Drawing on research from complexity science, this presentation explores why organizations slow down as they grow and how architectural and organizational patterns, like those in microservices and at Amazon, can create superlinear scaling. It explains that by fostering networked structures over rigid hierarchies, companies can mimic the innovative and resilient properties of cities.
Mark Mashottton of Los Alamos National Laboratory (LANL) discusses the lab's 70-year history in applied AI, its current focus on using agentic AI to accelerate scientific discovery, and the critical architectural and governance principles required for successful AI collaboration within the high-stakes U.S. federal and national security landscape.
A detailed summary of a workshop on building and deploying production-minded AI coding agents using Dagger. The session covers creating controlled, observable, and test-driven agent workflows and integrating them into CI/CD systems like GitHub Actions for automated, reliable software development.
Traditional benchmarks and leaderboards are insufficient for production AI. This summary details a practical, multi-layered evaluation strategy, moving from foundational system performance to factual accuracy and finally to safety and bias, using open-source tools like GuideLLM, lm-eval-harness, and Promptfoo.
Madhavan Ramanujam, a leading expert on monetization, shares a framework for pricing and scaling AI products. He discusses why AI companies can capture 25-50% of the value they create, how to choose the optimal pricing model using a 2x2 of attribution and autonomy, and provides advanced tactics for negotiation and reframing POCs as business case creation.
Philipp Krenn from Elastic demystifies the 'R' in RAG, arguing that modern retrieval is a sophisticated blend of classic keyword search (like BM25) and modern vector search. This workshop explores the fundamentals of lexical analysis, scoring, dense/sparse vectors, and advanced hybrid search techniques like Reciprocal Rank Fusion (RRF).