Krista Opsahl-Ong from Databricks introduces Agent Bricks, a platform designed to overcome the key challenges of productionizing enterprise AI agents. The talk covers common use cases, the difficult trade-offs between cost and quality, and how Agent Bricks uses automated evaluation and advanced optimization techniques to build cost-effective, high-performance agents.
This paper challenges the prevailing "bigger is better" narrative in AI, arguing that Small Language Models (SLMs) are not just sufficient but often superior for agentic AI tasks due to their efficiency, speed, and specialization. The discussion explores the paper's core arguments, counterarguments, and the practical implications of adopting a hybrid LLM-SLM approach.
Stefan Saroiu from Microsoft Research details Project STEMA's six-year journey tackling the DRAM security flaw, Rowhammer. He discusses how academic research kept the industry honest about DDR4 vulnerabilities, the development of their in-DRAM defense, Panopticon, and its evolution into the industry standard PRAC for DDR5, while highlighting that significant challenges and research opportunities remain.
Ben Lorica and David Talby of 'The Data Exchange' podcast analyze the 2025 AI Governance Survey, revealing a significant gap between AI adoption and mature risk management. While 30% of organizations have models in production, many lack robust governance frameworks, incident response plans, and comprehensive monitoring, often prioritizing speed-to-market over safety and compliance.
In the fifth edition of the a16z Consumer AI 100, an analysis of the most-used AI-native products reveals a market that is beginning to stabilize after a period of chaotic growth. Key trends identified include the continued dominance of AI companionship and creative tools, the significant market entry of major players like Google and xAI's Grok, the rise of Chinese AI companies on the global stage, and the emergence of a powerful new category: "vibe coding." The data suggests a future of increased verticalization, prosumer tool adoption, and the development of more sophisticated network effects beyond simple data acquisition.
Daniel Terhorst-North explores the learnable traits of great programmers, challenging the notion of innate talent. He outlines a three-part framework focusing on effective execution, selecting appropriate tools, and fostering a psychologically safe team environment, emphasizing that greatness is achieved through deliberate practice and a focus on product and people over code.