Don’t people google their newly coined acronyms? GRAPE is already Gradient-Ascent-Pulse-Engineering, which is arguably “machine learning” (optimal control)
Readit Newsgoerz commented on Understanding RL for model training, and future directions with GRAPE arxiv.org/abs/2509.04501... · Posted by u/sonabinu
I wrote this plugin for Neovim to be able to edit Jupyter notebook .ipynb files directly in Neovim. The native format for notebooks is quasi-binary json. This plugin uses the jupytext tool (https://github.com/mwouts/jupytext) to convert that json to a plain text representation (markdown or code) on the fly, and back to json when the file is saved.
For someone coming from Matlab, is Julia a valid replacement?
Very much so. See https://docs.julialang.org/en/v1/manual/noteworthy-differenc... for some details
goerz commented on Apple unveils M3, M3 Pro, and M3 Max apple.com/newsroom/2023/1... · Posted by u/ehPReth
my intel died last week. had to get an m2 pro and THIS drops today. kinda salty that my intel couldn' have held out till the M3 was available
“Last week” is well within the 2-week return window.
goerz commented on Julia and Mojo Mandelbrot Benchmark discourse.julialang.org/t... · Posted by u/npalli
I certainly consider Julia to be a Lisp, and I’m pretty sure that’s what the person you responded meant, too. His point remains true: Julia appears to have little chance of overtaking Python, except in some tiny niche areas. And even in these niche areas, I fear that Julia will end up losing to Mojo.
I really hope I am wrong. I love Julia and would like to see it succeed everywhere, but it doesn’t seem to be happening.
I’m not sure I would consider the scientific computing that Julia targets a “tiny” niche. It’s worth remembering that Python is 30 years older than Julia, and Julia has only been in a really usable state for 4 or 5 years. You can’t expect it to displace Numpy/Scipy/etc overnight. Especially if you include machine learning, where there’s just huge momentum with large corporations having massively invested in Python frameworks for at least 10 years. Subjectively, I’m seeing quite a lot of growth in Julia. It’s certainly a much stronger language than the Python/C++ combination that currently has the biggest market share in that area.
Also, there’s nothing wrong with niches. Julia is undoubtedly less of a general-purpose language like Python, but it very much shines in its domain.
One thing I noticed on the page:
> Fortran is a relatively small language that is surprisingly easy to learn and use
I suppose you could say the same of C. C is also a small language, but if you don't know your way around the libraries and tooling you're going to have a bad time. I wonder if the same is true for Fortran.
True, but C and Fortran have very different semantics that make them suitable for very different fields. In particular, C does not have great semantics for linear algebra. Also, C has many footguns due to its use of pointers, while Fortran has virtually none.
You still need to learn the tooling, but the tooling is actually pretty similar for Fortran/C/C++ (same compiler/linker toolchain). Libraries aren't that much of a thing in Fortran, but to the extent that they are, this again is similar to C/C++.