Can someone explain how so many folks managed to build decision models within days or weeks after Typesafe came out with Jev? Is this concept of decision models been in the works for a while? Is it easy to copy?
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.
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.
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.
Wow, Cloudflare is definitely buying some goodwill from me. Just consistently interesting new releases alongside and solid products at great prices.
Seems nearly too good to be true.
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.