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).
Have you tried Jev and compared it against your alternative classifiers? Should take half an hour to do that, then you’ll have your answer (or someone who already did it can tell you here).
I did this yesterday with user data to find duplicates and determine which conflicting fields should win in a merge. Took me about an hour-ish to run the experiment. I provided a good amount of context for each pair. It was underwhelming / OK at best and didn't add any value to my existing merge workflow, so I threw it out after the experiment (I kept the findings though). It was quite easy to setup and use via openrouter and extremely cheap and fast.
For fun I also tried using it at my local LLM router (all of my prompts and responses go through it for personal analytics) to decide which model to route tasks to based complexity etc. Again, underwhelming for my purposes, and so I'm not using it.
Not sure what real world use case it's best at, but I agree that it's simple enough to implement it yourself and see if it fits the type of work you are doing.
gatapia2 · · focus · HN ↗
I don't understand the hype.
brabel · · focus · HN ↗
rapind · · focus · HN ↗
For fun I also tried using it at my local LLM router (all of my prompts and responses go through it for personal analytics) to decide which model to route tasks to based complexity etc. Again, underwhelming for my purposes, and so I'm not using it.
Not sure what real world use case it's best at, but I agree that it's simple enough to implement it yourself and see if it fits the type of work you are doing.