<?xml version="1.0" encoding="UTF-8"?><oembed><type>video</type><version>1.0</version><html>&lt;iframe src=&quot;https://www.loom.com/embed/1d4af7c2cdb64cc7bae0de4f8ae494dc&quot; frameborder=&quot;0&quot; width=&quot;1280&quot; height=&quot;960&quot; webkitallowfullscreen mozallowfullscreen allowfullscreen&gt;&lt;/iframe&gt;</html><height>960</height><width>1280</width><provider_name>Loom</provider_name><provider_url>https://www.loom.com</provider_url><thumbnail_height>960</thumbnail_height><thumbnail_width>1280</thumbnail_width><thumbnail_url>https://cdn.loom.com/sessions/thumbnails/1d4af7c2cdb64cc7bae0de4f8ae494dc-00001.gif</thumbnail_url><duration>486.395</duration><title>Understanding How to Use Mirall0x</title><description>Hey guys, it&apos;s Stef! In this video, I&apos;ll guide you through the process of using Mirall0x. I&apos;ll explain how to build your CSV file and provide instructions on using Mirall0x. We&apos;ll cover important columns and data you&apos;ll need, as well as the parameters and weights for analysis. I&apos;ll show you how to upload your CSV file, choose networks, and set weights for behaviors. We&apos;ll also explore the plot and GitHub score, and I&apos;ll explain how to interpret the results. Finally, I&apos;ll demonstrate how to retrieve specific data. By the end of this video, you&apos;ll have a clear understanding of how to use Mirall0x effectively.</description></oembed>