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Comment by patagurbon | Hacker News Reader

There's no language I'd want to teach (applied) math in more than Julia. Lean is obviously kind for anything involving a proof, but for teaching anything with linear algebra involved the syntax alone is enough of a reason to prefer Julia.

Optimization, specifically the kind that JuMP tackles, is another field where teaching is probably easier in Julia than any other language

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Since you've posted in HN about the state of Julia in 2024, I think you have the knowledge, and perhaps the desire, to provide information about what the improvement are in Julia with respect to problems expressed in the 2022 long discussion by Yuri Vishnevsky (1).

Anyway the problem I see is that other languages and tools are also improving, for example the author of this post (2) "I am reluctant to shift to Symbolics, as the calculus specifics (limits and integrals) aren't as competitive".

(1) https://discourse.julialang.org/t/discussion-on-why-i-no-lon...

(2) https://github.com/jverzani/CalculusWithJulia.jl/issues/50

Edited for grammar and spelling.

As to (1), all the bugs Yuri filed seem to be fixed, save https://github.com/JuliaLang/julia/issues/39385, for which there is a largish PR that's still wending it's way to being merged.

All the bugs he listed as "filed by others" also seem to have been fixed, again save for one: https://github.com/JuliaLang/julia/issues/36069.

I'm not sure I understand what you're getting at with (2).

My question from (2) would be: is Symbolics still not on par with SymPy?

That's a very one dimensional phrasing. Symbolics has a ton of things SymPy doesn't have. SymPy has some things that Symbolics doesn't have. Which one is "better" depends on which things you need to use. SymPy still has a few classic math things that Symbolics doesn't (though some aspects like prime number pieces are deliberately left out because it's covered by other existing Julia packages, and IMO shouldn't be a part of SymPy in the first place), and Symbolics has plenty of things that SymPy doesn't have around optimized codegen and parallelized Groebner basis algorithms.

That said, Symbolics was released in 2022. The thread you posted was from 2022. In 2026, it does support limits (https://github.com/SciML/SymbolicLimits.jl), integrals, (https://docs.sciml.ai/SymbolicIntegration/dev/) etc., and in fact that symbolic integration is much more advanced than SymPy, except on symbolic integration of ODEs (surprisingly, though that has been pretty deliberate since that domain almost never has an analytical solution so it's not the most useful to real practitioners and is more of a teaching tool)

Have you ever looked into APL or BQN?

I have, compared to Julia they’re both just overall too slow. Same with K and Uiua and Matlab.

Do you seriously need performance to teach integrals or derivatives?

I understand TFA is scoped to calculus, but the parent post scoped their usage of Julia to "anything with linear algebra involved", so I would say in that case you should teach languages that are practical for linear algebra. I threw Matlab under the bus because it's generally slow (although there are ways to optimize it a bit more) and expensive, but I would teach matlab or julia before apl or bqn. Also bqn is a very new language, it's not ready to be used for classroom teaching purposes.

BQN is not intended for this!

https://mlochbaum.github.io/BQN/commentary/math.html

Too dissimilar to the blackboard although I really do like them

I must’ve meant best* instead of kind oop

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