Nathan Labenz: belief

Transcript context

…That's the one. It it looks it looks for, like, the Pareto frontier. So it just it's it's looking for, like, the best prompt across all of your eval set. We're looking into tweaking the system right now so that we can assign weights to evals. Well, like, this eval counts as, like, 10 of this other one because it's really important. But right now, it's just, like, treating every eval as equal. How Fine tuning as a dimension in this whole model situation. This is another thing if I go back in time, I'm like, I definitely expected a lot more fine tuning than we are getting. OpenAI's retired its or on the verge of they've certainly announced, and I think maybe at this point have pulled the trigger on retiring their fine tuning product. We've got thinking machines trying to answer that call with their own model that's specifically to be fine tuned. Yeah. Is that gonna be part of the future of Lindy teammate? Yes. I think fine tuning is the last result. It's something you do once you've every other option because it's such a pain in the ass and it's expensive. But it's it's gotten a lot easier because now we have AGI, and so you can just ask Cloud to fine tune for you. Just give it a just give it a dataset. Bringing the dataset together is still, like, complicated, and, like, sanitizing is complicated, and it's it's still it's still the pain. So it's it's it's a less result. It's it's not the low hanging fruit. You should do everything else before you fine tune, including debark. But then, yeah, at some point, get there, and this is why, like, a lot of companies are the first ones to fine tune because they they have existed the low hanging fruit. And at some point and and they have the resources to fine tune. And so it it it does make sense. It does give you more performance for cheaper. Do you think that that…

Stored transcript either side of the excerpt. The highlighted words are the published quote; the surrounding text is unedited source, never generated.