Fix some broken links (#2002)

JuliaLang · Dec 31, 2023 · f29cb56 · f29cb56
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diff --git a/_layout/foot_general.html b/_layout/foot_general.html
@@ -62,7 +62,7 @@
         <li><a href="https://github.com/JuliaLang/julia/security/policy">Report a Security Issue</a></li>
         <li><a href="https://github.com/search?q=is%3Aopen+is%3Aissue+language%3AJulia+label%3A%22help+wanted%22">Help Wanted Issues</a></li>
         <li><a href="https://github.com/search?q=is%3Aopen+is%3Aissue+language%3AJulia+label%3A%22good+first+issue%22">Good First Issue</a></li>
-        <li><a href="https://docs.julialang.org/en/v1/devdocs/reflection/">Dev Docs</a></li>
+        <li><a href="https://docs.julialang.org/en/v1/devdocs/init/">Dev Docs</a></li>
       </ul>
     </div>
     <div id="footer-bottom" class="row">

diff --git a/about/help.md b/about/help.md
@@ -32,7 +32,7 @@
  - To contribute a bug fix or other patch to core Julia, see the [Julia Developer's Guide](https://github.com/JuliaLang/julia/blob/master/CONTRIBUTING.md).
  - To contribute to the official Julia documentation, use the [contributing guide](https://github.com/JuliaLang/julia/blob/master/CONTRIBUTING.md#improving-documentation) to contribute a documentation patch.
  - To contribute to the official Julia Language website, see the [README for the website repository](https://github.com/JuliaLang/www.julialang.org#readme). 
- - To announce your package to the Julia community, post on the Julia Discourse under the [package announcement](https://discourse.julialang.org/c/community/packages/47) section. 
+ - To announce your package to the Julia community, post on the Julia Discourse under the [package announcement](https://discourse.julialang.org/c/package-announcements/60) section. 
 
 ## Need to contact the Julia Language?
  - Contact `[email protected]` and let us know how we can help!

diff --git a/blog/2019/02/julia-entities.md b/blog/2019/02/julia-entities.md
@@ -18,13 +18,13 @@ One official group of people representing the Julia project does exist: [Julia S
 
 ## The Julia Lab
 
-A lot of the early work on the development of core Julia was done at MIT in what is now known as [The Julia Lab](https://julia.mit.edu) under the direction of professor Alan Edelman. All of the co-creators of Julia have been part of the lab at some point. In those early days, we didn’t call it the Julia Lab yet, it was just an office where some people worked on high performance computing, numerical linear algebra, [random matrix theory](https://en.wikipedia.org/wiki/Random_matrix), and this new programming language called Julia. Although not formally part of the Julia Lab, [Jeremy Kepner](https://www.ll.mit.edu/biographies/jeremy-kepner) of Lincoln Laboratory was an early believer in and funder of the lab’s work on Julia and continues to support this work. Since those early days, things have gotten a lot more organized under the leadership (in chronological order) of [Jiahao Chen](https://jiahao.github.io), [Andreas Noack](https://github.com/andreasnoack) and [Valentin Churavy](https://github.com/vchuravy), and the Julia Lab remains a steady source of major innovations and contributions to Julia.
+A lot of the early work on the development of core Julia was done at MIT in what is now known as [The Julia Lab](https://julia.mit.edu) under the direction of professor Alan Edelman. All of the co-creators of Julia have been part of the lab at some point. In those early days, we didn’t call it the Julia Lab yet, it was just an office where some people worked on high performance computing, numerical linear algebra, [random matrix theory](https://en.wikipedia.org/wiki/Random_matrix), and this new programming language called Julia. Although not formally part of the Julia Lab, [Jeremy Kepner](https://www.ll.mit.edu/biographies/jeremy-kepner) of Lincoln Laboratory was an early believer in and funder of the lab’s work on Julia and continues to support this work. Since those early days, things have gotten a lot more organized under the leadership (in chronological order) of [Jiahao Chen](https://github.com/jiahao), [Andreas Noack](https://github.com/andreasnoack) and [Valentin Churavy](https://github.com/vchuravy), and the Julia Lab remains a steady source of major innovations and contributions to Julia.
 
