Daniel Avila
Daniel Ávila is the Co-Founder of CodeGPT, an AI SaaS platform transforming software development with an AI-powered code assistant specialized in understanding large codebases through knowledge graphs. This approach enables CodeGPT to navigate complex repositories, providing developers with deeper context and smarter code suggestions. With over 15 years of experience as a software engineer, Daniel has focused his career on improving developer productivity and scaling technology businesses. He co-founded BoxMagic, a SaaS platform for managing fitness class bookings, now operating in more than 10 countries. Today, with CodeGPT surpassing 1.7 million downloads and empowering thousands of developers worldwide, Daniel continues to push the boundaries of AI in software development. Daniel holds a degree in Computer Science from Duoc UC and later specialized in IT Management Engineering at Pontificia Universidad Católica de Chile (PUC). Throughout his career, he has also helped multiple startups scale into international markets, gaining deep expertise in building and expanding tech-driven businesses. Backed by Techstars, Daniel now leads CodeGPT's mission to redefine how developers interact with code — enabling them to ship scalable, high-impact solutions by leveraging advanced AI models capable of deeply understanding codebases.
Speaker Sessions
Building Smarter Code Assistants by Mapping Large Codebases Into Knowledge Graphs
In this workshop, we’ll first explore the common challenges developers face when working with large codebases, especially when traditional code assistants struggle to provide context for complex projects. Understanding the relationships between files, functions, and dependencies is crucial, but most tools fail to capture this structure, leaving developers with limited insights. Next, we’ll dive into solving this problem by using CodeGPT to load your own code repository. We’ll visualize how the platform builds a Knowledge Graph from your code, transforming it into a structured base of knowledge. This graph will then be used throughout the software development process, from generating code to verifying tasks, providing smarter and more accurate insights to enhance your workflow.