This episode is for builders experimenting in AI. We dive deep into technical aspects of agent based workflows, data models, and interesting new fundamental research. Madhav Singhal is a lead AI researcher at Replit focused on the frontier of what AI can enable for Replit users. In this episode, we dive into specific applications of models across code generation, testing, programming languages and more. We get fairly technical into how data maps to ML models and the types of agent based workflows that work today or may work in the future. We cover why focusing time on new model architectures may not be as relevant for founders to spend time on and how everything with new technology always maps to thinking about what solves the core problem efficiently. Enjoy this deep dive into the technical aspects of agent based workflows.
