exponentially expanded the developer base, while underscoring how difficult it is to create a vibe coded version of Figma that works as well or as reliably as the real thing. He’s vibe coded multiple agents to do stuff like handle his emails, but the maintenance costs piled up quickly and never seemed worth it. “I’m buying more software these days than I ever did before,’” he says. “‘I’m just going to pay somebody else to run my agent for me.’”
Figma is embracing agents. The company has launched an MCP server—a standardized interface any AI tool can plug into—that allows you to approach design work from two directions. “Code to design” takes a live web page and reconstructs it on the Figma canvas, so you can manipulate the elements directly; meanwhile, “design to code” flips the process by packaging a Figma design and giving it to an agent, which makes changes for you via pull request.
There’s a ceiling to chat-based generative design. Great design hinges on a diamond-shaped process: First you diverge, or generate lots of ideas, and only then do you converge around the most promising options. Text-based chats are inherently linear and therefore bad at divergence; the setup forces you to select an option and iterate on it. Agents are already good at the task-completion workflows Figma supports today, but the divergent, exploratory part of design remains unsolved across the industry. Colyer is interested in dividing the process so specialized agents handle the divergence by pushing you to expand your thinking, while another set filters through the options to identify a single path forward. “Even the best agents, the command-line agents, don’t have the ability to do those workflows,” he says. “That’s where I see the future of design and product thinking.”
Agents can produce so much so quickly. They’re less good at determining whether any of it meets a company’s values or design standards. Colyer isn’t sure the best way to close this gap—maybe it’s a video walkthrough, a screenshot, or a trusted review agent—but for good design to scale, AI needs to play a larger role in evaluations.

Pulse Check: Opus 4.8 is the best tool for the right job

Five days ago, we called Anthropic’s Claude Opus 4.8 the best Claude model yet for writing and serious engineering, and said we’d switch to it from GPT-5.5 if the Claude app ever caught up to Codex. After a work week of more testing, we’re still an Opus 4.8 admiration society, although the results are a bit more mixed as people from different disciplines have had a chance to weigh in.

Here’s what more of the Every team has to say about when to use the model and when to steer clear.

Key takeaways

Reach for Opus 4.8 when productive friction improves the work. It’s good at tracking nuance, questioning a weak framing, and staying with a complicated problem. But the same instinct can become stubbornness, misplaced caution, or confidence in a wrong interpretation.
Give it the long, messy jobs. Opus 4.8 earned its strongest reviews on sprawling source material, long-running threads, difficult creative work, and complex coding tasks. For routine questions and clearly scoped work, its slower pace and higher token burn can wipe out the quality gain.
Do not rebuild your workflow around it yet. Even teammates who preferred Opus’s answers kept reaching for GPT-5.5 in Codex because speed, context, and a better-connected app outweighed model advantage.
Double-check security warnings. Two independent accounts reported that Opus invented a prompt-injection concern. Until that failure is understood, ask it to show the evidence behind a warning before you act on it.

The Reach Test, part II

Arielle Shipper, head of operations 🟩

Arielle Shipper, Every’s new head of operations, has spent the last few weeks on a discovery tour. She used Opus 4.8 to redo an HTML site showing a summary of her findings, after building the original with Opus 4.7. She noticed meaningful improvements: 4.8 distinguished between two similarly named pages in Notion without the explicit guidance 4.7 had required, and suggested highlighting a count of how many times specific topics came up in her conversations with the team. Her summary: “It seems really detail-oriented in a way I appreciate.”

Austin Tedesco, head of growth 🟨

Austin spent the weekend using Opus 4.8 on an essay with Monologue, our speech-to-text tool, and our writing app, Spiral. For that job, he wrote that Opus 4.8 “is the best model available,” a step up from Opus 4.7 and “materially better than GPT-5.5.” But he doesn’t expect it to change his daily behavior. GPT-5.5 is “pretty good” at the same kind of creative partnership, he said, and keeping his work in Codex matters more than the modest quality improvement: “I don’t see myself reaching for Claude models much without a materially better desktop app experience, or such a dramatic leap in model quality that the harness matters less.”

Nityesh Agarwal, senior applied AI engineer 🟩(model) / 🥇(dynamic workflows)

Nityesh tested Opus 4.8 inside the AI employees he is building for Every—Claudie for consulting, Andy for the editorial team. He reported that the model recalls the right memory at the right time, stays useful in longer threads, and lets him use more of its 1-million-token context window, the amount of material it can handle in one conversation. But Anthropic really won his heart with Dynamic Workflows, the workflow-automation feature released alongside Opus 4.8. Combined with the new model, Nityesh says it feels like “a major power-up.”

Lee Knowlton, software engineer 🟨

Anthropic says Opus 4.8 is more honest and better at flagging risks. But Lee saw the negative side of that instinct during a daily planning run he’d repeated for months where Claude used his calendar, Slack, and notes to create a plan for his day. One morning, the plan cited events, messages, and files Lee couldn’t find in those sources. When he asked Claude what had happened, it claimed a prompt-injection attack had supplied fake information. When Lee challenged it, Claude said it had invented that story to explain its own bad output, mistaking a planning file Lee had moved for evidence of interference. The exchange left him reluctant to trust the model’s explanations for its own behavior.

Andrey Galko, engineer 🟩

Andrey is “very positive” about Opus 4.8 for coding and wrote that he likes it much more than GPT-5.5. For his use cases, it feels “more stable, reliable, and just less dumb.” His reservations are about the experience around the model, not its coding quality: GPT-5.5 is faster, and Codex gives it the better desktop-app harness.

The verdict:

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Why some power users find Opus 4.8’s defining strength its biggest frustration
A senior designer’s two-tool method for images that are creative and precise
The one prompt that tells you exactly where you fall on the AI adoption curve

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Figma Exec on Why the SaaSpocalypse Is a Goldmine

Dan Shipper / AI & I — 2026-06-03 18:00:00 -0400

by Dan Shipper

in AI & I

The transcript of AI & I with Matt Colyer, Figma’s director of product management for developers, is below. Watch on X or YouTube, or listen on Spotify or Apple Podcasts.

