I like R and used it two ways. 1) Scheme-like functionalish 2) Tiddyverse and found Julia to be a lot of talk but seemed clunky to me.
The Tiddyverse sounds epic.
Julia 1.10
81–90 of 104 posts
Re: Julia 1.10
#82Earlier quoted context omitted.
As much as it pains me to say it, I don't think Julia will. It looks to me like the practical problems with Julia, while addressable, are being addressed too slowly. There is simply too many rough edges and usability problems as it is now, and at the current pace it will take maybe 10 or 15 years to address them. On the other hand, the major use case for Julia is to have a fast, dynamic language. And it seems to me t…
What's wrong with JS/TS or Lua as a fast dynamic language?
I mean, JS, TS, and Python all have at least one major problem: threading. I don’t recall how Lua threads work but I have a feeling it too suffers.
Re: Julia 1.10
#83The load time improvements are amazing. Thanks to everyone that was involved. I've been using it locally for months now simply because of this feature and I had to update my "how to deal with compile-time" blog post ( https://sciml.ai/news/2022/09/21/compile_time/ ) to basically say system images really aren't needed anymore with these improvements. With that and the improvements to parallel compilation I tend to not…
For historical reasons I'm typically skeptical of claims that "TTFX" have improved. However, I just tried out Julia 1.10 and it is noticeably quicker than the last version I was using (1.8).
It takes some time to precompile packages, but once that's done I'm seeing the first plot pop up in fractions of a second.
Compliments to the Julia team! I'm looking forward to trying out this new version.
Re: Julia 1.10
#84Earlier quoted context omitted.
Julia was ahead of the game with automatic differentiation which took a few years for Python to get support. And it's still ahead of the game with integrating machine learning with scientific modeling. Macros and multiple dispatch are a game changer, and it allows people to hack and iterate on Julia far easier than on Python. And don't get me started on how nice JuMP.jl is for mathematical optimization.
> Julia was ahead of the game with automatic differentiation which took a few years for Python to get support. In what way is this true? Looks like Julia didn't exist until 2012. If I remember correctly, theano was the big AD thing in python at that point.
Theano existed, but it didn't use autodiff at the time, most loss functions had preprogrammed derivative functions. Python first had native autodiff in June of 2013 with the ad package, but previously had bindings to Fortran and C++ libraries that supported autodiff in different use cases. Julia first had native autodiff in April of 2013 with ForwardDiff.jl.
Re: Julia 1.10
#85Earlier quoted context omitted.
aren't most languages invented and initially developed within a private company? Go, Dart, Java, JavaScript, C#, F#, VBA, Kotlin, Erlang, C(at AT&T), Rust (at Mozilla). The list is probably very long
Most of those are open source, and while initially developed at a private company, most are run by non-profits (such as the Rust Foundation for Rust). Also, the language itself is not usually the main product at those companies.
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Re: Julia 1.10
#86The load time improvements are amazing. Thanks to everyone that was involved. I've been using it locally for months now simply because of this feature and I had to update my "how to deal with compile-time" blog post ( https://sciml.ai/news/2022/09/21/compile_time/ ) to basically say system images really aren't needed anymore with these improvements. With that and the improvements to parallel compilation I tend to not…
For historical reasons I'm typically skeptical of claims that "TTFX" have improved. However, I just tried out Julia 1.10 and it is noticeably quicker than the last version I was using (1.8). It takes some time to precompile packages, but once that's done I'm seeing the first plot pop up in fractions of a second. Compliments to the Julia team! I'm looking forward to trying out this new version.
I think v1.9 is where most of the magic happened there — packages gained the ability to list out their precompilation work during installation, which reduced compilation (and associated invalidations) at runtime.
Re: Julia 1.10
#87Earlier quoted context omitted.
For historical reasons I'm typically skeptical of claims that "TTFX" have improved. However, I just tried out Julia 1.10 and it is noticeably quicker than the last version I was using (1.8). It takes some time to precompile packages, but once that's done I'm seeing the first plot pop up in fractions of a second. Compliments to the Julia team! I'm looking forward to trying out this new version.
I think v1.9 is where most of the magic happened there — packages gained the ability to list out their precompilation work during installation, which reduced compilation (and associated invalidations) at runtime.
1.10 had some substantial `using` time improvements (roughly 2-3x) from just hunting down lots of the slow parts in the compiler and fixing them.
Re: Julia 1.10
#88Earlier quoted context omitted.
That page is a bit marketing
No different from the usual rewrite in Zig and Rust articles on HN, and more industry relevant.
Any page like that which is industry relevant is going to have to be associated with a company because you need the marketing team to go through the effort to get the case studies approved by each and every company. It's not an easy process since for example something like the Instron case study (https://juliahub.com/case-studies/auto-crash-simulation/) has details of upcoming projects like the Catapult Light which are major cost improvements passed on to customers, but you then have to go through the whole process of "well should we share this and let our competitors know what we used to get this advantage?" and contracts have to be signed before such a page can ever be built. The JuliaHub website has a whole list of case studies which have undergone this process and I couldn't see how you would get such detailed industrial accounts otherwise.
For open source accounts, there's the SciML showcase page https://sciml.ai/showcase/. Thats very focused in just one domain though, and I tend to just update it with what I remember to put in there so it probably only has about 1/4 of the blogs and news articles that it should, and the "External Applications Libraries and Large Projects using SciML" part is woefully incomplete, but at least it gives a picture of what's going on. It's hard to keep those kinds of pages up to date because exponential growth means that page requires exponential work.
Re: Julia 1.10
#89> tanpi is now defined. It computes tan(πx) more accurately than `tan(pix)` (#48575).
unescaped * caused website to italise text between formulas
Re: Julia 1.10
#90I like R and used it two ways. 1) Scheme-like functionalish 2) Tiddyverse and found Julia to be a lot of talk but seemed clunky to me.
> and found Julia to be a lot of talk but seemed clunky to me
This is a bit vague, any concrete example?