31–40 of 124 posts
I wonder how much of actual Mathematica use is outside of mathematics.
The new AsymptoticSum and AsymptoticSolve functions sound useful though!
Still don't understand why they don't have a good JSON REST API story. It's like they have no expectation of Mathematica ever being useful in a server based production environment.
I really think that Mathematica and Matlab failed to capitalize on the rise of AI and ML in the last decade. Seems like Python is the only go-to language these fields.
Still don't understand why they don't have a good JSON REST API story. It's like they have no expectation of Mathematica ever being useful in a server based production environment.
Care to elaborate? I was one of the developers behind APIFunction and GenerateHTTPResponse. If you are you talking about the ability to deploy a REST API to our cloud I think you are wrong and I'd be happy to show you how.
It takes me a few minutes to do it with Python using Chalice and AWS Lambda.
I'm also not a fan of the primitive URL query syntax. Why isn't there a way to generate an OpenAPI? A schema?
> nowadays the vast majority of what the Wolfram Language (and Mathematica) does isn’t what’s usually considered math I wonder how much of actual Mathematica use is outside of mathematics. The new AsymptoticSum and AsymptoticSolve functions sound useful though!
I think the people at Wolfram and heavyweight Wolfram Language users use Eclipse - which provides support for debugging. Though running with Eclipse in one window and Notebook in another is a bit of a dogs dinner.
In the Notebook environment I found the current debugging functionality to be pretty terrible. The GUI needs to work much more like a modern IDE, to support easy single stepping, inspection and editing of values, so us mere mortals can figure out what is going wrong when results aren't what you expect (and this is half of what programming is, after all).
In fact it might provide that somewhere among the layers of complexity of its meta-debugging whatnots. But I never figured it out, and really I shouldn't have to be fighting complexity when I'm just trying to find out why my program isn't working.
A decent debugger would help us understand the product better than any number of new documentation functions.
Earlier quoted context omitted.
https://mathics.github.io/ is open source partial clone. Frankly with out a source of income it wouldn't be what it is today.
Mathics is neat, but it is mostly an interface for a small part of the Sympy library and tries to mimic in a partial ad-hoc way the Mathematica syntax. Mathics also seems to be on life-support in terms of developer activity. Sympy or Sage are much better examples of open source computer algebra systems, but they lack the pattern-matching weirdly-lispy language design of Mathematica (the Wolfram language, which while…
A professional license costs 3500€. Which is probably worth it if you use it every day, but not so much if you just use it just once a month to make a graph or integrate something.
They have a "hobby" license for 350€, which is reasonable, but according to their license it can't be used for anything work related.
I think this is an issue with a lot of professional software. It's priced for heavy users and if you are just an occasional user, you are out of luck.
I know I'm dreaming, Stephen is rather on the opposite side, but if he would open source it all/most of it, he'd be one of the most famous people around. I wonder how he'd move financially wise with that kind of bold move?
Seems like he's far from open sourcing it. His team just published a long blog post ( https://blog.wolfram.com/2019/04/02/why-wolfram-tech-isnt-op... ) explaining why they're closed source.
The problem is network effects. The blog post is right that if you are running a large deployment of things like R or Python, you are likely paying vendors and getting support contracts and all these things so that it's not really free-as-in-beer to you. But there are a lot of people not yet at that scale where it is free as in beer to them. An ecosystem based around R or Python has a lot of people who can't afford to pay for Mathematica. Those people are writing blog posts, or answering Stack Overflow questions. If I've just come up with a novel technique and I want to get it out there (either as someone in academia or elsewhere), my broadest reach is going to be to release R or Python code. Everyone with Mathematica can install R if they're curious, people who use R can't just install Mathematica on a whim to look at one research paper. If I'm an employee at a small company that is using Excel for analysis still, it's really easy to smuggle in R or Python and do an ad hoc pilot project that gets adopted, whereas it'll take me six months to get Mathematica through the approval process, and they'll probably tell me no.
So more packages get written for the open source ecosystems, more documentation gets written, I can share my work with more people. Even if R and Python are in every other way worse than Mathematica, over time, the network effects will drive more and more people to R and Python.