zlacker

I do feel this divide, but from what I've read, and ehat I've observed, it's more a divide between people who understand the limited use-cases where machine learning is useful, and people who believe it should be used wherever possible.

For software engineering, it is useless unless you're writing snippets that already exist in the LLMs corpus.

>>tovej+(OP)
> For software engineering, it is useless unless you're writing snippets that already exist in the LLMs corpus.

If I give something like Sonnet the docs for my JS framework, it can write code "in it" just fine. It makes the occasional mistake, but if I provide proper context and planning up front, it can knock out some fairly impressive stuff (e.g., helping me to wire up a shipping/logistics dashboard for a new ecom business).

That said, this requires me policing the chat (preferred) vs. letting an agent loose. I think the latter is just opening your wallet to model providers but shrug.

>>rglove+Y8
If you need a shipping dashboard, then yeah, that's a very common, very simple use-case. Just hook up an API to a UI. Even then I don't think you'll make a very maintainable app that way, especially if you have multiple views (because the LLMs are not consistent in how they use features, they're always generating from scratch and matching whatever's closest).

What I'm saying is that whenever you need to actually do some software design, i.e. tackle a novel problem, they are useless.