Python has been at the center of my work in machine learning and AI for more than a decade. It is where I start from scratch, experiment with ideas, and build systems that help me understand how large language models really work.

In this keynote, I will look at what it means to build and study LLMs in Python today. Starting from small, from-scratch implementations, I will show how Python and PyTorch help us understand modern model architectures, compare new designs against reference code, and learn details that papers often leave out. I will then connect those implementation lessons to current LLM trends, especially the push to reduce inference costs and KV-cache pressure as reasoning models and agentic workflows need longer contexts. At the end, I will also share a practical roadmap of libraries, open projects, and learning resources for going from first principles to real-world LLM development.