Software craft in the AI era

What this guide covers ​

Every few years a tool comes along that is supposed to end the profession. Frameworks, no-code, and now agents. The profession keeps outliving the prediction, because typing was always a small part of the work. The core is understanding what needs to happen, judging whether it is right, and carrying the consequences once it runs. This cluster is about that durable part.

I say that without nostalgia. I use the new tools constantly. But the more powerful the tooling gets, the more weight lands on the fundamentals it does not replace.

What stays true ​

Some pieces here are about timeless technique. The magic of tries and DFS is one: making slow software fast by choosing the right data structure. That kind of work does not go away however good generation gets, because you have to know what you are looking for to recognise it when it appears. The same holds for the warning in stop copy-paste engineering: whoever adopts code without understanding it is building on someone else's assumptions. That was true in the Stack Overflow days and it is still true with an agent.

It is almost always your process ​

When AI adoption hurts somewhere, my first question is rarely what the model got wrong. You don't have an AI problem, you have a process problem is the diagnosis that fits most often: AI introduces no new category of failure, it exposes the existing gaps in your process at a pace you can no longer ignore. The scaled-up version of that story is your 10x developer is gated by your pipeline: code got cheap, but the pipeline that turns code into value never grew with it, so the bottleneck moves to review, deployment and decision-making.

Judgement and reading code ​

To judge what a model gives you, you have to understand what it is. The brilliant parrot problem explains what an LLM actually does when it thinks: next-token prediction, extraordinarily good, and fundamentally blind to truth. Once you see that, calibration follows on its own. You stop trusting tone and confidence, and you go back to the only test that counts: read it yourself, understand it yourself. How it ends when you skip that test is documented in the day Claude deleted my production database. By now that story is my shortest argument for discipline.

A durable profession ​

Which leaves the question of where the profession itself is heading. The most resilient job is eating its seed corn looks at the numbers: engineers turn out to be the most resilient role in tech, while the inflow of juniors dries up. The skills in this cluster, reading, judging, building process, are exactly the skills becoming scarce. Bad news if you were counting on the tools to take over. Good news if you keep yours sharp.

Where this touches quality ​

Craft without a yardstick stays a feeling. The AI and code quality cluster makes it concrete, and the hinge piece is speed got cheap, judgement didn't: the economics behind why judgement became the scarce resource. The rest of that guide is under related topics below.

Below are three starting points, then every post in this cluster, newest first.