WEBVTT captioned by gabriele
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Hello, I'm very excited to tell you
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about shared philosophies
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between the Julia programming language and Emacs.
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While Julia and Emacs might look like
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different pieces of software,
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I think there is profound commonalities between the two.
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Let's start by introducing Julia.
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Julia is a high-level dynamic programming language.
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Julia is free and open source software
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and is used primarily for scientific computing.
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The reason Julia is used for scientific computing
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is that while Julia is high level
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and has a syntax that looks like Python or MATLAB,
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Julia can be high performance.
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I use it to develop climate models
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that run on hundreds of GPUs.
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Models that are traditionally developed with C, C++, or Fortran.
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But how is this possible?
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How can Julia be high performance
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but also high level at the same time?
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What makes Julia, Julia?
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Well, what makes Julia, Julia
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is the idea of multiple dispatch.
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Multiple dispatch is the concept where a function call is resolved
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by looking at the types of every single argument involved.
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So, let's explore this with this example.
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Here, I define a function add that takes two objects
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and sums them together.
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And I call add with two different types.
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First with just integers and second with floats.
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So, let's look at what this produces.
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Here is the output of add in Julia.
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So, first we have add, a function with one method.
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I'm going to explain this in a second.
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And then we have our return values 12 and 12.0.
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What we cannot see here is that
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Julia has specialized code
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for the two different function calls.
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For integers and for floating points.
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Let's make this more explicit by specifically providing
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a new method for the case with floating point.
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So here, now I have an add function
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specifically for floating point. Instead of taking
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A + B, this returns A exponent B. Let's call this.
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And what we can see here is that
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now we have two methods.
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So, we add a new method to the same function.
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This is a method that is
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specifically for floating points.
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And instead of having the value 12, we have 100.
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And this is where the trick lies.
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Julia compiles the most, um, specialized version
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that can be compiled. So, a version with integers,
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a version with floats. And in this,
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compiling is an actual compilation with LLVM
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with optimization and so on.
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This is not just ahead of time compilation.
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Soon as the Julia knows the type,
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a function is compiled if it's not compiled already
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and then it's used.
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When types are stable and well inferred,
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this can lead to code that is as performant
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or comparable to C and Fortran.
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So, this is what makes Julia, Julia.
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Multiple dispatch with just ahead of time compilation
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of highly efficient code.
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So now, what makes Emacs, Emacs?
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Well, in my opinion, what makes Emacs, Emacs
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is interactivity, extensibility, and community.
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And I claim that Julia has the same three.
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Interactivity, extensibility, and community
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are three key pillars for Julia.
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More specifically, Julia encourages a
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REPL-driven, introspective, interactive workflow.
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It's largely open to extension and modification
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to the point that most of Julia is written in Julia.
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And Julia has a thriving and welcoming community
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with lots of packages. So, let me showcase
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a little bit of this REPL-driven, introspective,
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interactive workflow with the hope that commonalities
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with Emacs will emerge naturally.
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So, let's start by opening a Julia REPL.
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Here, I have a Julia REPL.
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Let me give you a tour of the Julia REPL.
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So, the REPL comes with lots of useful features,
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from a shell to a package manager.
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So, for example, let's add the random package.
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Um, yeah, I have the random package.
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I can look at what's inside.
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We have the statistics with random
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in this particular environment.
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Environments are fully declarative.
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So here we have the dependencies of this environment.
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And I can explore in this manifest,
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the specific versions that are used.
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So we have a shell, we have a package manager,
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and then we have a very powerful help system.
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So, for example, I can ask for help for length.
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And here we can see we have, well,
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the help for length. Lots of information about
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how to call length, the expected return values,
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examples. And now you can probably start seeing that
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this is not that different from calling length.
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So this is the output for length,
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or for help for length in in Emacs.
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So we have help, and we can do more.
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We can even look at the source code for length.
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So now, what we can see here is that now, well,
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we cannot see because it's zoomed in
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because the font size is huge, but in this page here,
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we can see the implementation of length.
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It's this line here in the middle,
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or these few lines here in the middle.
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And as you... Let's do this again.
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As we can see here at the bottom,
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what we are looking at,
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this is the source code of Julia.
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We can change this.
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There's even a macro edit
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if you want to change its length.
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And yeah, I use the word macro.
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Julia supports metaprogramming.
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And actually metaprogramming is
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one of the key features in Julia.
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It's used extensively in the core,
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but it's also used extensively in packages,
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both to extend the Julia ecosystem and functionalities,
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but also to develop full domain specific languages.
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Some of the useful macros are, well, I don't know,
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like time. Here, we have a built-in
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basic performance tool in in in Julia.
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And I want to showcase more introspection, macros.
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But for that, I'm going to do it slightly different.
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I'm going to open a file
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example.jl where I define a
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function, or our
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function add, there was an asterisk
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and I will go back to that in a second.
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So now, I am going to include this this file,
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and I can call my function add, one and two,
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and we get three. And now, what I can do is this.
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I can look at what code gets compiled
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when I call my when I call 1 + 2.
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And here, now we can see
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that there is some integer stuff.
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But if I make this floating point,
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now the compiled code changes.
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Now, maybe assembly code
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is a little bit too hard to read,
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so I can look at the LLVM IR representation.
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In this case we can see that there is promotion.
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The promotion will probably go away
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if I make everything float. So this we have F add,
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floating point add for a double,
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but we can also look at
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the Julia lowered representation
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after the abstract syntax tree is produced.
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The reason I put this in a file is because
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now what I can do is I can change this.
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And now, one and two will be two.
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So this to me is very reminiscent
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of how I work in Emacs,
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where there is a global state
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that I can access and modify any time
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with no restrictions. And this happens in in Julia too.
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Typically, we don't want to modify functions
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that are in other packages or they are in base,
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but we can do that. For example,
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I can change what is plus for integers.
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And if I change with this plus
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and make it so that any two integers return zero,
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well, I can do this. This will break Julia because,
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well, Julia is built in Julia.
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So if we break this, well, nothing will work.
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But I can do that. This to me is one of
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the signs of the powerful, introspective,
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and powerful interactive type of workflows
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that Julia enables.
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Finally, I want to talk about the general registry.
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This is the equivalent of Melpa.
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It comes with with Julia.
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But this is very akin to Melpa.
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It's built upon Git essentially.
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It's collaborative, as relies heavily on GitHub, GitLab.
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It's heavily automated.
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And comes with lots and lots of tools and packages.
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What's beautiful about all these tools and packages
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is that in the same way many of Emacs packages
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just play nicely with each other
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without any input from the developers,
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the same is true for Julia packages.
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The Julia packages are highly composable,
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so two developers can develop
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two distinct packages
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that end up playing nicely together for free
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because of the intrinsic structure, intrinsic way
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Julia objects are built.
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So, with all of this, I also want to mention that
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the community, in addition to have all these packages,
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is highly active, highly collaborative.
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The community meets regularly on places like Slack,
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as opposed to the Emacs community
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that I'd say maybe meets on Reddit.
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So, with all of this, I want to thank you
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for your attention, enjoy Emacs, and enjoy Julia.