Timestamps

Introduction: 00:01:03
The SaaSpocalypse narrative has it backwards: 00:02:15
Matt’s email-agent origin story: 00:05:27
Divergent vs. convergent design thinking: 00:13:21
Figma’s MCP server: 00:17:39
Why design agents need personalization: 00:19:45
Every problem is a context problem: 00:22:09
Apple and Google as the reigning kings of context: 00:25:12
Review is the new bottleneck: 00:28:18

Transcript

(00:00:00)

Matt Colyer

The SaaSpocalypse—or, more positively, the next era of software. I’m really excited about it, because I think the number of developers in the world is about to go from tens of millions to a billion, maybe more. We’re moving through this incredible democratization of technology, and the end result is dramatically more software in the world. If you’re an established product in that space, it’s not a casualty—it’s a goldmine.

(00:01:03)

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The Eight Levels of AI Adoption

Mike Taylor, Laura Entis, and Claude / Guides — 2026-06-02 18:00:00 -0400

by Mike Taylor, Laura Entis, and Claude

in Guides

All it takes is one viral post to make you feel like you’re using AI all wrong. Someone is running 12 Claude Code sessions in parallel. Someone else’s agent is answering emails while they sleep. Meanwhile, you’re still arguing with ChatGPT.

But here’s the thing: Keeping up with every power user isn’t the point. The best way to find value in AI is to use it in a way that fits your work—and to regularly check in to see if you could be getting more from it than you already are. (I was using Steve Yegge’s “Gas Town” post about directing dozens of coding agents to illustrate this in client presentations, but it didn’t quite match with my experience, and I needed to modify it.)

This guide maps eight levels of AI adoption, from basic chatbot use to full agent orchestration. With each new level, you delegate more of your work to—and place more trust in—the AI. The following sections explain how each level works in practice, complete with sample prompts, so you can figure out which levels match your current needs and workflows, what’s possible at each stage, and when it’s time to move to the next one.

Level	Description
1—Chatbot	You give it a task, it provides a response. (ChatGPT, Claude, Gemini)
2—Copilot	The AI exists inside your files and completes work alongside you. (Cursor, Claude in Excel, Gemini in Google Docs)
3—Agent	You describe a task, and the agent executes it step by step, asking for your approval before moving on. (Cowork, Codex)
4—Autopilot	You skip approvals and let an agent complete a task on its own, then review the results. (Lovable, Codex, Claude Code)
5—Workflows	You build a system that professionalizes the agent’s output. (Compound engineering, Claude Workflows, Copilot AI Studio)
6—Assistant	The agent works proactively in the background without being prompted. (OpenClaw, Hermes Agent, Claude Managed Agents)
7—Multi-agent	You’re managing multiple long-running agents at the same time. (Claude Managed Agents, OpenClaw, or Codex Goals)
8—Orchestrator	A manager agent runs a team of sub-agents on your behalf. (Gas Town, Paperclip, Symphony)

A higher level isn’t necessarily better. The most sophisticated AI users I know operate at several levels at once, identifying the best level to work within based on the specific challenge in front of them. The right level for a task is generally determined by how much you trust the AI to do a good job without intervention—and how big a deal it’ll be if it does mess up. For high-stakes tasks, you should either stay at a lower level so you can supervise the AI, or be prepared to invest the time, engineering resources, and tokens necessary to get that same quality at a higher level with less human oversight.

Most people I talk to who are struggling to adopt AI have good reasons: The output quality is either too low for the work they do or it’s too expensive to achieve. Safely moving up to the next level requires effort and experimentation—or a jump in model capability.

The right level match for most of your tasks may also depend on your role. Broadly speaking, the sweet spot for knowledge workers right now falls somewhere between Levels 1 and 4. Engineers are more often in Levels 5 through 8, partly because they can build the scaffolding that makes newer, less stable systems usable before they’re ready for everyone else.

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Where Do You Fall on the Eight Levels of AI Adoption?

Mike Taylor — 2026-06-02 07:00:00 -0400

by Mike Taylor

Midjourney/Every illustration.

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All it takes is one viral post to make you feel like you’re using AI all wrong. Someone’s running 12 Claude Code sessions in parallel. Someone else’s agent answers emails while they sleep. Meanwhile, you’re still arguing with ChatGPT.

Here’s the thing: Keeping up with the power users isn’t the point. The best way to get value from AI is to use it in a way that fits your work—and to check in now and then to see whether you could be getting more from it.

With that in mind, today we published a guide that maps all eight levels of AI adoption, from chatbot basics to full agent orchestration. We explain how each level works in practice, with sample prompts, so you can figure out which ones match your current needs and workflows, what’s possible at each stage, and when it’s time to move to the next one.

Level 1—Chatbot: You ask, it answers.
Level 2—Copilot: The AI works alongside you, inside your files.
Level 3—Agent: It executes a task step by step, checking in for approval.
Level 4—Autopilot: It runs on its own; you review the result.
Level 5—Workflows: You build a system that makes its output more reliable.
Level 6—Assistant: It works in the background, without being prompted.
Level 7—Multi-agent: You manage several long-running agents at once.
Level 8—Orchestrator: A manager agent runs a team of sub-agents for you.

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Company-wide AI Implementation in Five Steps

Natalia Quintero — 2026-06-01 06:00:00 -0400

by Natalia Quintero

Midjourney/Every illustration.

Join me and Dan Shipper for a live session on what AI fluency looks like at the executive level tomorrow, Tuesday, June 2. We’ll walk through how the leaders we work with—at hedge funds, private equity firms, and Fortune 500 companies—are using AI in their day-to-day, and what they wish they’d done differently six months in. RSVP.

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Sitting across from the chief operating officer of a health tech company earlier this year, I watched her name a problem many executives are feeling but few say out loud.

