Planet Python

Last update: January 04, 2026 04:43 AM UTC

January 04, 2026

EuroPython

Humans of EuroPython: Marina Moro López

EuroPython wouldn’t exist if it weren’t for all the volunteers who put in countless hours to organize it. Whether it’s contracting the venue, selecting and confirming talks & workshops or coordinating with speakers, hundreds of hours of loving work have been put into making each edition the best one yet.

Read our latest interview with Marina Moro López, a member of the EuroPython 2025 Programme Team and a former EuroPython speaker.

Thank you for contributing to the conference programme, Marina!

EP: What first inspired you to volunteer for EuroPython? And which edition of the conference was it?

I volunteered at EuroPython 2025 because I was a speaker at the 2024 edition and fell in love with the event, so I wanted to do my bit to help keep it amazing.

EP: What was your primary role as a volunteer, and what did a typical day look like for you?

I was involved in reviewing talks and putting together the schedule, as well as contacting keynote speakers and organizing the open spaces. A typical day was filled with Excel spreadsheets and emails :) 

EP: Could you share your favorite memory from contributing to EuroPython?

Honestly, the day the program team got together at the event. We shared an intimate moment exclusively for ourselves after all the hard work we had done, seeing how it was paying off.

EP: Is there anything that surprised you about the volunteer experience?

It may seem that organizing such a large event can be chaotic at times with so many people involved, so I was surprised to see that this wasn’t the case at all and that, in the end, we were all one big team.

EP: How has contributing to EuroPython impacted your own career or learning journey?

Without a doubt, an event like EuroPython gives you top communication and organizational skills. Also, in my particular case, and maybe this is a little silly, I am super proud to say that I did my first PR ever!

EP: What&aposs one misconception about conference volunteering you&aposd like to clear up?

Even if you don&apost have a tech background (like me), if you want to help, that&aposs reason enough to participate. You don’t need anything else.

EP: If you were to invite someone else, what do you think are the top 3 reasons to join the EuroPython organizing team?

Seeing how an event like this is created from the inside is incredible, plus the team is lovely, and you&aposll learn a lot because you’ll be surrounded by top people from the community.

EP: Thank you for your work, Marina!

January 04, 2026 02:26 AM UTC

January 03, 2026

Hugo van Kemenade

Localising xkcd

I gave a lightning talk at a bunch of conferences in 2025 about some of the exciting new things coming in Python 3.14, including template strings.

One thing we can use t-strings for is to prevent SQL injection. The user gives you an untrusted T-string, and you can sanitise it, before using it in a safer way.

I illustrated this with xkcd 327, titled “Exploits of a Mom”, but commonly known as “Little Bobby Tables”.

I localised most of the slides for the PyCon I was at, including this comic. Here they are!

PyCon Italia #

May, Bologna

PyCon Greece #

August, Athens

PyCon Estonia #

October, Tallinn

PyCon Finland #

October, Jyväskylä

PyCon Sweden #

October, Stockholm

Thanks #

Thanks to Randall Munroe for licensing the comic under a Creative Commons Attribution-NonCommercial 2.5 License. These adaptations are therefore licensed the same way.

Finally, here’s links for 2026, I recommend them all:

• PyCon Italia, 27-30 May: the CFP is open until 6th January
• PyCon Estonia, 8-9 October
• PyCon Greece, 12-13 October
• PyCon Sweden, TBA
• PyCon Finland, TBA

January 03, 2026 02:06 PM UTC

January 02, 2026

Real Python

The Real Python Podcast – Episode #278: PyCoder's Weekly 2025 Top Articles & Hidden Gems

PyCoder's Weekly included over 1,500 links to articles, blog posts, tutorials, and projects in 2025. Christopher Trudeau is back on the show this week to help wrap up everything by sharing some highlights and uncovering a few hidden gems from the pile.

[ Improve Your Python With 🐍 Python Tricks 💌 – Get a short & sweet Python Trick delivered to your inbox every couple of days. >> Click here to learn more and see examples ]

January 02, 2026 12:00 PM UTC

Glyph Lefkowitz

The Next Thing Will Not Be Big

The dawning of a new year is an opportune moment to contemplate what has transpired in the old year, and consider what is likely to happen in the new one.

Today, I’d like to contemplate that contemplation itself.

The 20th century was an era characterized by rapidly accelerating change in technology and industry, creating shorter and shorter cultural cycles of changes in lifestyles. Thus far, the 21st century seems to be following that trend, at least in its recently concluded first quarter.

The early half of the twentieth century saw the massive disruption caused by electrification, radio, motion pictures, and then television.

In 1971, Intel poured gasoline on that fire by releasing the 4004, a microchip generally recognized as the first general-purpose microprocessor. Popular innovations rapidly followed: the computerized cash register, the personal computer, credit cards, cellular phones, text messaging, the Internet, the web, online games, mass surveillance, app stores, social media.

These innovations have arrived faster than previous generations, but also, they have crossed a crucial threshold: that of the human lifespan.

While the entire second millennium A.D. has been characterized by a gradually accelerating rate of technological and social change — the printing press and the industrial revolution were no slouches, in terms of changing society, and those predate the 20th century — most of those changes had the benefit of unfolding throughout the course of a generation or so.

Which means that any individual person in any given century up to the 20th might remember one major world-altering social shift within their lifetime, not five to ten of them. The diversity of human experience is vast, but most people would not expect that the defining technology of their lifetime was merely the latest in a progression of predictable civilization-shattering marvels.

Along with each of these successive generations of technology, we minted a new generation of industry titans. Westinghouse, Carnegie, Sarnoff, Edison, Ford, Hughes, Gates, Jobs, Zuckerberg, Musk. Not just individual rich people, but entire new classes of rich people that did not exist before. “Radio DJ”, “Movie Star”, “Rock Star”, “Dot Com Founder”, were all new paths to wealth opened (and closed) by specific technologies. While most of these people did come from at least some level of generational wealth, they no longer came from a literal hereditary aristocracy.

To describe this new feeling of constant acceleration, a new phrase was coined: “The Next Big Thing”. In addition to denoting that some Thing was coming and that it would be Big (i.e.: that it would change a lot about our lives), this phrase also carries the strong implication that such a Thing would be a product. Not a development in social relationships or a shift in cultural values, but some new and amazing form of conveying salted meat paste or what-have-you, that would make whatever lucky tinkerer who stumbled into it into a billionaire — along with any friends and family lucky enough to believe in their vision and get in on the ground floor with an investment.

In the latter part of the 20th century, our entire model of capital allocation shifted to account for this widespread belief. No longer were mega-businesses built by bank loans, stock issuances, and reinvestment of profit, the new model was “Venture Capital”. Venture capital is a model of capital allocation explicitly predicated on the idea that carefully considering each bet on a likely-to-succeed business and reducing one’s risk was a waste of time, because the return on the equity from the Next Big Thing would be so disproportionately huge — 10x, 100x, 1000x – that one could afford to make at least 10 bad bets for each good one, and still come out ahead.

The biggest risk was in missing the deal, not in giving a bunch of money to a scam. Thus, value investing and focus on fundamentals have been broadly disregarded in favor of the pursuit of the Next Big Thing.

If Americans of the twentieth century were temporarily embarrassed millionaires, those of the twenty-first are all temporarily embarrassed FAANG CEOs.

The predicament that this tendency leaves us in today is that the world is increasingly run by generations — GenX and Millennials — with the shared experience that the computer industry, either hardware or software, would produce some radical innovation every few years. We assume that to be true.

But all things change, even change itself, and that industry is beginning to slow down. Physically, transistor density is starting to brush up against physical limits. Economically, most people are drowning in more compute power than they know what to do with anyway. Users already have most of what they need from the Internet.

