Working Overtime

What Board Games Taught Me About Working with AI

The skills I transferred to my writing agent from playing Settlers of Catan

TL;DR: In case you missed it, you can now see all of Every's upcoming camps and workshops in one place. Coming up this Friday: our Compound Engineering Camp, where Kieran Klaassen introduces the AI-native philosophy that helps Every ship products with single-person teams, and on February 24, learn Claude Code in one day in our live, beginner-friendly workshop taught by Mike Taylor.—Kate Lee

I'd been stuck on trying to build my own writing agent for months when I found myself scanning my board game shelf. Suddenly, the problem wasn't about AI anymore.

It was the end of Think Week, Every's twice-yearly retreat where we break to explore possibilities outside the flow of our regular work. The team was in a beach house in Panama, decked out in shorts and sunglasses with palm trees swaying in the background. I was under 10 inches of snow in Ohio, locked in a battle of wills with my dog about going outside.

From my laptop, I watched Austin Tedesco, Every's head of growth, demo a dashboard he built in one day that pulls data from PostHog and Stripe and gives him a complete view of signups and subscription revenue. COO Brandon Gell showed off an AI CFO that helps him steer the company. Head of consulting Natalia Quintero shared Claudie, an AI agent that she and applied AI engineer Nityesh Agarwal had built in two weeks with nothing but Claude Code and a dream.

Meanwhile, my momentum had stalled out as badly as my attempt to get my passport renewed in time for the trip. It was a stark contrast to how I'd felt six months earlier. In July, I was on a roll: I'd built a custom ChatGPT project that ran my entire drafting process. I'd developed an AI editor that could enforce Every's editorial sensibilities and written specialized Claude prompts called Skills that the whole editorial team used. But around September the wind fell out of my sails, and it hadn't quite been back since.

Watching my coworkers demo these systems made me want to take advantage of all the new capabilities of Opus and Codex, of agent-native architectures and the seemingly infinite possibilities popping up on all sides like Whac-A-Mole moles. I just had no clue where to start.

At the same time, my best friend and I were playing chicken about whether we'd brave the snow to get together. I scanned my board game shelf, stocked with everything from crowd pleasers like Wingspan and Codenames to five-hour behemoths that no one wants to play with me, ever. I was trying to decide what we'd play if she did come over… and then I was thinking about worker placement and area control and victory conditions.

What ensued was that strange, slightly vertigo-inducing feeling when two unrelated ideas fuse together in your head: What if I thought about my AI project as a board game?

Attio is the AI CRM that thinks fast and acts faster.

With Attio, AI isn't just a feature—it's the foundation. With powerful AI automations and research agents, Attio transforms your go-to-market motion into a data-driven engine, from intelligent pipeline tracking to product-led growth. Then ask Attio anything:

Prepare you for meetings with automatically compiled context
Create tasks and records while you work so you never miss a follow-up
Build powerful AI automations for your most complex workflows

Teams at Granola, Lightdash, and Wisper Flow are already experiencing the future of GTM with Attio.

Ready to build without limits?

Try Attio for free

Want to sponsor Every? Click here.

The art and science of 'the teach'

I spent the holidays teaching my nephews board games. Over four days, the four of us—a nine-year-old, a seven-year-old, my mom, and me—played five different games. It's not everyone's idea of a good time, but it is mine.

I like to think I have what's called "the teach" in board game lingo down to a science. Before you go into strategy, before "here's how you beat your brother," there's a more basic question: What are the pieces, and what do they do? This little wooden person is called a meeple. When you place it, you're claiming that road. This gem chip means you can afford more expensive cards. This sushi card is worth points if you collect three of them.

I knew I wanted to build a writing system that could take advantage of all these new capabilities and tools, but I wasn't even clear on the parts I was working with. I had a Claude project with some custom instructions and a few Google Docs that I'd manually edit whenever I wanted to change something. It worked well enough. But it didn't feel magical like those Think Week projects did.

I needed an example, a game I could study to help me understand the parts and what they might do. Fortunately, I already had one in mind.

The game on the shelf

Cora general manager Kieran Klaassen built a compound engineering plugin—a software development system for Claude Code that gets smarter the more you use it. Every time you fix a bug or have a new insight, you write it down and feed it back to the AI. Over time, the system learns your preferences and grows more capable.

