Welcome to AIE CODE - Jed Borovik, Google DeepMind

This talk introduces Google DeepMind's latest advancements, Gemini 3 Pro and Nano Banana Pro, highlighting their capabilities in building AI-powered applications. The core thesis is that these models empower anyone to build software by simplifying complex tasks through intuitive interfaces and powerful AI features, enabling a new generation of engineers to create at an unprecedented scale.

Anti-gravity is a new AI developer platform from Google DeepMind designed with an agent-first approach. It integrates an editor, a browser, and an agent manager to provide a cohesive environment for AI-powered software development. The platform aims to leverage advancements in AI models, particularly in areas like reasoning, multimodal capabilities, and tool use, to enable more ambitious and complex agentic workflows.

This talk explores the challenges and potential of measuring AI capabilities, particularly focusing on developer productivity and long-term task completion. It questions the extrapolation of current AI progress trends, suggesting that physical and economic constraints, alongside potential technological breakthroughs, could alter the trajectory of AI development. The discussion also delves into the complexities of evaluating AI in real-world scenarios beyond controlled benchmarks, highlighting the gap between AI capabilities and practical application in fields like software engineering and data science.

This talk explores building durable, production-ready AI agents by integrating the OpenAI Agents SDK with Temporal. It highlights how Temporal's distributed systems capabilities can provide essential durability, visibility, and scalability to agentic applications, which are often inherently stateful and prone to failures. The presentation demonstrates how to leverage Temporal's workflow and activity abstractions to manage agent execution, tool invocation, and error handling, ensuring that agent processes can reliably resume after interruptions.

This talk introduces Spec-Driven Development as a method to enhance AI agent development, focusing on improving control, code quality, and reliability. It proposes a structured workflow that compresses the software development lifecycle, moving from prompt to requirements, design, and execution, with an emphasis on creating reproducible results. The approach aims to integrate established software engineering practices with AI capabilities to tackle more complex problems.