scalability

Ex-Google engineers published a map of Google's internal tools and their open-source equivalents, providing a cheat code for building scalable infrastructure.

This paper proposes Node-Edge Policy Factorization (NEPF) to address scalability issues in solving Vehicle Routing Problems on multigraphs. It combines pre-encoding edge aggregation with a hierarchical reinforcement learning method to achieve state-of-the-art solution quality with faster training and inference.

The article analyzes the scalability limitations of using PostgreSQL as a job queue, specifically highlighting performance bottlenecks caused by MultiXact SLRU contention under high concurrency. It explains why this architecture fails in production despite working well in development and suggests considering alternatives.

Ben Dicken emphasizes that sharding is essential for building scalable databases and architecting data-intensive applications.

The article discusses the growing importance of reliability, security, and user protections as AI models become more capable and personalized.

OpenAI shares how it reimagined its support operations using AI to handle millions of requests annually by creating an operating model where every interaction improves the next. The approach combines chat/email/phone surfaces, continuously improving knowledge bases, and human-AI evaluation loops that empower support reps to act as builders and inform product improvements.

OpenAI presents evolution strategies (ES) as a scalable black-box optimization alternative to reinforcement learning for training neural network policies. ES simplifies the optimization problem by treating policy training as a stochastic parameter search that repeatedly samples and selects better parameter configurations based on reward feedback.

Linera introduces microchains to eliminate blockspace contention, offering real-time guarantees for AI agents and dApps.