That trial and error process is still happening with a LLM, but much faster, and with instantaneous cross-references to various forms of documentation that I would be looking up myself otherwise. It produces code of a quality that is dependent on the engineer knowing what they want in the first place and prompting for it and refining its output correctly.

It's the exact same process of sculpting code that the majority of the industry was doing "by hand" prior to the release of LLMs, but faster, and the harnesses are only getting better. To "vibe code" is to prompt vaguely and ignore the quality of the output. You're coming to a forum full of professionals and essentially telling us that you're getting really frustrated with your Scratch project.

I don't know if you're trying to lead a charge or whatever but good luck with that. As a senior SWE, it is clear to me that this is the new paradigm until something better than LLMs comes along. My workflows and efficiency have been vastly improved. I will admit that I have never really been a "I made a SMTP server in 3k of Rust" kind of guy, though.

If you understand good software architecture, architect it. Create a markdown document just as you would if you had a team of engineers working with you and would hand off to them. Be specific.

Let the AI do the implementation of your architecture.

The problem with this dev's approach is not AI, it's their use of it. They didn't ensure that the architecture made sense. They didn't look at the code and get a "feel" for it. They didn't do the whole build stuff, step back, refactor, rinse and repeat dance. The need for that hasn't gone away; if anything, it's even more important now. Because you can spit out code 100x faster than you could before, your tech debt compounds 100x faster. The earlier you refactor, the less work it is.

I usually give the agent a solid idea of what I want, often down to the API interfaces. Then every now and then, I'll go through the code and ensure that everything makes sense, and that I'm not just spitting out code that works, but building a codebase that scales.

This. I definitely agree with this statement at this point in AI-assisted development. This gets at the "taste" factor that is still intrinsically human, especially in software engineering. If you can construct and guide the overall architecture of an application or system, AI can conceivably fill in the smaller feature bits, and do so well. But it must have a strong architecture and opinionated field in which to play.

The ones who are “AI pilled” and the contagious lepers.