Inside the AI Workflows of Every’s Six Engineers

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Working alongside the engineers at Every, I sometimes wonder: What do they actually do all day? Building software, just like writing, is a creative act, and by definition, that means the process is messy. When I write, I go between Google Docs and whichever LLM I’m leaning on at the moment (currently, GPT-5). But what does that look like for the people building software?

Sure, I hear about the products they’re shipping in standup, and I get snippets of their workflows when we run Vibe Checks. But those moments are always in isolation, scattered whispers of a bigger conversation.

So I asked: What does the workflow of each of our engineers really look like? What stack have they built that makes it possible for six people to run four AI products, a consulting business, and a daily newsletter read by more than 100,000 people?

Experimenting at the edge: Yash Poojary, general manager of Sparkle

Yash Poojary used to be the kind of developer who insisted on doing everything from one lone laptop. A few weeks ago, he caved and added a Mac Studio—Apple’s high-performance desktop—to his setup. “I wanted to use my laptop for everything,” he admits, “but I felt bottleneck[ed] for testing things faster.”

The upgrade has paid off. Now he runs Claude Code on one machine and Codex on the other, feeding them the same prompt and codebase to see how they respond. He’s finding that the two models have distinct personalities. Claude Code is the “friendly developer,” great at breaking things down and explaining its reasoning, while Codex is the “technical developer,” more literal, more precise, and often able to land the right solution on the first try.

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Yash also recently launched a new version of Sparkle, our AI file organizer, complete with a redesigned interface that he worked on in Figma. Back in the dark ages (aka five months ago), Yash would take screenshots of the design and paste them into Claude so it could write the code. Now, with a Figma MCP integration, Claude can plug directly into the Figma file so it can read the design system itself—the colors, spacing, components—and translate that into working code. It saves steps and keeps Claude working from the real source of truth.

Outside of agents, Yash leans on Warp—a modern version of the developer’s command line, the text-based interface developers use to control their computers. Every time he pushes code, he jots down two lines about what he learned in a “learnings doc” and stores them in the cloud. After a few days, he has a rolling memory of recent context to feed back into his AI tools.

Even with all this experimentation, Yash emphasizes the importance of guardrails. He structures his day around one big task and a handful of smaller background ones, and he’s careful not to let AI-generated suggestions derail him. As he puts it: “The problem with CLIs [command line interfaces] is it’s easy to get derailed and lose focus on what you’re actually trying to build… so building guardrails into the system is essential.”

One way he’s doing that is with AgentWatch, an app he built that pings him when a Claude Code session finishes, letting him run multiple sessions simultaneously without losing track of them. Yash—and a smattering of others—have been using it of late; if you give it a try, DM him.

He’s also split his day into two modes: Mornings are for focused execution—just Codex and Claude Code, no new tools allowed—so shipping doesn’t stall. Afternoons are for exploration, when he experiments with new agents, apps, and features. That separation between “build” and “discover” has removed the productivity drag he used to feel when testing new tools.

Orchestrating the loop: Kieran Klaassen, general manager of Cora

For Kieran, everything with Cora starts with...

1. The tools Kieran Klaassen, Danny Aziz, Naveen Naidu, Andrey Galko and Nityesh Agarwal use look nothing like the ones they started with six months ago
2. One rule cuts through the chaos: "If it's not in Linear, it doesn't exist"
3. How OpenAI's latest models changed what one engineering lead will tolerate from AI