diff --git a/contributions/executable-tutorial/jmatsso-oschel/README.md b/contributions/executable-tutorial/jmatsso-oschel/README.md
index d66dff948..2819ff629 100644
--- a/contributions/executable-tutorial/jmatsso-oschel/README.md
+++ b/contributions/executable-tutorial/jmatsso-oschel/README.md
@@ -27,4 +27,7 @@ The flexibility and automation of the tool makes it suitable in fast moving envi
 
 The insights gained from local profiling with py-spy can directly inform what to monitor in production. For example, if local profiling highlights specific functions or parts of the code that are resource-intensive, these areas can become key monitoring metrics in production
 Py-spy works well in production environments, where it can be attached to running Python applications without restarting or modifying the code. This non-intrusive capability is critical in production monitoring, as downtime or code changes are often not acceptable.
-Some other features except that it can attach to running processes is that it has low overhead and also can produce flame-graphs for visualisation. Also py-spy can be used both for pre-deployment testing and post-deployment monitoring for example in canary or blue-green deployments
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+Some other features except that it can attach to running processes is that it has low overhead and also can produce flame-graphs for visualisation. Also py-spy can be used both for pre-deployment testing and post-deployment monitoring for example in canary or blue-green deployments
+
+Killercoda: https://killercoda.com/smissen/scenario/py-spy101
+Github: https://github.com/Smissen/py-spy-executable-tutorial
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