The big new feature in every operating system is a bunch of useless junk nobody really wants and is seeing remarkably little uptake. Social media and smartphones changed the world, true, but… those are both innovations from 2008. They’re just not new any more.

So we are all — collectively, culturally — looking for the Next Big Thing, and we keep not finding it.

It wasn’t 3D printing. It wasn’t crowdfunding. It wasn’t smart watches. It wasn’t VR. It wasn’t the Metaverse, it wasn’t Bitcoin, it wasn’t NFTs¹.

It’s also not AI, but this is why so many people assume that it will be AI. Because it’s got to be something, right? If it’s got to be something then AI is as good a guess as anything else right now.

The fact is, our lifetimes have been an extreme anomaly. Things like the Internet used to come along every thousand years or so, and while we might expect that the pace will stay a bit higher than that, it is not reasonable to expect that something new like “personal computers” or “the Internet”³ will arrive again.

We are not going to get rich by getting in on the ground floor of the next Apple or the next Google because the next Apple and the next Google are Apple and Google. The industry is maturing. Software technology, computer technology, and internet technology are all maturing.

There Will Be Next Things

Research and development is happening in all fields all the time. Amazing new developments quietly and regularly occur in pharmaceuticals and in materials science. But these are not predictable. They do not inhabit the public consciousness until they’ve already happened, and they are rarely so profound and transformative that they change everybody’s life.

There will even be new things in the computer industry, both software and hardware. Foldable phones do address a real problem (I wish the screen were even bigger but I don’t want to carry around such a big device), and would probably be more popular if they got the costs under control. One day somebody’s going to crack the problem of volumetric displays, probably. Some VR product will probably, eventually, hit a more realistic price/performance ratio where the niche will expand at least a little more.

Maybe there will even be something genuinely useful, which is recognizably adjacent to the current “AI” fad, but if it is, it will be some new development that we haven’t seen yet. If current AI technology were sufficient to drive some interesting product, it would already be doing it, not using marketing disguised as science to conceal diminishing returns on current investments.

But They Will Not Be Big

The impulse to find the One Big Thing that will dominate the next five years is a fool’s errand. Incremental gains are diminishing across the board. The markets for time and attention² are largely saturated. There’s no need for another streaming service if 100% of your leisure time is already committed to TikTok, YouTube and Netflix; famously, Netflix has already considered sleep its primary competitor for close to a decade - years before the pandemic.

Those rare tech markets which aren’t saturated are suffering from pedestrian economic problems like wealth inequality, not technological bottlenecks.

For example, the thing preventing the development of a robot that can do your laundry and your dishes without your input is not necessarily that we couldn’t build something like that, but that most households just can’t afford it without wage growth catching up to productivity growth. It doesn’t make sense for anyone to commit to the substantial R&D investment that such a thing would take, if the market doesn’t exist because the average worker isn’t paid enough to afford it on top of all the other tech which is already required to exist in society.

The projected income from the tiny, wealthy sliver of the population who could pay for the hardware, cannot justify an investment in the software past a fake version remotely operated by workers in the global south, only made possible by Internet wage arbitrage, i.e. a more palatable, modern version of indentured servitude.

Even if we were to accept the premise of an actually-“AI” version of this, that is still just a wish that ChatGPT could somehow improve enough behind the scenes to replace that worker, not any substantive investment in a novel, proprietary-to-the-chores-robot software system which could reliably perform specific functions.

What, Then?

The expectation for, and lack of, a “big thing” is a big problem. There are others who could describe its economic, political, and financial dimensions better than I can. So then let me speak to my expertise and my audience: open source software developers.

When I began my own involvement with open source, a big part of the draw for me was participating in a low-cost (to the corporate developer) but high-value (to society at large) positive externality. None of my employers would ever have cared about many of the applications for which Twisted forms a core bit of infrastructure; nor would I have been able to predict those applications’ existence. Yet, it is nice to have contributed to their development, even a little bit.

However, it’s not actually a positive externality if the public at large can’t directly benefit from it.

When real world-changing, disruptive developments are occurring, the bean-counters are not watching positive externalities too closely. As we discovered with many of the other benefits that temporarily accrued to labor in the tech economy, Open Source that is usable by individuals and small companies may have been a ZIRP. If you know you’re gonna make a billion dollars you’re not going to worry about giving away a few hundred thousand here and there.

When gains are smaller and harder to realize, and margins are starting to get squeezed, it’s harder to justify the investment in vaguely good vibes.

But this, itself, is not a call to action. I doubt very much that anyone reading this can do anything about the macroeconomic reality of higher interest rates. The technological reality of “development is happening slower” is inherently something that you can’t change on purpose.

However, what we can do is to be aware of this trend in our own work.

Fight Scale Creep

It seems to me that more and more open source infrastructure projects are tools for hyper-scale application development, only relevant to massive cloud companies. This is just a subjective assessment on my part — I’m not sure what tools even exist today to measure this empirically — but I remember a big part of the open source community when I was younger being things like Inkscape, Themes.Org and Slashdot, not React, Docker Hub and Hacker News.

This is not to say that the hobbyist world no longer exists. There is of course a ton of stuff going on with Raspberry Pi, Home Assistant, OwnCloud, and so on. If anything there’s a bit of a resurgence of self-hosting. But the interests of self-hosters and corporate developers are growing apart; there seems to be far less of a beneficial overflow from corporate infrastructure projects into these enthusiast or prosumer communities.

This is the concrete call to action: if you are employed in any capacity as an open source maintainer, dedicate more energy to medium- or small-scale open source projects.

If your assumption is that you will eventually reach a hyper-scale inflection point, then mimicking Facebook and Netflix is likely to be a good idea. However, if we can all admit to ourselves that we’re not going to achieve a trillion-dollar valuation and a hundred thousand engineer headcount, we can begin to consider ways to make our Next Thing a bit smaller, and to accommodate the world as it is rather than as we wish it would be.

Be Prepared to Scale Down

Here are some design guidelines you might consider, for just about any open source project, particularly infrastructure ones:

1. Don’t assume that your software can sustain an arbitrarily large fixed overhead because “you just pay that cost once” and you’re going to be running a billion instances so it will always amortize; maybe you’re only going to be running ten.

2. Remember that such fixed overhead includes not just CPU, RAM, and filesystem storage, but also the learning curve for developers. Front-loading a massive amount of conceptual complexity to accommodate the problems of hyper-scalers is a common mistake. Try to smooth out these complexities and introduce them only when necessary.

3. Test your code on edge devices. This means supporting Windows and macOS, and even Android and iOS. If you want your tool to help empower individual users, you will need to meet them where they are, which is not on an EC2 instance.

4. This includes considering Desktop Linux as a platform, as opposed to Server Linux as a platform, which (while they certainly have plenty in common) they are also distinct in some details. Consider the highly specific example of secret storage: if you are writing something that intends to live in a cloud environment, and you need to configure it with a secret, you will probably want to provide it via a text file or an environment variable. By contrast, if you want this same code to run on a desktop system, your users will expect you to support the Secret Service. This will likely only require a few lines of code to accommodate, but it is a massive difference to the user experience.

5. Don’t rely on LLMs remaining cheap or free. If you have LLM-related features⁴, make sure that they are sufficiently severable from the rest of your offering that if ChatGPT starts costing $1000 a month, your tool doesn’t break completely. Similarly, do not require that your users have easy access to half a terabyte of VRAM and a rack full of 5090s in order to run a local model.

Even if you were going to scale up to infinity, the ability to scale down and consider smaller deployments means that you can run more comfortably on, for example, a developer’s laptop. So even if you can’t convince your employer that this is where the economy and the future of technology in our lifetimes is going, it can be easy enough to justify this sort of design shift, particularly as individual choices. Make your onboarding cheaper, your development feedback loops tighter, and your systems generally more resilient to economic headwinds.

