I'll preface by saying by far my biggest gripe with Julia is the inability to deploy binaries easily. The only hope on easy deployment I suppose is to hope it becomes commonly installed enough that you can rely on it being on systems. But some of your complaints are a bit off (in my own opinion):
>and you can't quickly run a script
What is wrong with the following to run a script?
$ julia myscript.jl
If you have specific needs that demand, after hitting return, the few seconds of delay for the vast majority of scripts is an issue, you can pre-compile it ahead of time or simply use something like https://github.com/dmolina/DaemonMode.jl
Julia has issues as with all languages but "not being able to quickly run a script" is by far one of the easiest to work around.
> and you can't quickly run a script or REPL for development.
REPL- I disagree. Of course you can - that's how many of use Julia.
> And now Julia has competition from Mojo.
...maybe. The code-samples we've seen from Mojo look very similar to Python, obviously. And that is specifically why a lot of poeple love Julia.
The problems people are more and more interested in (machine learning, etc) are at their base mathematical problems. These languages are all tools to translate mathematics into computer instructions. The code should then ideally look as close to that math as possible. Spamming np.linalg, sp.sparse, and so forth over and over again is just ugly, and the entire Python workflow overly encourages object oriented design for concepts that are mathematically functions. And, well, should be functions. If you're working in these or related fields at all and write any code with Julia, it's hard to not fall in love.
Mojo may make Python faster. But it will still be, roughly, Python.
And considering that the Python 2 -> Python 3 transition has still not completed, I think it's premature to conclude Mojo's python upgrades will get anywhere.
>and you can't quickly run a script
What is wrong with the following to run a script?
$ julia myscript.jl
If you have specific needs that demand, after hitting return, the few seconds of delay for the vast majority of scripts is an issue, you can pre-compile it ahead of time or simply use something like https://github.com/dmolina/DaemonMode.jl
Julia has issues as with all languages but "not being able to quickly run a script" is by far one of the easiest to work around.
> and you can't quickly run a script or REPL for development.
REPL- I disagree. Of course you can - that's how many of use Julia.
> And now Julia has competition from Mojo.
...maybe. The code-samples we've seen from Mojo look very similar to Python, obviously. And that is specifically why a lot of poeple love Julia.
The problems people are more and more interested in (machine learning, etc) are at their base mathematical problems. These languages are all tools to translate mathematics into computer instructions. The code should then ideally look as close to that math as possible. Spamming np.linalg, sp.sparse, and so forth over and over again is just ugly, and the entire Python workflow overly encourages object oriented design for concepts that are mathematically functions. And, well, should be functions. If you're working in these or related fields at all and write any code with Julia, it's hard to not fall in love.
Mojo may make Python faster. But it will still be, roughly, Python.
And considering that the Python 2 -> Python 3 transition has still not completed, I think it's premature to conclude Mojo's python upgrades will get anywhere.