AI demands more engineering discipline. Not less

arent they still? or at least a lot. its too much current to win the swim race against the deluge of llm LOC. but i also disagree with some of the things the author just casually lays out, which is whether the LLMs can write good code. they write working code, but it looks written by a demogorgon and i get a bit ill seeing it. its bad but not bad in a way that a human would ever write, like i dont get that kind of sick reading spaghetti code written by new devs. it's a kind of sick like cthulhus eggs are hatching somewhere in your guts.

I enjoyed it, people post on blogs as a way to entertain themselves, not necessarily the reader.

I liked this article, and I see a lot of other commenters didn't, so I'll give my take:

When starting on a new codebase, how do you make yourself into a helpful contributor as quickly as possible? I go straight for the humans and their human docs. What problem was the system originally built to solve? What was the original design, and what were its biggest problems? Who is currently using it? If you know these, reading the code is much easier because you can guess why things were done the way they are.

Also, this blog post has gotten popular: https://blog.gpkb.org/posts/just-send-me-the-prompt/

I think Charity is observing a very old problem and expecting the new technology to lead to a new solution of some kind. I doubt she thinks even the current generation of tools are the end of the AI software development story. She's not saying we'll drop design docs right into Claude code and walk away (design docs aren't complete either, that's why when you're ramping up you also have to talk to people, read old tickets and postmortems, etc.)

What she's observing is that, in prod, people don't like infra where it's hard to tell how it got into is current state, and so infra-as-code is what we do now. She's also observing that, "it's hard to tell how it got into its current state" is the status quo with codebases, which other people have observed going back to "Programming as Theory Building" and earlier. And she's expecting that, analogous to infra, software development will somehow be done with tools focused on making "how the code got into its current state" clearer.

That is the problem imo. Most tech workers want a big check and no work. Gross. I like the work. But i do get wanting to get a nut with little effort.

I have a doubt that one of Three Virtues of a Programmer, laziness is still considered a virtue on AI coding era.

Now that AI coding speed and performance outperformed most of human. But AI still need human to be commanded. Yes, you can let AI agent manage sub-agents but still, human is at the top of manager who order AI what should be written.

So human must command and final say on when it's done.

Is laziness still a good virtue in AI era?