You've probably spotted those little circles of your friends' faces popping up on Facebook Reels. They look simple enough, but building them was a proper engineering challenge. In this episode, Pascal chats to Joseph and Subasree about Friend Bubbles, a feature that surfaces which of your close friends have been watching and reacting to the same Reels as you.

We get into the details of how prefetching keeps things snappy without wrecking scroll performance, why the team's ML model had to move from survey-based friend rankings to real-time interaction signals, and the surprising discovery that showing fewer bubbles actually made the whole feature click. If you've ever underestimated a "simple" feature, this one's for you.

Got feedback? Send it to us on Threads (https://threads.net/@metatechpod), Instagram (https://instagram.com/metatechpod) and don't forget to follow our host Pascal (https://mastodon.social/@passy, https://threads.net/@passy_). Fancy working with us? Check out https://www.metacareers.com/.

Links

• https://engineering.fb.com/2026/03/18/ml-applications/friend-bubbles-enhancing-social-discovery-on-facebook-reels/

• https://engineering.fb.com/2026/04/21/ml-applications/modernizing-the-facebook-groups-search-to-unlock-the-power-of-community-knowledge/

Timestamps

• Intro 0:06

• Meet the Engineers: Backgrounds and Roles 1:53

• Goals and Aspirations in Video Recommendations 4:20

• The Origin of Friend Bubbles 4:41

• Defining Success: Metrics and User Experience 5:40

• Client-Side Constraints and Challenges 6:57

• Feature Description: What Are Friend Bubbles? 8:31

• Initial Challenges and Performance Issues 9:29

• Architectural Changes for Performance 11:34

• Impact of Performance on User Experience 15:14

• Addressing Client-Side Challenges 16:58

• Model Development: From Surveys to Interactions 20:07

• Evolving the Model: Real-Time Data and User Interactions 23:35

• Exploring Model Training and Performance 24:58

• Feedback Loops and User Engagement 25:56

• The Role of AI in Development 29:49

• Collaboration Across Teams 32:17

• Future Directions for Friend Bubbles 34:02

• Safe Rollout Strategies for Features 35:22

• Outro 37:31

• Bloopers 38:27

Have you ever wondered how Meta makes config rollouts safe at scale? In this episode, Pascal sits down with Ishwari and Joe to discuss Meta's approach for propagating changes across services in seconds and discuss why speed increases the need for strong safeguards. Catch the episode to discover canarying and progressive rollouts, the health checks and monitoring signals used to catch regressions early, and how incident reviews focus on improving systems rather than blaming people. We also hear how data and early AI/ML are slashing alert noise and speeding up bisecting when something goes wrong.

Got feedback? Send it to us on Threads (https://threads.net/@metatechpod), Instagram (https://instagram.com/metatechpod) and don't forget to follow our host Pascal (https://mastodon.social/@passy, https://threads.net/@passy_). Fancy working with us? Check out https://www.metacareers.com/.

Links

• FFmpeg at Meta: Media Processing at Scale - https://engineering.fb.com/2026/03/02/video-engineering/ffmpeg-at-meta-media-processing-at-scale/

• Reliably Changing Configuration @ Scale - https://atscaleconference.com/reliably-changing-configuration-scale/

Timestamps

• Intro 0:06

• Introduction and Overview of Configuration Changes 2:31

• Understanding Configurations in Distributed Systems 4:02

• Meta's Configuration Management Systems 6:43

• Safeguards and Incident Prevention 9:22

• Deployment Mechanisms: Canary and Progressive Rollouts 12:06

• Challenges in Configuration Consumption 14:39

• Health Checks and Incident Response 17:13

• Mitigation Strategies for Configuration Issues 19:18

• Balancing Developer Velocity and Configuration Safety 21:09

• Data-Driven Improvements in Incident Management 22:12

• Leveraging AI for Change Detection 26:05

• Challenges in Deployment and Testing 28:21

• Reinventing Change Safety Strategies 30:24

• War Stories: Learning from Past Incidents 32:59

• Outro 36:10

At Meta, even seemingly simple engineering tasks—like updating an API—become monumental undertakings when you're dealing with millions of lines of code and thousands of engineers, especially if the changes are security-related. In today's episode, Pascal talks to Alex and Tanu about the challenges and learnings from the journey of making Meta's mobile frameworks more secure at a scale few companies ever experience. Tune in to this episode and join us as we explore the compelling crossroads of security, automation, and AI within mobile development.

Got feedback? Send it to us on Threads (https://threads.net/@metatechpod), Instagram (https://instagram.com/metatechpod) and don't forget to follow our host Pascal (https://mastodon.social/@passy, https://threads.net/@passy_). Fancy working with us? Check out https://www.metacareers.com/.

Links

• How AI Is Transforming the Adoption of Secure-by-Default Mobile Frameworks - https://engineering.fb.com/2025/12/15/android/how-ai-transforming-secure-by-default-mobile-frameworks-adoption/

• RCCLX: Innovating GPU Communications on AMD Platforms - https://engineering.fb.com/2026/02/24/data-center-engineering/rrcclx-innovating-gpu-communications-amd-platforms-meta/

• The Death of Traditional Testing: Agentic Development Broke a 50-Year-Old Field, JiTTesting Can Revive It - https://engineering.fb.com/2026/02/11/developer-tools/the-death-of-traditional-testing-agentic-development-jit-testing-revival/

Timestamps

• Intro & News 0:06

• Meet the Product Security Team 2:07

• Understanding the Intent System 4:13

• Security Challenges in Android's Intent System 6:44

• Proposed Solutions for Intent Security 9:39

• Meta's Unique Challenges at Scale 12:34

• Implementing a Secure Link Launcher Framework 15:32

• Leveraging AI for Contextual Understanding 17:55

• Navigating AI-Driven Code Modifications 20:47

• Developer Experience with AI Code Mods 21:49

• Validation Challenges in AI Code Generation 25:37

• Evolution of AI in Code Modifications 29:29

• Identifying AI's Strengths in Security 36:20

• Future Directions in AI and Framework Development 42:58

• Outro 46:58