HackerReposA FIELD GUIDE TO OPEN SOURCE
ProSponsor the shelf♧ 0
← Back to top all-time
JuliaLang
julia
Julia2012–2026
JuliaLang / julia

julia

The Julia Programming Language

178Distinct people
335Mentions
5Explicit recommenders
49.3KGitHub stars
REPOSITORY AT A GLANCE

What to know before exploring

What it is

The Julia Programming Language

GitHub

Where it is used

  • Aladdin — Aladdin uses Julia to power its analytics platform. BlackRock deploys Julia within the Aladdin system to run its analytics. Source

What people are saying

There's another approach for doing this: generate a random number between 1 and 2 (they all have the same exponent) and then subtract 1 (that's the default implementation in…

FabHK used a recommendation phrase. 5 indexed comments match. This is a phrase match, not a verdict.

No classified warning is attached to this page. Read the discussions before treating silence as approval.

Also in these discussions

Mentioned in the same Hacker News threads. That is not proof they are substitutes.

This week on Hacker News

Nov 8–Nov 15 UTC · Direct repository links and separately labeled related project stories. Indexed through 11/15/2026, 10:49:33 PM UTC.

No direct repository links were found in the indexed HN discussions over the past seven days. Older discussions are available below.

Mentions over time

HN mentions by year335 dated mentions

Observed mentions: 2012-02-18 12:55:37 UTC to 2026-10-12 16:48:37 UTC.

0102030402012-01-01 00:00:00 UTC to 2013-01-01 00:00:00 UTC (exclusive): 12 mentions; 1 submissions, 11 comments20122013-01-01 00:00:00 UTC to 2014-01-01 00:00:00 UTC (exclusive): 13 mentions; 1 submissions, 12 comments2014-01-01 00:00:00 UTC to 2015-01-01 00:00:00 UTC (exclusive): 42 mentions; 7 submissions, 35 comments2015-01-01 00:00:00 UTC to 2016-01-01 00:00:00 UTC (exclusive): 26 mentions; 4 submissions, 22 comments2016-01-01 00:00:00 UTC to 2017-01-01 00:00:00 UTC (exclusive): 26 mentions; 4 submissions, 22 comments2017-01-01 00:00:00 UTC to 2018-01-01 00:00:00 UTC (exclusive): 19 mentions; 5 submissions, 14 comments2018-01-01 00:00:00 UTC to 2019-01-01 00:00:00 UTC (exclusive): 29 mentions; 5 submissions, 24 comments2019-01-01 00:00:00 UTC to 2020-01-01 00:00:00 UTC (exclusive): 31 mentions; 4 submissions, 27 comments20192020-01-01 00:00:00 UTC to 2021-01-01 00:00:00 UTC (exclusive): 22 mentions; 3 submissions, 19 comments2021-01-01 00:00:00 UTC to 2022-01-01 00:00:00 UTC (exclusive): 37 mentions; 2 submissions, 35 comments2022-01-01 00:00:00 UTC to 2023-01-01 00:00:00 UTC (exclusive): 38 mentions; 1 submissions, 37 comments2023-01-01 00:00:00 UTC to 2024-01-01 00:00:00 UTC (exclusive): 22 mentions; 4 submissions, 18 comments2024-01-01 00:00:00 UTC to 2025-01-01 00:00:00 UTC (exclusive): 8 mentions; 0 submissions, 8 comments2025-01-01 00:00:00 UTC to 2026-01-01 00:00:00 UTC (exclusive): 6 mentions; 0 submissions, 6 comments2026-01-01 00:00:00 UTC to 2027-01-01 00:00:00 UTC (exclusive): 4 mentions; 1 submissions, 3 comments. Incomplete calendar period.2026*

* An incomplete calendar period is marked with an outlined bar. Its count is not directly comparable with a full period.

Counts come from the repository’s dated source evidence. Index cutoff: 2026-11-02 08:16:59 UTC.

