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Comment by oneoff786 | Hacker News Reader

Seems pretentious to me. I’ve never bothered to look through many things I use. I look extensively at how to use them and what the API offers. I have a good intuition for how most models work. I don’t really care about the specifics of the implementations.

I have more important things to do. The hacker mentality, imo, is about identifying what’s useful for you to explore to accomplish whatever you need. Often that’s a lot of glue between things that other people built. Other times it’s tweaking the internals to do something a bit different.

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If you think it’s about implementation details, you’re misunderstanding. It’s about understanding the principles behind it.

As an example, it’s more about understanding the statistics and linear algebra around estimating uncertainty in GLM regression estimates, than about reading the code for how the statsmodels library implements that.

If you want to understand the intuition behind a model you should read a paper, book, or even a blog before the repo imo.

This is not about the hacker mentality. This is a researcher mentality from a daily life perspective. Some people just aren’t curious. I like to understand the math and the computing models behind many things I use. That doesn’t mean I want to know what’s happening in Windows internals or something just because I use Windows everyday. But if I’m creating an app connecting to Office DLLs, I want to know what it does beyond “here’s a bunch of methods and constants you can use”.

I’d further argue that the nature of a hacker / power user is to break things apart once you want to get deep enough. If I need to know where in the cluster my instance of some software got lost into, I should be able to investigate all the tools I have available to somehow find it. Not just give up and say some garbage collector will get it for me.

I see what you're saying, but the post above seems to indicate that understanding those models SHOULD be important to you if your job is to run those models and explain results and make corporate decisions based off your forecasts. You shouldn't be just passing data through and thinking that it's not your job to actually understand things. The subtlety matters a lot as the software hitting some edge case could completely skew the results. There is a vague line drawn somewhere that tells you what is necessary to learn and what is superfluous. Finding the line isn't easy, but those that label too much as superfluous will likely get more erroneous results and that is a problem.

With regards to your API statement, I'm just as guilty regarding reading the code, but I do run some manual tests to ensure that my script calling the database actually does what I think it should. Is that good enough? Who knows :)

That's all fine, as long as you still understand the underlying assumptions and pitfalls. Many people who skim documentation and throw things together haphazardly do not.

Skim documentation != going through repos

I agree with you. Super powers are seeing value in doing something, and then finding the easiest and most efficient path to get there.

That said, sometimes I do like to read the code in libraries I use but often this is more for enjoyment with occasionally learning something interesting.

How can you see the value in doing something if you don’t understand the underlying concepts?

It depends. Sometimes for work or personal reasons I just need to get something done and API docs, etc. is all I need for that. And sometimes I dig deep. I have over 50 US patents and enjoy technical work, so sometimes I do dive deep.

"it's pretentious to know what you're doing" just be a haxxor xD

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