Mike Christensen from Ably critiques the standard HTTP streaming (SSE) approach for AI chat applications, highlighting its fragility and limitations. He introduces the "durable session" pattern, a persistent, shared resource built on pub/sub principles, to create resilient, multi-device AI experiences with live, bidirectional control.
Varant Zanoyan and Nikhil Simha Raprolu of Zipline AI explain why traditional feature stores are the wrong abstraction. They detail the journey of Chronon, the open-source engine born at Airbnb and battle-tested at Stripe, which focuses on compute, orchestration, and real-time correctness to solve the hardest data engineering challenges in ML, from fraud detection to powering modern AI agents with features and embeddings.
Rakesh Kumar from Lyft details the evolution of their real-time feature generation platform, from cron jobs to a sophisticated streaming architecture using Apache Beam and Flink. Key discussions include solving the 'hot shard' problem with geohashes, building a custom geospatial feature store, and optimizing pipelines with YAML-based configurations.