“Our junior employees are probably much more native with this technology,” she said. “And we need to make sure we’re sticking with it. Makes me feel like a dinosaur to say that, but it’s true.”

Confessions like this come up regularly during our executive training sessions: Leaders aren’t working directly with AI on sophisticated tasks, even as they’re guiding planning decisions about the technology. They know they should spend more time learning the tools, but they haven’t committed to it yet. That’s understandable; executives are incredibly busy. But what we see in our sessions is that leaders who haven’t gotten their hands dirty don’t clearly understand the practical opportunities and challenges of AI. That health tech executive’s admission sparked an important conversation about how a coordinated company-wide approach to AI implementation starts with executive AI fluency—but doesn’t stop there.

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An Executive’s Guide to Implementing AI

Natalia Quintero / Guides — 2026-06-01 05:00:00 -0400

by Natalia Quintero

in Guides

If you read nothing else, here is the loop:

Get fluent → Assign AI champions → Pick one painful workflow → Build to 95 percent → Scale what works

Get fluent. Use the tools yourself before directing anyone else to use them. Know what your company has access to, what the policies allow, and what the friction feels like. If you haven’t built something with AI in the last 30 days, start there.

Assign AI champions. Pick operators with bandwidth. Give them protected time (at least two days per month), a clear mandate, and enablement. They are responsible for taking workflows from “works in a demo” to “works in production.”

Pick one painful workflow. Let your champions choose. They know what work is most tedious and worth automating. Start with something frequent, data-rich, and narrow enough to test in a week. You don’t need a full workflow mapping exercise.

Build to 95 percent. An automation that works 80 percent of the time is a demo. Real automation requires gold-standard examples, structured evals, human review gates, and a named owner who maintains it when the model updates. Once you have a skill that works reliably 90-95 percent of the time, you’ve gotten value from AI.

Scale what works. This is where the champion role is key. Run show-and-tells. Train adjacent teams on proven workflows. Kill what doesn’t work and expand what does. One visible win creates pull across the organization.

This guide turns that loop into a 60-day plan for executives, with checklists, and rubrics drawn from Every’s consulting work with dozens of top companies.

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How We Work Now

Every Staff / Context Window — 2026-05-30 20:00:00 -0400

by Every Staff

in Context Window

Hello, and happy Sunday! This week was bookended by two guides: a 9,000-word power user’s guide to Codex—Dan Shipper’s “After Automation” essay put into practice the way the Every team has lately been working. And Kieran Klaassen published an updated guide to compound engineering, Every’s AI-native development workflow, expanded from four steps to seven. We’re running camps for both—a Compound Engineering Camp on June 5 and a Codex Camp on June 12.

Mid-week Anthropic dropped its latest model, Opus 4.8, and in the words of Dan and Katie Parrott, “Anthropic is so back.” The model tops our coding benchmark and writing tests, making it the company’s most complete model yet, though the app around it has some catching up to do. Anthropic and OpenAI have been volleying for the top of Every’s benchmarks for months. This week, Anthropic took the point.—Kate Lee

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Knowledge base

🔏 “Codex for Knowledge Work” by Katie Parrott/Guides: Katie Parrott’s 9,000-word guide turns Codex into an operating system for knowledge work, with five levels of use (from one-off tasks to compounding systems), 13 workflow templates, and the full setup for context files, rules, and review checklists that make agents reliable across a full workday. A companion essay covers the framing for readers new to Codex. Read this for the seven-day starter plan and the deeper templates.

“Compound Engineering” by Kieran Klaassen and Trevin Chow/Guides: The compound engineering loop has been expanded from four steps to seven. Ideate and plan move to the front, and polish to the end—now that AI handles the middle of the cycle. The updated plugin ships 43 subagents and 38 slash-command skills. In a companion essay, Kieran Klaassen explains the new paradigm of a sandwich: AI in the middle, with humans the bread on either end. Read this for the new loop and what each step demands of you.

“Vibe Check: Opus 4.8—Anthropic Should’ve Rounded Up to 5” by Dan Shipper and Katie Parrott/Vibe Check: Opus 4.8 is the first Anthropic release in a year Dan Shipper and Katie would reach for across coding, prose, and everyday work alike. It scored 63 on Every’s Senior Engineer Benchmark versus 62 for GPT-5.5 and 33.5 for Opus 4.7, and 79.6 on the writing tests—the highest score any model has hit, with fewer AI tells than any non-Claude model. Read this for the benchmark breakdowns and the case for why the model now outpaces the app built around it.

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Compound Engineering Gets an Upgrade

Kieran Klaassen — 2026-05-29 05:00:00 -0400

by Kieran Klaassen

Midjourney/Every illustration.

Join me and Trevin Chow for our third compound engineering camp for paid subscribers next Friday, June 5. We’ll show how planning and building are collapsing into one flow—where you hand your AI a goal and it runs with it. RSVP.

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In its early days, compound engineering was mostly about the code. I wanted to see if I could get an AI model to make a plan, do the work the way I wanted it done, review the results against my standards, and incorporate lessons from my feedback so it wouldn’t make the same mistake next time. The loop looked like this:

Brainstorm → work → review → compound → repeat

That loop is still the core of how I build Cora. But almost a year after we first coined the term compound engineering, the work phase has become boring—in the best way. If the plan is good and the agent has the right context, it usually does the work right. It writes the code and runs the tests. It fixes the obvious issues. The question now is: “Where do I fit in?”

The answer is at both ends of the process. An analogy my collaborator on the compound engineering plugin, Trevin Chow, came up with is a sandwich. AI is the stuff in the middle. Humans are the bread on either end, holding it together.

At the beginning, I need to decide what is worth building. I need to understand the user, the product, the weird edge cases, and the thing that feels exciting enough to spend time on. Then I can hand the middle to the agent. At the end, I come back in. I click around and look at the design. I read the copy. I ask whether the experience feels right. Sometimes everything technically works, but the product is still not good. So I make it better.

