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.
Professor Andreas Zeller discusses his interactive 'Debugging Book,' arguing that systematic, automated debugging is a critical but neglected skill. He explores powerful techniques like delta debugging and automated repair, explaining how developers can build their own tools to make debugging a more plannable and efficient process.
Distinguished Engineer Chris Hay demonstrates how to run and program Large Language Models (LLMs) locally in just two lines of Python code. The tutorial covers setting up a local environment with Ollama and UV, using a custom library for simplified interaction, and explores advanced topics like asynchronous streaming, persona customization with system prompts, and managing multi-turn conversations.
Author Fabrizio Romano discusses his decade-long journey writing "Learning Python Programming," detailing the book's evolution from GUI-focused content to command-line applications. He explores the controversial but ultimately beneficial introduction of typing in Python, the role of AI as a powerful tool for developers, and the critical importance of nurturing junior talent rather than replacing them with AI, emphasizing the enduring need for fundamental programming skills and human expertise.
Gal Peretz introduces CodeAct, a paradigm where LLMs generate and execute Python code for tool interaction, offering a more flexible and powerful alternative to traditional JSON-based function calling for building complex, production-ready AI agents.
Akshay Agrawal, CEO of Marimo, discusses how Marimo addresses the critical flaws of traditional notebooks like Jupyter. He explains its reactive architecture, the benefits of storing notebooks as pure Python files for version control and reusability, and its AI-native features that leverage runtime context for more intelligent LLM-assisted coding.