<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Social-Media on AI VOID</title><link>https://ai-blog.noorshomelab.dev/tags/social-media/</link><description>Recent content in Social-Media on AI VOID</description><generator>Hugo</generator><language>en</language><lastBuildDate>Fri, 19 Jun 2026 00:00:00 +0000</lastBuildDate><atom:link href="https://ai-blog.noorshomelab.dev/tags/social-media/index.xml" rel="self" type="application/rss+xml"/><item><title>Reel Friends and Friend Bubbles: Building Social Discovery at Billions Scale - Technical Case Study</title><link>https://ai-blog.noorshomelab.dev/case-studies/reel-friends-friend-bubbles-case-study/</link><pubDate>Fri, 19 Jun 2026 00:00:00 +0000</pubDate><guid>https://ai-blog.noorshomelab.dev/case-studies/reel-friends-friend-bubbles-case-study/</guid><description>&lt;h2 id="executive-summary"&gt;Executive Summary&lt;/h2&gt;
&lt;p&gt;The &amp;lsquo;Reel Friends&amp;rsquo; initiative and its flagship &amp;lsquo;Friend Bubbles&amp;rsquo; feature represent a significant engineering achievement at Meta, addressing the complex challenge of enhancing social discovery within the high-velocity content stream of Facebook Reels. This case study delves into the technical journey behind &amp;lsquo;Friend Bubbles,&amp;rsquo; a feature designed to surface close friends&amp;rsquo; engagement with Reels content. It highlights the iterative evolution of the underlying machine learning models, the architectural decisions made to scale to billions of users, and the solutions implemented to overcome significant engineering hurdles, including divergent user behaviors across iOS and Android platforms and the discovery of crucial signals for relationship strength estimation.&lt;/p&gt;</description></item></channel></rss>