Distributed Readings

Detecting unauthorized or malicious behavior from users and AI agents accessing enterprise applications in real-time to prevent insider threats and data exfiltration.

Making complex global Internet traffic and trend data accessible to non-technical users without requiring deep knowledge of data querying or analytics systems.

Bot mitigation systems were relying on point-in-time risk assessments, which failed to detect sophisticated agentic behavior that evolved over time and mimicked legitimate user patterns.

Real-time AI agents with long-lived stateful bidirectional streams cannot be effectively load-balanced using traditional request-response metrics because server capacity is obscured by the nature of continuous conversations.

How to efficiently rank ads at scale by leveraging temporal signals from user action sequences rather than relying on static, manually engineered sparse features across billions of daily interactions.

Netflix needed to efficiently query billions of nodes and edges in a real-time distributed graph while maintaining low latency and high availability across their microservices ecosystem.

Providing users with the ability to create isolated, controlled Media over QUIC (MoQ) relays with granular permission management for publishers and subscribers.

Cloudflare needed to track, measure, and analyze Internet disruptions caused by natural disasters, government-mandated shutdowns, and DNS infrastructure events to understand their global impact on connectivity.

Enabling low-latency point queries on vast quantities of data stored in a data lake for online services.