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+# Productivity tools and Profiling
+
+## Productivity tools
+
+```{questions}
+   - Do you have preferences on the visual aspects of the code
+     and how it should look?
+   - Do you use any tools that help you create better looking
+     code faster?
+```
+
+```{objectives}
+   - Learn tools that can help you be more productive.
+   - Learn how to follow standards that other people have created
+     and how to pick your own favorite.
+```
+
+> [!IMPORTANT]  
+> Left to do: Summarize [Productivity tools lecture](/content/productivity.rst) in 20 minutes.
+
+
+## Profiling
+
+```{questions}
+   - When shall we worry about the performance of our code?
+   - How do we find bottlenecks in our code?
+   - How do we measure improvements in running time and memory usage?
+```
+
+```{objectives}
+   - Understand when improving code performance is worth the time and effort.
+   - Learn how to use profilers in Python.
+   - Use `scalene` to find and optimize bottlenecks in a given code example.
+```
+
+
+> [!IMPORTANT]  
+> Left to do:
+> Give 20 minutes introduction to profiling:
+> - [ ] Discuss when to profile
+> - [ ] Discuss breifly manual profiling
+> - [ ] Introduce function call profilers
+> - [ ] Introduce line profilers
+> - [ ] Visualize one code example using `scalane`
+
+## Exercise
+> [!IMPORTANT]
+> Prepare two exercises for the last 20 minutes of this lecture.
+> Left to do:
+> - [ ] Provide exercise in pure python, Radovan has some ideas
+> - [ ] Provide exercise showing the improvement in performance when introducing numpy and/or pandas, Gregor will work on this
+
+