Engineering at Meta
type: channel
description: Engineering at Meta Blog
date: 2026-02-04T21:14:53Z
update period: hourly
update frequency: 1
update base: 2000-01-01T12:00+00:00
generator agent: https://wordpress.org/
items: No Display? No Problem: Cross-Device Passkey Authentication for XR Devices
items: Rust at Scale: An Added Layer of Security for WhatsApp
items: Adapting the Facebook Reels RecSys AI Model Based on User Feedback
items: CSS at Scale With StyleX
items: Python Typing Survey 2025: Code Quality and Flexibility As Top Reasons for Typing Adoption
items: DrP: Meta’s Root Cause Analysis Platform at Scale
items: How We Built Meta Ray-Ban Display: From Zero to Polish
items: How AI Is Transforming the Adoption of Secure-by-Default Mobile Frameworks
items: Zoomer: Powering AI Performance at Meta’s Scale Through Intelligent Debugging and Optimization

No Display? No Problem: Cross-Device Passkey Authentication for XR Devices
type: item
date: 2026-02-04T22:00:07Z
subject: Security & Privacy
subject: Virtual Reality
description: We’re sharing a novel approach to enabling cross-device passkey authentication for devices with inaccessible displays (like XR devices). Our approach bypasses the use of QR codes and enables cross-device authentication without the need for an on-device display, while still complying with all trust and proximity requirements. This approach builds on work done by the FIDO [...] Read More... The post No Display? No Problem: Cross-Device Passkey Authentication for XR Devices appeared first on Engineering at Meta.
encoded: We’re sharing a novel approach to enabling cross-device passkey authentication for devices with inaccessible displays (like XR devices). Our approach bypasses the use of QR codes and enables cross-device authentication without the need for an on-device display, while still complying with all trust and proximity requirements. This approach builds on work done by the FIDO Alliance and we hope it will open the door to bring secure, passwordless authentication to a whole new ecosystem of devices and platforms. Passkeys are a significant leap forward in authentication, offering a phishing-resistant, cryptographically secure alternative to traditional passwords. Generally, the standard cross-device passkey flow, where someone registers or authenticates on a desktop device by approving the action on their nearby mobile device, is done in a familiar way with QR codes scanned by their phone camera. But how can we facilitate this flow for XR devices with a head-mounted display or no screen at all, or for other devices with an inaccessible display like smart home hubs and industrial sensors? We’ve taken a novel approach to adapting the WebAuthn passkey flow and FIDO’s CTAP hybrid protocol for this unique class of devices that either lack a screen entirely or whose screen is not easily accessible to another device’s camera. Our implementation has been developed and is now broadly available on Meta Quest devices powered by Meta Horizon OS. We hope that this approach can also ensure robust security built on the strength of existing passkey frameworks, without sacrificing usability, for users of a variety of other screenless IoT devices, consumer electronics, and industrial hardware. The Challenge: No Screen, No QR Code The standard cross-device flow relies on two primary mechanisms: QR code scanning: The relying party displays a QR code on the desktop/inaccessible device, which the mobile authenticator scans to establish a secure link. Bluetooth/NFC proximity: The devices use loc…

