Arpan Writes

Latest — 04 Mar 2026

The Problem With "Expertise" (And How to Break It Apart)

Last week I introduced the DK/PK/EJ framework — the three components that explain why my daughters and I drew three different fish from the same tutorial. We all had the same Domain Knowledge (we knew what a fish looked like) and the same Process Knowledge (the tutorial steps). The

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The Autonomous Agent Gap: Why Perfect Instructions Still Fail

Last month, my daughters and I all followed the same pencil drawing tutorial on a tablet. We used the same steps, the same order, and the same reference image of a koi fish, yet we ended up with three very different results. My older daughter’s had a sense of

Content Engineering: an attempt at solving AI Slop, with AI

I can run 3+ dev agents through a Linear backlog. My content pipeline still ends with me, a printed draft, and a ballpoint pen. Here's what I built to close the gap.

Building an engineering team with Claude Code & Linear

If you removed the people from an engineering team, what would actually be left? Not the codebase. Not the PRs. Not the incident postmortems. Not the shipped releases. What’s left is the operating system: the roles, the rituals, the review standards, the definition of done, and the escalation paths

What I learned trying to delegate my job to Claude Code

I spent twenty years doing knowledge work without ever writing down what I actually do. Building an AI agent to delegate to made me realize just how much I kept in my head.

Everyone is a manager now

AI just made everyone a first-time manager. The question isn't whether you're good at the work. The question is whether you can encode what "good" means so the work ships without you.

In spec-driven development, the spec is where decisions live

When execution is cheap and discovery is expensive, "we'll figure it out later" stops working. The spec is where decisions survive.

How I ship production code with AI without writing any code.

I built a system to 10x my development velocity as a solo founder. It's a structured, gate-enforced model that makes AI-driven software delivery reliable, auditable, and self-improving. This is how.