, Titanium [2nd Floor]
Python’s static typing ecosystem has long been shaped by mypy, but a new contender has entered the space: ty, a high-performance type checker from Astral that has recently exited alpha. With a focus on speed, modern ergonomics, and tight tooling integration, Ty represents a new direction for Python type checking.
In this talk, we’ll explore what ty looks like in practice. We’ll cover its core features, how it behaves on real-world codebases, and what changes when type checking becomes fast enough to run constantly. We’ll also compare ty directly with mypy, highlighting strengths, limitations, and trade-offs teams should understand before adopting it.
This session will help Python developers evaluate whether ty is ready for production use today—and what it suggests about the future of Python typing tools.
Static typing in Python has matured significantly over the past decade, with mypy becoming the de facto standard for many teams. At the same time, developers continue to struggle with slow feedback loops, noisy errors, and friction in CI and local workflows. ty, a new type checker from Astral.sh, aims to address these issues with a fundamentally different set of design priorities—and it has now reached a post-alpha, production-ready stage.
This talk takes a practical, experience-based look at ty from the perspective of a Python developer using it on real code. We’ll start by briefly reviewing the current state of Python type checking and the problems that motivated ty’s design. From there, we’ll dive into ty’s feature set, performance characteristics, and developer experience, focusing on what actually changes when type checking becomes fast and ergonomic enough to feel “always on.”
A central part of the talk will be a direct comparison with mypy: where ty already excels, where it behaves differently, and where mypy remains the better choice today. Rather than framing this as a replacement story, we’ll explore the trade-offs between the two tools and what kinds of teams benefit most from each.
By the end of the session, attendees will have a clear mental model of how ty works, how mature it is today, and whether it’s a good fit for their own projects. More broadly, we’ll look at what ty signals about the future direction of Python’s typing ecosystem.
Data Engineer / ML Engineer at inovex GmbH.
I’m passionate about building innovative and impactful digital solutions and sharing practical insights that create sustainable value for customers and teams.