Rust at Scale: An Added Layer of Security for WhatsApp
type: item
date: 2026-01-27T15:00:09Z
subject: Security & Privacy
subject: WhatsApp
description: WhatsApp has adopted and rolled out a new layer of security for users – built with Rust – as part of its effort to harden defenses against malware threats. WhatsApp’s experience creating and distributing our media consistency library in Rust to billions of devices and browsers proves Rust is production ready at a global scale. [...] Read More... The post Rust at Scale: An Added Layer of Security for WhatsApp appeared first on Engineering at Meta.
encoded: WhatsApp has adopted and rolled out a new layer of security for users – built with Rust – as part of its effort to harden defenses against malware threats. WhatsApp’s experience creating and distributing our media consistency library in Rust to billions of devices and browsers proves Rust is production ready at a global scale. Our Media Handling Strategy WhatsApp provides default end-to-end encryption for over 3 billion people to message securely each and every day. Online security is an adversarial space, and to continue ensuring users can keep messaging securely, we’re constantly adapting and evolving our strategy against cyber-security threats – all while supporting the WhatsApp infrastructure to help people connect. For example, WhatsApp, like many other applications, allows users to share media and other types of documents. WhatsApp helps protect users by warning about dangerous attachments like APKs, yet rare and sophisticated malware could be hidden within a seemingly benign file like an image or video. These maliciously crafted files might target unpatched vulnerabilities in the operating system, libraries distributed by the operating system, or the application itself. To help protect against such potential threads, WhatsApp is increasingly using the Rust programming language, including in our media sharing functionality. Rust is a memory safe language offering numerous security benefits. We believe that this is the largest rollout globally of any library written in Rust. To help explain why and how we rolled this out, we should first look back at a key OS-level vulnerability that sent an important signal to WhatsApp around hardening media-sharing defenses. 2015 Android Vulnerability: A Wake-up Call for Media File Protections In 2015, Android devices, and the applications that ran on them, became vulnerable to the “Stagefright” vulnerability. The bug lay in the processing of media files by operating system-provided libraries, so WhatsApp and other applicatio…

Adapting the Facebook Reels RecSys AI Model Based on User Feedback
type: item
date: 2026-01-14T20:51:33Z
subject: ML Applications
subject: Video Engineering
description: We’ve improved personalized video recommendations on Facebook Reels by moving beyond metrics such as likes and watch time and directly leveraging user feedback. Our new User True Interest Survey (UTIS) model, now helps surface more niche, high-quality content and boosts engagement, retention, and satisfaction. We’re doubling down on personalization, tackling challenges like sparse user data [...] Read More... The post Adapting the Facebook Reels RecSys AI Model Based on User Feedback appeared first on Engineering at Meta.
encoded: We’ve improved personalized video recommendations on Facebook Reels by moving beyond metrics such as likes and watch time and directly leveraging user feedback. Our new User True Interest Survey (UTIS) model, now helps surface more niche, high-quality content and boosts engagement, retention, and satisfaction. We’re doubling down on personalization, tackling challenges like sparse user data and bias, and exploring advanced AI to make recommendations even smarter and more diverse. Our paper, “Improve the Personalization of Large-Scale Ranking Systems by Integrating User Survey Feedback” shares full details on this work. Delivering personalized video recommendations is a common challenge for user satisfaction and long-term engagement on large-scale social platforms. At Facebook Reels, we’ve been working to close this gap by focusing on “interest matching” – ensuring that the content people see truly aligns with their unique preferences. By combining large-scale user surveys with recent advances in machine learning, we are now able to better understand and model what people genuinely care about, which has led to significant improvements in both recommendation quality and overall user satisfaction. Why True Interest Matters Traditional recommendation systems often rely on engagement signals – such as likes, shares, and watch time – or heuristics to infer user interests. However, these signals can be noisy and may not fully capture the nuances of what people actually care about or want to see. Models trained only on these signals tend to recommend content that has high short-term user value measured by watch time and engagement but doesn’t capture true interests that are important for long-term utility of the product. To bridge this gap, we needed a more direct way to measure user perception of content relevance. Our research shows that effective interest matching goes beyond simple topic alignment; it also encompasses factors like audio, production style, mood, and moti…

