[relativ575]

From TFA:

> LLMs and the platforms powering them are quickly becoming one-stop shops for any ML-related tasks. From my perspective, the real revolution is not the chat ability or the knowledge embedded in these models, but rather the versatility they bring in a single system.

Why use another piece of software if LLM is good enough?

[comex]

Performance. A museum visitor may not have a good internet connection, so any solution that involves uploading a photo to a server will probably be (much) slower than client-side detection. There’s a thin line between a magical experience and an annoying gimmick. Making people wait for something to load is a sure way to cross that line.

Also privacy. Do museum visitors know their camera data is being sent to the United States? Is that even legal (without consent) where the museum is located? Yes, visitors are supposed to be pointing their phone at a wall, but I suspect there will often be other people in view.

[titzer]

Cost. Same reason you don't deliver UPS packages with B-2 bombers.

[msp26]

The cost of LLM inference is cheap and will continue to decrease. More traditional methods take up far more of an engineer's time (which also costs money).

If I have a project with a low enough lifetime inputs I'm not wasting my time labelling data and training a model. That time could be better spent working on something else. As long as the evaluation is thorough, it doesn't matter. But I still like doing some labelling manually to get a feel for the problem space.