Browse past weeks of engineering reads.
Ensuring reliable and fast storage access to support exponential growth in model capabilities and training dataset sizes while maintaining computational efficiency during rapid AI innovation cycles.
Meta needed to improve the throughput and compute efficiency of retrieval systems for recommendation engines that process user-generated content at massive scale.
Ensuring end-to-end encrypted messages and conversation history survive device loss, device switches, and extended offline periods without compromising encryption guarantees.
Meta needed to migrate their legacy data ingestion system to a new architecture while maintaining reliability and consistency for real-time social graph snapshots at massive scale.
How to enable end-to-end encrypted backups for messaging applications while ensuring recovery codes remain inaccessible to Meta, cloud providers, and other third parties.
Meta needed to handle massive-scale media processing (encoding, transcoding, filtering) across its family of apps, requiring efficient orchestration of complex audio/video pipelines using FFmpeg at an unprecedented scale.
Meta's large-scale infrastructure relies on jemalloc for memory allocation, but the codebase had accumulated maintenance burden and needed modernization to keep pace with evolving hardware and workload demands.