[sswatson]

Recently I've used Claude Code to build a couple TUIs that I've wanted for a long time but couldn't justify the time investment to write myself.

My experience is that I think of a new feature I want, I take a minute or so to explain it to Claude, press enter, and go off and do something else. When I come back in a few minutes, the desired feature has been implemented correctly with reasonable design choices. I'm not saying this happens most of the time, I'm saying it happens every time. Claude makes mistakes but corrects them before coming to rest. (Often my taste will differ from Claude's slightly, so I'll ask for some tweaks, but that's it.)

The takeaway I'm suggesting is that not everyone has the same experience when it comes to getting useful results from Claude. Presumably it depends on what you're asking for, how you ask, the size of the codebase, how the context is structured, etc.

[hunterpayne]

Its great for demos, its lousy for production code. The different cost of errors in these two use cases explains (almost) everything about the suitability of AI for various coding tasks. If you are the only one who will ever run it, its a demo. If you expect others to use it, its not.

[sswatson]

As the name indicates, a demo is used for demonstration purposes. A personal tool is not a demo. I've seen a handful of folks assert this definition, and it seems like a very strange idea to me. But whatever.

Implicit in your claim about the cost of errors is the idea that LLMs introduce errors at a higher rate than human developers. This depends on how you're using the LLMs and on how good the developers are. But I would agree that in most cases, a human saying "this is done" carries a lot more weight than an LLM saying it.

Regardless, it is not good analysis to try to do something with an LLM, fail, and conclude that LLMs are stupid. The reality is that LLMs can be impressively and usefully effective with certain tasks in certain contexts, and they can also be very ineffective in certain contexts and are especially not great about being sure whether they've done something correctly.