 There are several other groups at MIT that have made major contributions to Julia over the years besides the Julia Lab. [Steven Johnson](https://en.wikipedia.org/wiki/Steven_G._Johnson), also a [professor at MIT](https://math.mit.edu/~stevenj/), heads up his own group but in his spare time has become one of the most prolific contributors to Julia (currently #10 by commits). The [JuMP](http://jump.dev/) and [Cassette](https://github.com/JuliaLabs/Cassette.jl) projects are primarily developed by current and former members of professor Juan Pablo Vielma’s optimization research group at [MIT Operations Research Center](https://orc.mit.edu/). There are so many research groups at MIT using Julia these days, making major contributions to its ecosystem and to science, that it has become commonplace for Alan to discover that some neighbor of his at [CSAIL](https://www.csail.mit.edu) is using Julia completely unbeknownst to him. So definitely don’t consider this list exhaustive—I just didn’t want to appear to give the Julia Lab all the credit for work on Julia coming out of MIT. And of course, there’s incredible work being done on Julia by people at many other universities all around the world.
 
 ## NumFOCUS
 
-NumFOCUS is a US 501(c)(3) nonprofit organization promoting open practices in scientific research, data science, and scientific computing by serving as a fiscal sponsor for open source projects and organizing community-driven educational programs in these areas. Through NumFOCUS, both [individuals](https://numfocus.org/membership) and [corporations](https://numfocus.org/sponsors) have the opportunity to donate to any of NumFOCUS’s fiscally sponsored projects including Julia, NumPy, Jupyter, Pandas, JuMP, rOpenSci, QuantEcon, and [many others](https://numfocus.org/sponsored-projects/). This is possible because these projects have entered into a contractual and legal relationship with NumFOCUS. This means that a set of people serving as the project’s leadership body has signed an agreement stating that they, or their successors, will manage the technical direction and programs of the project within the scope of the organization’s nonprofit status. NumFOCUS is the legal and financial administrator of project funds, disbursing as requested by the project’s leadership body with ultimate oversight by the NumFOCUS board of directors.
+NumFOCUS is a US 501(c)(3) nonprofit organization promoting open practices in scientific research, data science, and scientific computing by serving as a fiscal sponsor for open source projects and organizing community-driven educational programs in these areas. Through NumFOCUS, both [individuals](https://numfocus.org/support) and [corporations](https://numfocus.org/sponsors) have the opportunity to donate to any of NumFOCUS’s fiscally sponsored projects including Julia, NumPy, Jupyter, Pandas, JuMP, rOpenSci, QuantEcon, and [many others](https://numfocus.org/sponsored-projects/). This is possible because these projects have entered into a contractual and legal relationship with NumFOCUS. This means that a set of people serving as the project’s leadership body has signed an agreement stating that they, or their successors, will manage the technical direction and programs of the project within the scope of the organization’s nonprofit status. NumFOCUS is the legal and financial administrator of project funds, disbursing as requested by the project’s leadership body with ultimate oversight by the NumFOCUS board of directors.
 
 Despite what “fiscally sponsored project” may sound like, NumFOCUS does not have a big bag of money that it gives out to projects however it wants to. Rather, it means that NumFOCUS is legally able to accept donations from individuals, companies, private foundations and government entities to be spent on the fiscally sponsored projects. There are [other projects](https://numfocus.org/sponsored-projects/affiliated-projects) affiliated with NumFOCUS that are not fiscally sponsored, including Conda, Cython, Dask, SciPy, and scikit-learn. These projects can’t receive money through NumFOCUS because they don’t have the same legal relationship.
 

diff --git a/blog/2020/08/julia-1.5-highlights.md b/blog/2020/08/julia-1.5-highlights.md
@@ -110,7 +110,7 @@ The speedup is actually not that large, which is a testament to the fact that Ju
 very efficient, but note the difference in allocations: 250004 on 1.4 versus 2 on 1.5. The
 difference is all those view objects which don't need to heap allocated anymore.
 
-We would like to thank [RelationalAI](https://www.relational.ai/) for sponsoring this work.
+We would like to thank [RelationalAI](https://relational.ai/) for sponsoring this work.
 
 ## Multithreading API stabilization & improvements
 

diff --git a/blog/2020/09/juliacon-2020-open-source-bof-follow-up.md b/blog/2020/09/juliacon-2020-open-source-bof-follow-up.md
@@ -48,7 +48,7 @@ Another flavor of this problem can exist when open-sourced code features many do
 
 #### Organizations That Don't Use OSS In The First Place
 
-One attendee brought up that, for many organizations, the first barrier to allowing employee OSS contributions is to allow internal usage of OSS at all. It can be hard for decision makers at organizations that have no prior OSS experience to come to terms with the traditional motivations and arguments for OSS. They may even harbor [common misconceptions](https://www.dreamsongs.com/IHE/IHE-29.html#58965) about how the process actually works. Luckily, there's [a plethora of material available online](https://bfy.tw/OvDl) that targets exactly this audience that organization members can bundle up in a manner that's most effective for their organization.
+One attendee brought up that, for many organizations, the first barrier to allowing employee OSS contributions is to allow internal usage of OSS at all. It can be hard for decision makers at organizations that have no prior OSS experience to come to terms with the traditional motivations and arguments for OSS. They may even harbor [common misconceptions](https://www.dreamsongs.com/IHE/IHE-29.html#58965) about how the process actually works. Luckily, there's a plethora of material available online that targets exactly this audience that organization members can bundle up in a manner that's most effective for their organization.
 