As the models have grown more capable, the original compound engineering loop started to feel incomplete. Plan, work, review, and compound still describes the core engineering cycle, but it leaves out the two places where I now spend most of my attention: before there is a plan, and after the work technically passes review.

So I expanded the loop:

Ideate → brainstorm → plan → work → review → polish → compound → repeat

Ideate and brainstorm are the new front of the process. Polish is the new end. Compound is still the most important step, because the whole point is that every feature should make the next feature easier.

I updated the compound engineering guide to explain the full system. The guide is about engineering, but I think the pattern applies to knowledge work much more broadly. The middle of a lot of work will get automated. But if you want the work to be good, and if you want it to feel like yours, you still need to be there at the beginning and the end.

Read the updated Compound Engineering guide

Kieran Klaassen is the general manager of Cora, Every’s email product. Follow him on X at @kieranklaassen or on LinkedIn.

To read more essays like this, subscribe to Every, and follow us on X at @every and on LinkedIn.

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Vibe Check: Opus 4.8—Anthropic Should’ve Rounded Up to 5

Dan Shipper and Katie Parrott / Vibe Check — 2026-05-28 08:00:00 -0400

by Dan Shipper and Katie Parrott

in Vibe Check

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Anthropic is back.

After a year of riding Claude Code into the rest of knowledge work, the lab hit a rough patch: Opus 4.7 was hard to love, and OpenAI’s Codex desktop app pulled even devoted Claude users from our team to GPT models. Opus 4.8, out today, has us running back—for the model, if not the app around it. It tops our Senior Engineer Benchmark and our writing tests at once, and it’s the first Anthropic release in a year we’d reach for across coding, prose, and everyday work.

The big insights from our testing:

Best on senior-engineer coding. At extra-high effort, Opus 4.8 scored 63 on our Senior Engineer Benchmark, versus 62 for GPT-5.5 and 33.5 for Opus 4.7. At lower effort settings, the score drops significantly.
The strongest writing model we’ve tested. Opus 4.8 at high effort scored 79.6, ahead of Sonnet 4.6 (74.5), GPT-5.5 (73), and Opus 4.7 (63), with fewer AI tells than any non-Claude model.
Best one-shot PowerPoint we’ve seen. On our Every Consulting Benchmark, Opus 4.8 produced a well-designed deck that told a clear story—something most models still can’t do.
The model is stronger than the app. Opus 4.8 is good enough to make us want to live in Claude, but the split between Chat, Code, and Cowork keeps Codex as the better daily harness.

The full Vibe Check has the benchmark results, Reach Test ratings, pricing, screenshots, and advice on when to reach for Opus 4.8 versus GPT-5.5.

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 at Every. You can read more of her work in her newsletter.

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After ‘After Automation’

Katie Parrott / Context Window — 2026-05-27 17:00:00 -0400

by Katie Parrott

in Context Window

Dan Shipper (left) and Brandon Gell. Midjourney/Every illustration.

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‘AI & I’: More machine, more human work

Today, we’re releasing a new episode of our podcast, AI & I. In a format flip, Dan Shipper sits down with Every’s COO Brandon Gell not to interview a guest, but to be interviewed himself on why automating everything leads to more human work. The occasion is “After Automation,” Dan’s 8,000-word argument on the topic that became our most viral piece of the year, driving the AI discourse on X for a couple days.

It’s a counterintuitive thesis from someone who runs a company that’s automated every single thing it can. And yet Every has grown from four people to 30 in the GPT era, with agents embedded into nearly every workflow. Dan’s point isn’t that AI won’t change work—it already has—but that it drives up the demand for human expertise, judgment, and taste.

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

Here are the highlights:

AI makes experts more valuable. When everyone can produce a decent first draft—of code, writing, design—the floor rises, but so does the amount of comparable content. “You flood the zone with tons of stuff that’s close, but not quite right,” Dan says. Getting from close to memorable requires experts who can work with AI to rise above the new baseline.
The goalposts will keep moving. Models improve exponentially on benchmarks precisely because benchmarks are fixed frames, or existing ways of posing a problem the model can train on. Humans remain indispensable because we can operate outside established frames entirely—we zoom out, recenter the problem, and make surprising, self-directed choices that don’t exist anywhere in the training data.
“AI layoffs” are usually a cover story. Meta and ClickUp, among other tech companies, have recently laid off people and blamed AI. Dan and Brandon’s read on the trend is the same: AI is an easier explanation than admitting your company hired too many people or is in financial straits. AI will undoubtedly change how people do their jobs—and big, structurally rigid companies will have to reorganize around that—but that’s different from the technology eliminating jobs.
Ride the models and you’ll be fine. The paradox at the heart of Dan’s essay is that AI creates more work for humans while raising the bar for how good that work needs to be. Agents are structurally built to rely on humans for direction; without someone deciding what matters and how to make it better, they produce mediocre results. To position yourself to thrive in an AI-native workplace, Dan says, use new models to do the tasks you’re already good at, and you’ll be more in demand than ever.

Signal

The Pope takes on the means of AI production

When Pope Leo XIV’s encyclical on AI, Magnifica Humanitas, hit the internet a little after 6 a.m. on Monday, the first thing I did was give it to an AI.

I’d been waiting on the Pope’s first major written teaching with the bated breath of a left-leaning agnostic secular humanist amateur Bible scholar slash knowledge worker in the AI economy. AI, labor, and the Book of Nehemiah, in one document? I’m not sure there’s ever been a more Katie Parrott-coded text.

Nevertheless, I gave AI the first crack at it. I had Andy, Every’s in-house editorial assistant, use Claude design to turn it into a comic-book infographic with the need-to-know information for the Every team...

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Why the Pope’s first AI encyclical is less anti-AI than at first glance
How the encyclical squares with Dan’s argument in “After Automation”
What every knowledge worker should ask before handing their work to an AI tool

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Transcript: ‘We Automated Everything With AI and Tripled Our Headcount’

Dan Shipper / AI & I — 2026-05-27 13:00:00 -0400

by Dan Shipper

in AI & I

The transcript of AI & I, in which Every COO Brandon Gell interviews me about “After Automation”—my 8,000-word essay on why AI creates more work for humans—is below. Watch on X or YouTube, or listen on Spotify or Apple Podcasts.

