Smaller models have been able to do these sorts of tasks, but a little slower, for a while now. Give a small Qwen 3.8 model a classification task and force a structured output, and it'll do a good job. I've used Qwen 0.8b for basic image classification in <500ms on my local machine for a while now.

There are a few technical details that can reduce the latency significantly (covered in the post) but the real insight has been from watching the reaction to Jev and seeing that there's enough of a market interest to offer it as a distinct thing. The underlying concept/approach was already there.

Transformers output a set of probabilities over outputs. For ChatGPT etc, those are predictions of what the next token will be. But it can also be a structured list of options or classes. Jev mostly innovated on the interface, API, and product concept around this, and made it click for a large number of people. Unfortunately for Jev, it's very easy to copy an API, and any pretrained LLM can be adapted to work in this way.

Jev created accessible/programmatic ergonomics around a general purpose classifiers, and did it very well; ie intuitive api and structured data approach.

Anyone can copy that and apply to an array of models - stripped down LLMs or already slim/highly performant traditional classification architectures (just wrap inference with an api that inputs/outputs the same structured data).

Jev, I think, would say their advantage is the intelligence of their models and training data including calibration: https://medium.com/code-applied/calibrated-classifiers-makin... (which i still struggle with in the general application... there's no free lunch with these things).

Most answers explain the LLM-based approach to these models, which is also what Typesafe did with Jev. However, depending on what you need, there are far simpler classification models, and for a lot of use cases, these models can be way faster and more accurate than Jev

But, for these adhoc models, you need to understand the task more, collect some data and train the model (on CPU, no need for GPU). So Jev-like models are a great way of getting a hosted general decision model, but if you have a very narrow task or set of tasks, you might be better off with some more basic models that you can run on the same server you run other things or even on your laptop

You can use already trained large transformer models to make one, so it doesn't require the kind of high-scale compute, high quality data, data cleanup, reinforcement, and so on training that say an LLM does.

What's new is "smart" decision models than you can supposedly use on anything without additional training.

If you have a very narrow use case you can train a BERT based decision model on a laptop an hour if you have good data to train it on. It'll answer faster than the roundtrip to clef/jev and use <1gb memory

You just have to fine tune an LLM like Qwen on some synthetic data to do so. There was even someone that had a model that was exactly like Typesafe and published their work a year before Jev (but wasn't marketed as heavily since it was academic).

The question of AI in automation is "can it make decisions in a consistent and predictable manner, with near 100% determinism?"

Many people seem to have run into the same question and started working out the answer.

Read this - https://magazine.sebastianraschka.com/p/classifier-history-a...

The concept existed a year before Jev or so. See Laya

It's not a new concept, it just took someone adding on to the approach and refining it. I never deep dove it, but I assume JEV is sort of like how Sora works? They had a blog post about how it has a sort of tiny LLM, which OpenAI's small LLMs are insanely good and well defined. I think any lab tackling this with a from-scratch model could yield affordable alternatives that are highly competitive.

It seems insanely obvious at least to me, that JEV is the new hot thing for the AI field since they give you stronger output that isn't... flat out wrong, that alone is impressive.

They are not too difficult to train if you already have infra to train regular LLMs. You can typically replace a few layers train them alone and you're off to the races.

Getting training data that works well for calibrated classification objectives is difficult.

I hear conflicting opinions (including my own) about how well calibrated each of these are. Jev seems to be the best.

But the jev release made obvious the PMF for these models, and the underlying reality is that calibration really doesn't matter much when you're replacing usecases where people were using damn LM head softmax probabilities before, which are nowhere near calibrated.

So now everyone simply finetunes qwen and makes a compared-to-regular-LLM vastly cheaper decision model. And it works for majority of usecases. People mostly only care about accuracy, not confidence.

That's probably driven by their own internal need to show the model images of emails and webpages to detect phishing, despite obfuscation of the underlying HTML.