What are the key pain points in the current AI development workflow, and in what areas do potential opportunities lie for improving this process?

I have run into some issues using LangChain—just random errors that I feel like aren't my fault that I haven't looked into it enough. But I feel it's just normal issues, normal API issues, normal documentation. They're normal issues you get if you're using cutting edge technology. I have to read the source code sometimes because the code is more up-to-date than the docs but that's just normal for a startup. I still don't know the best way to deploy LangChain applications.

They have a list of ten different things and I used Vercel and it sucked. Every startup is trying to become the startup that people deploy LangChain apps on, so they're making a bunch of noise. It's not clear how to deploy, or the best way to deploy LangChain apps. But I didn't have too much friction. I ran into some errors when I was trying to build an agent this past weekend, where it's querying different APIs. But I don't really see that as a big issue. 

In terms of where could I see more startups being built and products being built for this ecosystem? I'd say, just looking at all the modules that LangChain has for agents or documents or vector stores. Pinecone has the vector store, but they have other modules, and I feel like all of those modules could probably build a few startups around there, or there will probably be a few startups if there aren't already. Just extending the capabilities and making it as easy as LangChain, basically. It just works. It's dumb, simple, stupid. Its hosting is free for a low quota.