I started using Claude Code in December last year. In four months, I’ve delivered what my team would have needed two or three years to ship — at least. That sounds like a happy ending. It isn’t quite.

Last year I went to the Google Cloud Summit with a few colleagues. We saw a lot of impressive projects built around agents and LLMs, and walked out a bit deflated. As a data team — analysts, engineers, but not “pure” software developers — we could see the value clearly, but we’d seen this movie before: the moment we wanted to build something real, we’d be queuing up for backend developer time, or pricing out an external consultant to implement agents for us. Same bottleneck as always, just with a fancier label.

A year later, that bottleneck has quietly dissolved. We built it all ourselves — fast — with Claude Code as the agent in our IDEs. The projects we thought we’d have to outsource, we shipped on quiet afternoons.

And it isn’t just the work-shaped projects. I’m finally shipping ideas that have sat in my personal backlog for literally ten years — the kind of thing where every time you open the notebook you remember why you closed it last time. Now they’re real, deployed, and — the part I keep being surprised by — they’re solid. Tests, sensible error handling, security defaults that hold up to scrutiny. Not vibey demos. Things I’d hand to a friend without a list of caveats.

When people ask how much faster I work with the agent, my honest answer is that “faster” is the wrong axis. I built this chart for an internal talk last week to explain why.

I wrote three months ago about the context files and memory system behind my AI coding agent. This post is the follow-up I wish existed back then: the concrete tricks and patterns that make the agent reliably useful, not just occasionally impressive. Most of these are small. They compound.