I’m not sure about that. I used to use LabView and its various libraries often. The whole thing felt scattered and ossified. I’d take a python standard library any day.

Commercial software polish is lipstick on a pig. A pig that will never be anything else and will eventually die as a pig.

Ugly os software at least has potential to grow internally. Long lived commercial software is a totting carcass with fresh coat of paint every now and then.

Well, that doesn't sound too bad. But this is a high enough barrier for Mathematica to not see wide spread use.

I don't remember what the pricing has been throughout the years. But I do remember that for some of the time I couldn't really afford Mathematica. And the license I wanted was also a bit too expensive to justify for a piece of software that only I would be using within an organization.

Because it is also about enough other people around you not being able to justify the expense. And about companies not wanting to pay a lot of money for licenses so they can lock their computations into an ecosystem that is very small.

Mathematica is, in the computing world, pretty irrelevant. And I'm being generous when I say "pretty": I have never encountered it in any job or even in academia. People know of it. They just don't use it for work.

It would have been nice if the language and the runtime had been open source. But Wolfram didn't want to go in that direction. That's a perfectly fine choice to make. But it does mean that as a language, Mathematica will never be important. Nor will knowing how to program in it be a marketable skill.

(To Stephen Wolfram it really doesn't matter. He obviously makes a good living. I'm not sure I'd bother with the noise and stress coming from open sourcing something)

It still creates a class of haves and have-nots which prevents forming a community.

Plus you buy a version of it, and then someone else is on another version, and you don't have the same features, and the tiny community is fragmented.

I’d like to use it sporadically, but they charge a lot for people in academia, and for my use case it’s simply not worth it.

I’m using xcas now, it’s working pretty well for my humble needs.

Yeah, I was one of those schmucks that used sympy / python instead of mathematica in my physics coursework. Policy was "mathematica is recommended and supported, but you can bring your own tools if you want to and can make them work."

In retrospect, doing the work in mathematica would have probably stretched my brain more (in a good way!) since it provides a different and powerful way of solving problems vs other languages...maybe I'll have to revisit it. Perhaps even try advent of code with it?

While python did get the job done, it feels like the ceiling (especially for power users) is so much higher in mathematica.

Mathematica is awesome for weird, one-off tasks in fields that I'm unfamiliar with, since the documentation is excellent, and the functionality is really broad (so I don't need to figure out how to install a specialty program for every one-off task). But for fields that I'm experienced with or tasks that I'm planning on running frequently, I'll usually just use Python, since most of the Python libraries have more functionality and run quicker than Mathematica.

(Mathematica is of course much better than Python at symbolic math, but this isn't what you are asking about)

I quite like Sage. Python is a much better language than Wolfram (yes, he named it after himself...). In Wolfram, there is no real scoping (even different notebooks share all variables, Module[] is incredibly clumsy), no real control flow (If[] is just a function), and no real error handling. When Wolfram encounters an exception, it just prints a red message and keeps chugging along with the output of the error'd function being replaced by a symbolic expression. This usually leads to pages and pages of gibberish and/or crashes the kernel (which for some reason is quite difficult to interrupt or restart). Together with the notebook format and the laughable debugger, this makes finding errors extremely frustrating.

The notebooks are also difficult to version control (unreadable diffs for minor changes), and unit testing is clearly just an afterthought. Also the GUI performance is bad. Put more than a hand full of plots on a page, and everything slows to a crawl. What keeps me coming back is the comprehensive function library, and the formula inputs. I find it quite difficult to spot mistakes in mathematical expressions written in Python syntax.

Yeah, but that was my point, similar capabilities, not a bunch of tools glued together, poorly integrated, as you point out.

Prob I am a very small minority here, but I used Maple a lot in the past and I liked it a lot. More standard syntax than mathematica, and overall much much easier to debug. In particular, as far as I remember, maple allowed for more transparency in the mathematical methods used in some computation than mathematica, as I could just see what it was doing exactly when an unexpected result came or I wanted to understand some method. When you work a lot with integrals, limits and special functions there is always a shit ton of assumptions and cases all over the place, and if something is missed somewhere the computation can just get wrong. There were some few times I would actually get wrong results in mathematica. When the language is more like a black box, it is hard to know what happened.

Yes!

Reminds me of the “Stop writing Dead Programs” talk. https://news.ycombinator.com/item?id=33270235

No. I work in VC backed startups. Externally they might say that for investors, but talk to a software engineer. Agentic IA is not working, it will regurgitate code from examples online, but if you get it to do something complex or slightly novel, it falls flat on its face.

Maybe it will someday be good enough, but not today, and probably not for at least 5 years.

Maxima, despite its shortcomings, is a great tool for symbolic computation.