Nim v2.0 released
Nim v2.0 released
I'm curious to the reasoning behind moving a bunch of standard library stuff to external packages, listed at the end of the blog post. Faster compiling? Smaller executables? Faster development for each now separate package?
I don't know the specific motivation here but in general it's for package development to not be tied to the language release. People also generally have different backwards compatibility expectations for the stdlib vs a regular library and that constrains the development of the package. In Python the meme is that stdlib is where packages go to die. Not all large stdlib languages feel that way. Clojure, for example, has a pretty sizeable stdlib while being code frozen without a lot of demand for change. In general, however, language developers prefer not having things in stdlib.
I saw that list, and figured that they were distancing themselves from obsolete encryption (MD5 & SHA-1), as well as remove database management from their scope (which seems like the right move, IMO).
It seems like maybe there are some new features that weren't in the previous release candidate? I don't remember default values for objects being a thing. Maybe just me though?
That is a new and welcome change, in my opinion. I have a bit of experience with Nim and when you defined a type (object), you couldn't define default values. The workaround was making or overloading a
new()function with default values instead.Oh, yeah, don't get me wrong, I like the change. I just figured the difference between a release candidate and the actual release would just be bug fixes and such.
I want to love Nim but during my trial run with it. It was a pain in the ass to get set up on my Mac in a way that I could use it easily ie as a repl for quick and dirty prototyping and learning
Doesn't MacOS have
homebrewthat takes care of these things?No issue setting up Nim itself (and I realize my complaint is not fault to Nim itself) but it would be great if this complimentary jupyter kernel for Nim would work on MacOS... Hasn't been maintained in a while: https://github.com/stisa/jupyternim/issues/38
Would be very useful for my workflow as someone who wants to explore Nim for data science-y type tasks.
Anyone know of an alternative Nim jupyter kernel?