View mention counts and date ranges
UTC ranges include their start and exclude their end. Select a period to filter the source evidence.
PeriodStart (UTC)End (exclusive, UTC)MentionsSubmissionsComments
20122012-01-01 00:00:00 UTC2013-01-01 00:00:00 UTC12111
20132013-01-01 00:00:00 UTC2014-01-01 00:00:00 UTC13112
20142014-01-01 00:00:00 UTC2015-01-01 00:00:00 UTC42735
20152015-01-01 00:00:00 UTC2016-01-01 00:00:00 UTC26422
20162016-01-01 00:00:00 UTC2017-01-01 00:00:00 UTC26422
20172017-01-01 00:00:00 UTC2018-01-01 00:00:00 UTC19514
20182018-01-01 00:00:00 UTC2019-01-01 00:00:00 UTC29524
20192019-01-01 00:00:00 UTC2020-01-01 00:00:00 UTC31427
20202020-01-01 00:00:00 UTC2021-01-01 00:00:00 UTC22319
20212021-01-01 00:00:00 UTC2022-01-01 00:00:00 UTC37235
20222022-01-01 00:00:00 UTC2023-01-01 00:00:00 UTC38137
20232023-01-01 00:00:00 UTC2024-01-01 00:00:00 UTC22418
20242024-01-01 00:00:00 UTC2025-01-01 00:00:00 UTC808
20252025-01-01 00:00:00 UTC2026-01-01 00:00:00 UTC606
2026 (partial)2026-01-01 00:00:00 UTC2027-01-01 00:00:00 UTC413

From the project README

An excerpt from the repository, fetched Nov 12, 2026.

Read the project’s introduction
Documentation Continuous integration Code coverage The Julia Language Julia is a high-level, high-performance dynamic language for technical computing. The main homepage for Julia can be found at julialang.org. This is the GitHub repository of Julia source code, including instructions for compiling and installing Julia, below. Resources Homepage: https://julialang.org Install: https://julialang.org/downloads/ Source code: https://github.com/JuliaLang/julia Documentation: https://docs.julialang.org Packages: https://julialang.org/packages/ Discussion forum: https://discourse.julialang.org Zulip: https://julialang.zulipchat.com/ Slack: https://julialang.slack.com (get an invite from https://julialang.org/slack/) YouTube: https://www.youtube.com/user/JuliaLanguage Code coverage: https://coveralls.io/r/JuliaLang/julia Contributing to Julia We welcome contributions from developers of all experience levels, including bug fixes, documentation improvements, tests, and performance enhancements. New contributors are encouraged to start by reading CONTRIBUTING.md. ImportantIf your pull request contains substantive contributions from a generative AI tool, please disclose so with details, and review all changes before opening. This also applies to other content, such as issues, discussions, and comments. Learning Julia Learning resources Binary Installation The recommended way of installing Julia is to use juliaup which will install the latest stable julia for you and help keep it up to date. It can also let you install and run different Julia versions simultaneously. Instructions for this can be found here. If you want to manually download specific Julia binaries, you can find those on the Manual Downloads page. The downloads page also provides details on the different tiers of support for OS and platform combinations. If everything works correctly, you will get a julia program and when you run it in a terminal or command prompt, you will see a Julia banner and an interactive prompt into which you can enter expressions for evaluation. You can read about getting started in the manual. Note: Although some OS package managers provide Julia, such installations are neither maintained nor endorsed by the Julia project. They may be outdated, broken and/or unmaintained. We recommend you use the official Julia binaries instead. Building Julia First, make sure you have all the required dependencies installed. Then, acquire the source code by cloning the git repository: git clone https://github.com/JuliaLang/julia.git and then use the command prompt to change into the resulting julia directory. By default, you will be building the latest unstable version of Julia. However, most users should use the most recent stable version of Julia. You can get this version by running: (replace [tag] with the desired tag) git checkout [tag] To build the julia executable, run make from within the julia directory. Building Julia requires 2GiB of disk space and approximately 4GiB of virtual memory. Note: The build process will fail badly if any of the build directory's parent directories have spaces or other shell meta-characters such as $ or : in their names (this is due to a limitation in GNU make). Once it is built, you can run the julia executable. From within the julia directory, run ./julia Your first test of Julia determines whether your build is working properly. From the julia directory, type make testall. You should see output that lists a series of running tests; if they complete without error, you should be in good shape to start using Julia. You can read about getting started in the manual. Detailed build instructions, should they be necessary, are included in the build documentation. Uninstalling Julia By default, Julia does not install anything outside the directory it was cloned into and ~/.julia. Julia and the vast majority of Julia packages can be completely uninstalled by deleting these two directories. Source Code Organization The Julia source code is organized as follows: Directory Contents base/ source code for the Base module (part of Julia's standard library) cli/ source for the command line interface/REPL contrib/ miscellaneous scripts deps/ external dependencies doc/src/ source for the user manual etc/ contains startup.jl src/ source for Julia language core stdlib/ source code for other standard library packages test/ test suites Terminal, Editors and IDEs The Julia REPL is quite powerful. See the section in the manual on the Julia REPL for more details. On Windows, we highly recommend running Julia in a modern terminal, such as Windows Terminal from the Microsoft Store. Support for editing Julia is available for many widely used editors: Emacs, Vim, Sublime Text, and many others. For users who prefer IDEs, we recommend using VS Code with the julia-vscode plugin. For notebook users, Jupyter notebook support is available through the IJulia package, and the Pluto.jl package provides Pluto notebooks.
Read the full README on GitHub

