Machine learning engineering

Jul 20, 2026

Enterprise Agents Have a Structure Problem - Ishita Daga, Tesla

Ishita Daga, a Senior ML Engineer at Tesla, explains why enterprise AI agents fail, highlighting that current fixes like larger models or more RAG are insufficient. She identifies ambiguity, staleness, and user preference as key structural problems. Daga proposes a solution built on semantic retrieval infrastructure: a hierarchical approach to knowledge sources, including a curated semantic layer and metadata graphs. She also details a robust context life cycle with live data sources and continuous feedback loops to combat staleness. The challenge of integrating individual preferences, requiring agents to reason over business concepts rather than raw schemas, is also discussed.

Jul 15, 2026

Recursive Model Improvement — Lee Robinson, Cursor, SpaceXAI

Lee Robinson of Cursor outlines a comprehensive strategy for recursive AI model improvement, centered on a two-loop training framework. He details how Cursor enhances both user-feedback-driven outer loops and high-quality evaluation inner loops, introducing novel methods like textual feedback and addressing reward hacking. The discussion extends to scaling compute infrastructure through partnerships with SpaceX, Colossus, and Terafab, and leveraging agent-based automation to streamline research and foster a future where models continuously train and improve themselves.

Jul 14, 2026

WTF Is the Context Layer? The Missing Infrastructure for Production Agents — Prukalpa Sankar

Prukalpa Sankar argues that while AI models are increasingly intelligent, their practical business utility is limited by a lack of contextual intelligence. She proposes a "context layer" as a crucial solution, akin to a "GitHub for context," to centralize and manage business knowledge, expertise, and norms. This system aims to provide AI agents with the shared, versioned, and continuously learning context necessary to overcome the challenges of isolated systems and context sprawl, ultimately differentiating companies in an AI-driven world.

Jul 08, 2026

Building an ACP-Compatible Agent Live — Bennet Fenner, Zed

This session explores building an AI coding agent that integrates with the Agent Client Protocol (ACP), Zed's open-source JSON RPC-based standard for agent-client communication. It covers essential architectural elements, including protocol design, session lifecycle management, real-time streaming of model output via session updates, and sophisticated handling of tool calls, such as proxied file system operations and self-modifying code for new capabilities like a terminal tool.

Jun 17, 2026

🔬 The Limits of AI in Science - Why We Need Self-Driving Labs — Joseph Krause, Radical AI

Joseph Krause, CEO of Radical AI, details how his company uses Self-Driving Labs (SDLs) and AI scientists to overcome the experimental bottleneck in materials science. By automating the full loop of hypothesis generation, synthesis, characterization, and testing, Radical AI is accelerating the discovery of novel alloys for aerospace, defense, and semiconductor applications, achieving 10x the pace of traditional methods. Krause explains why materials science is uniquely challenging for AI, how human intuition trains the AI, and why experimental data, not models, forms the core competitive advantage in this rapidly evolving, geopolitically significant field.