Shopify CTO Mikhail Parakhin details their aggressive internal AI adoption, revealing the internal stack—Tangle, Tangent, and SimGym—that powers reproducible ML, auto-research, and customer simulation. He also discusses how AI shifts the software bottleneck to PR review and CI/CD and explains their use of Liquid AI for low-latency, non-transformer workloads.
Jacob Lauritzen, CTO of Legora, argues that as AI agents tackle more complex work, the bottleneck shifts from task execution to planning and review. He proposes a framework for human-agent collaboration based on increasing 'trust' and 'control', and advocates for moving beyond simple chat interfaces to high-bandwidth, domain-specific artifacts like documents and structured reviews for more effective collaboration.
IBM security experts discuss the implications of using AI agents like OpenClaw for penetration testing, the challenges posed by AI-generated ephemeral software, and the strategic allocation of security budgets in an era of rapidly growing ransomware threats.
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.