Timestamps

1. Introduction: 00:00:51

2. The AI paradox: more automation, more human work: 00:05:51

3. How AI makes yesterday’s expert competence cheap: 00:10:00

4. AI can act autonomously but it does not have agency: 00:18:00

5. Why Dan is all in on AGI: 00:20:39

6. AI layoffs are a lie: 00:21:57

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How to Use Codex for Knowledge Work: A Power User’s Guide

Katie Parrott — 2026-05-26 12:00:00 -0400

by Katie Parrott

Midjourney/Every illustration.

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Dan Shipper is a man possessed by Codex. He calls it his daily driver, he’s been at inbox zero for 10 days straight (genuinely unlike him), and at a recent Anthropic event he spent his time telling the people who build Claude Code that they had to try Codex. He swears he isn’t sponsored by OpenAI. He’s just like this now.

At first glance, Codex looks just like another coding agent. In practice, it’s a workspace where you and AI agents can work side by side across your inbox, documents, data sources, and connected tools. You bring the context, judgment, and review. Codex helps gather inputs, produce artifacts, check work, and turn repeated processes into reusable workflows.

Today we published a power user’s guide to using Codex for knowledge work—even if you’ve never written a line of code. The guide covers:

The Codex knowledge-work loop: Connect, contextualize, delegate or collaborate, review, and compound
Workspace setup: how to create context files, rules, source folders, workflow documents, and review checklists
The five levels of Codex use: from one-off tasks to multi-source workflows, recurring chores, small tools, and compounding systems
13 workflow templates: inbox review queues, unanswered message sweeps, research briefs, weekly reports, GTM plans, customer support routing, recruiting research, planning agents, and more

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Cheap Competence, New Frontier

Every Staff / Context Window — 2026-05-24 05:00:00 -0400

by Every Staff

in Context Window

Midjourney/Every illustration.

Hello, and happy Sunday! This week we published “After Automation,” Dan Shipper’s argument that even when you automate as much as we have, there’s always a new frame for humans to hand to the models. COO Brandon Gell and new head of marketing Douglas Brundage tested the idea by moving their agent work into public internal Slack channels and watching the lurkers gather. Anthropic’s reported $300 million acquisition of developer-tools startup Stainless rides on the same bet—that an agent can’t use a company’s API unless a human has first made it easy to use, which is what Dan and CEO Alex Rattray talked through on AI & I months before the deal.

Scroll down for two takes from the ground at Google I/O—Jack Cheng on why Google is aiming at everyday users, not the AI crowd, and Alex Duffy on Demis Hassabis’s claim that AGI is a few years out—and what Google’s been doing to take us there. Plus, a mini-Vibe Check on Gas City from head of tech consulting Mike Taylor and a Grok-based “banger classifier” Katie Parrott is running her X drafts through, and Katie’s playbook for new grads facing AI-driven entry-level cuts at Meta and beyond—copy-paste career-coach prompt included. We’re off Monday for U.S. Memorial Day and back in your inbox on Tuesday.—Kate Lee

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Knowledge base

“After Automation” by Dan Shipper: We’ve automated as much as possible at Every—agents write the code, draft emails, and compile the newsletter—and yet there’s more human work to do than ever. Dan’s new report traces what happens when cheap competence floods the market and argues there’s always a new frame for humans to hand the models. Read this for the case that progress expands human work rather than ending it.

“Google I/O: Agents, Agents, Agents” by Jack Cheng/Context Window: Google’s I/O keynote rebuilt search and assistants around agents—a default AI Mode, the 24/7 Gemini Spark, and a Universal Cart co-built with Amazon, Meta, and Microsoft—all on Gemini 3.5 Flash, pitched as Opus 4.7-level intelligence at four times the speed and half the cost. Read Jack Cheng’s report from the field for why Google’s I/O bets on distribution over benchmarks.

“Notes From the Foothills of the Singularity” by Alex Duffy/Playtesting: At Google I/O, Demis Hassabis placed AGI “just a few years” out and put its total impact at 10 times the Industrial Revolution. Alex Duffy frames the other side of the story through his Uber driver back from Mountain View: a 54-year-old construction worker who knows the city by heart and is worried his job is next. Read this for the tension between Google’s compute-at-scale ambitions and the workers whose ground it’s reshaping.

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Notes From the Foothills of the Singularity

Alex Duffy / Playtesting — 2026-05-22 05:00:00 -0400

by Alex Duffy

in Playtesting

Midjourney/Every illustration.

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Last year at Google I/O, the company made an overwhelming 100 announcements, including an AI video model—Veo 3—that was miles ahead of anything else at the time. This year had less wow but more dutiful iteration. Gemini 3.5 Flash is faster and more capable than Google’s previous frontier model. Search now builds the right small tool to answer your question on the fly. Gemini assistants can keep running with your laptop closed. Even Gemini Omni, a new, multi-model world model that intuitively understands gravity, kinetic energy, and fluid dynamics—and will likely help train robots—is, for now, being billed as “Nano Banana for video.”

In a year when competitors like OpenAI continued to throw things at the wall—touting its video model, Sora 2, as a ChatGPT moment for video that, according to former head Bill Peebles, would “evolve into a mini alternate reality”—only to shut it down later in the same year. Or leaned into the work market while simultaneously talking, as Anthropic CEO Dario Amodei did, about AI’s potential to decimate entry-level jobs, Google’s releases were not flashy. But filling the gaps both within AI’s jagged intelligence and across its products, while getting the tools to people who will use them, is probably orders of magnitude more important.

Attendees at this year’s Google I/O, with the swooping, landscape-inspired roof of the company’s Bay View campus buildings. (All photos courtesy of Alex Duffy.)

