I agree that this is a cool. That being said, the results show that we have a long, long way to go. The topics are pretty incoherent: what are "would", "should"

I agree that this is a cool. That being said, the results show that we have a long, long way to go. The topics are pretty incoherent: what are "would", "should", and "use" doing in there? The words have no context, so (for example) "self" clearly refers to the python reserved keyword, but you have no way of knowing that. Not to mention (as another comment brings up), the topics aren't named so it's pretty hard to actually figure out what they're about. If we think about real-world usage this output would be practically useless - it tells you that people talk about investing in r/investing and pytorch in r/pytorch. If you want meaningful, actionable information about what people are talking about in a large corpus of unstructured text data then for the forseeable future you'll need to involve humans in the loop even if ML assistance plays a big part.