架构· 平台工程
系统设计、平台工程与架构演进
GenRecs Tokenization: Transforming Ads Recommendation at Meta with Stable Model Vocabulary
Generative Recommenders (GenRecs) represent a paradigm shift from scoring every ad to reasoning about ad content and user preferences together in order to predict the best outcome for advertisers. Leveraging the world k…
The Thin Orchestrator: How We Made Our AI Agents More Reliable by Giving the Router Less to Do
Sometimes, giving an AI agent all of the knowledge it needs is the wrong approach. At Meta, we’ve seen notable improvements in efficiency and accuracy by keeping the orchestration layer of our data pipeline agents delib…
27 分钟 · Meta Engineering
Inside Prometheus: Meta’s Journey to a Gigawatt-Scale Supercluster
With the recent release of the Muse family of foundational models, we want to take you inside the infrastructure that has made it all possible. The first phase of Prometheus, our gigawatt-scale supercluster, is already …
30 分钟 · Meta EngineeringLATEST STORIES
最新内容
How Meta Built Forum With AI-Native Engineering精选导读
Meta’s newest app, Forum, is for anyone who wants to go deeper into the conversations in their Facebook groups. To build Forum, Meta engineers adopted a whole new way of working by bringing an AI agent into the developm…
2026 World Cup: Breaking Records, Spiky Charts and Reliability at Scale精选导读
Every four years, the World Cup becomes the largest planned stress test of our infrastructure. This year it also set an all-time record! There are a few moments throughout the year, like New Years Eve, that really let u…
Sapienz Receives ISSTA 2026 Impact Paper Award精选导读
Ke Mao, Mark Harman, and Yue Jia have been recognized with the ACM SIGSOFT International Symposium on Software Testing and Analysis (ISSTA) 2026 Most Influential Paper Award for their 2016 research, “Sapienz: multi-obje…
Meta’s Generative Ads Model (GEM): Scaling the Central Model and Compute Engine for Frontier Ads AI精选导读
Last year, we introduced GEM, the industry’s largest recommendation system (RecSys) foundation model. GEM established Meta’s first LLM-inspired paradigm for ads recommendation, driving major performance gains. Building …
Meta’s Generative Ads Model (GEM): Scaling the Central Model and Compute Engine for Frontier Ads AI精选导读
Last year, we introduced GEM, the industry’s largest recommendation system (RecSys) foundation model. GEM established Meta’s first LLM-inspired paradigm for ads recommendation, driving major performance gains. Building …
OCP Summit 2026: Powering AI with Open Innovations in Scale-Up, Scale-Out, and Scale-Across Networks精选导读
At the 2026 Open Compute Project Global Summit, Meta is sharing the next generation of open networking infrastructure, including: Our first liquid-cooled network rack (IcePack), and 102.4T switch platform (IceCube). Fla…
Shipping Media Over QUIC at Meta Scale for Instagram Live Ingest精选导读
Meta has begun deploying Media over QUIC (MoQ), an emerging open protocol for real-time video streaming at scale. MoQ will help simplify our protocol stack for live streaming video and enhance overall user experience fo…
Turning Opportunistic AI Inference Capacity Into Better Ads Recommendations精选导读
At Meta, we’ve built a system that enhances ads recommendations by leveraging idle inference capacity in our fleet. Two elasticity levers in the system allow ads recommendation work to expand across opportunistic capaci…
NTS: Authenticated Time at Meta精选导读
Meta’s public time service now speaks NTS (Network Time Security, RFC 8915) at nts.meta.com. Packets are authenticated, so a device can verify the time came from us and was not modified on the way. Our NTS servers hold …