Sam Khavari, the creator of Pydantic, introduces Monty, a new, secure, and high-performance Python interpreter written in Rust. Monty is designed specifically for AI agents, bridging the gap between simple, limited tool-calling and complex, slow, full-featured sandboxes.
A technical guide on migrating ML experiments from Neptune to Weights & Biases, covering the migration script, API-level code changes, and best practices for organizing projects and analyzing results in the W&B platform before the Neptune sunset.
Sam Keen, author of 'Clean Architecture with Python', discusses with Max Kirchoff how to pragmatically apply architectural principles to Python. They explore the critical link between architecture and testability, thoughtful dependency management through layered design, and how these principles enhance modern AI-assisted coding workflows by providing clear structure and scope.
Fabrizio Romano, author of "Learning Python Programming," discusses the evolution of his book with Naomi Ceder. Key topics include the strategic shift from GUIs to CLIs, the evolving perspective on Python's type hinting, and the dual role of AI as a powerful tool and a potential threat to junior developer growth. Fabrizio emphasizes the importance of fundamental skills, mentorship, and the human element in the age of AI.
Sam Keen discusses his book “Clean Architecture with Python,” explaining how to apply architectural principles in a pragmatic, Pythonic way. The conversation covers thoughtful dependency management, the onion model, and the crucial link between good architecture and testability. Sam also explores how these principles provide a robust foundation for AI-assisted coding by creating well-defined, scoped problems for AI agents, ultimately leading to more maintainable and resilient software.
Yusuf Olokoba, founder of Muna, details a compiler-based approach to transform Python AI functions into self-contained native binaries. This talk explores the technical pipeline, including custom AST-based tracing, type propagation, and the strategic use of LLMs for code generation, enabling a universal, OpenAI-style client for running any model on any platform.