What I had hoped to do for writing, Kieran's plugin had already solved for code. If I could understand how it worked, I could find a way to apply it to writing. So I opened Claude Code, pointed it at the compound engineering plugin's GitHub repository, and said: How does this thing work?

From there, my board game brain took over. I knew how to do this: Dump the pieces on the table, figure out what each one does, learn the moves, and play until the strategy clicks. In this case, the "table" was my desktop and the "pieces" were lines of code. But the principle is the same.

What are the pieces?

Every game comes with components: tokens, cards, dice, and boards. Before you know what anything means, you need to know what you're holding.

Claude Code gave me an inventory of the pieces in the compound engineering "box": agents, commands, skills, and configuration files. The point wasn't to take a strict inventory but to identify the categories: actors, actions, stored knowledge, and preferences. Once you have the categories, you can ask what goes in each one for your domain.

Claude helped here, too. It proposed writing equivalents to the engineering components—instead of a Rails reviewer, a developmental editor. Instead of a security auditor, a fact-checker.

The most important mapping was the simplest. CLAUDE.md, where Kieran encodes his engineering taste in plain language, became TASTE.md, where I encoded writing style. It was the same concept: voice, sentence preferences, and a "kill list" of words I never want to see. When you use the plugin, Claude Code loads this file at the beginning of each writing session.

What moves can you make?

In a board game, this is the action phase: Place a worker, spend a resource, buy a property. Each action has rules, and the rules define what the pieces can do—and therefore what's possible in the game.

Kieran's plugin has a four-step loop: Plan, work, review, compound. You research and plan before you build. You review what you built. Then you compound—capture what you learned so the system is smarter the next time around.

The writing equivalent mapped onto a sequence I already knew from years of editing, even if I'd never laid it out this cleanly. Brainstorm: Surface raw material when you don't have an idea yet. Interview: Pressure-test an idea you do have—what's the claim, what's the evidence, why should anyone care? Outline: Organize the material into a skeleton first. Draft: Expand that structure into prose, give the skeleton flesh. Edit: Review the big picture, zoom down to sentence level, then do a final check before you publish.

Each stage has a job. Each one feeds the next, and—here's the part that took me the longest to accept—you don't skip steps. The temptation with AI is to jump straight to a draft. But a draft built on a bad outline is a fast way to produce polished garbage.

How do the moves fit together?

The best board games, though, aren't just a string of moves one after another. Instead, they interlock and repeat those moves in such a way that early decisions have outsized effects in later rounds. In Settlers of Catan, the settlement you put up in round two funds the city you build in round eight. In Ticket to Ride, the routes you claim early lock out your opponents and determine which coasts you can connect later. Games like these reward thinking in systems.

In Kieran's plan-work-review-compound loop, the last step matters most. When you solve a problem, the system captures what you learned and surfaces it the next time something similar comes up. The system has a memory.

Building the writing equivalent of that memory was the hardest part. I'd pre-loaded the system with Every's editorial philosophy, but when I ran through it as though I were a test user, the AI got mixed up, and all of my (Katie's) preferences showed up in what was supposed to be the user's personal profile. The system was supposed to learn your taste. Instead, it was handing you mine.

The solution was to split the system into two layers: a "defaults" file that holds an opinionated baseline for good writing, and a taste file that starts empty and fills up over time.

That moment crystallized another important lesson from the board game framework: The engine only reveals its flaws when I actually play.

How do you win?

Every board game has a victory condition—the objective that gives all the components and moves their meaning. Without it, you're shuffling pieces around on a table.

The victory condition for compound writing itself is straightforward: Each piece of writing makes the next one easier. The system gets smarter about you—your voice, your instincts, your recurring mistakes—and over time, the gap between what you mean and what shows up on the page gets smaller.

But building compounding writing taught me that I also was playing a second, bigger game—the game of learning how to learn AI systems in the first place.

This bigger game is far from over. I'm still testing, still refining, still discovering rules I'd encoded wrong or principles I'd forgotten to encode at all. Which is how learning any board game works. You don't play perfectly the first time. You fumble through a round, misunderstand a rule, lose badly, and say, "Okay, now I get it—let's play again." That's what learning AI feels like when you stop trying to understand everything at once.

So dump the pieces on the table. Play a round. Lose. Compound what you learned. Then play again.

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

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

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

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

Get paid for sharing Every with your friends. Join our referral program.

For sponsorship opportunities, reach out to sponsorships@every.to.

Help us scale the only subscription you need to stay at the edge of AI. Explore open roles at Every.