Good afternoon! I'm Jiang Wenxin. This work is done in collaboration with Yin Jian. We are both PhD students at CityUHK. Before today's talk, let me do a quick survey first: I'm sure many of you have noticed that Python 3.14 has officially launched. But how many of you have already checked out what's new in 3.14? If you have, please raise your hand. (Let me see.) All right, thank you. (Great!) Looks like many of you are already familiar with the new features. (Okay, not so many? No worries!) That’s exactly why I’m here today. So the goal of this talk is simple: I wanna share my experience in how to catch up with the latest changes. We’ll take a look at several changes from Python 3.10 to 3.14. You’ll see how these changes are making Python even more enjoyable to use. Let's get started.
Actually, the release of Python now is stable: a new major version launches every October. This chart is a screenshot from the official Python versions page. Each row is a version, and you can see the entire journey — from adopting new features, to only fixing bugs and security, and finally reaching its end-of-life. The blue line shows us where we are today, right after the release of Python 3.14.
So, how do we keep up with all these changes? I’d like to highlight two key resources: the 'What's New' pages and PEPs. Think of the 'What's New' page as a summary for each release. It's a perfect way for us to quickly get a high-level overview of the most important updates. But when we need more details of a specific feature, here's the PEP. Python Enhancement Proposal, is the official design document. It gives you the technical details, motivation and rationale behind a change. Here's how an idea becomes a Python feature: It usually starts with discussion on public forums. If there's enough interest, someone will draft a proposal. They will find a core dev to sponsor it, submit it as a pull request to the PEPs repository. Then, discuss and refine it. The proposal eventually goes to the Steering Council for a final decision. This open process is a core strength of the Python ecosystem. In short, the What's New page tells us what has changed, while the PEP explains why and how.
Although the Whats New page is quite long, it's very well-organized. We can first start with the Release Highlights. This section is the official summary and gives you the information about the most critical updates. Then, we'll move on to the New Features. Here, you’ll find the major language changes, which are often highly relevant to our coding work. Finally, we can dive into the details. For example, if you're interested in specific areas like optimizations, C API, or module changes, you can jump to those sections directly. This way, you can focus on what matters most to you.
I'll introduce the update in Python with an example from Python 3.10: Structural Pattern Matching. This introduced the match/case statement, like the switch in C++, providing an elegant alternative to long if/elif/else chains. In this code, the status variable can be matched against a specific value, a set of values or a wildcard. This declarative style makes our code easy to write and read.
Now, I wanna highlight the multi-year effort to make Python faster and achieve its truly parallel execution. Python 3.11 marked the real beginning of this journey. Python 3.11 delivered a free speed boost, reducing runtime without requiring any changes to your code. It's about 25% faster on average, thanks to a faster startup of the interpreter and runtime optimizations. This impressive performance improvement set the stage for the next step: tackling true parallelism.
The main challenge of taking advantage of multi-CPU systems has always been the Global Interpreter Lock, or GIL, which kept Python stuck on just one CPU core at a time. But with Python 3.13, this problem was addressed through an experimental free-threaded mode. Of course, there are some trade-offs. Single-threaded code might run slightly slower. C extensions also need updates to ensure compatibility, which means extra effort for third-party library developers. And memory usage increased slightly.
Python 3.14 takes two steps further. First, the free-threaded mode is now officially supported. Second, the ability to manage multiple interpreters is now exposed in the standard library. With these significant developments, Python finally unlocks its ability to execute truly parallel code across multiple CPU cores.
Let’s quickly recap the key takeaways. First, I'd like to remind you that Python now follows a predictable annual release schedule. Second, the Whats New pages provide a high-level summary, while the corresponding PEPs provide technical details. Third, before adopting new features in production code, it's crucial to check compatibility and test whether they work well in your case. And finally, all I want to say is: stay curious and keep exploring new features. Python is evolving rapidly, and there's always something new to learn.
Thank you for your attention. You can find me on GitHub at LucaJiang, and the slides of this talk are available online.