Making Sense of Millions of Conversations for AI Agents

Hugo Bowne-Anderson is an independent data and AI consultant with extensive experience in the tech industry. He is the host of the industry Vanishing Gradients, where he explores cutting-edge developments in data science and artificial intelligence. As a data scientist, educator, evangelist, content marketer, and strategist, Hugo has worked with leading companies in the field. His past roles include Head of Developer Relations at Outerbounds, a company committed to building infrastructure for machine learning applications, and positions at Coiled and DataCamp, where he focused on scaling data science and online education respectively. Hugo's teaching experience spans from institutions like Yale University and Cold Spring Harbor Laboratory to conferences such as SciPy, PyCon, and ODSC. He h

Ivan Leo is an AI engineer at Manus, building the future of agents. Previously at 567 Labs, he explored how large language models think and how to optimize their real-world performance through rigorous experimentation and engineering. He is also one of the lead maintainers of Instructor, an open-source library with millions of downloads that helps developers get structured outputs from LLMs. Ivan’s background spans full-stack engineering at Credit Suisse and collaborations with teams at HubSpot, Raycast, and OpenAI. Based in Singapore, he’s part of a new generation of engineers turning research breakthroughs into reliable, production-grade AI systems.