diff --git a/docs/blog/posts/learn-async.md b/docs/blog/posts/learn-async.md
index ea88891..461a89f 100644
--- a/docs/blog/posts/learn-async.md
+++ b/docs/blog/posts/learn-async.md
@@ -16,6 +16,10 @@ authors:
 
 Today, I will introduce you to various approaches for using asyncio in Python. We will apply this to batch process data using `instructor` and learn how to use `asyncio.gather` and `asyncio.as_completed` for concurrent data processing. Additionally, we will explore how to limit the number of concurrent requests to a server using `asyncio.Semaphore`.
 
+!!! notes "Github Example"
+
+    If you want to run the code examples in this article, you can find them on [jxnl/instructor](https://github.com/jxnl/instructor/blob/main/examples/learn-async/run.py)
+
 We will start by defining an `async` function that calls `openai` to extract data, and then examine four different ways to execute it. We will discuss the pros and cons of each approach and analyze the results of running them on a small batch.
 
 ## Understanding `asyncio`
diff --git a/requirements-doc.txt b/requirements-doc.txt
index 2e98b1b..b03ccb9 100644
--- a/requirements-doc.txt
+++ b/requirements-doc.txt
@@ -9,6 +9,4 @@ pytest
 aiohttp==3.8.2
 yarl==1.8.1
 frozenlist==1.3.1
-git+https://${GH_TOKEN}@github.com/squidfunk/mkdocs-material-insiders.git
-mkdocs-minify-plugin
-mike
\ No newline at end of file
+mkdocs-minify-plugin
\ No newline at end of file