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.
Sander Dieleman from Google DeepMind provides a behind-the-scenes look at the key components of training large-scale diffusion models for audio-visual data. The talk covers the entire pipeline, from the critical role of data curation and latent representations to the mechanics of diffusion, network architectures, sampling with guidance, and advanced control signals.
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.