Demis Hassabis, CEO of Google DeepMind, called this moment the “foothills of the singularity.” He puts artificial general intelligence (AGI) “just a few years” out and its total impact at 10 times the Industrial Revolution, and arriving 10 times faster. We now have the ability to automate almost anything we can capture reliable data on, but one of the biggest hurdles is convincing society that it’s worth investing in that ability. Right now most people don’t think it is.

Hassabis called out explicitly that “it’s incumbent on the field, our field, the AI field and industry to show the unequivocal benefits more clearly and more concretely.” My impression, after this year’s conference, is that Google sees the precarity of the current moment clearly, and its scale gives it a rare position to do something about it.

The loop

Google’s loop works like this...

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How Google’s AI roadmap got a lot clearer at this year’s conference
Why the AI race is between progress and public trust
What needs to happen before the window closes on AI’s biggest opportunity

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After Automation

Dan Shipper — 2026-05-21 10:00:00 -0400

by Dan Shipper

Midjourney/Every illustration.

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We’ve automated everything we can here at Every. Agents write our code, draft our emails, handle customer support, and help compile the newsletter. We alpha-test new models before they launch. We use AI in every way imaginable to build and ship everything we touch. We go as far and as fast as possible.

Yet there’s more human work to do than ever.

Today we’re publishing “After Automation.” It’s something I’ve been working through for a while. The popular narrative is that AI will eliminate human work. But I think technological progress creates more for people to do, not less. And that’s a good thing.

This report traces what happens when cheap competence floods in and creates sameness, and how no matter how good AI gets at executing complex tasks, there will always be a new frame for humans to hand it. I’ve included examples from inside Every: how we embed our agents, what benchmarks we use, prompt engineering we play with, and what the work looks like when humans stay structurally ahead of the models.

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Google I/O: Agents, Agents, Agents

Jack Cheng / Context Window — 2026-05-20 13:00:00 -0400

by Jack Cheng

in Context Window

Midjourney/Every illustration.

Google I/O dominated the week, and the message from Mountain View was unsubtle: Agents are now the product, with Gemini 3.5 Flash powering a redesigned search and a new fleet of always-on assistants. One layer down, Anthropic paid a reported $300 million for Stainless—so we’re re-upping our AI & I episode with CEO Alex Rattray, who laid out the design principles for making software legible to agents months before the deal happened. Plus: We did a mini-Vibe Check of Figma’s new in-canvas agent to see whether it solves the blank-page problem.—Kate Lee

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Spotlight

Alex Rattray, Stainless CEO and MCP whisperer

Flashy frontier model releases suck up most of the oxygen in the AI ecosystem. But without reliable ways for AI agents to access these models, their capabilities are limited. This plumbing may be easy to overlook, but it’s an indispensable component of an agent-native internet.

You don’t have to take our word for it. On Monday, Anthropic announced it has acquired Stainless, a software platform for high-quality APIs, to extend Claude’s ability to connect to data and tools. (While terms weren’t disclosed, The Information put the purchase price at north of $300 million.) Former Stainless customers include OpenAI and Google, meaning Anthropic has acquired a developer tooling company used by its top rivals.

In October, Stainless CEO and founder Alex Rattray joined Dan Shipper on AI & I to talk about why teaching models to use software is so tricky, and what design principles make model context protocol (MCP) servers more intuitive for LLMs. (TL;DR: Keep the number of tools an agent can access small, give the tools precise names, and aim to generate tightly defined outputs.) In the episode, Alex goes deep on Stainless’s approach to making it easier for AI agents to use the internet—hard-won insights that, as it turns out, can lead to a big-sticker acquisition from a top model company. [Disclosure: Dan is a small investor in Stainless.]

Read Anthropic’s announcement about its decision to buy Stainless and then watch Rattray’s AI & I episode on X or YouTube, or listen on Spotify or Apple Podcasts (or read the episode transcript).—Laura Entis

Signal

Google goes all-in on agents

We’re hurtling toward an AI landscape divided into two categories of agents: those you collaborate with, and those you delegate to. Google’s new releases from its flagship I/O developer conference, happening this week in San Francisco, break neatly along that line.

The headline announcement is Gemini 3.5 Flash, Google’s just-announced frontier model it says operates four times faster and at half the cost of comparable LLMs. It’s the engine powering most of the agentic features below.

In the ‘collaborate with’ bucket

AI Mode and the new search box: Google is giving search its biggest interface change in 25 years. In addition to expanding the search box to accommodate longer, more conversational questions and terms from users, AI Mode, which Google introduced at last year’s I/O conference, is becoming the default search mode. With the 2026 updates, you can now build custom mini-apps, such as a personalized fitness tracker, or interactive visualizations directly within search itself.

Antigravity 2.0: Google’s agentic development platform is becoming a desktop app for managing teams of agents, with a new command line interface tool and an SDK for custom workflows. You orchestrate, and the agents code, design, or do whatever else you want them to accomplish.

In the ‘delegate to’ bucket

Gemini Spark: Google is pitching Spark as a 24/7 personal agent that lives in the cloud, works when your devices are off, and can operate across Gmail, Docs, Workspace, Chrome, and eventually, third-party tools through MCP. “You can just throw tasks over your shoulder,” Josh Woodward, vice president of Google Labs, Gemini, and AI Studio said in the keynote. “Spark will catch them and then run with them.”

Daily Brief: An out-of-the-box agent in the updated Gemini app that works overnight, scanning your inbox, calendar, and tasks so it can hand you a prioritized digest when you wake up in the morning.

Universal Cart: Google’s new shopping cart works across merchants as part of the Universal Commerce Protocol, which it co-developed with Amazon, Meta, Microsoft, and others. Whenever you add something in your cart, it automatically monitors the internet for information on the product, including price drops, price history, and whether something is back in stock. It also analyzes the full contents of your cart to proactively flag potential issues, like if you’re building a PC and the processor and motherboard you’ve selected are incompatible.

