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I know nothing about the PhD candidate and professor who co-authored this, but I don't think this statement makes sense in general. Typically CS labs at univer
by rryan 2mo ago
I know nothing about the PhD candidate and professor who co-authored this, but I don't think this statement makes sense in general.
Typically CS labs at universities contain people who have not spent a significant amount of time exposed to large industry codebases and the corresponding complexity. I think the post would have more credibility coming from e.g. the platforms team at a tech company with a monorepo.
- jrflo 2mo agoThere is something to be argued about industry vs academic experience but this post has nothing to do with large industry codebases
- raddan 2mo agoWell, they’re also probably not scaling up in the same way as any of the commercial AI offerings, let alone the frontier labs. Faculty at Stanford probably have some decent hardware to play with, but they do not have data centers. I don’t doubt that they know their way around CUDA/PTX, but it’s not clear how relevant their message is given that their research code is very likely not being deployed in production, or at scale.
- rryan 2mo agoDisagree. The abstractions they are talking about aren't like, the y-combinator. They're talking about boring software engineering abstractions.
- JMiao 2mo agosounds like it might help to know something about the phd candidate and professor
- rryan 2mo agoHeh, not really