The Jev model is economically, but also in terms of compute, a much more efficient model. A normal LLM goes token by token, each token in a separate step. Whereas Jev just returns all the results the first round. So it is much better at classification than LLMs.

Compared to traditional ML classification, Jev works without training, like a LLM.

Could you link to some of these classification models that can be used as versatile and perform with similar quality, speed and cost?

It reminds me a bit of what Ansible got right: user communication. The underlying tech may have existed for a long time, but the genius is presenting it to a regular developer in a way that reads "yes, even you can understand ML, just using a little JSON". The contribution of that should not be understated, as has been clearly evident recently.

The way I see this (I havent played around with Jev or layla the OSS version) is that classifiers have always existed and a recognised tool in the ML world. But, the norm is that one needs to not only know what to classify as, but determine what weights to use to classify the input.

Jev came in, and added that magic of "you dont need to train your classifier or determine the weights" if you dont want to, and just get the classified answer out. I think that's what is making people see this with a glitter in their eyes.

I think it’s timing. So many devs trying to squeeze their subscriptions, build more tooling to throughputMaxx. 6 months ago, i speculate it launches pretty flat.

For a while is the keyword. It’s just vibe coders have just discovered the classifiers

It’s because it’s practically useful and enabled things that were impractical previously.

It's appealing not having to fine-tune separate model for each use case

So you have more flexibility to get on with building, evolve your business logic etc

Classifier models are extremely niche and trained for a singular purpose. A utility classifier that you can one-shot on almost any topic or need is a dramatically different beast.

Is it truly useful or accurate or beneficial? To be seen. But it's the idea that has everyone so captivated. An expert system that is an expert at most everything is a lot more useful than an expert system that is an expert at choosing a bar of soap, for instance.

Feels like astroturfing.

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Jev is creating a sort of identity crisis for me, because the number of absolutely clueless folks parroting the classifier thing is the first time I've seen this sort of mass psychosis in CS upfront.

Like even 5 minutes of tinkering captures why this isn't anymore like BERT or any past classification model than ChatGPT is like those old Markov Chain generators, yet folks cannot shut up about how this is nothing new.

Absolutely scary and makes me wonder how much of the field is just people super confidently discrediting otherwise promising/interesting directions for development for a cheap dunk!