Under 5 minutes to a deployed LLM endpoint — Audry Hsu, RunPod

This talk explores building and deploying AI-powered applications, focusing on the capabilities and accessibility of Google DeepMind's latest models and tools. It highlights the multimodal nature of Gemini models, their integration into AI Studio for rapid prototyping, and the potential for creating complex applications with features like code execution, search grounding, and dynamic world generation. The presentation emphasizes practical demonstrations over slides, showcasing how developers can leverage these advancements for diverse projects.

This talk explores the rapid development and deployment practices within the OpenClaw project, likening the process to a "dark factory" that ships code at an unprecedented velocity. It argues that the sheer speed of development necessitates a shift from traditional code review to a more managerial approach, where engineers oversee swarms of autonomous agents. The core thesis is that effective engineering in this new paradigm relies on process, intuition, and managing agent workflows rather than solely on individual coding prowess.

This talk introduces a framework for building AI agents that can interact with a computer, leveraging the AI SDK. The core thesis is that effective agents require an agent runtime, tools for interaction, and a computational environment like a sandbox file system for state persistence and code execution. The presentation demonstrates how to integrate these components to create more capable and autonomous AI agents.

This talk introduces RunPod's Flash, a Python SDK designed to streamline the development and deployment of AI models on GPU infrastructure. The core thesis is that developers can significantly reduce iteration time by deploying functions directly to the cloud from their local IDE, eliminating the need for manual commits, Docker builds, and server configuration. This allows for rapid testing and iteration on GPU-accelerated workloads.

This talk argues that reinforcement learning (RL) is crucial for moving AI models, particularly large language models (LLMs), from pilot stages to production. The speaker posits that the common failure of GenAI pilots stems from the "myth of the last mile," where initial MVPs built on proprietary models or instruction fine-tuning lack systematic improvement pathways. RL, by its nature, allows for the mathematical integration of feedback, leading to more effective model steering and enabling scaled, cost-efficient, and faster deployments.