CSS at Scale With StyleX
type: item
date: 2026-01-12T18:34:59Z
subject: Culture
subject: Open Source
subject: Web
subject: Meta Tech Podcast
description: Build a large enough website with a large enough codebase, and you’ll eventually find that CSS presents challenges at scale. It’s no different at Meta, which is why we open-sourced StyleX, a solution for CSS at scale. StyleX combines the ergonomics of CSS-in-JS with the performance of static CSS. It allows atomic styling of components [...] Read More... The post CSS at Scale With StyleX appeared first on Engineering at Meta.
encoded: Build a large enough website with a large enough codebase, and you’ll eventually find that CSS presents challenges at scale. It’s no different at Meta, which is why we open-sourced StyleX, a solution for CSS at scale. StyleX combines the ergonomics of CSS-in-JS with the performance of static CSS. It allows atomic styling of components while deduplicating definitions to reduce bundle size and exposes a simple API for developers. StyleX has become the standard at companies like Figma and Snowflake. Here at Meta, it’s the standard styling system across Facebook, Instagram, WhatsApp, Messenger, and Threads. On this episode of the Meta Tech Podcast, meet Melissa, a software engineer at Meta and one of StyleX’s maintainers. Pascal Hartig talks to her about all things StyleX—its origins, how open source has been a force multiplier for the project, and what it’s like interacting with large companies across the industry as they’ve adopted StyleX. Download or listen to the episode below: You can also find the episode wherever you get your podcasts, including: Spotify Apple Podcasts Pocket Casts The Meta Tech Podcast is a podcast, brought to you by Meta, where we highlight the work Meta’s engineers are doing at every level – from low-level frameworks to end-user features. Send us feedback on Instagram, Threads, or X. And if you’re interested in learning more about career opportunities at Meta visit the Meta Careers page. The post CSS at Scale With StyleX appeared first on Engineering at Meta.

Python Typing Survey 2025: Code Quality and Flexibility As Top Reasons for Typing Adoption
type: item
date: 2025-12-22T14:00:16Z
subject: Culture
subject: DevInfra
description: The 2025 Typed Python Survey, conducted by contributors from JetBrains, Meta, and the broader Python typing community, offers a comprehensive look at the current state of Python’s type system and developer tooling. With 1,241 responses (a 15% increase from last year), the survey captures the evolving sentiment, challenges, and opportunities around Python typing in the [...] Read More... The post Python Typing Survey 2025: Code Quality and Flexibility As Top Reasons for Typing Adoption appeared first on Engineering at Meta.
encoded: The 2025 Typed Python Survey, conducted by contributors from JetBrains, Meta, and the broader Python typing community, offers a comprehensive look at the current state of Python’s type system and developer tooling. With 1,241 responses (a 15% increase from last year), the survey captures the evolving sentiment, challenges, and opportunities around Python typing in the open-source ecosystem. In this blog we’ll cover a summary of the key findings and trends from this year’s results. Who Responded? The survey was initially distributed on official social media accounts by the survey creators, and subsequently shared organically across further platforms including Reddit, email newsletters, Mastodon, LinkedIn, Discord, and Twitter. When respondents were asked which platform they heard about the survey from, Reddit emerged as the most effective channel, but significant engagement also came from email newsletters and Mastodon, reflecting the diverse spaces where Python developers connect and share knowledge. The respondent pool was predominantly composed of developers experienced with Python and typing. Nearly half reported over a decade of Python experience, and another third had between five and 10 years. While there was representation from newcomers, the majority of participants brought substantial expertise to their responses. Experience with type hints was similarly robust, with most respondents having used them for several years and only a small minority indicating no experience with typing. Typing Adoption and Attitudes The survey results reveal that Python’s type hinting system has become a core part of development for most engineers. An impressive 86% of respondents report that they “always” or “often” use type hints in their Python code, a figure that remains consistent with last year’s Typed Python survey. For the first time this year the survey also asked participants to indicate how many years of experience they have with Python and with Python typing. We found…

