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).
The "moat" is data. And compute. But mostly data.
It is a classifier, that you don't have to train, that seems to work well, and they have made available by API.
One of the reasons for LLM success has not just been that they are "smart" but that they are smart while not requiring you to collect your own data and perform your own training.
There is also the marginal advantage of it not requiring a PhD to know how to use. But most companies can get a competent data scientist on board, so that is not a major problem, they could find someone to train their classifiers. But even a good data scientist can't make data appear out of nowhere. If you can get a pretty good result without needing data and training, for cheap, you're going to take it.
(Let's put aside the fact that almost every company using LLMs these days is irresponsibly NOT comparing the results with their own internal gold standard datasets for validation and calibration. They are just winging it. And Jev allows them to continue to do that.)
gatapia2 · · focus · HN ↗
I don't understand the hype.
radarsat1 · · focus · HN ↗
The "moat" is data. And compute. But mostly data.
It is a classifier, that you don't have to train, that seems to work well, and they have made available by API.
One of the reasons for LLM success has not just been that they are "smart" but that they are smart while not requiring you to collect your own data and perform your own training.
There is also the marginal advantage of it not requiring a PhD to know how to use. But most companies can get a competent data scientist on board, so that is not a major problem, they could find someone to train their classifiers. But even a good data scientist can't make data appear out of nowhere. If you can get a pretty good result without needing data and training, for cheap, you're going to take it.
(Let's put aside the fact that almost every company using LLMs these days is irresponsibly NOT comparing the results with their own internal gold standard datasets for validation and calibration. They are just winging it. And Jev allows them to continue to do that.)