Uploaded March 2026 | Updated September 2026, 2 weeks ago
*Alena Osipova* joins PyStories to talk about Kiwi.comβs support for the Python community, her volunteering experience at EuroPython, as well as the Prague local user groups and PyLadies chapters across Europe.
*Interviewer:* LaΓs Carvalho
π₯ Interview recorded at EuroPython 2024.
π Be part of EuroPython and connect with welcoming Python communities across the globe: https://europython.eu/ β€οΈ
π€ Interested in supporting EuroPython? You are welcome to reach out to sponsoring@europython.eu!
*Alena Osipova* joins PyStories to talk about Kiwi.comβs support for the Python community, her volunteering experience at EuroPython, as well as the Prague local user groups and PyLadies chapters across Europe.
*Interviewer:* LaΓs Carvalho
π₯ Interview recorded at EuroPython 2024.
π Be part of EuroPython and connect with welcoming Python communities across the globe: https://europython.eu/ β€οΈ
π€ Interested in supporting EuroPython? You are welcome to reach out to sponsoring@europython.eu!

![Write async. Get sync for free - Thom Lane
[EuroPython 2026 - S4 on 2026-07-15]
π€ *Write async. Get sync for free by Thom Lane*
π https://ep2026.europython.eu/session/write-async-get-sync-for-free
π Abstract:
When you write a Python library, you have a dilemma. Ship async-only? Ship blocking-only? Or write everything twice and watch both copies drift?
Synchronicity (https://github.com/modal-labs/synchronicity) removes this dilemma. Write your library once (in async) and get a blocking interface for free, while avoiding the boilerplate and limitations of `asyncio.run()` wrappers. Weβll talk through how to handle persistent connections, async generators, clean tracebacks, type hints and more.
Weve used it in production at Modal for years, and its fully open source. We hope it can save you from writing your own library twice!
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License: https://creativecommons.org/licenses/by-nc-sa/4.0/ Write async. Get sync for free - Thom Lane](https://i.ytimg.com/vi/MOZL9XgepxM/mqdefault.jpg)


