We are just showing the versatility of the platform through a real world use case. LDA is hard to implement/scale for the untrained same as many other machine learning, optimization, graph traversing,etc algorithms. What we are building is crowd-sourced and generalized API where all these algorithms can be combined and used together to really make any application smarter.
The demo we show here is a version of how we used our platform to generate tags for all entries in our API by combining algorithms that existed already in Algorithmia. (modified for performance over quality due to the volume that HN would bring).
It's only hard because most libriaries I've seen have so little documentation available. It's simple once you understand the library. We need people picking these libraries up, implementing them on weekends on fun projects, documenting their work and code, and publishing it for everyone to learn from.
The demo we show here is a version of how we used our platform to generate tags for all entries in our API by combining algorithms that existed already in Algorithmia. (modified for performance over quality due to the volume that HN would bring).
Cheers.