DrP: Meta’s Root Cause Analysis Platform at Scale
type: item
date: 2025-12-19T17:35:13Z
subject: Data Infrastructure
subject: ML Applications
description: Incident investigation can be a daunting task in today’s digital landscape, where large-scale systems comprise numerous interconnected components and dependencies DrP is a root cause analysis (RCA) platform, designed by Meta, to programmatically automate the investigation process, significantly reducing the mean time to resolve (MTTR) for incidents and alleviating on-call toil Today, DrP is used [...] Read More... The post DrP: Meta’s Root Cause Analysis Platform at Scale appeared first on Engineering at Meta.
encoded: Incident investigation can be a daunting task in today’s digital landscape, where large-scale systems comprise numerous interconnected components and dependencies DrP is a root cause analysis (RCA) platform, designed by Meta, to programmatically automate the investigation process, significantly reducing the mean time to resolve (MTTR) for incidents and alleviating on-call toil Today, DrP is used by over 300 teams at Meta, running 50,000 analyses daily, and has been effective in reducing MTTR by 20-80% By understanding DrP and its capabilities, we can unlock new possibilities for efficient incident resolution and improved system reliability. What It Is DrP is an end-to-end platform that automates the investigation process for large-scale systems. It addresses the inefficiencies of manual investigations, which often rely on outdated playbooks and ad-hoc scripts. These traditional methods can lead to prolonged downtimes and increased on-call toil as engineers spend countless hours triaging and debugging incidents. DrP offers a comprehensive solution by providing an expressive and flexible SDK to author investigation playbooks, known as analyzers. These analyzers are executed by a scalable backend system, which integrates seamlessly with mainstream workflows such as alerts and incident management tools. Additionally, DrP includes a post-processing system to automate actions based on investigation results, such as mitigation steps. <img loading="lazy" decoding="async" class="alignnone size-full wp-image-23498" src="https://engineering.fb.com/wp-content/uploads/2025/12/Meta-DrP-image-1.png" alt="" width="1999" height="1125" srcset="https://engineering.fb.com/wp-content/uploads/2025/12/Meta-DrP-image-1.png 1999w, https://engineering.fb.com/wp-content/uploads/2025/12/Meta-DrP-image-1.png?resize=580,326 580w, https://engineering.fb.com/wp-content/uploads/2025/12/Meta-DrP-image-1.png?resize=916,516 916w, https://engineering.fb.com/wp-content/uploads/2025/12/Meta-DrP-image-1.p…

How We Built Meta Ray-Ban Display: From Zero to Polish
type: item
date: 2025-12-17T14:00:17Z
subject: Culture
subject: Virtual Reality
subject: Meta Tech Podcast
description: We’re going behind the scenes of the Meta Ray-Ban Display, Meta’s most advanced AI glasses yet. In a previous episode we met the team behind the Meta Neural Band, the EMG wristband packaged with the Ray-Ban Display. Now we’re delving into the glasses themselves. Kenan and Emanuel, from Meta’s Wearables org, join Pascal Hartig on [...] Read More... The post How We Built Meta Ray-Ban Display: From Zero to Polish appeared first on Engineering at Meta.
encoded: We’re going behind the scenes of the Meta Ray-Ban Display, Meta’s most advanced AI glasses yet. In a previous episode we met the team behind the Meta Neural Band, the EMG wristband packaged with the Ray-Ban Display. Now we’re delving into the glasses themselves. Kenan and Emanuel, from Meta’s Wearables org, join Pascal Hartig on the Meta Tech Podcast to talk about all the unique challenges of designing game-changing wearable technology, from the unique display technology to emerging UI patterns for display glasses. You’ll also learn what particle physics and hardware design have in common and how to celebrate even the incremental wins in a fast-moving culture. Download or listen to the episode below: You can also find the episode wherever you get your podcasts, including: Spotify Apple Podcasts Pocket Casts The Meta Tech Podcast is a podcast, brought to you by Meta, where we highlight the work Meta’s engineers are doing at every level – from low-level frameworks to end-user features. Send us feedback on Instagram, Threads, or X. And if you’re interested in learning more about career opportunities at Meta visit the Meta Careers page. The post How We Built Meta Ray-Ban Display: From Zero to Polish appeared first on Engineering at Meta.

