I don't understand Jev. Its a generic classifier right? Like the classifiers we were building 15 years ago with random forrests and logistic regressions, but just generic. What's so revolutionary? And how can the accuracy be any better than a custom trained classifier that can be built in a day (an hour using Claude).
> Like the classifiers we were building 15 years ago with random forrests and logistic regressions
I think no one mentioned here, but the obvious difference is that Jev can spit out decisions directly from natural language input. None of these ML models could do that, and other than a full-fledged LLM (which is optimized for conversation and agentic tasks) or some classical NLP models (which underperform compared to LLMs, AFAIK), there is nothing right now that rivals Jev-like models.
Of course it's not perfect, but seems like the right step forward for quick classification/decision tasks based on natural language.
As far as I can tell from just looking at the documentation, yeah, it looks like a "better than nothing" and "definitely better than LLMs for a few tasks" tool, and quite possibly something that I'd build into a demo, but I'd be really careful deploying it in any kind of product where exactness matters.
gatapia2 · · focus · HN ↗
I don't understand the hype.
pedrosbmartins · · focus · HN ↗
I think no one mentioned here, but the obvious difference is that Jev can spit out decisions directly from natural language input. None of these ML models could do that, and other than a full-fledged LLM (which is optimized for conversation and agentic tasks) or some classical NLP models (which underperform compared to LLMs, AFAIK), there is nothing right now that rivals Jev-like models.
Of course it's not perfect, but seems like the right step forward for quick classification/decision tasks based on natural language.
Yoric · · focus · HN ↗