So, please design your open source libraries, applications, and services to run on smaller devices, with less complexity. It will be worth your time as well as your users’.

But if you can fix the whole wealth inequality thing, do that first.

Acknowledgments

Thank you to my patrons who are supporting my writing on this blog. If you like what you’ve read here and you’d like to read more of it, or you’d like to support my various open-source endeavors, you can support my work as a sponsor!

January 02, 2026 01:59 AM UTC

Seth Michael Larson

New ROM dumping tool for SNES & Super Famicom from Epilogue

Just heard the news from the WULFF Den Podcast that Epilogue has released pre-orders for the next ROM backup tool in their “Operator” series for the Super NES (SNES) and Super Famicom called the “SN Operator”. The SN Operator pre-order costs $60 USD plus shipping.

This is great news for collectors and people interested in owning and playing SNES and Super Famicom games without a subscription service and for cheaper than purchasing a console, currently hovering around $120 USD on eBay. If there's only one or two games you're interested in playing and ownership isn't a huge deal for you: Nintendo Switch Online offers a substantial SNES and Super Famicom library for $20/year.

Most importantly, these devices are important to provide legal pathways for enthusiasts to archive and play their aging collection on newer storage mediums and devices. Emulation does not equate to piracy, and ROM dumpers are an important tool for legal emulation and archiving. I've previously written about using the GB Operator from Epilogue with Ubuntu to successfully archive my Game Boy, Game Boy Color, and Game Boy Advance ROMs and save files.

I personally won't be buying this product as my collection doesn't have any SNES games. Is there a game in particular that I missed out on from this era? Let me know.

Their website also features a teaser for another upcoming Operator! The Wulff brothers guessed that this would likely be an N64 Operator, especially given the N64's prominence with the new Analogue 3D console. An N64 operator similarly wouldn't be super useful for me, I am holding out hope for a SEGA Genesis/Mega Drive Operator in the future.

Thanks for keeping RSS alive! ♥

January 02, 2026 12:00 AM UTC

January 01, 2026

Python Morsels

Implicit string concatenation

Python automatically concatenates adjacent string literals thanks to implicit string concatenation. This feature can sometimes lead to bugs.

Table of contents

1. Strings next to each other
2. Implicit string concatenation
3. Implicit line continuations with implicit string concatenation
4. Concatenating lines of text
5. Bugs caused by implicit string concatenation
6. Linter rules for implicit string concatenation
7. Implicit string concatenation is a bug and a feature

>>> print("Hello" "world!")

>>> print("Hello" "world!")
Helloworld!

>>> print("Hello", "world!")
Hello world!

>>> print("Hello" "world!")
Helloworld!

Read the full article: https://www.pythonmorsels.com/implicit-string-concatenation/

January 01, 2026 05:43 PM UTC

Seth Michael Larson

Cutting spritesheets like cookies with Python & Pillow 🍪

Happy new year! 🎉 For an upcoming project on the blog requiring many video-game sprites I've created a small tool (“sugarcookie”) using the always-lovely Python image-processing library Pillow. This tool takes a spritesheet and a list of mask colors, a minimum size, and then cuts the spritesheet into its component sprites.

I'm sure this could be implemented more efficiently, or with a friendly command line interface, but for more own purposes (~10 spritesheets) this worked just fine. Feel free to use, share, and improve. The script is available as a GitHub gist, but also included below.

Source code for sugarcookie

#!/usr/bin/env python
# /// script
# requires-python = ">=3.13"
# dependencies = [
#   "Pillow",
#   "tqdm"
# ]
# ///
# License: MIT
# Copyright 2025, Seth Larson

import os.path
import math
from PIL import Image
import tqdm

# Parameters
spritesheet = ""  # Path to spritesheet.
masks = {}  # Set of 3-tuples for RGB.
min_dim = 10  # Min and max dimensions in pixels.
max_dim = 260

img = Image.open(spritesheet)
if img.mode == "RGB":  # Ensure an alpha channel.
    alpha = Image.new("L", img.size, 255)
    img.putalpha(alpha)

output_prefix = os.path.splitext(os.path.basename(spritesheet))[0]
data = img.getdata()
visited = set()
shapes = set()
reroll_shapes = set()


def getpixel(x, y) -> tuple[int, int, int, int]:
    return data[x + (img.width * y)]


def make_2n(value: int) -> int:
    return 2 ** int(math.ceil(math.log2(value)))


with tqdm.tqdm(
    desc="Cutting cookies",
    total=int(img.width * img.height),
    unit="pixels",
) as t:
    for x in range(img.width):
        for y in range(img.height):
            xy = (x, y)
            if xy in visited:
                continue
            inshape = set()
            candidates = {(x, y)}

            def add_candidates(cx, cy):
                global candidates
                candidates |= {(cx - 1, cy), (cx + 1, cy), (cx, cy - 1), (cx, cy + 1)}

            while candidates:
                cx, cy = candidates.pop()
                if (
                    (cx, cy) in visited
                    or cx < 0
                    or cx >= img.width
                    or cy < 0
                    or cy >= img.height
                    or abs(cx - x) > max_dim
                    or abs(cy - y) > max_dim
                ):
                    continue
                visited.add((cx, cy))
                rgba = r, g, b, a = getpixel(cx, cy)
                if a == 0 or (r, g, b) in masks:
                    continue
                else:
                    inshape.add((cx, cy))
                    add_candidates(cx, cy)
            if inshape:
                shapes.add(tuple(inshape))
        t.update(img.height)

max_width = 0
max_height = 0
shapes_and_offsets = []
for shape in sorted(shapes):
    min_x = img.width + 2
    min_y = img.height + 2
    max_x = -1
    max_y = -1
    for x, y in shape:
        max_x = max(x, max_x)
        max_y = max(y, max_y)
        min_x = min(x, min_x)
        min_y = min(y, min_y)
    width = max_x - min_x + 1
    height = max_y - min_y + 1

    # Too small! We have to reroll this
    # potentially into another shape.
    if width < min_dim or height < min_dim:
        reroll_shapes.add(shape)
        continue

    max_width = max(max_width, width)
    max_height = max(max_height, height)
    shapes_and_offsets.append((shape, (width, height), (min_x, min_y)))

# Make them powers of two!
max_width = make_2n(max_width)
max_height = make_2n(max_height)

sprite_number = 0
with tqdm.tqdm(
    desc="Baking cookies",
    total=len(shapes_and_offsets),
    unit="sprites"
) as t:
    for shape, (width, height), (offset_x, offset_y) in shapes_and_offsets:
        new_img = Image.new(mode="RGBA", size=(max_width, max_height))
        margin_x = (max_width - width) // 2
        margin_y = (max_height - height) // 2
        for rx in range(max_width):
            for ry in range(max_height):
                x = rx + offset_x
                y = ry + offset_y
                if (x, y) not in shape:
                    continue
                new_img.putpixel((rx + margin_x, ry + margin_y), getpixel(x, y))
        new_img.save(f"images/{output_prefix}-{sprite_number}.png")
        sprite_number += 1
        t.update(1)

When using the tool you may find yourself needing to add additional masking across elements, such as the original spritesheet curator's name, in order for the cutting process to work perfectly. This script also doesn't work great for sprites which aren't contiguous across their bounding box. There's an exercise left to the reader to implement reroll_shapes, a feature I didn't end up needing for my own project. Let me know if you implement this and send me a patch!

