31–40 of 309 posts
Going by Tiobe there is gulf between the top 4 languages and everything else. To put it into perspective Julia is only twice as relevant as Prolog and on par with Scratch.
Of course I don't necessarily think Tiobe is a great metric for this but it was quoted in the article.
I wish we would see a larger fraction of the energy invested into propagating the 'Pythonic' approach were _properly_ redirected into improving relevant aspects of Julia.
Earlier quoted context omitted.
Julia has the focus on scientific and numerical computing, and is overtaking the python/numpy combo in that niche. In addition to being considerably faster than python, it also has quite some innovative libraries in the area. This can also extend into machine learning, where python has been the go to language, despite its limitations. For other areas, like web programming, there is no sign of Julia replacing Python i…
> and is overtaking the python/numpy combo in that niche No, it's isn't. Julia is growing but it's far from overtaking Python at this point. > For other areas, like web programming, there is no sign of Julia replacing Python in the forseable future. That's where Go comes in.
And yes, there are ways to AOT compile as well.
Earlier quoted context omitted.
You can call Python directly from Julia https://github.com/JuliaPy/PyCall.jl so much of the Python library ecosystem (say, matplotlib) is available to be used in Julia programs. That helps the adoption story quite a bit. You can do the number-crunching in Julia where performance counts, and then analyse and present the results using Python.
- Using Python directly is a better experience than calling Python from Julia - I've never run into unsolveable performance issues with Python So I guess I'm not in the target audience unless I just happen to be curious about a new language? That's kind of my overall point - even if Julia is a good language on its own and I work in data science, I don't have reasons to pick it over Python.
Not until it addresses the JIT overhead.
In my tests I frequently switch between cpython, pypy and julia (depending on the libraries/task I want to perform) and I haven't found the JIT overhead to be worse than pypy on average. Count me as one of the 1-based index haters, but I do love multiple dispatch and the language in general. As a language for explorative tools and analysis is on par of python (strict preference between the two according to taste). To…
Julia is a nice language, it's just tough to compete with Python. - The beginner experience in Julia is still much worse than it is in Python. Stuff that should work intuitively sometimes doesn't, and when you get a cryptic error message, it's difficult to find relevant help online. And when you do find help, some of it is out of date because the language has changed over the past few years. - You can squeeze a lot o…
A similar enabler in a new field could help Julia burst in as a general language. My 2c.
> It will be important for it to be adopted as a first-class target language by CPU/GPU vendors.” I’m not sure what the Apple M1 SoC with AMX [1] means for Julia within the Apple ecosystem. [1] https://news.ycombinator.com/item?id=25801500
In particular, multiple dispatch makes operator overloading so much nicer than Python’s fragile “dunder” methods like `__add__` and `__radd__`.
Earlier quoted context omitted.
> and is overtaking the python/numpy combo in that niche No, it's isn't. Julia is growing but it's far from overtaking Python at this point. > For other areas, like web programming, there is no sign of Julia replacing Python in the forseable future. That's where Go comes in.
I second this. Python is actually starting to get significant traction in the scientific community. Depending on the field, R, Fortran and Matlab (and even C++) still have a huge lead. It's nice that Julia is getting noticed, but it's a distant blip in the radar. The sci community is really hard to move from existing battle-tested and performant libraries.