@kneath I mean - the ethical is up for debate considering the data-in it harvested. And who gets the money. (Eco side is up for debate obviously - I've seen arguments on both sides for that.)

But I 100% totally understand where you come from in general. Genuinely.

@kneath
Like your example. It looks good. I'm sure there are no errors. But a lot of it's errors aren't obvious, and it can miss things.

I'm not trying to say they're not useful. I've used them on and off for years. I just think people naturally over-assign intelligence because of the wow-factor.

Again, I'm open to being wrong on this one.

@kneath I feel other languages having gotchas and LLMs **sounding** like they know intent, isn't the same thing though.

LLMs can be pretty impressive. But understanding what it's asking you, and giving you tailored responses is it's main job.

But anyone who's used it has also spent 6 hours going in circles because it's no where near any form of AGI.

I understand the dislike for the specific question, but it does show the fundemental issue. I'm sure there are other examples out there.

@kneath When it's going to understand intent is an interesting question. Considering the scaling is starting to plateau, and the data is drying up.) It's anyone's guess. We'll see.

Also - miss you!

@kneath Absolutely fair. And totally; context.

But that's why weather reports are usually a few lines per time unit. The key facts. Rather than blog post size each.

As you said, right now until it doesn't understand intent. (Think the old "Should I walk to the carwash" question.) So in today's age - I really do feel it.