Walk up to any building's address plaque for its story Wikipedia info & link to historical photos from Old NYC.

Comments URL: https://news.ycombinator.com/item?id=48978276

Points: 5

# Comments: 2

That said I love this as is and will definitely be playing & sharing it.

If you're just saying different tools are best suited for different problems, apologies - that's my take as well.

Also this is a remarkable (and realistic) evaluation of where these systems are for general work which speaks to both the room to grow as well as the pace: https://www.remotelabor.ai/

For some practical examples of what the leading consumer grade AI can do, Ethan Mollick consistently has great writeups with demos: https://www.oneusefulthing.org/p/what-it-feels-like-to-work-...

Are you suggesting that losing electricity in the modern age (entirely absent AI) doesn't upend one's world?

You seem to be saying "we should avoid this thing because we'll become dependent on it," but we're highly dependent on all manners of technology for all sorts of things and would seem to be better for it.

Skeptics continue to move the goalposts on what constitutes this mattering, but the fact that frontier systems are making novel maths & sciences discoveries and I can run an LLM on my phone for simple tasks that would've been unthinkable a few years ago are testaments to the directionality of the tech.

If you aren't seeing remarkable things being done with this tech, I'd argue you aren't looking hard enough. I understand there's a lot of noise obscuring the signal, but that's always the case with a "big thing."

The only way to get a sense for these systems is to use them on things you know well, and everyone knows different things at different levels.

People also tend to underestimate how fast this is moving and base their take on dated and subpar systems for a variety of reasons, a key one being that the firehose is too big for any one person to have a proper focus on all of it.

The crucial breakdown here sounds like either lack of proper context/harness or insufficiently capable model (there's a huge gulf between GPT-5.5/Opus 4.8/Fable class models and anything not from the big three) or both.

Most of the skepticism I encounter on this front is due to lack of proper direction, process involving planning and review before execution, and appropriate attention given to evaluation and feedback loops.

If you asked the smartest person in the world to YOLO a task with the sort of instruction the average denier uses to evaluate an LLM, you'd likely find they wouldn't get back what they were expecting either - and if you're evaluating on subpar models/tools, you shouldn't be surprised to get subpar results.

This stuff is moving fast, and if you aren't evaluating SoTA on at least a quarterly basis, you're going to have a bad time.

I was told growing up "you won't always have a calculator in your pocket" and yet now my phone has an offline LLM on it.