The highest quality codebase

"Hey claude, I get this error message: <X>", and it'll often find the root cause quicker than I could.

"Hey claude, anything I could do to improve Y?", and it'll struggle beyond the basics that a linter might suggest.

It suggested enthusiastically a library for <work domain> and it was all "Recommended" about it, but when I pointed out that the library had been considered and rejected because <issue>, it understood and wrote up why that library suffered from that issue and why it was therefore unsuitable.

There's a significant blind-spot in current LLMs related to blue-sky thinking and creative problem solving. It can do structured problems very well, and it can transform unstructured data very well, but it can't deal with unstructured problems very well.

That may well change, so I don't want to embed that thought too deeply into my own priors, because the LLM space seems to evolve rapidly. I wouldn't want to find myself blind to the progress because I write it off from a class of problems.

But right now, the best way to help an LLM is have a deep understanding of the problem domain yourself, and just leverage it to do the grunt-work that you'd find boring.

Very easy to write it off when it spins out on the open-ended problems, without seeing just how effective it can be once you zoom in.

Of course, zooming in that far gives back some of the promised gains.

Edit: typo

The love/hate flame war continues because the LLM companies aren't selling you on this. The hype is all about "this tech will enable non-experts to do things they couldn't do before" not "this tech will help already existing experts with their specific niche," hence the disconnect between the sales hype and reality.

If OpenAI, Anthropic, Google, etc. were all honest and tempered their own hype and misleading marketing, I doubt there would even be a flame war. The marketing hype is "this will replace employees" without the required fine print of "this tool still needs to be operated by an expert in the field and not your average non technical manager."

As we speak, my macOS menubar has an iStat Menus replacement, a Wispr Flow replacement (global hotkey for speech-to-text), and a logs visualizer for the `blocky` dns filtering program -- all of which I built without reading code aside from where I was curious.

It was so vibe-coded that there was no reason to use SwiftUI nor set them up in Xcode -- just AppKit Swift files compiled into macOS apps when I nix rebuild.

The only effort it required was the energy to QA the LLM's progress and tell it where to improve, maybe click and drag a screenshot into claude code chat if I'm feeling excessive.

Where do my 20 years of software dev experience fit into this except beyond imparting my aesthetic preferences?

In fact, insisting that you write code yourself is becoming a liability in an interesting way: you're going to make trade-offs for DX that the LLM doesn't have to make, like when you use Python or Electron when the LLM can bypass those abstractions that only exist for human brains.