Marketplaces are about to get weird. With Pedro Chaves and Donné Stevenson: agents picking your house, negotiating deals, even talking to other agents for you. Less browsing. Less choice. More automation. Convenience… or giving up control?

A new paradigm is emerging for building applications that process large volumes of data, run for long periods of time, and interact with their environment. It’s called Durable Execution and is replacing traditional data pipelines with a more flexible approach. Durable Execution makes regular code reliable and scalable. In the past, reliability and scalability have come from restricted programming models, like SQL or MapReduce, but with Durable Execution this is no longer the case. We can now see data pipelines that include document processing workflows, deep research with LLMs, and other complex and LLM-driven agentic patterns expressed at scale with regular Python programs. In this session, we describe Durable Execution and explain how it fits in with agents and LLMs to enable a new class of machine learning applications.

Chaigent combines Chainlit and Vertex AI to deliver a code-first, serverless AI agent platform that avoids costly per-seat licensing fees. It empowers developers to build highly customizable, enterprise-grade agents using a scalable pay-as-you-go architecture.

mAIdAI is a lightweight personal AI assistant built with Google Chat, Cloud Run, and Vertex AI, designed to automate repetitive micro-tasks. By grounding the model with a local markdown context file, it provides highly personalized workflow assistance directly within your chat environment.

We present LingBot-World, an open-sourced world simulator stemming from video generation. Positioned as a top-tier world model, LingBot-World offers the following features. (1) It maintains high fidelity and robust dynamics in a broad spectrum of environments, including realism, scientific contexts, cartoon styles, and beyond. (2) It enables a minute-level horizon while preserving contextual consistency over time, which is also known as "long-term memory". (3) It supports real-time interactivity, achieving a latency of under 1 second when producing 16 frames per second. We provide public access to the code and model in an effort to narrow the divide between open-source and closed-source technologies. We believe our release will empower the community with practical applications across areas like content creation, gaming, and robot learning.

In today’s era of massive generative models, it's important to understand the full scope of AI systems' performance engineering. This talk discusses the new O'Reilly book, AI Systems Performance Engineering, and the accompanying GitHub repo (https://github.com/cfregly/ai-performance-engineering). This talk provides engineers, researchers, and developers with a set of actionable optimization strategies. You'll learn techniques to co-design and co-optimize hardware, software, and algorithms to build resilient, scalable, and cost-effective AI systems for both training and inference.

A hands-on beginner roadmap for learning Kubernetes, designed to walk you through core concepts (like clusters, pods, services, deployments, storage, RBAC, autoscaling, etc.) with simple explanations, CLI examples, and practical exercises. By following it you build real experience and are prepared to use Kubernetes locally or on cloud platforms like GKE or EKS.

Experimenting with LLMs is easy. Running them reliably and cost-effectively in production is where things break. Most AI teams never make it past demos and proofs of concept. A smaller group is pushing real workloads to production—and running into very real challenges around infrastructure efficiency, runaway cloud costs, and reliability at scale. This session is for engineers and platform teams moving beyond experimentation and building AI systems that actually hold up in production.