33 posts
On its face the new Friend Bubbles feature looks simple enough. It highlights Reels your friends have watched and reacted to. But sometimes the features that seem the most straightforward require the deepest engineering work. On this episode of the Meta Tech Podcast, Pascal Hart…
Meta’s data ingestion system, which our engineering teams leverage for up-to-date snapshots of the social graph, has recently undergone a significant revamp to enhance its reliability at scale. Moving from our legacy system to our new architecture required a large-scale migratio…
We’re rolling out version 1.1 of Labyrinth, the encrypted storage system and protocol that secures messages and history on Messenger. Labyrinth 1.1 enhances the reliability of end-to-end encrypted backups with a new sub-protocol that helps messages survive the loss of a device,…
The HSM-based Backup Key Vault Meta’s HSM-based Backup Key Vault provides the foundation for end-to-end encrypted backups for WhatsApp and Messenger. The system allows people to protect their backed-up message history with a recovery code, ensuring that the recovery code is stor…
We’ve fundamentally transformed Facebook Groups Search to help people more reliably discover, sort through, and validate community content that’s most relevant to them. We’ve adopted a new hybrid retrieval architecture and implemented automated model-based evaluation to address…
We’re sharing insights into Meta’s Capacity Efficiency Program, where we’ve built an AI agent platform that helps automate finding and fixing performance issues throughout our infrastructure. By leveraging encoded domain expertise across a unified, standardized tool interface th…
We’re sharing lessons learned from Meta’s post-quantum cryptography (PQC) migration to help other organizations strengthen their resilience as industry transitions to post-quantum cryptography standards. We’re proposing the idea of PQC Migration Levels to help teams within organ…
At Meta, WebRTC powers real-time audio and video across various platforms. But forking a large open-source project like WebRTC within our monorepo presents unique challenges – over time, an internal fork can drift behind upstream, cutting itself off from community upgrades. We’r…
As AI increases developer speed and productivity it also increases the need for safeguards. On this episode of the Meta Tech Podcast, Pascal Hartig sits down with Ishwari and Joe from Meta’s Configurations team to discuss how Meta makes config rollouts safe at scale. Listen in t…
AI coding assistants are powerful but only as good as their understanding of your codebase. When we pointed AI agents at one of Meta’s large-scale data processing pipelines – spanning four repositories, three languages, and over 4,100 files – we quickly found that they weren’t m…
This is the second post in the Ranking Engineer Agent blog series exploring the autonomous AI capabilities accelerating Meta’s Ads Ranking innovation. The previous post introduced Ranking Engineer Agent’s ML exploration capability, which autonomously designs, executes, and analy…
0Meta continues to lead the industry in utilizing groundbreaking AI Recommendation Systems (RecSys) to deliver better experiences for people, and better results for advertisers. To reach the next frontier of performance, we are scaling Meta’s Ads Recommender runtime models to LL…
Meta is continuing its long-term roadmap to help the construction industry leverage AI to produce high-quality and more sustainable concrete mixes, as well as those exclusively produced in the United States. Concurrent with the 2026 American Concrete Institute (ACI) Spring Conve…
Friend bubbles in Facebook Reels highlight Reels your friends have liked or reacted to, helping you discover new content and making it easier to connect over shared interests. This article explains the technical architecture behind friend bubbles, including how machine learning…
Meta’s Ranking Engineer Agent (REA) autonomously executes key steps across the end-to-end machine learning (ML) lifecycle for ads ranking models. This post covers REA’s ML experimentation capabilities: autonomously generating hypotheses, launching training jobs, debugging failur…