How AI Is Transforming the Adoption of Secure-by-Default Mobile Frameworks
type: item
date: 2025-12-15T17:00:25Z
subject: Android
subject: iOS
subject: ML Applications
subject: Security & Privacy
description: Meta’s secure-by-default frameworks wrap potentially unsafe OS and third-party functions, making security the default while preserving developer speed and usability. These frameworks are designed to closely mirror existing APIs, rely on public and stable interfaces, and maximize developer adoption by minimizing friction and complexity. Generative AI and automation accelerate the adoption of secure frameworks at [...] Read More... The post How AI Is Transforming the Adoption of Secure-by-Default Mobile Frameworks appeared first on Engineering at Meta.
encoded: Meta’s secure-by-default frameworks wrap potentially unsafe OS and third-party functions, making security the default while preserving developer speed and usability. These frameworks are designed to closely mirror existing APIs, rely on public and stable interfaces, and maximize developer adoption by minimizing friction and complexity. Generative AI and automation accelerate the adoption of secure frameworks at scale, enabling consistent security enforcement and efficient migration across Meta’s vast codebase. Sometimes functions within operating systems or provided by third parties come with a risk of misuse that could compromise security. To mitigate this, we wrap or replace these functions using our own secure-by-default frameworks. These frameworks play an important role in helping our security and software engineers maintain and improve the security of our codebases while maintaining developer speed. But implementing these frameworks comes with practical challenges, like design tradeoffs. Building a secure framework on top of Android APIs, for example, requires a thoughtful balance between security, usability, and maintainability. With the emergence of AI-driven tools and automation we can scale the adoption of these frameworks across Meta’s large codebase. AI can assist in identifying insecure usage patterns, suggesting or automatically applying secure framework replacements and continuously monitoring compliance. This not only accelerates migration but also ensures consistent security enforcement at scale. Together, these strategies empower our development teams to ship well-secured software efficiently, safeguarding user data and trust while maintaining high developer productivity across Meta’s vast ecosystem. How We Design Secure-by-Default Frameworks at Meta Designing secure-by-default frameworks for use by a large number of developers shipping vastly different features across multiple apps is an interesting challenge. There are a lot of competing concerns…

Zoomer: Powering AI Performance at Meta’s Scale Through Intelligent Debugging and Optimization
type: item
date: 2025-11-21T21:00:15Z
subject: Data Center Engineering
subject: Data Infrastructure
subject: ML Applications
description: We’re introducing Zoomer, Meta’s comprehensive, automated debugging and optimization platform for AI. Zoomer works across all of our training and inference workloads at Meta and provides deep performance insights that enable energy savings, workflow acceleration, and efficiency gains in our AI infrastructure. Zoomer has delivered training time reductions, and significant QPS improvements, making it the [...] Read More... The post Zoomer: Powering AI Performance at Meta’s Scale Through Intelligent Debugging and Optimization appeared first on Engineering at Meta.
encoded: We’re introducing Zoomer, Meta’s comprehensive, automated debugging and optimization platform for AI. Zoomer works across all of our training and inference workloads at Meta and provides deep performance insights that enable energy savings, workflow acceleration, and efficiency gains in our AI infrastructure. Zoomer has delivered training time reductions, and significant QPS improvements, making it the de-facto tool for AI performance optimization across Meta’s entire AI infrastructure. At the scale that Meta’s AI infrastructure operates, poor performance debugging can lead to massive energy inefficiency, increased operational costs, and suboptimal hardware utilization across hundreds of thousands of GPUs. The fundamental challenge is achieving maximum computational efficiency while minimizing waste. Every percentage point of utilization improvement translates to significant capacity gains that can be redirected to innovation and growth. Zoomer is Meta’s automated, one-stop-shop platform for performance profiling, debugging, analysis, and optimization of AI training and inference workloads. Since its inception, Zoomer has become the de-facto tool across Meta for GPU workload optimization, generating tens of thousands of profiling reports daily for teams across all of our apps. Why Debugging Performance Matters Our AI infrastructure supports large-scale and advanced workloads across a global fleet of GPU clusters, continually evolving to meet the growing scale and complexity of generative AI. At the training level it supports a diverse range of workloads, including powering models for ads ranking, content recommendations, and GenAI features. At the inference level, we serve hundreds of trillions of AI model executions per day. Operating at this scale means putting a high priority on eliminating GPU underutilization. Training inefficiencies delay model iterations and product launches, while inference bottlenecks limit our ability to serve user requests at scale. Remov…