Gergely Orosz, author of The Pragmatic Engineer, discusses the bizarre trend of 'token maxing' in Big Tech, the evolving role of software engineers in the AI era, and why companies are heavily investing in internal AI infrastructure despite uncertain productivity gains.
Tuomas Artman, Cofounder and CTO of Linear, discusses the paradoxical challenge AI introduces to software development. While AI agents accelerate shipping features, this speed can lead to a decline in software quality. He argues for prioritizing "tasteful," high-quality software as the ultimate competitive advantage and shares Linear's unique internal practices, such as the "Zero-Bug Policy" and "Quality Wednesdays," designed to cultivate a deep-seated culture of quality.
Giedrius Praspaliauskas from AWS explores the ten stages of the API lifecycle, demonstrating how modern AI, Generative AI, and agentic systems are reshaping API strategy, development, security, and governance on AWS using services like API Gateway, AppSync, and Amazon Q.
Richmond Alake from Oracle explains the critical role of Agent Memory in building adaptive AI agents, detailing the four types of memory, the limitations of Retrieval-Augmented Generation (RAG), and the architecture of the modern agent stack.
Adria Grondin, developer of the Locally AI app, provides a technical walkthrough on running large language models like Google's Gemma on an iPhone using Apple's MLX framework. The talk covers the necessary tools, performance expectations, the importance of quantization, and the growing MLX ecosystem.
This hands-on workshop details the construction of a sophisticated, dual-part AI system for producing high-quality technical content. It begins with an MCP-powered deep research agent that autonomously plans, searches the web, and analyzes sources like YouTube to synthesize a grounded research artifact. The second part is a constrained, deterministic writing workflow that transforms this research into polished, non-sloppy content using an innovative "Evaluator-Optimizer" pattern for iterative refinement. The session emphasizes crucial AI engineering principles, such as choosing between agentic and workflow-based architectures, and concludes with a deep dive into implementing practical observability and evaluation pipelines to ensure the system is both measurable and improvable.