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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, 9:01:12 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

12 sources
Except that the matrix multiplication benchmark uses pure javascript (see the source here: https://github.com/JuliaLang/julia/blob/master/test/perf/per...). I wonder how JS would do with bindings to LAPACK. And then there is a bias in those benchmarks, see for example the ones for quicksort: in Python they only time the duration of the sort itself, whereas (at least in Julia and JS) they time both the creation of the random array and the time needed to sort it.
In The fastest Statistical Programming Language is …Javascript?
I think it's hard to overstate how important this is. Consider the number of bolt-in solutions there are to get decent performance in python: numexpr, weave, pysco (dead), the emerging island of NumPyPy, theano, the brilliant/crazy chimera language cython, and now perhaps numba. The multi-modal nature of development affects libraries (too few devs needing too many hammers) and even the very sustainability of the ecosystem -- as evidenced by myriad numpy-discussion threads in the past year [0][0.1] about core implementation issues with 100-150+ messages, but about three people [1] of those who have the requisite interest, C chops, Python and CPython API understanding, and time to do work in the NumPy core. As a result the core has been mostly stuck in a local maximum for years now. Contrast Julia: Arrays are written in the language itself
Read the rest
[2]. Folks practically off-the-street with single-digit weeks of Jl experience are implementing non-trivial array datatypes [3][4]. Julia appears to have the flexibility to allow the kind of fine-tuned optimizations that are seen in the guts of a BLAS library [5] while simultaneously facilitating carefree, throw-away data exploration scripts by MATLAB refugees. If anything, I fear that Julia may be susceptible to the LISP curse if extreme care is not taken to ensure the interoperability that is needed to build an ecosystem. (The Python straitjacket is a blessing in this respect). On the other hand, maybe this time github will help balance the equation... [0] http://thread.gmane.org/gmane.comp.python.numeric.general/44... [0.1] http://thread.gmane.org/gmane.comp.python.numeric.general/48... [1] http://thread.gmane.org/gmane.comp.python.numeric.general/49... [2] https://github.com/JuliaLang/julia/blob/master/base/array.jl [3] https://groups.google.com/d/topic/julia-dev/YYRa6Iveevg/disc... [4] https://groups.google.com/d/topic/julia-dev/x3xFSa8iCog/disc... [5] https://groups.google.com/d/msg/julia-dev/vETgqnpesDk/OuZaB7...
In Julia, Python and Cython
Constructing and manipulating code in Julia is a bit more complicated than Lisp — because Lisp lists are so damned simple. However, not by much. I think most of the complexity of code that generates code is inherent. Sometimes being that meta just makes your brain hurt. Our printf implementation (https://github.com/JuliaLang/julia/blob/master/j/printf.j) is about as bad as it gets and it's still pretty understandable — aside from the inherent complexity of implementing printf and the separation of what's computed at compile time and what's computed at run time.
In Why we created Julia - new language for fresh approach to technical computing
Actually, it is quite easy to implement 0 based indices or any other indexing scheme in julia, since all the array indexing code is implemented in julia itself. https://github.com/JuliaLang/julia/blob/master/j/array.j#L15... I personally would find multiple indexing schemes confusing both for usage as well as to develop and maintain. Given that 1 based indexing seems to be a popular choice among many similar languages, we just went ahead with that.
In Why we created Julia - new language for fresh approach to technical computing

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