Meta Tech Podcast

Explore the fascinating world of Pyrefly, a groundbreaking type checker for Python built with Rust. Discover the motivations behind its creation and how it dramatically improves performance and developer experience. The hosts delve into the historical evolution of Python's type system, highlighting a cultural shift towards embracing diverse programming languages. Learn about the challenges and innovations in building an incremental type checker, and peek into the future of type checking with exciting tools and community engagement.

To ensure that everyone has access to resilient, high-speed and low-latency connections to Meta services, no matter where in the world they are, Meta makes large-scale investments into subsea cable infrastructure. The recently announced Project Water worth will, Once complete, reach five major continents and span over 50,000 km (longer than the Earth’s circumference), making it the world’s longest subsea cable project using the highest-capacity technology available.  In this episode, host Pascal talks with another Pascal and his colleague Andy who are involved at every stage of these projects and share the surprising challenges one has to deal with when working on the largest subsea cable project in the world. 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 Project Waterworth: https://engineering.fb.com/2025/02/14/connectivity/project-waterworth-ai-subsea-infrastructure/  Timestamps Intro 0:06 Introduction Andy 2:14 Introduction Pascal 3:21 Why do we build our own subsea cable infrastructure? 4:15 Current state of Meta-owned subsea cables 6:20 Project Waterworth 7:40 Why invest in more subsea cables? 9:00 What does a cable look like? 11:14 The process of laying subsea cable 16:39 Unexpected findings on the ocean floor 19:25 Shallow vs deep ocean 21:12 Merging different cable types 24:00 What happens when a cable breaks? 25:04 Memorable challenges 27:42 Cable capacity 29:51 The long history of subsea cables 33:07 What's next? 36:27 Outro 39:02

Join Pascal and Sabrina on the latest Meta Tech Podcast episode as they discuss the evolution and future of GraphQL. From client-side consistency to innovative APIs, learn how GraphQL is making developers' lives easier and enhancing user experiences. Discover surprising insights into the challenges of building a mobile GraphQL platform and how it's transforming product development at Meta.  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 GraphQL: https://graphql.org/  Relay: https://relay.dev/  Sabrina at GraphQL Conf 2024: https://www.youtube.com/watch?v=PGBC-0E-kco  Timestamps Intro 0:06 Introduction Sabrina 1:42 Sabrina's team 2:47 What's GraphQL? 3:18 Relay and Mobile GraphQL Clients 4:01 GraphQL Consistency Engine 4:54 Pando Mobile GraphQL Client 7:16 Interfacing with Pando 8:03 Code generation 9:14 Inventing new features 10:43 The hidden complexity behind pagination 11:52 Working inside the GraphQL spec 16:00 Complexity tradeoffs 18:30 State of GraphQL at Meta 21:16 Measuring success 24:58 Optimistic Mutations 27:31 Collaboration model 31:42 Preventing early adoption 34:43 The challenge of migrating FBApp 37:10 What's next for mobile GraphQL? 40:22 Outro 41:54  

Explore the fascinating world of multimodal AI and its application in Ray-Ban Meta glasses. Discover how integration of image recognition technology enhances user interactions and the challenges faced in wearable tech. Learn about the collaborative efforts among researchers and engineers that drive innovation forward. Delve into the empowering Be My Eyes initiative, which aids the visually impaired with audio guidance. Unlock the transformative potential of open source contributions in advancing AI and experience the future of smart wearable technology!

How do you translate roughly ten million lines of Java code to Kotlin? Clicking in your the IDE gets pretty repetitive after a while and doesn’t work if you have custom APIs and requirements for null safety. Eve and Jocelyn, two software engineers on the Mobile Infra Codebases Team have taken on this challenge and talk host Pascal through the unexpected difficulties when embarking on the journey to (close to) 100% Kotlin in our Android codebase. 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 Meta Engineering Blog - Translating Java to Kotlin at Scale: https://engineering.fb.com/2024/12/18/android/translating-java-to-kotlin-at-scale/  Open-source transformations: https://github.com/fbsamples/kotlin_ast_tools  Mobile @Scale Conference recordings: https://www.youtube.com/watch?v=L7xSnbrk4CI Timestamps Intro 0:06 Introduction Eve 1:11 Introduction Jocelyn 2:15 Team mission 2:44 The scale of Meta's codebase 3:40 Why is there so much code? 4:34 Why migrate to Kotlin? 5:45 Isn't Kotlin slow to compile? 7:51 Why not use Android Studio's converter? 8:28 Nullability differences 10:04 Meta Codemod Service 14:50 Kotlin codemod stages 17:07 Headless J2K 20:14 Open-source transformations 23:14 Java Nullsafe 24:47 Leveraging Linters 26:01 Fixing build errors 27:24 Unexpected challenges 29:33 State of the union 33:44 Outro 36:10 Outtakes 37:08

