Software Engineering Daily

Pydantic AI with Samuel Colvin

101 snips
Dec 4, 2025
Samuel Colvin, creator of Pydantic and Pydantic AI, joins Gregor Vand to dive into the evolution of Pydantic from a simple type-safe library to a robust AI framework. They discuss the significance of type safety in AI applications, revealing how Pydantic AI enforces data validation. Samuel also shares insights on Logfire, an observability platform, and its developer-centric features. The duo emphasizes the importance of modular agent design and the interplay between AI and human expertise, showcasing the future of AI tooling in Python.
Ask episode
AI Snips
Chapters
Transcript
Episode notes
INSIGHT

Runtime Types As Safety Net

  • Samuel Colvin built Pydantic to enforce Python type hints at runtime because silent type errors are dangerous.
  • The library proved the idea worked and gradually became widely adopted in production Python codebases.
INSIGHT

Rust Backing For Speed

  • Pydantic v3 will introduce a Rust-backed struct type to hold data for big performance gains.
  • That enables direct binary/Parquet paths and array/table primitives for high-performance workflows.
ADVICE

Make Observability Native To Python

  • Make instrumenting Python apps feel as natural as writing Python by offering a smooth SDK experience.
  • Emit OpenTelemetry-compliant traces and provide a fast UI so developers can debug instantly.
Get the Snipd Podcast app to discover more snips from this episode
Get the app