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by jrflo10 hours ago |
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upvoteby rryan10 hours ago|
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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 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.

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upvoteby jrflo9 hours ago|
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There is something to be argued about industry vs academic experience but this post has nothing to do with large industry codebases
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upvoteby raddan9 hours ago|
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Well, 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.
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upvoteby rryan7 hours ago|
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Disagree. The abstractions they are talking about aren't like, the y-combinator. They're talking about boring software engineering abstractions.
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upvoteby JMiao9 hours ago|
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sounds like it might help to know something about the phd candidate and professor
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upvoteby rryan7 hours ago|
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Heh, not really
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upvoteby vatsachak10 hours ago|
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The machine learning community decided to choose Python as the main language, which costs probably tens of millions of dollars in bugs every year due to it not being type checked (astral sh is fixing this though)
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upvoteby raddan9 hours ago|
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Hasn’t mypy been a thing for years already?

The main problem with Python is that, if you’re not doing your work in highly optimized numeric libraries (which are usually written in C), then you’re paying terrible overheads to use the Python interpreter. Type checking doesn’t help with this because Python is dynamically typed but static types could in principle. Is that what you mean?

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upvoteby vatsachak8 hours ago|
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I mean yes the python interpreter is not great, but I'm talking about bugs created by accidentally passing a negative number where a positive one should go, using a tensor of the wrong size etc.

I don't have any ml experience with python only numerical libraries and I always have to be careful what I'm doing otherwise an experiment can go Kaput. Types stop that.

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upvoteby 10 hours ago|
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deleted
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upvoteby nostrebored10 hours ago|
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why?
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upvoteby jrflo9 hours ago|
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I think it's generally fair to assume that you don't become a Stanford CS professor by being bad at writing code and creating abstractions, and that the average professor (let alone one at a prestigious university) is more knowledgable than your average HN commentor
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upvoteby nostrebored8 hours ago|
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Professors have no reputation for writing good code. Academic code is fairly notorious for being under abstracted, hard to follow, messy, etc.

If a passerby told me this story I’d have the same guess though!

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