Thanks for keeping RSS alive! ♥

January 01, 2026 12:00 AM UTC

December 31, 2025

The Python Coding Stack

Mulled Wine, Mince Pies, and More Python

I’ve been having too much mulled wine. And wine of the standard type. And aperitifs before meals and digestifs after them…the occasional caffé corretto, too. You get the picture…

No wonder I can’t remember what articles I wrote this year here at The Python Coding Stack. So make sure you adjust your expectations for this end-of-year review post.

Parties and Gatherings

And there’s another thing I can never remember, especially at this time of year when large-ish gatherings are more common. How many people are needed in a group to have a probability greater than 50% that two people share a birthday? This could be an ice-breaker in some awkward gatherings, but only if you’re with a geeky crowd. Although the analytical proof is cool, writing Python code to explore this problem is just as fun. Here’s my article from February exploring the Birthday Paradox:

This post also explores some tools from the itertools module. Iteration in Python is different from its implementation in many other languages. And the itertools module provides several tools to iterate in a Pythonic way. Later in the year, I explored more of these tools in The itertools Series. Here’s the first post, exploring Yteria’s adventures in a world a bit similar to ours, yet different:

Here’s the whole series following Yteria’s other adventures and the itertools module:

Christmas Decorations

And something else you can’t avoid at this time of year is all the Christmas decorations you’ll find everywhere. Christmas trees, flashing lights, street displays, and…

…Python has its own decorations. You can adorn functions with Python’s equivalent of tinsel and angels:

This post is the most-read post on The Python Coding Stack in 2025. It also has a follow-up post that explores more:

Python’s decorators don’t necessarily make functions pretty—they make them more versatile. However, Python’s f-strings are there to make displayed outputs look pretty. And what if you want your own custom fancy f-string format specifiers?

Endless Visits to Coffee Shops

I spend a lot of time in coffee shops over the holidays. It’s a great place to meet people for a quick catch-up. And to drink coffee. Coffee featured a few times in articles this year here on The Python Coding Stack.

One of these coffee-themed posts followed Alex’s experience with opening his new coffee shop and explored parameters and arguments in Python functions:

Another one narrates one of my trips to a coffee shop and how it helped me really understand the difference between == and is in Python—equality and identity:

Board Games

Who doesn’t play board games over the holidays? We certainly do. And that means we need a dictionary present to resolve Scrabble-related disagreements. This year, we also played Boggle, another word-based game. So, the dictionary had to work overtime.

And dictionaries work overtime in Python, too. They’re one of the most important built-in data structures. Here’s a question: Are Python dictionaries ordered? The answer is more nuanced than you might think at first:

And to understand how Python dictionaries work, it’s best to understand hashability:

This article is part of The Club, the special area on The Python Coding Stack for premium members. The Club launched this year and includes more articles, an exclusive forum, videos, and more… This premium content is in addition to the free articles, which will always remain a key part of The Python Coding Stack. To make sure you don’t miss a thing here on The Python Coding Stack, join The Club by becoming a premium subscriber:

Subscribe now

And it’s not just dictionaries that play an important role in Python. Indeed, in Python, we often prefer to focus on what a data type can do rather than what it is. Here’s another short post in The Club on this topic:

Unwanted Gifts?

Did you receive gifts you don’t need or want? Or perhaps, you have received the same gift more than once? Python can help, too. Let’s start by removing duplicate presents from the Christmas tree list:

And what about the used wrapping paper and food packaging? You can recycle some of it. But some must end up in the garbage. Python has its own trash bin, too:

Magic

This time of year can feel magical. And maybe it’s for this reason that TV stations here keep showing the Harry Potter films during the Christmas holiday season. I’m a Harry Potter fan, and I’ve written Harry Potter-themed posts and series in the past. And there was one this year, too:

One thing that’s not magic in Python is its behind-the-scenes operations. Python’s special methods deal with this, and once you know the trick, it’s no longer magic. Here’s a post that explores some of these special methods:

Queueing in The Cold

I avoided queueing in the cold this year, but I’ve done this so many times in past Christmas holidays. Queueing for a skating rink or for a Christmas fair. Queueing to get mulled wine from a street stall. If you could skip the queue, would you?

And if it’s cold, you’ll need to zip your jacket well. Python’s zipping and unzipping also feature in this year’s posts:

End-of-Year Reflections

Let me spare you all my Python-related stuff—the courses, articles, updates here on The Stack, and all that. Instead, my news for 2025 was my return to an old interest: track and field athletics. I even started a new Substack to document my adventures in track and field:

Back on the Track

My journey as I return back to track and field athletics after a 25-year gap… as a masters athlete (translation: old) and an aspiring coach

By Stephen Gruppetta

And I’ve written some posts with a track-and-field theme, too. Here’s one of these:

But the end of the year is also a time for reflecting on one’s life, past and future. Recently, a Python object has done just that:

Looking forward to a great new year in the Python world and here on The Python Coding Stack. Wishing you all a Happy New Year!

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For more Python resources, you can also visit Real Python—you may even stumble on one of my own articles or courses there!

Also, are you interested in technical writing? You’d like to make your own writing more narrative, more engaging, more memorable? Have a look at Breaking the Rules.

And you can find out more about me at stephengruppetta.com

December 31, 2025 09:12 PM UTC

Django Weblog

DSF member of the month - Clifford Gama

For December 2025, we welcome Clifford Gama as our DSF member of the month! ⭐

Clifford contributed to Django core with more than 5 PRs merged in few months! He is part of the Triage and Review Team. He has been a DSF member since October 2024.

You can learn more about Clifford by visiting Clifford's website and his GitHub Profile.

Let’s spend some time getting to know Clifford better!

Can you tell us a little about yourself (hobbies, education, etc)

I'm Clifford. I hold a Bachelor's degree in Mechanical Engineering from the University of Zimbabwe.

How did you start using Django?

During my first year in college, I was also exploring open online courses on EDx and I came across CS50's introduction to web development. After watching the introductory lecture -- which introduced me to git and GitHub -- I discovered Django's excellent documentation and got started on the polls tutorial. The docs were so comprehensive and helpful I never felt the need to return to CS50. (I generally prefer comprehensive first-hand, written learning material over summaries and videos.)

At the time, I had already experimented with flask, but I guess mainly because I didn't know SQL and because flask didn't have an ORM, I never quite picked it up. With Django I felt like I was taking a learning fast-track where I'd learn everything I needed in one go!

And that's how I started using Django.

What projects are you working on now?

At the moment, I’ve been focusing on improving my core skills in preparation for remote work, so I haven’t been starting new projects because of that.

That said, I’ve been working on a client project involving generating large, image-heavy PDFs with WeasyPrint, where I’ve been investigating performance bottlenecks and ways to speed up generation time, which was previously around 30 minutes 😱.

What are you learning about these days?

I’ve been reading Boost Your Git DX by Adam Johnson and learning how to boost my Git and shell developer experience, which has been a great read. Aside from that, inspired by some blogs and talks by Haki Benita, I am also learning about software design and performance. Additionally, I am working on improving my general fluency in Python.

What other framework do you know and if there is anything you would like to have in Django if you had magical powers?

I am not familiar with any other frameworks, but if I had magic powers I'd add production-grade static-file serving in Django.

Django libraries are your favorite (core or 3rd party)?

The ORM, Wagtail and Django's admin.

What are the top three things in Django that you like?

• The community
• The documentation
• Djangonaut Space and the way new contributors are welcomed

How did you start contributing to Django?

I started contributing to Django in August last year, which is when I discovered the community, which was a real game changer for me. Python was my first course at university, and I loved it because it was creative and there was no limit to what I could build with it.

Whenever I saw a problem in another course that could be solved programmatically, I jumped at it. My proudest project from that time was building an NxN matrix determinant calculator after learning about recursion and spotting the opportunity in an algebra class.

