Harrison Chase: belief

Transcript context

…Yeah. I have to confess, I did not know that you cared. It's not like a judgment on Langchain. I was just like, you know, you write an adapter for it and you're done, right? Like how much further does it go for Langchain? In terms of like, for you, it's one of the, you know, the model IO modules and that's it. But like, you seem very personally, very passionate about it, but I don't know what the Langchain specific angle for this is, for fine-tuning local models, basically. Like you're just passionate about local models and privacy and all that, right? And open source. Well, I think there's a few different things. Like one, like, you know, if we think about what it takes to build a really reliable, like context-aware reasoning application, there's probably a bunch of different nodes that are doing a bunch of different things. And I think it is like a really complex system. And so if you're relying on open AI for every part of that, like, I think that starts to get really expensive. Also like, probably just like not good to have that much reliability on any one thing. And so I do think that like, I'm hoping that for like, you know, specific parts at the end, you can like fine-tune a model and kind of have a more specific thing for a specific task. Also, to be clear, like, I think like, I also, at the same time, I think open AI is by far the easiest way to get started. And if I was building anything, I would absolutely start with open AI. So. It's something I think a lot of people are wrestling with. But like, as a person building apps, why take five vendors when I can take one vendor, right? Like, as long as I trust Azure, I'm just entrusting all my data to Azure and that's it. So I'm still trying to figure out the real case for local models in production. And I don't know, but fine-tuning, I think, is a good one. That's why I guess open AI worked on fine-tuning.…

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