One of the first major use cases for Generative AI was as a coding assistant. But developers face many challenges throughout the software development lifecycle beyond just writing the next line of code. In order to reduce developer friction and keep developers in flow, eBay has been leveraging AI to address multiple challenges. During this talk, Brad will discuss a variety of challenges that eBay developers face and how they are applying Generative AI to them.

Join Jasmine Robinson for an illuminating session that delves into the transformative power of Generative AI (GenAI) in reshaping software development at Netflix. As a pioneering advocate for GenAI tools in Developer Productivity, Jasmine has been instrumental in navigating the complex pathways of Legal and Security to integrate these cutting-edge technologies into Netflix’s engineering framework. In this session, Jasmine will share her expert insights and provide a comprehensive roadmap for safely and securely onboarding GenAI in your organization. You will also discover the innovative suite of GenAI tools that Netflix developers leverage to drive creativity and innovation, ensuring seamless integration into their development workflow.

This presentation covers five strategies to help speed up Maven builds, focusing on practical steps like parallelizing builds, optimizing Maven profiles, and leveraging caching. We’ll explore how tweaking your configuration can significantly reduce build times. The aim is to provide actionable techniques that developers can immediately apply to make their Maven builds faster—directly impacting productivity by shortening the feedback loop for code compilation and testing wait times.

Mobile testing remains an unresolved challenge especially at our scale, encompassing thousands of developers and over 3,000 simultaneous experiments. Manual testing is usually carried out, but with high overhead, and cannot be done extensively for every minor code change. Furthermore, the substantial maintenance costs of these tests significantly hinder their adaptability and reusability across diverse cities and languages (imagine having to hire manual testers or mobile engineers for the 50+ languages that we operate in!), which makes it really difficult for us to efficiently scale testing and ensure Uber operates with high quality globally.

To solve these problems, we created DragonCrawl, a system that uses large language models (LLMs) to execute mobile tests with the intuition of a human. It decides what actions to take based on the screen it sees and its goals, and independently adapts to UI changes, just like a real human would. In this talk, we will deep dive into our architecture, challenges, results, MCP and how we went from DragonCrawl to OmegaCrawl: A system to automate many repetitive tasks