After COVID lockdown, I gave programming up for a while. With more time on my hands, I found myself prioritizing programming over core courses, so I took a break. Last year, I returned to it when I faced a problem that I could only solve with Django. My goal was simply to build an app quickly and go back to being a non-programmer, but along the way I thought I found a bug in Django, filed a ticket, and ended up writing a documentation PR. That’s when I really discovered the Django community.

What attracted me most was that contributions are held to high standards, but experienced developers are always ready to help you reach them. Contributing was collaborative, pushing everyone to do their best. It was a learning opportunity too good to pass up.

How did you join the Triage and Review team?

About the time after I contributed my first PR, I started looking at open tickets to find more to work on, and keep on learning.

Sometimes a ticket was awaiting triage, in which case the first step was to triage it before assigning it to working on it, and sometimes the ticket I wanted was already taken, in which case I'd look at the PR if available. Reviewing a PR can be a faster way to learn about a particular part of the codebase, because someone has already done most of the investigative part of work, so I reviewed PRs as well.

After a while I got an invitation from Sarah Boyce, one of the fellows, to join the team. I didn't even know that I could join before I got the invitation, so I was thrilled!

How the work is going so far?

It’s been rewarding. I’ve gained familiarity with the Django codebase and real experience collaborating with others, which already exceeds what I expected when I started contributing.

One unexpected highlight was forming a friendship through one of the first PRs I reviewed.

SiHyun Lee and I are now both part of the triage and review team, and I’m grateful for that connection.

What are your hobbies or what do you do when you’re not working?

My main hobby is storytelling in a broad sense. In fact, it was a key reason I returned to programming after a long break. I enjoy discovering enduring stories from different cultures, times, and media—ranging from the deeply personal and literary to the distant and philosophical. I recently watched two Japanese classics and found I quite love them. I wrote about one of the films on my blog, and I also get to practice my Japanese, which I’ve been learning on Duolingo for about two years. I also enjoy playing speed chess.

Do you have any suggestions for people who would like to start triage and review tickets and PRs?

If there’s an issue you care about, or one that touches a part of the codebase you’re familiar with or curious about, jump in. Tickets aren’t always available to work on, but reviews always are, and they’re open to everyone. Reviewing helps PRs move faster, including your own if you have any open, sharpens your understanding of a component, and often clarifies the problem itself.

As Simon Charette puts it:

“Triaging issues and spending time understanding them is often more valuable than landing code itself as it strengthen our common understanding of the problem and allow us to build a consistent experience accross the diverse interfaces Django provides.”

And you can put it on your CV!

Is there anything else you’d like to say?

I’m grateful to everyone who contributes to making every part of Django what it is. I’m particularly thankful to whoever nominated me to be the DSF Member of the month.

I am optimistic about the future of Django. Django 6.1 is already shaping up with new features, and there are new projects like Django Bolt coming up.

Happy new year 🎊!

Thank you for doing the interview, Clifford and happy new year to the Django community 💚!

December 31, 2025 08:42 PM UTC

"Michael Kennedy's Thoughts on Technology"

Python Numbers Every Programmer Should Know

There are numbers every Python programmer should know. For example, how fast or slow is it to add an item to a list in Python? What about opening a file? Is that less than a millisecond? Is there something that makes that slower than you might have guessed? If you have a performance sensitive algorithm, which data structure should you use? How much memory does a floating point number use? What about a single character or the empty string? How fast is FastAPI compared to Django?

I wanted to take a moment and write down performance numbers specifically focused on Python developers. Below you will find an extensive table of such values. They are grouped by category. And I provided a couple of graphs for the more significant analysis below the table.

Acknowledgements: Inspired by Latency Numbers Every Programmer Should Know and similar resources.

Source code for the benchmarks

This article is posted without any code. I encourage you to dig into the benchmarks. The code is available on GitHub at:

https://github.com/mikeckennedy/python-numbers-everyone-should-know

📊 System Information

The benchmarks were run on the sytem described in this table. While yours may be faster or slower, the most important thing to consider is relative comparisons.

TL;DR; Python Numbers

This first version is a quick “pyramid” of growing time/size for common Python ops. There is much more detail below.

Python Operation Latency Numbers (the pyramid)

Attribute read (obj.x)                              14   ns
Dict key lookup                                     22   ns              1.5x attr
Function call (empty)                               22   ns
List append                                         29   ns              2x attr
f-string formatting                                 65   ns              3x function
Exception raised + caught                          140   ns             10x attr
orjson.dumps() complex object                      310   ns        0.3 μs
json.loads() simple object                         714   ns        0.7 μs   2x orjson
sum() 1,000 integers                             1,900   ns        1.9 μs   3x json
SQLite SELECT by primary key                     3,600   ns        3.6 μs   5x json
Iterate 1,000-item list                          7,900   ns        7.9 μs   2x SQLite read
Open and close file                              9,100   ns        9.1 μs   2x SQLite read
asyncio run_until_complete (empty)              28,000   ns         28 μs   3x file open
Write 1KB file                                  35,000   ns         35 μs   4x file open
MongoDB find_one() by _id                      121,000   ns        121 μs   3x write 1KB
SQLite INSERT (with commit)                    192,000   ns        192 μs   5x write 1KB
Write 1MB file                                 207,000   ns        207 μs   6x write 1KB
import json                                  2,900,000   ns      2,900 μs   3 ms   15x write 1MB
import asyncio                              17,700,000   ns     17,700 μs  18 ms    6x import json
import fastapi                             104,000,000   ns    104,000 μs 104 ms    6x import asyncio

Python Memory Numbers (the pyramid)

Float                                               24   bytes
Small int (cached -5 to 256)                        28   bytes
Empty string                                        41   bytes
Empty list                                          56   bytes          2x int
Empty dict                                          64   bytes          2x int
Empty set                                          216   bytes          8x int
__slots__ class (5 attrs)                          212   bytes          8x int
Regular class (5 attrs)                            694   bytes         25x int
List of 1,000 ints                              36,856   bytes         36 KB
Dict of 1,000 items                             92,924   bytes         91 KB         
List of 1,000 __slots__ instances              220,856   bytes        216 KB
List of 1,000 regular instances                309,066   bytes        302 KB         1.4x slots list
Empty Python process                        16,000,000   bytes         16 MB

Python numbers you should know (detailed version)

Here is a deeper table comparing many more details.

Memory Costs

Understanding how much memory different Python objects consume.

An empty Python process uses 15.77 MB

Strings

The rule of thumb for ASCII strings is the core string object takes 41 bytes, with each additional character adding 1 byte. Note: Python uses different internal representations based on content—strings with Latin-1 characters use 1 byte/char, those with most Unicode use 2 bytes/char, and strings with emoji or rare characters use 4 bytes/char.

Numbers

Numbers are surprisingly large in Python. They have to derive from CPython’s PyObject and are subject to reference counting for garabage collection, they exceed our typical mental model many of:

• 2 bytes = short int
• 4 bytes = long int
• etc.

Collections

Collections are amazing in Python. Dynamically growing lists. Ultra high-perf dictionaries and sets. Here is the empty and “full” overhead of each.

Classes and Instances

Slots are an interesting addition to Python classes. They remove the entire concept of a __dict__ for tracking fields and other values. Even for a single instance, slots classes are significantly smaller (212 bytes vs 694 bytes for 5 attributes). If you are holding a large number of them in memory for a list or cache, the memory savings of a slots class becomes meaningful - about 30% less memory usage. Luckily for most use-cases, just adding a slots entry saves memory with minimal effort.

Aggregate Memory Usage (1,000 instances):

Basic Operations

The cost of fundamental Python operations: Way slower than C/C++/C# but still quite fast. I added a brief comparison to C# to the source repo.