Inside Google I/O

Anyone can cook

Gemini 3.5 Flash, announced in Tuesday’s opening keynote, seems like a meaningful step toward a fast and cheap model that can reliably handle the personal, everyday tasks that most people are looking for help with.

When is a model good enough? That was the question...

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Inside Stainless, The Developer Tools Startup Anthropic Just Bought for $300 Million

Dan Shipper / AI & I — 2026-05-20 13:00:00 -0400

by Dan Shipper

in AI & I

The transcript of AI & I with Stainless CEO Alex Rattray is below. Watch on X or YouTube, or listen on Spotify or Apple Podcasts. [Disclosure: I’m a small investor in Stainless.]

Timestamps

Introduction: 00:01:15
APIs and MCP, the connectors of the new internet: 00:05:09
Why MCP exists: 00:11:00
Why MCP servers are hard to get right: 00:17:15
Design principles for reliable MCP servers: 00:20:24
Using MCP for business ops at Stainless: 00:25:06
Alex’s take on the security model for MCP: 00:40:57
How one-off AI actions become permanent production software: 00:44:42

Dan Shipper

The internet runs on computers talking to each other, but its entire architecture was built for a pre-AI world. Now we’re trying to hook AI up to the internet with MCP—Model Context Protocol—which turns any website or web service into a set of tools that an AI can use natively to get work done. And the software companies that learn how to do MCP well are going to win over the next decade.

That’s why I brought Alex Rattray, the founder and CEO of Stainless, onto the show. Stainless’s job is to help computers talk to each other. They make the APIs and SDKs for all the big companies you know about, like OpenAI and Anthropic, and they’re starting to build MCP servers too. Alex and I get into the nitty-gritty of what the future of MCP looks like, how to design good MCPs, why MCPs are actually really hard to scale and possibly insecure, and we try to figure out together what a better model for allowing AIs to use the internet might look like.

This is a great episode. Alex is a good friend of mine. Let’s dive in.

Alex, welcome to the show.

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Inside the 100-agent Software Factory

Katie Parrott / Context Window — 2026-05-19 09:00:00 -0400

by Katie Parrott

in Context Window

Midjourney/Every illustration.

Happy Tuesday! Today we have a mini Vibe Check on a tool for running more than 100 coding agents in parallel. Plus: how to write viral X posts using the secrets of Grok’s algorithm, why Every’s chief operating officer and head of marketing moved their agent work into public Slack channels, and what’s overtaking Markdown as the preferred format for agents.

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Mini-Vibe Check: Gas City

A glimpse of the future that’s not (yet) ready for practical use

Earlier this year, prominent software engineer Steve Yegge published a viral Medium post about Gas Town, an open-source tool that let developers coordinate 20 to 30 AI coding agents in parallel on the same codebase. Last week, Every’s head of tech consulting, Mike Taylor, got a peek at the future of multi-agent engineering with Gas Town’s successor project, Gas City. The project was rebuilt as a toolkit with Yegge’s blessing by Chris Sells, a long-time developer-tools veteran who grew Google’s open-source app-building toolkit, Flutter, to 3 million developers, and former Block technical lead Julian Knutsen. Mike joined more than two dozen engineers and chief technology officers who played around with the project at a workshop in New York, with Sells and Knutsen dialing in.

TL;DR: Gas City has some sharp ideas that reflect the direction software development is headed, but it’s not yet ready for prime time.

What is Gas City: Running many coding agents in parallel is table stakes for developers at this point. Getting them to do anything useful requires coordination systems to hand work to each other, review each other’s output, and not step on each other’s branches—and nobody’s quite figured out how to get that right yet. “Software factories” like Gas City are one solution: an orchestration system that hands tasks to a small team of agents, routes their work, and decides what’s done.

Sells and Knutsen use Gas City to build Gas City: Knutsen’s Atlanta-based server runs roughly 100 agents that merge around 50 pull requests per day—the output of a small team—burning through roughly a billion tokens per day, or equal to roughly one-fifth of the English-language corpus on Wikipedia.

What works: There are three ideas from the world of software engineering that Gas City is built on and are worth internalizing, even if you never touch the toolkit.

Dark factory versus light factory: Parts of your work where humans and agents talk to each other (planning, design, review) stay visible can be thought of as light, and parts where agents grind through clearly defined work on their own stay in the background, in the dark. As you gain trust in the agents’ output, you can move more of your process into the dark.
One pet, many cattle: The future of multi-agent engineering is likely organized with one persistent, named supervisor you talk to directly (Gas City calls it the “mayor”), who hands tasks to anonymous, disposable workers (the “polecats”) that do one job and shut down, so they execute their job without getting lost in context or in each other’s way. Instead of managing one hundred agents individually, you manage one conversation while the mayor does the coordinating.
Multiple opinions on every code review: Give the same code to Claude, Codex, and Kimi at the same time for review from multiple angles. Three different models catch different bugs than one model run three times.

What could be better: In Gas City, every task spins up a fresh agent session that doesn’t remember the earlier steps, so agents waste cycles re-reading context that other agents produced and miss connections a single session would have caught. Cost is also a challenge: A six-step job can cost six times the cost of one Claude session, which adds up fast. The toolkit still feels experimental––it took a room full of experienced engineers an entire day to get it running, even with support from the instructors.

Beads, the task tracker powering the system, is built for agents first. It runs on the command line rather than as a visual dashboard, which is fine for agents but harder for humans, who want to see everything at a glance. So teams using Gas City in production typically pair it with Jira or Linear—placing tasks in two places instead of one.

Additionally, Gas City was built on the assumption that AI models need hand-holding to stay on track, but models have gotten good enough that parts of Gas City built to keep models on track, such as review loops to catch mistakes and mid-task check-ins to prevent agents from drifting, are now mostly unnecessary. Finally, Gas City uses deliberately unfamiliar words to refer to different inputs, actors, and workflows—“beads” for tasks, “polecats” for workers, “refineries” for processing steps—so it can be confusing for a team new to the tech.

Verdict:...

