Development

I mandated AI tooling across my engineering org expecting faster code. I got faster code. It was the least interesting thing that happened.

Here is the thing I got wrong, and I got it wrong in the most ordinary way possible: I thought the bottleneck was typing.

An AI agent will hand you two hundred lines in ninety seconds, and it will look plausible. The risk is not that the code is bad; it is that confident, unreviewable work arrives faster than anyone can catch it. CasaFlow is our answer: a staged pipeline of Claude Code skills, built on Duro's open-source Jig, that walks a piece of work from brainstorm to merged PR and leaves a durable artifact at every step.

The parts that matter most sit between the stages. One gate makes you explain what the last stage produced before the next one opens; the other demands evidence before anything gets called done. Neither cares whether you are a person or an agent. Process stops being a tax on the work and becomes exhaust from it.

Releases used to have a single point of failure, and it was me. The process was ad hoc and quietly nerve-wracking, the kind of thing where you clear your afternoon and hope. Nobody wanted to own it, so it fell to the person with the most access and the most to lose.

What fixed it is unglamorous underneath: staged environments, automated checks, human approval gates, git as the source of truth. On top sits a deadpan British rail announcer calling stations, a steam whistle on departure, a bell at Production Terminal, and delays attributed to leaves on the line. The whimsy is not decoration. It is the adoption strategy, and it is why rotating ownership stuck.

Ninety days ago I rebuilt my engineering team from scratch: four engineers, one shared start date, a clean slate. Thirty days in they shipped to production, and my first reaction was not pride. It was suspicion. Output that good, that fast, that early is usually output you are borrowing against, and with AI in the loop the reflex only gets louder.

So I watched, closely, looking for the corner that had been cut somewhere I could not see. It never showed up. Throughput roughly doubled, review coverage went from 24% to 85%, reverts and hotfixes more than halved, and time-to-merge got slower on purpose. What I found instead was the reason the speed held, and a better question to ask the next time good work arrives ahead of schedule.

I've shipped more in the last few months than at any point in my career, and I've never been this tired at the end of a day. Both are true, and the relationship between them is the part nobody's writing about. Some of it is my own doing — when attempting something costs almost nothing, you attempt everything, and I've been a kid in the candy store. The rest is structural. The boilerplate, the builds, the Googling: never the valuable parts of my day, but they were the low-intensity parts, and removing them leaves a concentrate of pure judgment with the rest stops engineered out.

It's an operational problem rather than a personal one. Tripling output can quietly mean tripling cognitive load, and that doesn't show up on any dashboard. I've stopped reading the exhaustion as a sign I'm doing it wrong.

The 10x productivity narrative assumes AI has changed the equation. It has, but not where most people are looking. AI makes architected work faster; unarchitected work just fails faster.

The shift happened for me while debugging authentication across three platforms and three languages — two days of coordination compressed into an hour. From there it became a method: a workspace where the model sees the whole stack at once, MCP connections to the real database instead of guesses about it, and planning documents that get stress-tested, argued with, and revised right up until the feature ships. What the method still needs is someone who already knows what good output looks like.

A guide to getting the most out of Copilot as a Drupal sidekick, built around knowing where its strengths stop. It will churn out boilerplate all day, but it is not a stand-in for actually understanding the code you are shipping.

That opening line matches my own experience with AI coding tools and autocompletions pretty closely. They are genuinely handy, and I would not want to give them up. But much like AI text and images, they will also confidently hand you complete nonsense, and noticing that is still your job.