 Jupyter Notebooks are key to data scientists and machine learning engineers, but its file structure makes code review challenging. Have you tried observing code changes in a Jupyter file? Looks like this. It's hard to read. GitLab automatically strips out the noise and displays a cleaner version of the diff for these files. Let's see it in action. I have this Jupyter Notebook where I want to find common items between two datasets and return a list with them. My code works. So I will push this notebook and create a merge request. Here I'm using JupyterLab terminal to communicate with my remote. Here we go. So let's create a new merge request in GitLab. In the merge request page, in changes, I can already see how my notebook changes are displayed in a cleaner and more readable way. Cool. Let's take advantage of that and ask Eduardo to review my code. With cleaner diffs in the Jupyter files, it will be easier to collaborate and review code. Alright, let's fast forward and check my notebook code review. Eduardo suggests to get rid of the for loop in the code and go for a more efficient data structure. Using sets to compute the intersection. Alright. Let's refactor the code in my Jupyter Notebook applying Eduardo's suggestion. Let's use sets to compute intersection between these two datasets and get rid of the for loop. Alright. And yes, the execution was faster. Let's push the changes to our merge request again. I add, commit and push to the MR. And it's done. Let's take a look at our merge request. As we can see here, now in GitLab, it is easier to compare changes in Jupyter Notebook files. In this case, Eduardo can quickly see that we added his suggestion thanks to more human readable diffs. Let's resolve this thread and merge to the main branch. Great. GitLab cleaner diffs in Jupyter Notebook files make it easier to run cold reviews and treat notebooks as collaborative files. Stay tuned and let's continue learning at GitLab.