 #### Licensing, Patents, and IP
 

diff --git a/blog/2021/01/arrow.md b/blog/2021/01/arrow.md
@@ -41,6 +41,6 @@ It also supports multithreaded reading/writing by default, memory compression/de
 
 We're excited to see further progress of using arrow-formatted in Julia; as it stands, we believe the Julia implementation to be one of the easiest, yet powerful and flexible implementations for the arrow format. Being able to work interactively with arrow datasets to explore, integrate easily with so many other formats, and process the data as-is is a powerful tool for anyone working with data.
 
-Checkout the [package documentation](https://arrow.juliadata.org/stable/) or [format documentation](https://arrow.apache.org/docs/index.html) if you're interested in learning more; and as always, feel free to swing by the [#data Slack channel](https://julialang.slack.com/messages/data/) with questions or to chat, or [start a new topic](https://discourse.julialang.org/c/domain/data/16) in the Data discourse domain. Happy arrowing!
+Checkout the [package documentation](https://arrow.apache.org/julia/stable) or [format documentation](https://arrow.apache.org/docs/index.html) if you're interested in learning more; and as always, feel free to swing by the [#data Slack channel](https://julialang.slack.com/messages/data/) with questions or to chat, or [start a new topic](https://discourse.julialang.org/c/domain/data/16) in the Data discourse domain. Happy arrowing!
 
 A big thanks to JuliaHub (formerly Julia Computing) for helping sponsor the work on the Julia implementation.
diff --git a/blog/2021/01/contributhon.md b/blog/2021/01/contributhon.md
@@ -16,7 +16,7 @@ This event's goals were to...
 > - ...provide promotional and technical benefits to the Julia community as whole.
 > - ...and have a huge amount of fun!
 
-Now, in 2021, I'm happy to report on all of the great contributions made as part of the event last month by the wonderful folks at [Beacon Biosignals](https://beacon.bio/), [Invenia](https://www.invenia.ca/), [TriScale innov](https://www.triscale-innov.com/), [RelationalAI](https://www.relational.ai/), and [PumasAI](https://pumas.ai/).
+Now, in 2021, I'm happy to report on all of the great contributions made as part of the event last month by the wonderful folks at [Beacon Biosignals](https://beacon.bio/), [Invenia](https://www.invenia.ca/), [TriScale innov](https://www.triscale-innov.com/), [RelationalAI](https://relational.ai/), and [PumasAI](https://pumas.ai/).
 All shapes and sizes of contribution were welcome at the event: we released whole packages, made PRs, caught bugs, started new projects, and planned out our OSS roadmaps/backlogs for future work.
 
 Here is a sampling of the standouts:

diff --git a/blog/2021/04/gsod-2020-wrapup.md b/blog/2021/04/gsod-2020-wrapup.md
@@ -49,6 +49,6 @@ It is always worth acknowledging that without the hard work of our mentors, none
 
 ## Closing Thoughts
 
-Thank you to all of the technical writers and mentors mentioned above who made this GSoD a success. We are also happy to share that we have submitted our application for the GSoD 2021-2022 Season and are anticipating a decision by mid-April. You can find our [proposal on the Julia Language Website under the JSoC Tab](https://julialang.org/jsoc/gsod/proposal/).
+Thank you to all of the technical writers and mentors mentioned above who made this GSoD a success. We are also happy to share that we have submitted our application for the GSoD 2021-2022 Season and are anticipating a decision by mid-April.
 
 {{about_the_author}}
diff --git a/blog/2021/10/DEQ.md b/blog/2021/10/DEQ.md
@@ -4,7 +4,7 @@
 @def title = "Composability in Julia: Implementing Deep Equilibrium Models via Neural ODEs"
 @def authors = """Qiyao Wei, Frank Schäfer, Avik Pal, Chris Rackauckas"""  
 
-The [SciML Common Interface](https://scimlbase.sciml.ai/dev/) defines a complete
+The [SciML Common Interface](https://docs.sciml.ai/SciMLBase/stable/) defines a complete
 set of equation solving techniques, from differential equations and optimization
 to nonlinear solves and integration (quadrature), in a way that is made to
 mix with machine learning naturally. In this sense, there is no difference
@@ -30,7 +30,7 @@ of methods in machine learning literature. For background on DiffEqFlux and Neur
 please see the previous blog post [DiffEqFlux.jl – A Julia Library for Neural Differential Equations](https://julialang.org/blog/2019/01/fluxdiffeq/).
 
