Conor Hoekstra demonstrates how to achieve exponential productivity by combining AI-assisted development, array programming, and high-performance computing. Using a financial dashboard app built entirely with AI (Vibe Coding), he showcases a custom array-based DSL with a dual backend (interpreted BQN and compiled NVIDIA Parrot for GPUs), urging developers to fully embrace modern tools and elevate their expectations of what is possible.
This workshop by Mahmoud Mabrouk, CEO of Agenta AI, delves into building calibrated LLM-as-a-judge evaluations that reliably align with human judgment. It highlights how miscalibrated judges lead to false confidence and presents a practical workflow, including designing use-case specific metrics, detailed data annotation, and optimizing judge prompts using the GAPA algorithm. The talk emphasizes the importance of iterative debugging, model selection, and custom reflection templates for achieving trustworthy and effective LLM evaluations.
Microsoft researchers unpack the New Future of Work Report 2025, exploring AI's real-world impact. They discuss adoption trends, the shift in job tasks, and the crucial distinction between viewing AI as a simple tool versus a collaborator. The conversation emphasizes moving beyond pure efficiency to consciously design a future where AI supports human flourishing and meaningful work.
Matthew Meckes from AWS makes a compelling case for Java's central role in the future of enterprise AI. This talk explores how Spring AI empowers developers to build robust, production-ready agentic applications by integrating LLMs with existing Java services, moving beyond proofs-of-concept to solve real-world business problems.
Circle CEO Jeremy Allaire delves into how programmable money and the Arc blockchain will power the emerging AI agentic economy. He explains how stablecoins like USDC offer an internet-native financial infrastructure for micro-transactions and large settlements, addressing the limitations of traditional banking for AI agents. The discussion covers the foundational principles of full-reserve banking, the unique attributes of Arc blockchain for machine-driven economic activity, the tokenization of real-world assets, and a bold vision for AI's potential to drive double-digit GDP growth and foster new on-chain organizational structures within the next decade.
Daniel Lütgehetmann of inait introduces "digital brains," biologically accurate computational models of real brains, as a solution to current AI's limitations in physical world interaction. Unlike traditional AI that struggles with dynamic environments and skill accumulation, these digital brains leverage biologically inspired learning rules to achieve dramatically faster learning in robotics and complex systems, demonstrating potential for real-world adaptability and efficiency.