Don’t people google their newly coined acronyms? GRAPE is already Gradient-Ascent-Pulse-Engineering, which is arguably “machine learning” (optimal control)
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.
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.
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++.
C++ and Rust don’t really have good semantics for scientific computing (linear algebra). They’re great for many other domains like “system’s programming”, where Fortran doesn’t compete, of course. The only language that’s similarly (in fact, more) expressive as Fortran for scientific computing today, as well as holding up for performance, is Julia
How has it “not panned out”? Julia has been in a useable state only for a few years, and is still rapidly developing. Prior to Julia 1.0, I would have said Fortran (which I have 15 years experience with) was the most productive language for scientific computing. Today, Julia is, by a wide margin. In fact, our benchmarks show Julia matching or beating Fortran’s performance, to say nothing of the mich increased flexibility.
Yes, you can absolutely (and we will have to) mitigate hot temperatures with building design, but only up to the point where you have wet bulb temperatures >35C over several days. There are heavily populated areas that are in real danger of having something like that happening, and this is what that would look like: https://www.orbitbooks.net/orbit-excerpts/the-ministry-for-t...
It’s much easier to heat something than to cool it. You can build a fire without any technology at all, but you can’t do anything about unsurvivable heat without a working power grid.
Has it “lost” preference? I was more under the impression that it just hasn’t yet gained as much popularity as it deserves based on its technical merits, due to being quite a young language and the entrenchment of a large existing Python ecosystem.
Although I'm technically not a native speaker, I consider myself completely fluent, and a competent writer. I still find Grammarly or similar services quite useful, even if just to catch stupid "grammatical typos" that a traditional spellchecker would miss. I basically treat it like autocorrect.
Yeah, Grammarly is moving in the wrong direction for me. What I’d like to see is better integration with text editors etc, especially in a plain text environment. 99% of what I write is Markdown, LaTeX, or some other markup language, which Grammarly does not deal well with at all. LanguageTool is much better in this respect, although it doesn’t find nearly as many mistakes as Grammarly