Meta Tech Podcast

How do you add end-to-end encryption to a messenger that people have already been using for years, without taking away the message history they expect to be there? Emma Connor works on the team behind Labyrinth, Messenger's encrypted storage protocol. We get into where the encryption keys actually live, which turns out to be nowhere near Meta, and why a six-digit PIN is far harder to attack than six digits ought to be. Emma also explains the hardware security modules that Meta deliberately locked itself out of, what happens when you lose a device and want it cut out of your account, how key transparency lets you verify who you are talking to without having to trust us, and why Labyrinth 1.1 moved to post-quantum cryptography. All of this and more is discussed in this episode.
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
Opaque Rust Crate - https://crates.io/crates/opaque-ke

Labyrinth 1.1: Making End-to-End Encrypted Backups Even More Reliable - https://engineering.fb.com/2026/05/11/security/labyrinth-1-1-end-to-end-encrypted-e2ee-backups-more-reliable/

The Labyrinth Encrypted Message Storage Protocol - https://engineering.fb.com/wp-content/uploads/2026/05/Minos-Updates-2026-Encrypted-Backups-White-Paper.pdf

MTP E54: Building Key Transparency at WhatsApp - https://pca.st/orlsiq1l

MTP E65: Getting Ready for Post-Quantum Cryptography - https://pca.st/4klppivy

NTS: Authenticated Time at Meta - https://engineering.fb.com/2026/10/06/production-engineering/nts-authenticated-time-at-meta/

Timestamps
Intro 0:06

Introduction Emma 2:25

The Complexity of End-to-End Encryption 3:42

User Expectations and Mental Models 5:35

The Labyrinth System Overview 7:17

Key Management and Security 7:54

Rate-limiting passcode guesses 13:01

Evolution of Backup Systems 16:45

Rolling Out New Storage Protocols 17:07

Ensuring Data Integrity During Migration 18:34

Multi-Device Support Challenges 18:48

Building Custom Cryptography Solutions 20:20

Revoking Device Access 23:56

Labyrinth 1.1: 26:24

Preparing for Quantum Resistance 29:20

Outro 36:16

Pascal is going on Recharge for August so there will be no regular interview episode. However, we want to hear your questions for our mailbag episode. Send them to us on Instagram (https://www.instagram.com/metatechpod) or Threads (https://www.threads.com/@metatechpod) or via good old email to techpodcast@meta.com.

What changes for you as a developer when the device moves from your pocket to your face? The battery shrinks by an order of magnitude, the thermal budget is whatever won't make your nose uncomfortable, and your mental model of "one process, one device" goes out the window.
Rey and Dmitrii talk us through their custom power monitors, how they've extended Perfetto to give a coherent trace across a multi-device system, why robotic arms make surprisingly good QA engineers, and what it takes to catch the kind of regression that quietly costs you thirty minutes of call time. All of this and more is discussed in this episode.
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
Migrating Data Ingestion Systems at Meta Scale: https://engineering.fb.com/2026/05/12/data-infrastructure/migrating-data-ingestion-systems-at-meta-scale/ 

Timestamps
Intro 0:05

Introduction and Setup 1:51

Battery Life Optimization in AI Glasses 1:57

Challenges in Testing and CI Architecture 5:37

Detecting and Managing Power Regression 8:27

Developer Tools and Best Practices 13:04

Advanced Monitoring and Synchronization Techniques 16:53

Advanced Power Monitoring in Wearables 21:26

Real-World Examples of Performance Optimization 23:53

Battery Life Improvements Through Engineering 25:21

Engineering Culture and Performance Mindset 27:49

Continuous Testing and Profiling Strategies 29:36

Balancing Profiling Overhead and Accuracy 32:15

Simulating Real-World Scenarios in Testing 34:27

Best Practices for Efficient Wearable Code 37:21

Outro 40:26

Your smart glasses run all day on a battery narrower than your pinky finger and building it required reinventing how batteries are made. In this episode, Pascal talks to Karthik and Myuran, the engineers behind Meta's steel can battery technology, to explore why traditional pouch cells couldn't cut it for the ultra-slim temple arms of AI glasses like Meta Ray-Bans and the Oakley Vanguards. Tune in to learn how Meta designed, built, and scaled the batteries powering your glasses, wristbands, and cases from first prototype to mass production.
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
SilverTorch: Index as Model — A New Retrieval Paradigm for Recommendation Systems - https://engineering.fb.com/2026/05/26/ml-applications/silvertorch-index-as-model-new-retrieval-paradigm-recommendation-systems/
Timestamps
Intro and News 0:06

Guest intros 1:49

The problem with existing batteries 4:16

Pouch vs Steel Can Batteries 6:40

What does lower impedence mean? 10:27

Power requirements 12:25

Synchronising two batteries 16:02

Manufacturing never-done-before batteries 23:11

Software vs hardware iteration cycles 28:12

Collaborations across the globe 30:51

Market compliance 37:00

Outro 42:24

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