![Inside Python 3.15s JIT Optimizer - Ken Jin
[EuroPython 2026 - S1 on 2026-07-15]
π€ *Inside Python 3.15s JIT Optimizer by Ken Jin*
π https://ep2026.europython.eu/session/inside-python-3-15-s-jit-optimizer
π Abstract:
Python 3.15s JIT is finally starting to see real speedups on benchmarks. Core to that is a revamped JIT optimizer. *What* the JIT selects to compile and *how* it optimizes code has seen significant improvements thanks to the work of many contributors. In this talk, I will cover in-depth how the Python JIT optimizes your Python code, and how this was an amazing community team effort spanning multiple continents.
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License: https://creativecommons.org/licenses/by-nc-sa/4.0/ Inside Python 3.15s JIT Optimizer - Ken Jin](https://i.ytimg.com/vi/NN_kHSIw0SY/mqdefault.jpg)
![Args: Amazing or Approaching? - Evan Kohilas
[EuroPython 2026 - S1 on 2026-07-16]
π€ *Args: Amazing or Approaching? by Evan Kohilas*
π https://ep2026.europython.eu/session/args-amazing-or-approaching
00:00 - Intro
01:21 - Arguments vs Parameters
01:55 - Example intro
02:21 - Problems of adding optional parameters
03:20 - Solution via wrapper functions
03:59 - Solution via function overloading
04:29 - Pythons lack of function overloading
05:10 - Solution via Pythons default arguments
05:52 - Problems of Pythons default arguments
07:16 - How other languages use the builder pattern to solve optional arguments without function overloading
07:56 - Problems of the builder pattern
08:33 - Problems of function overloading
09:07 - Benefits of Pythons keyword arguments
11:30 - re.sub example problem
13:30 - re.sub example solution using the `*` parameter
13:37 - How to enforcing keyword arguments using the `*` parameter
14:25 - Problems when using the `*` parameter
15:14 - Solution via PEP736
15:31 - Solution via linters
16:34 - The `/` parameter, and problems of Pythons keyword arguments
18:06 - How the `/` parameter helps with parameter renaming
18:32 - Benefits of Swifts handling of external and internal parameter names
19:36 - How Javascript similarly handles external and internal parameter names
20:24 - How the `**` operator in Python allows for unpack arguments from dictionaries
21:08 - How the `...rest` property allows Javascript to handle unexpected arguments
21:21 - How the `**kwargs` parameter allows Python to handle unexpected arguments
22:19 - How Python 2 used to handle parameter unpacking
22:53 - Problems of `**kwargs` and solution using the `/` parameter
23:40 - How the `*args` parameter allows an arbitrary number of arguments
25:15 - Are arbitrary arguments a good idea? Why doesnt Rust support them?
25:38 - How arbitrary arguments make it difficult to understand function definitions
26:01 - Typing arbitrary arguments
26:36 - Recap of Pythons function parameter and argument system
27:37 - Outro
π Abstract:
From default args, to `**kwargs`, and everything in-between, Pythons comprehensive argument system lends itself to some of the most effective code, through encouraging readability, reuse, and easy refactoring.
But is that really true? Why, and why not?
What could we learn from other languages?
And what else could lie in their future?
In this session, well give a rundown of Pythons comprehensive function argument system and how its features allow for safety, conciseness, and expressiveness (whether youre calling or writing functions).
Well then give examples for where that system lacks, where it could bite you, and give suggestions for what more could be done on Python code to fix those limitations.
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License: https://creativecommons.org/licenses/by-nc-sa/4.0/ Args: Amazing or Approaching? - Evan Kohilas](https://i.ytimg.com/vi/NQRU9bmVkyw/mqdefault.jpg)
![From Pixels to Insights: Python for Earth Observation - ELENI TOKMAKTSI
[EuroPython 2026 - S2 on 2026-07-16]
π€ *From Pixels to Insights: Python for Earth Observation by ELENI TOKMAKTSI*
π https://ep2026.europython.eu/session/from-pixels-to-insights-python-for-earth-observation
π Abstract:
Due to climate change, *large wildfires* continue affecting all continents of the Earth, leading to forest loss and exacerbating environmental impacts. Even more, current changing weather conditions associated with global warming will further increase fire danger to a global extent. Investigating these phenomena in a data-driven manner helps in decision making in pre-intervention and post-restoration following wildfire events. *Satellites* orbiting Earth can offer critical insights by capturing detailed images of the planetβs surface. Utilizing this data, environmental scientists perform detailed assessments to monitor an ecosystemβs loss and post-restoration progress. However, data from satellites arrive in the form of millions of raw pixels and turning them into analysis ready products is a *time-consuming* task full of *multi-step* and *error-prone* processes.
This talk introduces an end-to-end Python workflow to automate the processing of *Earth Observation (EO) data.* The presentation walks through pixels to insights showcasing a real example and highlighting both the power of automation in the field of remote sensing and the importance of EO data in *climate change monitoring.* After the session, the audience will understand how utilizing open Python packages such as *Rasterio* and *NumPy* with openly distributed data from the [European Space Agencyβs Sentinel-2 mission](https://dataspace.copernicus.eu/data-collections/copernicus-sentinel-missions/sentinel-2) can lead us quickly into crucial and spatially meaningful information. Although the demonstration focuses on wildfire events, the automated workflow is broadly applicable to other environmental fields such as land cover change detection, hydrological assessments and coastal studies.
*No prior knowledge is required*! The *goal* is not to delve into complex mathematical equations, but to showcase the power of automation in remote sensing with Python.
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License: https://creativecommons.org/licenses/by-nc-sa/4.0/ From Pixels to Insights: Python for Earth Observation - ELENI TOKMAKTSI](https://i.ytimg.com/vi/O9ZyixOwSrY/mqdefault.jpg)



![Update on the CPython JIT: What to expect in 3.15 - Diego Russo
[EuroPython 2026 - S1 on 2026-07-15]
π€ *Update on the CPython JIT: What to expect in 3.15 by Diego Russo*
π https://ep2026.europython.eu/session/update-on-the-cpython-jit-what-to-expect-in-3-15
π Abstract:
This talk is a follow-up to my EuroPython 2025 session on the foundations of the CPython JIT compiler. It looks at how the JIT has evolved since Python 3.14 and where it stands as Python 3.15 approaches release. We cover the transition of the JIT from an experimental feature towards a supported part of CPython, how it fits into the runtime today, and what that means in practice for users and distributors. The talk also explores how the JIT interacts with free-threaded builds, the current status of integration with external debuggers, and how these constraints shape real-world usage. Finally, we examine how the JIT performs today, where it already delivers substantial speedups, where it does not, and what realistic performance expectations look like for Python 3.15.
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License: https://creativecommons.org/licenses/by-nc-sa/4.0/ Update on the CPython JIT: What to expect in 3.15 - Diego Russo](https://i.ytimg.com/vi/QP9fiB_c8Kk/mqdefault.jpg)