 (Note: If you are interested in this work and are an undergraduate or graduate
-student, we have [Google Summer of Code projects available in this area](/jsoc/gsoc/diffeq/). This
+student, we have [Google Summer of Code projects available in this area](/jsoc/projects/). This
 [pays quite well over the summer](https://developers.google.com/open-source/gsoc/help/student-stipends).
 Please join the [Julia Slack](http://julialang.org/slack/) and the #jsoc channel to discuss in more detail.)
 
@@ -272,7 +272,7 @@ There are many ways that one can solve a rootfinding problem with different char
 One can directly use Newton's method, but this can require a good guess and may not distinguish
 between stable and unstable equilibrium. Julia packages like
 [NLsolve.jl](https://github.com/JuliaNLSolvers/NLsolve.jl) provide many good algorithms, and
-Bifurcation tools like [BifurcationKit.jl](https://rveltz.github.io/BifurcationKit.jl/dev/) give a whole host of other methods.
+Bifurcation tools like [BifurcationKit.jl](https://bifurcationkit.github.io/BifurcationKitDocs.jl/stable/) give a whole host of other methods.
 Given the importance of solving nonlinear algebraic systems and their differentiability, the SciML organization
 has put together a common interface package [NonlinearSolve.jl](https://nonlinearsolve.sciml.ai/dev/) that weaves
 together all of the techniques throughout the package ecosystem (bringing together methods from SUNDIALS, MINPACK,

diff --git a/blog/2021/11/julia-1.7-highlights.md b/blog/2021/11/julia-1.7-highlights.md
@@ -243,7 +243,7 @@ For those interested, here is the list of specific PRs that implement the main i
 
 These inference improvements were initially motivated by the needs of [JET.jl](https://github.com/aviatesk/JET.jl), a static analyzer for Julia, that is powered by the Julia compiler's type inference implementation.
 These inference improvements in 1.7 allow JET to analyze your program more correctly and faster –
-as a simple measurement, [when analyzing JET itself](https://gist.github.com/aviatesk/e2ffa4bfaee60f939ef4b65449fa394b),
+as a simple measurement, when analyzing JET itself,
 JET took `90` seconds to report `93` false-positive errors in 1.6,
 but in 1.7 and higher, JET can finish the analysis within `40` seconds and the number of false positives is reduced to `27`,
 thanks to both the type inference improvements and [several inferrability improvements of Julia Base](https://github.com/JuliaLang/julia/pulls?q=is%3Apr+is%3Amerged+inferrability).
@@ -253,7 +253,7 @@ thanks to both the type inference improvements and [several inferrability improv
 *Elliot Saba*, *Viral B Shah*, *Mosè Giordano*
 
 Julia v1.7 introduces a new BLAS demuxing library called [libblastrampoline (LBT)](https://github.com/staticfloat/libblastrampoline), that provides a flexible and efficient way to switch the backing BLAS library at runtime.
-Because the BLAS/LAPACK API is "pure" (e.g. each BLAS/LAPACK invocation is separate from any other; there is no carryover state from one API call to another) it is possible to switch which BLAS backend actually services a particular client API call, such as a [DGEMM](http://www.netlib.org/lapack/explore-html/d1/d54/group__double__blas__level3_gaeda3cbd99c8fb834a60a6412878226e1.html) call for a `Float64` `Matrix`-`Matrix` multiplication.
+Because the BLAS/LAPACK API is "pure" (e.g. each BLAS/LAPACK invocation is separate from any other; there is no carryover state from one API call to another) it is possible to switch which BLAS backend actually services a particular client API call, such as a Double Precision General Matrix Multiply call for a `Float64` `Matrix`-`Matrix` multiplication.
 This statelessness enables us to easily switch from one BLAS backend to another without needing to modify client code, and combining this with a flexible wrapper implementation, we are able to provide a single, coherent API that automatically adjusts for a variety of BLAS/LAPACK providers across all the platforms that Julia itself supports.
 
 The wrapper itself consists of assembly routines to jump to a stored function pointer, using the same assembly chunks that the [Procedure Linkage Table (PLT)](https://www.technovelty.org/linux/plt-and-got-the-key-to-code-sharing-and-dynamic-libraries.html) uses in every dynamic library on your operating system.