We design and build calm, durable software.

We keep the stack deliberately boring. We minimize dependencies, skip frameworks we don't need, and reach for simple, established tools, like the databases and services that have been proven for years, rather than whatever is new and exotic. Boring technology is predictable: fewer failure modes, more answers when something breaks, and far less to maintain down the road. The novel, clever choice is usually the one you regret, so we'd rather the system be obvious.

High-quality AI-written code starts with a well-crafted design: we break the work down to the right granularity and plan each increment in detail before any code is written. From there AI does the building, fast, but a senior engineer is shaping all of it, reviewing every change for the right abstractions, clean structure, sound API and data-modeling decisions, and code that extends instead of duplicates. We use AI at every stage and review its work at every stage too. Its output usually looks right, which is exactly why it takes someone who knows the whole system to catch where it quietly isn't.

We build in thin vertical slices, each one cutting through the whole stack, from interface to data, to land a complete, working change instead of a layer of half-built scaffolding. Slicing this way keeps scope honest and integration risk low. There's always something real to run, review, and ship, rather than a pile of parts waiting to be wired together at the end.

Parallelism is what unlocks the speed. We keep many features in flight at once, each broken into small slices that run side by side instead of one after another. Isolated workspaces, a separate git worktree per stream, keep them from colliding, so nothing bleeds between them. The whole workflow is async: the AI agents signal us the moment a plan or a change is ready for review, so instead of watching one thing run, we move from stream to stream handling whatever is ready. It's how three of us move on far more at once than a team several times our size.

Every plan, design, decision, and bit of research lives in git, right beside the code it shaped, and all of it is revisionable. That gives us a complete audit trail: not just what the software does, but why it's built that way, what we weighed, and what we ruled out. Nothing important is stuck in someone's head or a chat log that disappears. Months or new people later, the full reasoning is right there, and it's also what we hand back to AI so it builds on real history instead of guessing.