Meta智能眼镜引入隐私计算与本地数据处理架构
Meta工程团队发文阐述其为Meta AI智能眼镜打造的隐私数据处理机制。Meta认为智能眼镜是全天候AI助手的最佳形态，能结合个人上下文提供无感交互。为保障用户隐私，Meta在硬件端侧与云端交互中引入专用隐私保护架构，确保设备在理解用户视觉与环境上下文的同时，最大限度减少敏感数据泄露风险，平衡实时AI计算与数据安全要求。
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9月24日2026-09-24
9月22日2026-09-22
Meta开源Rebalancer：历经9年实战检验的高性能资源分配求解库
Meta宣布开源其内部使用了九年多的分配问题求解器Rebalancer。该库旨在解决大规模系统中的通用资源分配问题，通过解耦问题的定义规格、高效内存存储、求解算法与调试机制等核心环节，兼顾了通用性与高性能。Rebalancer在Meta内部广泛应用于各种复杂的基础设施资源调度与优化场景，为大规模分布式系统的负载平衡与资源规划提供了工业级参考实现。
9月21日2026-09-21
Meta公布Petal海缆计划：全球首条跨洋级Petabit多芯海底光缆
Meta披露其全新跨洋海底光缆项目“Petal”，旨在成为全球首条在跨洋距离上提供Petabit级别容量的海缆系统。该海缆全长约7000公里，连接美国与法国，预计将于2029年投入运营。Petal将成为全球首个大规模采用多芯光纤（MCF）技术的海底光缆系统，显著提升跨大西洋算力基础设施互联的数据传输容量与效率。
9月4日2026-09-04
ZGateway: Learnings from Putting a Proxy in Front of ZippyDB
We’re introducing ZGateway, the proxy we are using to unify traffic through ZippyDB, Meta’s most widely-used key value store. As a bonus, it also enables admission control, load balancing, cross-region resilience, and richer operations. ZippyDB is the most widely used key value store at Meta, backing product metadata, counters, and configuration, and can serve billions [...] Read More... The post ZGateway: Learnings from Putting a Proxy in Front of ZippyDB appeared first on Engineering at Meta .
9月2日2026-09-02
An Organizational Second Brain: Building an AI That Learns From Experts
We’ve built an AI agent that acts as a secondary expert for a given domain, making deep specialist knowledge readily available and preserved for anyone in an organization to access, share, and build upon. This is not a typical domain-specific agent. Its novelty comes from integrating two layers: A structured, auditable knowledge architecture separates what [...] Read More... The post An Organizational Second Brain: Building an AI That Learns From Experts appeared first on Engineering at Meta .
8月25日2026-08-25
MetaRoCE: A New RDMA Transport Built for AI-Scale Ethernet
Training and serving frontier AI models depends on fast, reliable networks that move data between GPUs without wasting compute cycles. To meet this challenge at scale, Meta designed MetaRoCE – a clean-sheet RDMA transport protocol purpose-built for AI workloads on commodity Ethernet. We’re releasing the MetaRoCE specification, a reference software implementation and a compliance test [...] Read More... The post MetaRoCE: A New RDMA Transport Built for AI-Scale Ethernet appeared first on Engineering at Meta .
MTIA 300: Meta’s First Training Chip with Built-in NICs and Communication-Offloading Engines
MTIA 300 is the first of Meta’s family of in-house training and inference accelerators optimized for training ranking and recommendation models. We’re sharing how MTIA 300’s built-in NIC chiplets allow it to meet the communication needs associated with training recommendation models with superior performance over general-purpose GPUs. By co-designing MTIA’s communication library, HCCL, alongside the [...] Read More... The post MTIA 300: Meta’s First Training Chip with Built-in NICs and Communication-Offloading Engines appeared first on Engineering at Meta .
8月12日2026-08-12
How We’re Building Scam Alert on WhatsApp With End-to-End Encryption and Verifiability Guarantees
WhatsApp is committed to helping people stay safe while protecting the privacy of their messages. As scam tactics evolve — from impersonation to social engineering to AI-generated lures — we’re always evolving as well, so that our protections stay ahead of scammers while protecting people’s personal messages with end-to-end encryption. Today, we’re sharing an early [...] Read More... The post How We’re Building Scam Alert on WhatsApp With End-to-End Encryption and Verifiability Guarantees appeared first on Engineering at Meta .
8月6日2026-08-06
From User Sequences to Scaling Laws: A Multi-Stage Architecture for Meta’s Ads Ranking
Every day, Meta’s recommendation platforms handle billions of user interactions, generating rich temporal signals that capture individual preferences and intent across products, ads, and content. In our 2024 post on sequence learning for ads recommendations, we showed how modeling the order and timing of user actions (rather than relying on static, manually engineered sparse features) [...] Read More... The post From User Sequences to Scaling Laws: A Multi-Stage Architecture for Meta’s Ads Ranking appeared first on Engineering at Meta .