Arithmetic

String Operations

String operations in Python are fast as well. Among template-based formatting styles, f-strings are the fastest. Simple concatenation (+) is faster still for combining a couple strings, but f-strings scale better and are more readable. Even the slowest formatting style is still measured in just nanoseconds.

List Operations

List operations are very fast in Python. Adding a single item usually requires 28ns. Said another way, you can do 35M appends per second. This is unless the list has to expand using something like a doubling algorithm. You can see this in the ops/sec for 1,000 items.

Surprisingly, list comprehensions are 26% faster than the equivalent for loops with append statements.

Collection Access and Iteration

How fast can you get data out of Python’s built-in collections? Here is a dramatic example of how much faster the correct data structure is. item in set or item in dict is 200x faster than item in list for just 1,000 items! This difference comes from algorithmic complexity: sets and dicts use O(1) hash lookups, while lists require O(n) linear scans—and this gap grows with collection size.

The graph below is non-linear in the x-axis.

Access by Key/Index

Length

len() is very fast. Maybe we don’t have to optimize it out of the test condition on a while loop looping 100 times after all.

Iteration

Class and Object Attributes

The cost of reading and writing attributes, and how __slots__ changes things. Slots saves ~30% memory on large collections, with virtually identical attribute access speed.

Attribute Access

Other Attribute Operations

JSON and Serialization

Comparing standard library JSON with optimized alternatives. orjson handles more data types and is over 8x faster than standard lib json for complex objects. Impressive!

Serialization (dumps)

Deserialization (loads)

Pydantic

Web Frameworks

Returning a simple JSON response. Benchmarked with wrk against localhost running 4 works in Granian. Each framework returns the same JSON payload from a minimal endpoint. No database access or that sort of thing. This is just how much overhead/perf do we get from each framework itself. The code we write that runs within those view methods is largely the same.

Results

File I/O

Reading and writing files of various sizes. Note that the graph is non-linear in y-axis.

Basic Operations

Pickle vs JSON (Serialization)

For more serialization options including orjson, msgspec, and pydantic, see JSON and Serialization above.

Database and Persistence

Comparing SQLite, diskcache, and MongoDB using the same complex object.

Test Object

user_data = {
    "id": 12345,
    "username": "alice_dev",
    "email": "alice@example.com",
    "profile": {
        "bio": "Software engineer who loves Python",
        "location": "Portland, OR",
        "website": "https://alice.dev",
        "joined": "2020-03-15T08:30:00Z"
    },
    "posts": [
        {"id": 1, "title": "First Post", "tags": ["python", "tutorial"], "views": 1520},
        {"id": 2, "title": "Second Post", "tags": ["rust", "wasm"], "views": 843},
        {"id": 3, "title": "Third Post", "tags": ["python", "async"], "views": 2341},
    ],
    "settings": {
        "theme": "dark",
        "notifications": True,
        "email_frequency": "weekly"
    }
}

SQLite (JSON blob approach)

diskcache

MongoDB

Comparison Table

Note: MongoDB is a victim of network access version in-process access.

Function and Call Overhead

The hidden cost of function calls, exceptions, and async.

Function Calls

Exceptions

Type Checking

Async Overhead

The cost of async machinery.

Coroutine Creation

Running Coroutines

asyncio.sleep()

Note: asyncio.sleep(0) is a special case in Python’s event loop—it yields control but schedules an immediate callback, making it faster than typical sleeps but not representative of general event loop overhead.

asyncio.gather()

Task Creation

Async Context Managers & Iteration

Sync vs Async Comparison

Methodology

Benchmarking Approach

• All benchmarks run multiple times and with warmup not timed
• Timing uses timeit or perf_counter_ns as appropriate
• Memory measured with sys.getsizeof() and tracemalloc
• Results are median of N runs

Environment

• OS: macOS 26.2
• Python: 3.14.2 (CPython)
• CPU: ARM - 14 cores (14 logical)
• RAM: 24.0 GB

Code Repository

All benchmark code available at: https://github.com/mkennedy/python-numbers-everyone-should-know

Key Takeaways

1. Memory overhead: Python objects have significant memory overhead - even an empty list is 56 bytes
2. Dict/set speed: Dictionary and set lookups are extremely fast (O(1) average case) compared to list membership checks (O(n))
3. JSON performance: Alternative JSON libraries like orjson and msgspec are 3-8x faster than stdlib json
4. Async overhead: Creating and awaiting coroutines has measurable overhead - only use async when you need concurrency
5. __slots__ tradeoff: __slots__ saves memory (~30% for collections of instances) with virtually no performance impact

Last updated: 2026-01-01

December 31, 2025 07:49 PM UTC

Zero to Mastery

[December 2025] Python Monthly Newsletter 🐍

73rd issue of Andrei's Python Monthly: A big change is coming. Read the full newsletter to get up-to-date with everything you need to know from last month.

December 31, 2025 10:00 AM UTC

December 30, 2025

Paolo Melchiorre

Looking Back at Python Pescara 2025

A personal retrospective on Python Pescara in 2025: events, people, and moments that shaped a growing local community, reflecting on continuity, experimentation, and how a small group connected to the wider Python ecosystem.

December 30, 2025 11:00 PM UTC

PyCoder’s Weekly

Issue #715: Top 5 of 2025, LlamaIndex, Python 3.15 Speed, and More (Dec. 30, 2025)

#715 – DECEMBER 30, 2025
View in Browser »

Articles & Tutorials

Projects & Code

Events

Happy Pythoning!
This was PyCoder’s Weekly Issue #715.
View in Browser »

[ Subscribe to 🐍 PyCoder’s Weekly 💌 – Get the best Python news, articles, and tutorials delivered to your inbox once a week >> Click here to learn more ]

December 30, 2025 07:30 PM UTC

Programiz

Python List

In this tutorial, we will learn about Python lists (creating lists, changing list items, removing items, and other list operations) with the help of examples.

December 30, 2025 04:31 AM UTC

December 29, 2025

Paolo Melchiorre

Django On The Med: A Contributor Sprint Retrospective

A personal retrospective on Django On The Med, three months later. From the first idea to the actual contributor sprint, and how a simple format based on focused mornings and open afternoons created unexpected value for people and the Django open source community.

December 29, 2025 11:00 PM UTC

Hugo van Kemenade

Replacing python-dateutil to remove six

The dateutil library is a popular and powerful Python library for dealing with dates and times.

However, it still supports Python 2.7 by depending on the six compatibility shim, and I’d prefer not to install for Python 3.10 and higher.

Here’s how I replaced three uses of its relativedelta in a couple of CLIs that didn’t really need to use it.

One #

norwegianblue was using it to calculate six months from now:

import datetime as dt

from dateutil.relativedelta import relativedelta

now = dt.datetime.now(dt.timezone.utc)
# datetime.datetime(2025, 12, 29, 15, 59, 44, 518240, tzinfo=datetime.timezone.utc)
six_months_from_now = now + relativedelta(months=+6)
# datetime.datetime(2026, 6, 29, 15, 59, 44, 518240, tzinfo=datetime.timezone.utc)

But we don’t need to be so precise here, and 180 days is good enough, using the standard library’s datetime.timedelta:

import datetime as dt

now = dt.datetime.now(dt.timezone.utc)
# datetime.datetime(2025, 12, 29, 15, 59, 44, 518240, tzinfo=datetime.timezone.utc)
six_months_from_now = now + dt.timedelta(days=180)
# datetime.datetime(2026, 6, 27, 15, 59, 44, 518240, tzinfo=datetime.timezone.utc)

Two #

pypistats was using it get the last day of a month:

import datetime as dt

first = dt.date(year, month, 1)
# datetime.date(2025, 12, 1)
last = first + relativedelta(months=1) - relativedelta(days=1)
# datetime.date(2025, 12, 31)

Instead, we can use the stdlib’s calendar.monthrange:

import calendar
import datetime as dt

last_day = calendar.monthrange(year, month)[1]
# 31
last = dt.date(year, month, last_day)
# datetime.date(2025, 12, 31)

Three #

Finally, to get last month as a yyyy-mm string:

import datetime as dt

from dateutil.relativedelta import relativedelta

today = dt.date.today()
# datetime.date(2025, 12, 29)
d = today - relativedelta(months=1)
# datetime.date(2025, 11, 29)
d.isoformat()[:7]
# '2025-11'

Instead:

import datetime as dt

today = dt.date.today()
# datetime.date(2025, 12, 29)
if today.month == 1:
 year, month = today.year - 1, 12
else:
 year, month = today.year, today.month - 1
 # 2025, 11
f"{year}-{month:02d}"
# '2025-11'

Goodbye six, and we also get slightly quicker install, import and run times.

