In this case, its people who have built BERT classifiers for years, pointing out you can just fine-tune some model to classify. Of course that's true, and still have value, but its not exactly turnkey. And Jev is surprisingly high quality at what its built for.

1 - https://news.ycombinator.com/item?id=9224

But what Jev actually has done that's new and interesting is that it allows you to define a new semantic decision at runtime in natural language, without training a model for that task, and get a fast, cheap, bounded, probability-bearing result designed to be consumed directly by software.

In other words, you don't have to fine tune a model. You should if super high quality is really important to you -- but you should also do that with other things that people reach for LLMs for as well. But for the rest of us for whom good enough is, well, good enough, and who aren't interested in training or fine-tuning models? Jev unlocks a lot off-the-shelf.

You should be impressed. It's doing something more novel than the haters suggest.

BeRT, BART and FLAN-T5, for text classification. All of those models are ~half a decade old now, are based on the transformer model, and small enough to run locally. They're not perfectly SOTA, but perform quite well in embedded applications. Fine for low-stakes stuff, I think.

Image classifiers are a hugely competitive space. You've got BeiT, DeiT, MobileOne, ConvNext, FastViT and several more models that all support image classification pipelines.