Introducing a new Android UI Framework like Jetpack Compose into an existing app is easy right? Import some AARs and code away. But what if your app has specific performance goals to meet, has existing design components, integrations with navigation and logging frameworks? That is where Summer and her team come in who handle large-scale migrations for Instagram. They aim to provide developers with the best possible experience when working on our code bases, even if that requires some temporary pain on the side of infrastructure teams that have to maintain multiple implementations at once. Why Summer thinks it is worth it, how they approach the rollout of a new framework and so much more is all discussed in episode 70. 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 Jetpack Compose: https://developer.android.com/compose Litho: https://fblitho.com/ Google Showcase: Meta built threads in only 5 months using Jetpack Compose: https://android-developers.googleblog.com/2023/10/meta-built-threads-in-only-5-months-using-jetpack-compose.html Flipper: https://fbflipper.com/ Timestamps Intro 0:06 Intro Summer 1:29 Notable differences moving from FB to IG 2:26 The Instagram Data & UI Architecture team 2:58 Why modernise? 3:44 Where has the risk paid off? 6:08 What does Compose look like? 7:49 Compose v Litho 11:15 Where does Litho still have the upper hand? 14:53 Meta contributions to Compose 16:38 Compose pitfalls 19:10 Rolling Compose out across the company 20:13 Design systems 22:12 Downsides of establishing another UI framework? 24:22 Rollout stages 28:43 Experimentation stage 32:32 Closed enrollment phase 38:15 Graduation criteria 39:38 Outro 42:20 Bants 44:04

Discover the intriguing debate on whether typing more enhances productivity or just adds to the workload. The discussion dives into Diff Authoring Time (DAT) as a fresh productivity metric and explores the evolution of the Hack programming language. Learn about the challenges of implementing incremental type checking in large codebases and the importance of developer experience. The hosts also highlight a groundbreaking experiment using typed mocking that led to significant time savings. Get insights on data-driven decision-making in developer infrastructure!

How do you build your own mixed reality headset from sketch to scale? That's exactly what Alfred Jones, VP of hardware engineering at Meta Reality Labs, discussed with host Pascal. From choosing the right display technology, battery, thermal budget and of course hitting the right price point. How he manages to not fall victim to choice paralysis and so much more in episode 68. Got feedback? Send it to us on Threads (https://threads.net/@metatechpod) or Instagram (https://instagram.com/metatechpod) and don’t forget to follow our host Pascal (https://threads.net/@passy_). Fancy working with us? Check out https://www.metacareers.com/. Links Caddy: https://engineering.fb.com/2024/07/18/virtual-reality/caddy-cad-mixed-reality-mr-meta/ Timestamps Intro 0:06 Alfred Introduction 1:40 Who do you work with? 3:23 Decision making frameworks 5:20 Is MR the final destination? 7:19 What makes good passthrough such a challenge? 10:18 How to build your own MR headset 13:51 Hardware design constraints 19:00 Prototype phases 22:34 Durability testing 26:23 Dogfooding at Meta 28:55 Magic wand for technical limitations 31:56 Outro 34:26

In this discussion, Sarita, who focuses on measuring developer productivity at Meta, and Moritz, who improves developer experience metrics, delve into the innovative concept of Diff Authoring Time (DAT). They explore how this metric captures the time taken to submit code changes and its implications for productivity evaluation. The conversation touches on the challenges of measuring developer efficiency, maintaining data privacy, and the potential for future enhancements in measuring metrics. Their insights reveal a data-driven approach to maximizing developer effectiveness in a tech-driven world.

Join Steve, a Jupyter expert from Meta, as he reveals the exciting features of Bento, Meta's customized Jupyter Notebooks. Learn how serverless capabilities and WebAssembly enhance performance and efficiency. Steve discusses innovative tools like scheduled notebooks, easier data integration from Google Sheets, and the upcoming multi-language support. Discover how Bento streamlines data analytics and fosters collaboration among engineers and data scientists while integrating AI to assist users. Get a glimpse of what's next for these groundbreaking tools!