What Hacker News says

19 sources
Right, so the way to address this is to provide guarantees that this kind of optimization /will/ occur and providing syntax for making writing "pseudo-mutating" code more convenient. There's a PR [1] for the latter, but it's been shelved while we focus on getting 1.0 out the door instead of adding new features. Optimization guarantees + convenient syntax provides everything you need without trashing the semantics of the language by bifurcating the type system into two incompatible kinds of values. [1] https://github.com/JuliaLang/julia/pull/21912
In Some Insights from a Julia Developer
This seems to be a terrible misinformed comment. Not only isn't there really a problem with using GPL tools in a commercial context, as long as they don't become part of your work product. GCC is probably used to compile a large percentage of commercial software. And I'm sure you're writing comments in Chrome, yet expect to retain the copyright on those words. But what makes this really asinine is that Julia isn't even GPL: https://github.com/JuliaLang/julia/blob/master/LICENSE.md Moreover, since I've been seeing this kind of hostility towards the GPL more and more often: it should be somewhat obvious to everyone that the current ecosystem of widely available, highest-quality OSS stacks is a rather surprising development. There are certainly parallel universes where parallel computing requires you to buy the compiler from IBM, and URIs en
Read the rest
d not in .html, but in .doc. The GPL and its advocates aren't the only ones who created this, but I'm pretty sure they were at least necessary. That includes not just the software they created, but also the force of the moral argument for software freedom they made.
In Julia Computing Raises $4.6M in Seed Funding
> So this is why every once in a while we see some article about Julia being as easy to use as Python while having nearly the performance of C. Here's a performance testimonial with links to real code and benchmarks against Fortran (not Python), from Steven G. Johnson, one of the authors of FFTW: https://discourse.julialang.org/t/julia-vs-fortran-complaint... I implemented an erfinv function Julia (https://github.com/JuliaLang/julia/pull/29874), and it was about 3x faster than Matlab or SciPy's erfinv function, both of which are taken from standard Fortran libraries. [...] I have found similar gains (compared to Fortran code called in SciPy) for other special functions, e.g. polygamma functions (https://github.com/JuliaLang/julia/pull/71255) and exponential integrals (https://github.com/JuliaMath/SpecialFunctions.jl/issues/19). The reason
Read the rest
Julia can beat the Fortran code is that metaprogramming makes it easy to apply performance optimizations that are awkward in Fortran. We have metaprogramming macros (@evalpoly) that can easily inline polynomial evaluations, whereas the Fortran code makes function calls that loop over look-up tables of polynomial coefficients. Even greater speedups are possible for evaluating polynomials of complex arguments, where there is a fancy recurrence from Knuth that is almost impossible to use effectively without code generation. In principle, the Fortran authors could have done the same inlining and gotten similar performance, but the code would have been much more painful to write by hand. (They could even have written a program to generate Fortran code, but that is even more painful.) > I'd rather deal with Cython apache license than deal with this GPL stuff for commercial use. The Julia core is MIT licensed. The language distribution is currently GPL licensed due to linked dependencies, but there has been a no-GPL build flag available for several releases. GPL (and other) dependencies are continually being removed, and there are only one or two (GPL) libraries left in the currently nightly build.
In Julia Computing Raises $4.6M in Seed Funding

Showing the 15 most recent of 19 sources. Enable JavaScript to search and browse every indexed mention.

MAKE YOURSELF AT HOME

A few shortcuts

/
Search the collection
?
Open this guide
Esc
Close dialogs and navigation

Your saved repositories and interests stay in this browser.