Pretty much every software problem we work on breaks down into steps that are solved in prior art.

Even if it weren't, if you're capable of explaining the context and constraints of your problem, then a modern LLM with effort=high will generally come up with a solution that's worth starting with because it's well-reasoned.

Moreover, you can start with the solution and then course-correct based on future information because refactoring is trivial with an LLM, yet human projects often ratchet into a local optimum because refactoring is too expensive.

Finally, I don't think "it's been built before" does as much work as it seems. I didn't fork a project. The LLMs reasoned about how to build the project from scratch using trade-offs that made sense for my needs, and they made reasoned, unsolicited deviations from kitty, xterm, and co, not just blindly doing what some ref impl did. Btw, it was still a lot of work because my project isn't just "kitty but swift".

Then the models went on to drive a well-reasoned incremental implementation of a system that lets me use the terminal running on my Macbook from my iPhone over tailscale which was basically novel software.

The point is that I don't really have to know how things work to build good software with modern models. LLMs can do things like read Linux source code and adversarially refine ideas such that the final idea is a good one. And my biggest influences on the project can be automated through the use of reusable markdown files.