Distributed Readings

Migrating a large-scale metrics pipeline from StatsD to OpenTelemetry while handling production traffic volumes without losing data or blocking dependent systems.

AI coding assistants were ineffective at making useful edits in large-scale data pipelines because they lacked sufficient understanding of complex, multi-repository codebases spanning multiple languages and thousands of files.

Safely deploying configuration changes at scale while minimizing the risk of widespread failures caused by faulty configurations.

Detecting safety hazards in real-time across hundreds of distributed operational sites using video feeds while maintaining low latency and managing the computational complexity of processing multiple camera streams.

Detecting sophisticated client-side security threats like zero-day exploits while minimizing false positives in real-time across millions of requests.

How to design a public DNS resolver that prioritizes user privacy while maintaining performance and trustworthiness at scale.

Dropbox needed to improve storage efficiency and resilience in Magic Pocket, their immutable blob store, when handling variable and changing workloads.

LinkedIn's logging infrastructure couldn't scale cost-effectively to handle the massive volume of operational logs across thousands of services.

Meta needed to automatically optimize low-level infrastructure and kernel-level parameters for AI ranking models to improve performance without manual tuning.

Meta needed to scale their ads ranking models to LLM-scale complexity and size while maintaining inference latency requirements for real-time ad serving.