31–40 of 144 posts
There is something elegant about having all functions at the top level (or module) scope, `foo(x, y, z)`, but I there is also something really nice about function names scoped to the thing/noun/subject: `x.foo(a, b)`. As far as I know Julia only does the former. Sometimes the latter seems a lot easier to deal with.
* TTFP - this is the big one. Every thing is a sluggish sometimes * Sluggish IDE in VSCode; I am on Windows so start up is a bit of a pain i often have to wait 20+ s for it to become responsive * Non-robust Basic Data Science coverage e.g. GLM fails on some datasets that R can handle. So it's the only code in my workflow that still has R in it
There is something elegant about having all functions at the top level (or module) scope, `foo(x, y, z)`, but I there is also something really nice about function names scoped to the thing/noun/subject: `x.foo(a, b)`. As far as I know Julia only does the former. Sometimes the latter seems a lot easier to deal with.
I just noticed that someone finally fixed that stretched minesweeper screenshot on Julia's landing page.
I started using Julia in 2017, and as a physics student who saw Python, Mathematica, and Matlab as a series of tools that I had to pull out sometimes to solve specific problems, it came as a great shock to me that I was suddenly interested in Julia for it's own sake. It's not an overstatement to say that this language totally changed my relationship with computers and programming.
I cannot remember exactly when I discovered Julia or where. But I remember I got intrigued early on and wrote a blogpost about 9 years ago on Tumblr, which is still there comparing Julia to Ruby of all things: https://assoc.tumblr.com/post/70484963303/getting-comfortabl... The normal thing today is comparing Julia to R, Matlab and Python. But my intro to Julia was actually trying to convert code examples in the O'Rei…
For unix tools, I assume you mean command line tools? In which case how do you ameliorate the long startup time on each invocation?
I'll just have packages for doing doing image file conversions in batch, modifying source code, changing configuration files etc.
Actually come to think about it, I used to have shell scripts in a pipeline to build an application for security. We had to obfuscate the source code and stuff like that. When I rewrote to Julia it all ran much faster. Even if there was a startup time, the workload was heavy enough that the higher performance of Julia easily outperformed bash.
As a replacement for Python, I've never understood the appeal, and it's probably not going to fill that niche. Still, as a replacement for Matlab/Mathematica, it's doing swimmingly.
All the best.
I cannot remember exactly when I discovered Julia or where. But I remember I got intrigued early on and wrote a blogpost about 9 years ago on Tumblr, which is still there comparing Julia to Ruby of all things: https://assoc.tumblr.com/post/70484963303/getting-comfortabl... The normal thing today is comparing Julia to R, Matlab and Python. But my intro to Julia was actually trying to convert code examples in the O'Rei…
I count myself as someone surprised at Julia's success. As a replacement for Python, I've never understood the appeal, and it's probably not going to fill that niche. Still, as a replacement for Matlab/Mathematica, it's doing swimmingly. All the best.
In that case, I guess we'll see. Python will never be a particularly good language for implementing such things, but has become pretty ok for using them once someone has implemented (in another language, and made the python bindings).
Network effect is the real barrier, python itself is very easy to toss for this use .