Meta's new Lattice system integrates ad delivery across platforms, enhancing performance through better signal processing. Advertisers must focus on providing stronger first-party data and creative that drives engagement, as the system learns more quickly than most can adapt.

Charlotte Qi discusses the challenges of serving large language models (LLMs) at Meta, focusing on the complexities of LLM inference and the need for efficient hardware and software solutions. She outlines the critical steps to optimize LLM serving, including fitting models to hardware, managing latency, and leveraging techniques like continuous batching and disaggregation to enhance performance.