I'm going back to writing code by hand

It is and has always been immensely helpful to understand what you are doing in any context.

There are some programmers who treat the job as just plumbing together what is to them completely incomprehensible black boxes, who treat the computer as a mystery machine that just does things "somehow", but these programmers will almost always be hacks that spend their entire career producing mediocre code.

There are things such a programmer can build, but they are very limited by their lack of in depth understanding, and it is only a tiny fraction of what a more competent programmer can put together.

To get beyond being a hack, you need to understand the entire stack, including the code that you didn't write, including both libraries, frameworks and the OS, and including the hardware, the networking layers, and so forth. You don't have to be an expert at these things by any means, but you do need to understand them and be comfortable treating them as transparent boxes that you may have to go in and fiddle with at some point to get where you need to go. Sometimes you need to vendor a dependency and change it. Sometimes you need to drop it entirely and replace it with something more fit for purpose you built yourself.

This struck me as odd too. 7 months? It wouldn’t take that long to write it in a new language.

Another thing I don’t see mentioned is code quality.

Vibe-coded code bases are an excellent example of why LLMs aren’t very good at writing code. It will often correct its own mistakes only to make them again immediately after and Inconsistent pattern use.

Recently Claude has been making some “interesting” code style choices, not inline with the code base it’s currently supposed to be working on.

That’s when you ask it to write tests to a good coverage, and then have it reimplement everything with the tests still passing…

We're still in the early ages and must discern hard what AI is good for, what it can maybe do, what it could potentially do and what it just can't do, and move those threshold marks very conservatively. AI is also cheap enough that it's worth shots of experiments. As long as you don't really rely on AI it's easy to test the capabilities of this new conversational autocomplete, and the random gains it offers can be magnificent (except when they aren't, of course).

What has generally worked for me is paraphrasing the old adage "Write the data structures and the code will follow" over to AI. Design your data, consider the design immutable and let the AI try fill in the necessary code (well, with some guidance). If it finds the data structures aren't enough, have it prompt you instead of making changes on its own. AI can do lot of the low-hanging fruit and often the harder ones as well as long as it's bound to something.

Yet, for now, AI at best has been something that relieves me from having to write a long string of boring code: it's not sustainable to keep developing stuff relying on AI alone. It's also great when quality is not an issue; for any serious work AI has not speeded me up noticeably. I still need to think through the hard parts, and whatever I gain in generating code I lose in managing the agents. But I can parallelise code generation, trying new approaches, and exploring out because AI is cheap. AI is also pretty good for going through the codebase and reasoning about dependencies whether in the context of adding a new feature or fixing a bug: I often let AI create a proof-of-concept change that does it, then I extract the important bits out of that and usually trim down the diffs down to at least 1/3 or less.

AI further helps with non-work, i.e. tasks that you have to do in order to fulfill external demands and requirements, and not strictly create anything solid and new. I can imagine AI creating various reports and summaries and documentation, perhaps mostly to be consumed and condensed by another AI at the receiving end. Sadly, all of this is mostly things not worth doing anyway.

Overall, I cringe under all the hype that's been laid on AI: it's a new tool that's still looking for its box or niche carveout, not a revolution.

I'm classing that as the second camp. Because you don't find it hard to do, it's just time consuming. It means you still know what you're doing and you're just using AI as a tool to accelerate your delivery. That's the optimal way to use in my experience if you want to actually deliver well architected software.