Dave Farley argues that modern software development should be treated as an engineering discipline, emphasizing an evolutionary and iterative approach to architecture and design. He outlines key principles for tackling complexity, focusing on optimizing for learning and ease of change through techniques like working in small steps, prioritizing testability, and managing coupling.
Tudor Girba introduces "Moldable Development," a paradigm where developers create thousands of custom tools to analyze and interact with their specific software context. He demonstrates this live using Glamorous Toolkit, transforming an unstructured text file into a browsable system with custom views, all within minutes.
Paul Slaughter, creator of Conventional Comments, and Adrienne Braganza Tacke discuss how a structured, empathetic approach to feedback can transform code reviews from frustrating processes into productive collaborations. They explore the philosophy behind labeling comments, the importance of fostering collaborative ownership, and practical strategies for maintaining both politeness and efficiency in asynchronous, remote environments.
Anthropic's Boris Cherny, creator of Claude Code, discusses the paradigm shift from traditional coding to agentic workflows. He covers the co-evolution of models and their "harnesses," the importance of "hackability" via features like slash commands, and provides practical tips for leveraging coding agents for tasks of varying complexity.
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.
Dexter Horthy of Human Layer explains why naive AI coding agents fail in complex software projects and introduces 'Advanced Context Engineering.' He details a spec-first, three-phase workflow (Research, Plan, Implement) designed to manage context intentionally, keeping utilization below 40% to maximize model performance. This approach uses subagents and frequent compaction to turn AI from a prototyping tool into a production-ready system for large, brownfield codebases.