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Here is one:
https://github.com/TakekazuKATO/TailRec.jl
It works by inspecting the code and rewriting a function to turn tail calls into loops.
The interesting bit is that it was very easy to write because of the strong macros in Julia.
It's acknowledged. The full redefinition of any function in any context was solved by RuntimeGeneratedFunctions and we use it extensively throughout SciML, ModelingToolkit, Pumas, etc. so it's fair to say struct redefinitions are the only thing left. That said, "struct redefinitions" are done all of the time in many contexts: if you know you may need to do this, you can just use a named tuple which acts just like an anonymous type and obeys dispatch on a named form that you can then hijack at runtime. With smart engineering then you can be redefining everything on the fly, and we do this through MTK quite regularly. That said, it would be nice to do the last feat of full redefinition of non-anonymous structs, and the latest proposal for how to do struct redefinition is https://github.com/JuliaLang/julia/issues/40399. So we both built and provided solutions for how to do it, showed how to do it in libraries, tutorials, videos, etc. How is that not acknowledging the point of view and doing something about it?
You might want to try and acknowledge the other point of view where I note that, hey, we did make this work but it was a smaller deal then we thought because we legally cannot employ it in many production contexts. We're still going to work out a few details for fun and ease of debugging, but given that we have extensively looked into and thought deeply about the whole issue, we have noticed that the last little bits are less useful than we had thought (at least in the contexts and applications I have been discussing, like clinical trial analysis). That doesn't mean we won't finish the last pieces, but given how much of it you can already do and how many teaching materials show how to do work around the issues in any real-world context, and how little of a real-world application the last few bits have, it shouldn't be surprising that the last pieces haven't been a huge priority. So instead of looking narrowly at one factor, I encourage you to take a more global view.
Clojure has a high performance data frame library that leverages new JVM vector API and high quality apache arrow protocol.
Talk related - https://youtu.be/5mUGu4RlwKE
https://github.com/zero-one-group/geni-performance-benchmark
This is a total non issue as indexing is an operation that is subject to multiple dispatch. For a humorous example see https://github.com/giordano/StarWarsArrays.jl
> The main pain point is probably the lack of standard, multi-environment packaging solutions for natively compiled code.
Are you talking about something like BinaryBuilder.jl[1], which provides native binaries as julia-callable wrappers?
--
well, you can always program in wenyan: https://github.com/wenyan-lang/wenyan
But seriously, it's 2021. We are no longer slave to ASCII, or even English.
Most of the newer math oriented langs have heavy use of unicode (julia, agda, lean, etc).