canonify docs

README.md

@@ -1,8 +1,7 @@
 # NonlinearSolve.jl
 
 [![Join the chat at https://julialang.zulipchat.com #sciml-bridged](https://img.shields.io/static/v1?label=Zulip&message=chat&color=9558b2&labelColor=389826)](https://julialang.zulipchat.com/#narrow/stream/279055-sciml-bridged)
-[![Stable](https://img.shields.io/badge/docs-stable-blue.svg)](http://nonlinearsolve.sciml.ai/stable/)
-[![Global Docs](https://img.shields.io/badge/docs-SciML-blue.svg)](https://docs.sciml.ai/dev/modules/NonlinearSolve/)
+[![Global Docs](https://img.shields.io/badge/docs-SciML-blue.svg)](https://docs.sciml.ai/NonlinearSolve/stable/)
 
 [![codecov](https://codecov.io/gh/SciML/NonlinearSolve.jl/branch/master/graph/badge.svg)](https://codecov.io/gh/SciML/NonlinearSolve.jl)
 [![Build Status](https://github.com/SciML/NonlinearSolve.jl/workflows/CI/badge.svg)](https://github.com/SciML/NonlinearSolve.jl/actions?query=workflow%3ACI)
@@ -16,8 +15,8 @@
 Fast implementations of root finding algorithms in Julia that satisfy the SciML common interface.
 
 For information on using the package,
-[see the stable documentation](https://nonlinearsolve.sciml.ai/stable/). Use the
-[in-development documentation](https://nonlinearsolve.sciml.ai/dev/) for the version of
+[see the stable documentation](https://docs.sciml.ai/NonlinearSolve/stable/). Use the
+[in-development documentation](https://docs.sciml.ai/NonlinearSolve/dev/) for the version of
 the documentation which contains the unreleased features.
 
 ## High Level Examples

docs/make.jl

@@ -8,7 +8,7 @@ makedocs(sitename = "NonlinearSolve.jl",
          clean = true, doctest = false,
          format = Documenter.HTML(analytics = "UA-90474609-3",
                                   assets = ["assets/favicon.ico"],
-                                  canonical = "https://nonlinearsolve.sciml.ai/stable/"),
+                                  canonical = "https://docs.sciml.ai/NonlinearSolve/stable/"),
          pages = pages)
 
 deploydocs(repo = "github.com/SciML/NonlinearSolve.jl.git";

docs/src/basics/FAQ.md

@@ -35,4 +35,4 @@ MATLAB 2022a achieves 1.66s. Try this code yourself: we receive 0.06 seconds, or
 This example is still not optimized in the Julia code and we expect an improvement in a near
 future version.
 
-For more information on performance of SciML, see the [SciMLBenchmarks](https://github.com/SciML/SciMLBenchmarks.jl)
+For more information on performance of SciML, see the [SciMLBenchmarks](https://docs.sciml.ai/SciMLBenchmarksOutput/stable/)

docs/src/index.md

@@ -7,7 +7,7 @@ ability to use sparse automatic differentiation for Jacobian construction and
 Jacobian-vector products. It interfaces with other packages of the Julia ecosystem
 to make it easy to test alternative solver packages and pass small types to
 control algorithm swapping. It also interfaces with the
-[ModelingToolkit.jl](https://mtk.sciml.ai/dev/) world of symbolic modeling to
+[ModelingToolkit.jl](https://docs.sciml.ai/ModelingToolkit/stable/) world of symbolic modeling to
 allow for automatically generating high-performance code.
 
 Performance is key: the current methods are made to be highly performant on