diff --git a/.circleci/config.yml b/.circleci/config.yml
index 549a6374246a0..999bba1f9f77f 100644
--- a/.circleci/config.yml
+++ b/.circleci/config.yml
@@ -26,7 +26,6 @@ jobs:
       image: ubuntu-2004:2022.04.1
     resource_class: arm.large
     environment:
-      ENV_FILE: ci/deps/circle-38-arm64.yaml
       TRIGGER_SOURCE: << pipeline.trigger_source >>
     steps:
       - checkout
diff --git a/doc/source/development/contributing_codebase.rst b/doc/source/development/contributing_codebase.rst
index 184060d3cf697..311120fc527d4 100644
--- a/doc/source/development/contributing_codebase.rst
+++ b/doc/source/development/contributing_codebase.rst
@@ -770,7 +770,7 @@ install pandas) by typing::
     your installation is probably fine and you can start contributing!
 
 Often it is worth running only a subset of tests first around your changes before running the
-entire suite (tip: you can use the [pandas-coverage app](https://pandas-coverage.herokuapp.com/)
+entire suite (tip: you can use the [pandas-coverage app](https://pandas-coverage.herokuapp.com/))
 to find out which tests hit the lines of code you've modified, and then run only those).
 
 The easiest way to do this is with::
diff --git a/doc/source/getting_started/index.rst b/doc/source/getting_started/index.rst
index abb3aad8dbd3e..d9cb1de14aded 100644
--- a/doc/source/getting_started/index.rst
+++ b/doc/source/getting_started/index.rst
@@ -24,7 +24,7 @@ Installation
 
         .. code-block:: bash
 
-            conda install pandas
+            conda install -c conda-forge pandas
 
     .. grid-item-card:: Prefer pip?
         :class-card: install-card
diff --git a/web/pandas/community/ecosystem.md b/web/pandas/community/ecosystem.md
index 60b043cf052ce..957a8d38b204c 100644
--- a/web/pandas/community/ecosystem.md
+++ b/web/pandas/community/ecosystem.md
@@ -58,7 +58,7 @@ target values with cutoff times that can be used for supervised learning.
 
 STUMPY is a powerful and scalable Python library for modern time series analysis.
 At its core, STUMPY efficiently computes something called a
-`matrix profile <https://stumpy.readthedocs.io/en/latest/Tutorial_The_Matrix_Profile.html>`__,
+[matrix profile](https://stumpy.readthedocs.io/en/latest/Tutorial_The_Matrix_Profile.html),
 which can be used for a wide variety of time series data mining tasks.
 
 ## Visualization
@@ -177,7 +177,7 @@ D-Tale integrates seamlessly with Jupyter notebooks, Python terminals, Kaggle
 
 ### [hvplot](https://hvplot.holoviz.org/index.html)
 
-hvPlot is a high-level plotting API for the PyData ecosystem built on `HoloViews <https://holoviews.org/>`__.
+hvPlot is a high-level plotting API for the PyData ecosystem built on [HoloViews](https://holoviews.org/).
 It can be loaded as a native pandas plotting backend via
 
 ```python
@@ -207,8 +207,7 @@ are utilized by Jupyter Notebook for displaying (abbreviated) HTML or
 LaTeX tables. LaTeX output is properly escaped. (Note: HTML tables may
 or may not be compatible with non-HTML Jupyter output formats.)
 
-See `Options and Settings <options>` and
-`Available Options <options.available>`
+See [Options and Settings](https://pandas.pydata.org/docs/user_guide/options.html)
 for pandas `display.` settings.
 
 ### [quantopian/qgrid](https://github.com/quantopian/qgrid)
@@ -355,7 +354,7 @@ Rigorously tested, it is a complete replacement for ``df.to_sql``.
 ### [Deltalake](https://pypi.org/project/deltalake)
 
 Deltalake python package lets you access tables stored in
-`Delta Lake <https://delta.io/>`__ natively in Python without the need to use Spark or
+[Delta Lake](https://delta.io/) natively in Python without the need to use Spark or
 JVM. It provides the ``delta_table.to_pyarrow_table().to_pandas()`` method to convert
 any Delta table into Pandas dataframe.
 
@@ -510,8 +509,8 @@ assumptions about your datasets and check that they're *actually* true.
 ## Extension data types
 
 Pandas provides an interface for defining
-`extension types <extending.extension-types>` to extend NumPy's type system. The following libraries
-implement that interface to provide types not found in NumPy or pandas,
+[extension types](https://pandas.pydata.org/docs/development/extending.html#extension-types) to extend NumPy's type system.
+The following librariesimplement that interface to provide types not found in NumPy or pandas,
 which work well with pandas' data containers.
 
 ### [cyberpandas](https://cyberpandas.readthedocs.io/en/latest)
@@ -540,7 +539,8 @@ Text Extensions for Pandas provides extension types to cover common data structu
 ## Accessors
 
 A directory of projects providing
-`extension accessors <extending.register-accessors>`. This is for users to discover new accessors and for library
+[extension accessors](https://pandas.pydata.org/docs/development/extending.html#registering-custom-accessors).
+This is for users to discover new accessors and for library
 authors to coordinate on the namespace.
 
   | Library                                                              | Accessor   | Classes               |