They went with python and it's just too general and verbose.
Edited: Added: I think if you love teaching calculus using Julia then the passion you transmit might compensate for the additional burden of using Julia plus SymPy.
Some of the design algorithm bits are hidden away, but I’d argue that a first course in DSP probably shouldn’t cover optimization details like Parks-McClellan.
As a language it’s very pluggable (both directions). I think it’s one the best languages in terms of interop out there at the moment.
That's the only time I've ever even heard it mentioned outside HN. I felt a lot of excitement for Julia's automatic differentiation during the ML era, because you had programmers with weak math backgrounds suddenly needing to do basic calculus (any dev from that era has the chain rule burnt into their brain).
Then the ML community settled on Python as their language, and if performance was an issue you wrote a library in C++, and obviously that was all sucked into the event horizon of LLMs so people "code" in whatever language the LLMs had the best training data for.
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and many other companies.
I use it routinely, but it isn't a company standard where I work.
But, on the other hand, you rarely need those kind of tools with Julia. The REPL+Revise in Julia is most often all you need. If you really want to jump into running code debugger style you have Infiltrator.jl
And in the year of our lord 2026, I’ll say that agents are very effective in debugging Julia, especially paired with persistent sessions/daemons like DaemonicCabal.jl or JuliaDaemon.jl.
Debugging worked quite good. I didn't look for static analysis.
But I find programming in any language in VSCode less polished, compared to e.g. programming C++ with Visual Studio, which works out of the box.
We still mog them on mass and volume though. 2.5 million of the average ant would only make the size of a human leg, if that.
From my quick skim, the book is quite light on proofs, which I think is a big mistake for intro calculus classes. Proofs are usually what you need to get a solid feel for the material, at least at the start. Developing intuition on how things work by playing around with Julia, and using that to inform exploration to come up with theorems, and using that data to help develop a proof would be a more interesting approach. Maybe they do this in later chapters. For example, when teaching eulers method for integrating the ODE, visually graphing the error compared to the actual as step size changes could give the reader some intuition about the order of the method.
Jupyter handles Julia too, but Pluto is designed specifically for it, and is lovely. I'm using it to teach an intro to ecological modelling course right now.
Optimization, specifically the kind that JuMP tackles, is another field where teaching is probably easier in Julia than any other language
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.
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).