diff --git a/2024/107-marco-polars.md b/2024/107-marco-polars.md
index 807bf49..3693130 100644
--- a/2024/107-marco-polars.md
+++ b/2024/107-marco-polars.md
@@ -14,17 +14,45 @@ https://www.meetup.com/data-umbrella
 - Polars documentation: https://docs.pola.rs/
 - Narwhals: https://narwhals-dev.github.io/narwhals/
 - Slides:  https://github.com/data-umbrella/event-transcripts/blob/main/resources/polars-narwhals.pdf
+- Video: Polars for Data Analysis in Python:  https://youtu.be/5V_MvnwTVwc
 
 ## About the Event
 When it comes to dataframes, pandas is the go-to library for many people. Yet Polars is taking the world by storm, and so many data practitioners are curious about trying it out. There is a learning curve though, as Polars introduces some concepts which pandas users might not be familiar with. This talk will be a deep dive into one of those concepts (expressions) and will focus on how you can understand them from a pandas perspective.
 
 The lessons learned will be useful beyond Polars, as they will also enable you to use Narwhals. Narwhals is a lightweight and extensible compatibility layer between dataframe libraries which is gaining traction (Altair, Marimo, scikit-lego, and more are currently using it) - like Polars, its API is also based on expressions. By learning this concept, you will not only be able to use Polars efficiently, but you'll also know how to build dataframe-agnostic tools.
 
-Polars: https://docs.pola.rs/
-Narwhals: https://narwhals-dev.github.io/narwhals/
 ```
 ## Timestamps
-00:00 Help us add timestamps
+00:00 Data Umbrella introduction
+05:07 Marco begins presentation
+06:15 Timeline / Agenda of presentation
+07:15 Why care (about Polars)?
+08:05 Polars crash course
+08:10 -- DataFrame
+09:02 -- Series
+09:38 -- Expressions
+10:28 Expressions: a light introduction / selection
+11:18 Functions: a detour
+13:10 scikit-learn uses Polars in some of their documentation
+13:53 Expressions: multiple inputs (and outputs)
+16:35 Expressions: summary
+17:15 What about group-by aggregations?
+18:20 pl.col(‘weight’).sum() (data type is preserved)
+18:47 Expressions in group-by
+20:05 pandas syntax comparison
+21:05 Expressions: can we use them in pandas
+23:20 Should pandas adopt the expressions in pandas? Let us know.
+23:30 Narwhals
+25:05 Conclusion
+26:49 Q: Do polars expressions support Python type annotations?
+27:35 Q: If I start a new project, should I use Polars and not pandas?
+28:58 Q: What size datasets can I use for Polars? Streaming in Polars
+30:03 Q: Is Narwhals a way to provide an array API equivalent for dataframes?
+31:20 Q: How is it able to achieve multi-threading?
+33:00 Q: Could we use Polars as input to scikit-learn?
+33:15 Q: When you make a copy of a dataframe, is it still a shallow copy?
+33:48 Q: Any disadvantage to using Polars?
+34:25 Q: Should I learn Polars instead of pandas?
 ```
 
 https://github.com/data-umbrella/event-transcripts/issues/92