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Learn to build with AI — from everyday life to production.
I share what I learn building with AI — from experiments that automate personal life with Claude Cowork to agentic development. Real trade-offs and open source for you to apply, whether in your routine or at your company. The goal is to help you build better — who writes it is a by-product, not the point.
Technical writing with explicit trade-offs
The first post. Why this space exists, and what I'm promising you — the honest version of a mid-career transition into AI Engineering.
What you'll walk away withwhat this space is, and the four promises behind it.
Open source for you to study, clone and use. It grows as automations graduate.
This site — a static React/Vite SPA, built in an agent-first loop with Claude Code.
A static SPA (no backend) served from S3 + CloudFront and provisioned with Terraform. Delivery is plan-first, with blocking CI gates and deploy on merge. The repo is the source of truth.
What you take awaywhere to start reading the repo, and what to reuse: the ADRs record the load-bearing decisions, from plan-first to deploy — with the trade-off each one cost.
The harness that builds this site — review personas, permission hooks, and a merge gate wired into the tool, not the prompt.
An agent produces plausible work fast; the bottleneck becomes trusting it. Here the verification is mechanical and the human keeps what is theirs: a reviewer judges the MR in a fresh context, and a hook refuses irreversible actions before the command runs, leaving them to you.
What you take awaywhat you can install into your own repo today, and what is a reference pattern — the README publishes how to tell them apart rather than leaving you to find out per file.