Distributed systems

Jul 30, 2026

Jeff Dean: The 1% Rule for Building in AI

Jeff Dean discusses the evolution of AI, drawing parallels between Google's past breakthroughs (like fitting search in RAM and the origin of TPUs) and current challenges. He emphasizes that AI is becoming an energy problem, driving the need for specialized inference hardware. Dean highlights 'context engineering' and multi-agent systems as crucial for long-running, complex AI tasks, and offers advice for startups on finding niches where they can outperform larger entities by focusing on specific domains, data, and models. He stresses the importance of clear specifications for agents and 'taste' in problem selection, encouraging founders to question fundamental assumptions and automate the scientific method to build 'AI that builds AI.'

Jul 30, 2026

Developer Productivity at a Developer Productivity Startup • Robert-Jan "RJ" Huijsman • GOTO 2025

Robert-Jan Huijsman, Founding Engineer at Reboot.dev, outlines a pragmatic approach to developer productivity by tackling friction and non-determinism in both human collaboration and software development. He shares Reboot.dev's strategies, including async workstreams, full-stack typing, unit testing with AI, and foundational engineering principles like retries, durable execution, and strong consistency, to build reliable and efficient systems.

Jul 29, 2026

Your Agent Didn't Fail. Your Harness Did. — Vinoth Govindarajan, OpenAI

Vinoth Govindarajan's talk addresses critical 'harness failures' in AI agents, arguing these, rather than model errors, are the root cause of most production incidents. He introduces the core contract: 'A model proposes, the harness commits, and a receipt proves it,' and outlines five key boundaries (state ownership, ordering, deadlines, authority, user-visible proof) that lead to failures like silent success and incomplete reality. The talk culminates in a practical 'run receipt audit' with five questions to diagnose and ensure reliable agent behavior.

Jul 28, 2026

Serving 2 Million Models Without Melting: Scaling the Hugging Face Hub — Arek Borucki, Hugging Face

Arek Borucki details how Hugging Face scales its infrastructure to serve millions of models and users, focusing on the evolution of search architecture using MongoDB Atlas and Apache Lucene, robust database scaling with a seven-node cluster and sharding, and dynamic frontend autoscaling with Kubernetes and KEDA to ensure an instant, seamless user experience.

Jul 23, 2026

Learned Execution Graphs for Anomaly Detection & Drift in APIs — Ritvik Pandya, JP Morgan Chase

Ritvik Pandya from JP Morgan introduces 'learn execution graphs'—short-lived DAGs representing API request processing—to detect anomalies and drift in high-throughput systems. This approach localizes performance issues to exact nodes, identifies skipped or reordered steps, and differentiates between transient anomalies and fundamental system drift (structural, volume, or behavioral). It leverages per-client baselines and OpenTelemetry data to reduce mean time to discovery (MTTD) from hours to seconds, enhancing system reliability and observability.

Jun 30, 2026

Plenary Talk 3​: Challenges and research opportunities for global hyperscale services

Jim Kleewein's talk outlines the immense challenges and critical research opportunities in building and operating global hyperscale services like Microsoft 365 and Azure. He emphasizes that at this scale, traditional approaches fail, necessitating a "new golden age of applied research" across areas like continuous availability, data management, security, and sustainability. Kleewein also discusses AI's powerful but limited role, stressing the ongoing need for human expertise, and highlights the ethical imperative to prevent failures that can have life-or-death consequences.