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 core feature is good quality out-of-the-box domain and cross-domain calibrated probability you can use as a threshold.
SO you do not need to have YOUR data to train classifier. All you need is to benchmark it on your process/data to see that it really is good for your domain (as authors promise) and choose a threshold for acting.
Ahh, and the most important thing you need to do - design well the input context (state), questions with their context (instructions) and output type (because the same question asked as Noul (yes probability) or Choice with yes/no variants give different probability of yes.
gatapia2 · · focus · HN ↗
I don't understand the hype.
flashnik · · focus · HN ↗
SO you do not need to have YOUR data to train classifier. All you need is to benchmark it on your process/data to see that it really is good for your domain (as authors promise) and choose a threshold for acting.
Ahh, and the most important thing you need to do - design well the input context (state), questions with their context (instructions) and output type (because the same question asked as Noul (yes probability) or Choice with yes/no variants give different probability of yes.