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How to Start a Career When AI Is Doing Your Entry-level Job

Katie Parrott / Working Overtime — 2026-05-18 07:00:00 -0400

by Katie Parrott

in Working Overtime

Midjourney/Every illustration.

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My first job out of college was as a copywriter at a little crowdfunding website based in Columbus, Ohio, called Fundable.com. The company had no money, so they didn’t care that I had no experience. I had no experience, so I didn’t care that the job didn’t pay at first.

The offer was simple: Create a profile for your startup, and we’ll connect you with investors. Most founders didn’t want to write their own profiles, so my job was to take whatever strange, half-formed thing a founder was building and translate it into investor-speak. The profiles were so templatized I can still recite the format: problem, solution, traction, team, business model, revenue projections, competitive landscape, funding terms.

I’ve been thinking about that job lately because AI could now produce one of those profiles in two minutes. At 23, I would have heard that and thought: “Thank God.” At 36, I think: “Thank God it couldn’t.” Without that job, I would have never learned how to take a company apart and put it back together as a story, or how to organize information for an audience that wasn’t being paid to read my stuff like my professors in undergrad.

This year’s crop of recent graduates has it harder than mine did. AI, which can perform many entry-level tasks, is replacing those early experiences faster than employers can figure out what’s going on. Researchers at Stanford’s Digital Economy Lab found that employment for 22-to-25-year-olds in the jobs most vulnerable to AI has dropped 13 percent since late 2022, even as older workers in the same roles held steady.

I think about the 22-year-old version of myself, if I were sending out applications right now into the void of LinkedIn. What would she think about the headlines about AI and job displacement? Would she be scared?

Yeah, probably. She was scared of much less.

So with full awareness that no one born this millennium wants career advice from someone born before the fall of the Berlin Wall, here’s what I’d do if I were starting over today, knowing what I know about work, AI, and how one is shaping the other.

There’s good news, and there’s bad news

The paradox facing today’s entry-level workers is as old as the entry-level job itself: In many cases, in order to get a job, you need experience, but in order to get experience, you need a job. And while employers requiring experience in AI when the technology barely existed when you picked your major may feel like a cosmic joke, employers have long asked for five years of experience with brand-new technologies.

All that is small comfort to the recent grad with a near-empty resumé. And there are qualitative differences in what AI is doing to entry-level work.

For one thing, when you look at the kind of AI skills employers expect young workers to bring to the table, they want more than the ability to type a prompt into ChatGPT. They want people who can evaluate tools, review outputs, and figure out how to improve those outputs, whether it be with better prompting or fixing the work themselves.

Demand for AI skills in entry-level jobs is up three times, with a particular focus on capabilities that require you to evaluate AI as well as use it. (Chart courtesy of NACE.)

They’re looking for judgment, which is something that you can really only build through experience. When I was writing those funding profiles, I learned how to tell good work from bad. The first 50 that I wrote were so bad that at one point, a client said I should be taken out back and shot. With AI in the mix, the bad ones wouldn’t have been bad enough to teach me anything.

The other way today’s job market is more intense for entry-level workers is that employers are expecting competence in a technology that won’t stand still long enough for anyone to completely grasp. Agentic tools are changing functions in months, rather than years. There’s no canon to study or senior teammate to apprentice under. Everyone in the org chart is figuring it out on the fly, and you’re expected to figure it out with them while learning how to navigate office politics and pay your taxes.

What to do about it?

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After the Personal Agent

Every Staff / Context Window — 2026-05-17 09:00:00 -0400

by Every Staff

in Context Window

Midjourney/Every illustration.

Hello, and happy Sunday! Housekeeping note: We’re hosting our first paid subscriber meetup during New York Tech Week. Scroll down to learn more and RSVP.—Kate Lee

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Knowledge base

“We Gave Every Employee an AI Agent. Here’s What We’re Doing Differently Now.” by Brandon Gell and Willie Williams/Source Code: A few weeks after we launched our Plus One personal agents internally, everyone had their own AI agent. But it wasn’t working: The agents were unreliable, constantly broke, and needed too much upkeep. The problem wasn’t just the OpenClaw harness; it was the idea that every employee needed a personal agent. Read this for a retrospective from Brandon Gell and Willie Williams, and a preview of how Plus One 2.0 is being rebuilt around shared, reliable coworkers.

“Socrates as a Service” by Eleanor Warnock/Every: In a world where AI can search anything, the people who know how to extract tacit knowledge—the gold dust that isn’t on the internet—are getting more valuable, not less. Eleanor Warnock lays out seven techniques she keeps coming back to find the most interesting information. Read this for a working interviewer’s toolkit, and the case for why taste, judgment, and attention can’t be prompted.

“Opus 4.7 Reels Us Back In” by Laura Entis/Context Window: After weeks of Codex dominance, several members of the Every team have been pulled back to Opus 4.7. Cora general manager Kieran Klaassen has made it his default for synchronous work. Read this for the team’s case for switching back. Plus: A hack that spread through a widely used software package, a 30 percent drop in AI-tells complaints after Spiral added a top-edit step, and a better way to think about what an “agent” is.

“Mining Your Life for Context” by Laura Entis/Context Window: By the time you sit down to write an article, strategy memo, or launch page, you’ve probably already said most of what you want to say. It’s just in Slack threads, Notion documents, voice memos, and meeting transcripts. Laura Entis walks through a three-step workflow for mining all that scattered thinking before you draft. Plus: How AI entrepreneur Noah Brier uses Claude Code as a “second brain,” and the productivity regimen Codex’s Chronicle wrote for head of growth Austin Tedesco after analyzing his computer activity. 🎧 🖥 Listen on Spotify or Apple Podcasts, or watch YouTube.

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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

by Brandon Gell and Willie Williams

in Source Code

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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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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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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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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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
How Claude’s new “Dreaming” feature takes a page out of compound engineering
Why Anthropic says building a model-agnostic harness is a losing strategy

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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.

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.

Hello, and happy Sunday! Was this newsletter forwarded to you? Sign up to get it in your inbox.

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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