Julia motivation: why weren't Numpy, Scipy, Numba, good enough?

It is also very complex, apparently implementing a

  1. parser,
  2. compiler, with loop fusion,
  3. a virtual machine to run the result on.

The problem with this approach is that it does not scale easily for generic programming. Looking at the issues, many of them are asking for an implementation of some operator or function.

The great thing about Julia, often underappreciated by outsiders, is that in the ideal case, orthogonal components can work together. Eg a Bayesian MCMC library can get derivatives via AD to estimate models with ODEs. If the code is written well, they don’t even need to know about each other, eg the ODE library authors don’t need to ask the AD library maintainers to include specific code for their functions.

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Do I say something stupid? Because I don’t understand your post.

[Including quotes for context.] Not at all, I’m very sorry if it came across that way. I had just switched over from Reddit and maybe was still stuck in an inappropriate mindset for this forum.

Anyway, it was just a playful way of pointing your attention to the “Summarize This Topic” button that automatically gets included at the top of long posts threads. It basically just shows you the “best” posts of the thread, with “best” defined by some heuristic based on a combination of likes and replies. Not perfect, but it usually gives you enough context to catch up with a long thread.

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That is really interesting. I never noticed this “Summarize Topic” button.

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