Bonus #

I recommend Adam Johnson’s tip to import datetime as dt to avoid the ambiguity of which datetime is the module and which is the class.

Header photo: Ver Sacrum calendar by Alfred Roller

December 29, 2025 04:53 PM UTC

Talk Python to Me

#532: 2025 Python Year in Review

Python in 2025 is in a delightfully refreshing place: the GIL's days are numbered, packaging is getting sharper tools, and the type checkers are multiplying like gremlins snacking after midnight. On this episode, we have an amazing panel to give us a range of perspectives on what matter in 2025 in Python. We have Barry Warsaw, Brett Cannon, Gregory Kapfhammer, Jodie Burchell, Reuven Lerner, and Thomas Wouters on to give us their thoughts.<br/> <br/> <strong>Episode sponsors</strong><br/> <br/> <a href='https://talkpython.fm/seer-code-review'>Seer: AI Debugging, Code TALKPYTHON</a><br> <a href='https://talkpython.fm/training'>Talk Python Courses</a><br/> <br/> <h2 class="links-heading mb-4">Links from the show</h2> <div><strong>Python Software Foundation (PSF)</strong>: <a href="https://www.python.org/psf/?featured_on=talkpython" target="_blank" >www.python.org</a><br/> <strong>PEP 810: Explicit lazy imports</strong>: <a href="https://peps.python.org/pep-0810/?featured_on=talkpython" target="_blank" >peps.python.org</a><br/> <strong>PEP 779: Free-threaded Python is officially supported</strong>: <a href="https://peps.python.org/pep-0779/?featured_on=talkpython" target="_blank" >peps.python.org</a><br/> <strong>PEP 723: Inline script metadata</strong>: <a href="https://peps.python.org/pep-0723/?featured_on=talkpython" target="_blank" >peps.python.org</a><br/> <strong>PyCharm</strong>: <a href="https://www.jetbrains.com/pycharm/?featured_on=talkpython" target="_blank" >www.jetbrains.com</a><br/> <strong>JetBrains</strong>: <a href="https://www.jetbrains.com/company/?featured_on=talkpython" target="_blank" >www.jetbrains.com</a><br/> <strong>Visual Studio Code</strong>: <a href="https://code.visualstudio.com/?featured_on=talkpython" target="_blank" >code.visualstudio.com</a><br/> <strong>pandas</strong>: <a href="https://pandas.pydata.org/?featured_on=talkpython" target="_blank" >pandas.pydata.org</a><br/> <strong>PydanticAI</strong>: <a href="https://ai.pydantic.dev/?featured_on=talkpython" target="_blank" >ai.pydantic.dev</a><br/> <strong>OpenAI API docs</strong>: <a href="https://platform.openai.com/docs/?featured_on=talkpython" target="_blank" >platform.openai.com</a><br/> <strong>uv</strong>: <a href="https://docs.astral.sh/uv/?featured_on=talkpython" target="_blank" >docs.astral.sh</a><br/> <strong>Hatch</strong>: <a href="https://github.com/pypa/hatch?featured_on=talkpython" target="_blank" >github.com</a><br/> <strong>PDM</strong>: <a href="https://pdm-project.org/latest/?featured_on=talkpython" target="_blank" >pdm-project.org</a><br/> <strong>Poetry</strong>: <a href="https://python-poetry.org/?featured_on=talkpython" target="_blank" >python-poetry.org</a><br/> <strong>Project Jupyter</strong>: <a href="https://jupyter.org/?featured_on=talkpython" target="_blank" >jupyter.org</a><br/> <strong>JupyterLite</strong>: <a href="https://jupyterlite.readthedocs.io/en/latest/?featured_on=talkpython" target="_blank" >jupyterlite.readthedocs.io</a><br/> <strong>PEP 690: Lazy Imports</strong>: <a href="https://peps.python.org/pep-0690/?featured_on=talkpython" target="_blank" >peps.python.org</a><br/> <strong>PyTorch</strong>: <a href="https://pytorch.org/?featured_on=talkpython" target="_blank" >pytorch.org</a><br/> <strong>Python concurrent.futures</strong>: <a href="https://docs.python.org/3/library/concurrent.futures.html?featured_on=talkpython" target="_blank" >docs.python.org</a><br/> <strong>Python Package Index (PyPI)</strong>: <a href="https://pypi.org/?featured_on=talkpython" target="_blank" >pypi.org</a><br/> <strong>EuroPython</strong>: <a href="https://tickets.europython.eu/?featured_on=talkpython" target="_blank" >tickets.europython.eu</a><br/> <strong>TensorFlow</strong>: <a href="https://www.tensorflow.org/?featured_on=talkpython" target="_blank" >www.tensorflow.org</a><br/> <strong>Keras</strong>: <a href="https://keras.io/?featured_on=talkpython" target="_blank" >keras.io</a><br/> <strong>PyCon US</strong>: <a href="https://us.pycon.org/?featured_on=talkpython" target="_blank" >us.pycon.org</a><br/> <strong>NumFOCUS</strong>: <a href="https://numfocus.org/?featured_on=talkpython" target="_blank" >numfocus.org</a><br/> <strong>Python discussion forum (discuss.python.org)</strong>: <a href="https://discuss.python.org/?featured_on=talkpython" target="_blank" >discuss.python.org</a><br/> <strong>Language Server Protocol</strong>: <a href="https://microsoft.github.io/language-server-protocol/?featured_on=talkpython" target="_blank" >microsoft.github.io</a><br/> <strong>mypy</strong>: <a href="https://mypy-lang.org/?featured_on=talkpython" target="_blank" >mypy-lang.org</a><br/> <strong>Pyright</strong>: <a href="https://github.com/microsoft/pyright?featured_on=talkpython" target="_blank" >github.com</a><br/> <strong>Pylance</strong>: <a href="https://marketplace.visualstudio.com/items?itemName=ms-python.vscode-pylance&featured_on=talkpython" target="_blank" >marketplace.visualstudio.com</a><br/> <strong>Pyrefly</strong>: <a href="https://github.com/facebook/pyrefly?featured_on=talkpython" target="_blank" >github.com</a><br/> <strong>ty</strong>: <a href="https://github.com/astral-sh/ty?featured_on=talkpython" target="_blank" >github.com</a><br/> <strong>Zuban</strong>: <a href="https://docs.zubanls.com/?featured_on=talkpython" target="_blank" >docs.zubanls.com</a><br/> <strong>Jedi</strong>: <a href="https://jedi.readthedocs.io/en/latest/?featured_on=talkpython" target="_blank" >jedi.readthedocs.io</a><br/> <strong>GitHub</strong>: <a href="https://github.com/?featured_on=talkpython" target="_blank" >github.com</a><br/> <strong>PyOhio</strong>: <a href="https://www.pyohio.org/?featured_on=talkpython" target="_blank" >www.pyohio.org</a><br/> <br/> <strong>Watch this episode on YouTube</strong>: <a href="https://www.youtube.com/watch?v=PfRCbeOrUd8" target="_blank" >youtube.com</a><br/> <strong>Episode #532 deep-dive</strong>: <a href="https://talkpython.fm/episodes/show/532/2025-python-year-in-review#takeaways-anchor" target="_blank" >talkpython.fm/532</a><br/> <strong>Episode transcripts</strong>: <a href="https://talkpython.fm/episodes/transcript/532/2025-python-year-in-review" target="_blank" >talkpython.fm</a><br/> <br/> <strong>Theme Song: Developer Rap</strong><br/> <strong>🥁 Served in a Flask 🎸</strong>: <a href="https://talkpython.fm/flasksong" target="_blank" >talkpython.fm/flasksong</a><br/> <br/> <strong>---== Don't be a stranger ==---</strong><br/> <strong>YouTube</strong>: <a href="https://talkpython.fm/youtube" target="_blank" ><i class="fa-brands fa-youtube"></i> youtube.com/@talkpython</a><br/> <br/> <strong>Bluesky</strong>: <a href="https://bsky.app/profile/talkpython.fm" target="_blank" >@talkpython.fm</a><br/> <strong>Mastodon</strong>: <a href="https://fosstodon.org/web/@talkpython" target="_blank" ><i class="fa-brands fa-mastodon"></i> @talkpython@fosstodon.org</a><br/> <strong>X.com</strong>: <a href="https://x.com/talkpython" target="_blank" ><i class="fa-brands fa-twitter"></i> @talkpython</a><br/> <br/> <strong>Michael on Bluesky</strong>: <a href="https://bsky.app/profile/mkennedy.codes?featured_on=talkpython" target="_blank" >@mkennedy.codes</a><br/> <strong>Michael on Mastodon</strong>: <a href="https://fosstodon.org/web/@mkennedy" target="_blank" ><i class="fa-brands fa-mastodon"></i> @mkennedy@fosstodon.org</a><br/> <strong>Michael on X.com</strong>: <a href="https://x.com/mkennedy?featured_on=talkpython" target="_blank" ><i class="fa-brands fa-twitter"></i> @mkennedy</a><br/></div>

