I would love to see things flipped around: Teach calculus as code, and work in the concepts after. This looks like a conventional approach with code tacked on after. What I see here: concept -> math notation -> code. I'd go: code -> concept -> math notation.
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
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".
The stated goal is to introduce math concept without computer language or use being a burden. But then I took a quick glimpse at the derivative chapter, and at the top there is "using Sympy", so you don't only use Julia but also sympy. Mathematica and Matlab are the alternative in a university setting in which free licenses for students are available, with strong numerical and symbolic capabilities . I think Julia is a lovely language, but perhaps Julia + Sympy is a difficult proposal for attaining the stated goal. S.Rational(1) + S.Rational(1,3) type of things from sympy are hard to digest.
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.
The Julia DSP module is pretty nice for learning. The basic objects are about what you would expect them to be, you can specify filters by their zeroes and poles, etc.
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.
Depends a bit. More traditional tools like static analysis/Debuggers/full fledged IDEs are somewhat lacking compared to your standard static programming language. Although there’s very good progress being made with JETLS on the static analysis part, and the VSCode extension is very mature.
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.
While I agree that agents can do pretty much all the coding, I still would love to learn to work with the language myself --- Julia was supposed to be a human-friendly/human-first programming language, with elegant syntax and all that, so it would be a pity not to try it as a human programmer.
Oh yeah absolutely. One of my favourite ways to interact with agents is to spin up an interactive common session where both me and it can run commands in a shared environment. You can ask the agent to setup a MWE for you to play with and further explore in the REPL for example.
I used it for a few hobby projects in VSCode. It's not 100% smooth, e.g. I couldn't compile and run my code with a single shortcut. It's supposed to be possible to show graphs in VSCode but I couldn't make that work.
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.
I wouldn't call them experimental. They just suck IMO, and that's my biggest gripe with using Julia, as language alone is nice, especially with slowly improving AOT. Of course you will also get replies with completely opposing views, so there is no other way than trying it yourself.
20 quadrillion ants is about 2.5 million ants per human - pretty insane to think about. I thought I had a grasp on their abundance, but I'd still probably have underestimated their numbers by an order of magnitude previously.
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.
Anyone using Julia outside university settings? I tried to get into it around 5 years ago, and it felt good, was conceptually interesting, but haven't seen any adoption or talks anywhere outside the academia
Yes - in multiple companies. It works perfectly fine in production, and is especially useful if mathematical optimisation is part of your app somewhere or you need to write any custom, performant algorithms. It's pretty flexible - you can use it for dynamic code like python scripts, you can write optimised data pipelines, it can function as pretty decent server (the async stuff is working well these days). Not sure on adoption, but it defintely works.
looking into it more with the release of juliac. i am able to keep algorithms in one place at a high level for simulation/visualization and running things real time in julia, then if I do everything right i can compile it down to a binary for other platforms using juliac
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.
Julia's interop is amazing. I've used AI to generate Julia code that calls cuBLASLt for efficient tensor FP8 computations. It achieves like 90% of maximal theoretical GPU performance withing a workflow written completely in Julia.
At a new-space startup I've worked at in the past we used it for some satellite (constellation) analysis work. Mechanical, Power, ConOps, that sort of stuff. MATLAB was too expensive and also overkill, as we didn't really need the toolboxes.
Seems like a fun way to teach basic calculus. I think a notebook style, maybe a Julia equivalent of Jupyter, would work better.
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.
They went with python and it's just too general and verbose.
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.
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.
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.
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.
ECMWF ASML Pumas JPMorgan Chase
and many other companies.
I use it routinely, but it isn't a company standard where I work.
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.
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.
Arguably, the effort of the reader manually transcribing the code into their own Jupyter notebook seems didactic.