[embedding-shape]

People generally sleep when you start talking about fine-tuned BERT and CLIP, although they do a fairly decent job as long as you have good data and know what you're doing.

But no, they want to pay $0.1 per request to recognize if a photo has a person in it by asking a multimodal LLM deployed across 8x GPUs, for some reason, instead of just spending some hours with CLIP and run it effectively even on CPU.

[Aurornis]

> they do a fairly decent job as long as you have good data and know what you're doing.

This is the bottleneck in my experience. Going for the expensive per-request LLM gets something shipped now that you can wow the execs with. Setting up a whole process to gather and annotate data, train models, run evals, and iterate takes time. The execs who hired those expensive AI engineers want their results right now, not after a process of hiring more people to collect and annotate the data.

[cestith]

I’m no ML engineer and far from an LLM expert. Just reading the article though it seemed to me that leveraging an SQL database here was a bigger issue than using traditional ML on the data, rather than the LLM being a win specifically. Just finding anything that was better suited than string matching on a RDBMS to the type of inputs seems like the natural conclusion when the complaint in the article itself was literally about SQL.

[keeda]

>... as long as you have good data and know what you're doing.

I think you've just identified, in a set-theoretic complementary manner, the TAM for GenAI.

[throwaway314155]

What's TAM?

[keeda]

https://en.wikipedia.org/wiki/Total_addressable_market

[efavdb]

Are you suggesting use the clip embedding for the text as a feature to train a standard Ml model on?

[daemonologist]

I think they're suggesting doing that with BERT for text and CLIP for images. Which in my experience is indeed quite effective (and easy/fast).

There have been some developments in the image-of-text/other-than-photograph area though recently. From Meta (although they seem unsure of what exactly their AI division is called): https://arxiv.org/abs/2510.05014 and Qihoo360: https://arxiv.org/abs/2510.27350 for instance.

[PaulHoule]

I think he is. I do things like that plenty.