December 29, 2025 08:00 AM UTC

Seth Michael Larson

Nintendo GameCube and Switch “Wrapped” 2025 🎮🎁

This is my last blog post for 2025 💜 Thanks for reading, see you in 2026!

One of my goals for 2025 was to play more games! I've been collecting play activity for my Nintendo Switch, Switch 2, and my Nintendo GameCube. I've published a combined SQLite database with this data for 2025 with games, play sessions, and more. Feel free to dig into this data yourself, I've included some queries and my own thoughts, too.

Here are the questions I answered with this data:

• What were my favorite games this year?
• Which game system did I play the most?
• Which games did I play the most?
• Which games did I play most per-session?
• When did I start and stop playing each game?
• Which game was I most consistently playing?
• When did I play games?
• Which day of the week did I play most?

What were my favorite games this year?

Before we get too deep into quantitative analysis, let's start with the games I enjoyed the most and defined this year for me.

The project took 4 months to complete, running from June to October. I published a script which analyzed Pikmin 2 save file data using this documentation on the save file format. From here the HTML table in the project page could be automatically updated as I made more progress. I published regular updates to the project page as the project progressed, too.

Pikmin 2 for the GameCube (NTSC-J, NTSC-M, and PAL) and the Switch.

My favorite game for the Switch family of consoles in 2025 is Kirby Air Riders. This game is pure chaotic fun with a heavy dose of nostalgia for me. I was one of the very few that played the original game on the Nintendo GameCube 22 years ago. I still can't believe this gem of a game only sold 750,000 units in the United States. It's amazing to see what is essentially the game my brother and I dreamed of as a sequel: taking everything great about the original release and adding online play, a more expansive world, and near-infinite customization and unlockables.

This game is fast-paced and fits into a busy life easily: a single play session of City Trail or Air Ride mode lasts less than 7 minutes from start to finish. I'll frequently pick this game up to play a few rounds between work and whatever the plans of the evening are. Each round is different, and you can pursue whichever strategy you prefer (combat, speed, gliding, legendary-vehicle hunting) and expect to have a fighting chance in the end.

Kirby Air Ride for the GameCube (NTSC-J and NTSC-M) and Kirby Air Riders for the Switch 2.

Which game system did I play the most?

Even with the Switch and Switch 2 bundled into one category I played the GameCube more in 2025. This was a big year for me and GameCube: I finally modded a platinum cube and my childhood indigo cube with the FlippyDrive and ETH2GC. I've got a lot more in store for the GameCube next year, too.

SQLite query

SELECT game_system, SUM(duration) AS d
FROM sessions
WHERE STRFTIME('%Y', date) = '2025'
GROUP BY game_system
ORDER BY d DESC;

Here's the same data stylized like the GitHub contributor graph. Blue squares represent days when I played GameCube and red squares I played the Switch or Switch 2, starting in June 2025:

I also played Sonic Adventure on a newly-purchased SEGA Dreamcast for the first time in 2025, too. Unfortunately I don't have a way to track play data for the Dreamcast (yet?) but my experience with the Dreamcast and Sonic Adventure will likely get its own short blog post eventually, stay tuned.

Which games did I play the most?

I played 9 unique titles this year (including region and platform variants), but which ones did I play the most?

SQLite query

SELECT game_name, SUM(duration) AS d
FROM sessions
WHERE STRFTIME('%Y', date) = '2025'
GROUP BY game_name
ORDER BY d DESC;

That's a lot of Pikmin 2, huh? This year collected all 245 unique treasures across the three regions of Pikmin 2 (JP, US, and PAL) including a 100% complete save file for the Japanese region. This is the first time I collected all treasures for a single Pikmin 2 play-through.

We can break down how much time was spent in each region and system for Pikmin 2:

SQLite query

SELECT game_system, game_region, SUM(duration) AS d
FROM sessions
WHERE STRFTIME('%Y', date) = '2025'
AND game_name = 'Pikmin 2'
GROUP BY game_system, game_region
ORDER BY d DESC;

You can see I even started playing the Switch edition of Pikmin 2 but bailed on that idea pretty early. Playing through the same game 3 times in a year was already enough :) The US and JP versions were ~9 hours each with PAL receiving less play time. This is due to PAL only having 10 unique treasures, so I was able to speed-run most of the game.

Which games did I play most per session?

This query sorta indicates “binge-ability”, when I did play a title how long was that play session on average? Super Mario Odyssey just barely took the top spot here, but the two Switch 2 titles I own were close behind.

SQLite query

SELECT game_name, SUM(duration)/COUNT(DISTINCT date) AS d
FROM sessions
WHERE STRFTIME('%Y', date) = '2025'
GROUP BY game_name
ORDER BY d DESC;

When did I start and stop playing each game?

I only have enough time to focus on one game at a time, so there is a pretty linear history of which game is top-of-mind for me at any one time. From this query we can construct a linear history:

• Pikmin 2 (June→Oct)
• Animal Crossing (July→Aug)
• Super Mario Odyssey (Oct)
• Pikmin 4 (Nov, “Decor Pikmin”)
• Mario Kart World (July→Nov)
• Kirby Air Riders (Nov→Dec)

I still want to return to Super Mario Odyssey, I was having a great time with the game! It's just that and Kirby Air Riders came out and stole my attention.

SQLite query

SELECT (
  game_name,
  MIN(date) AS fp,
  MAX(date)
)
FROM sessions
WHERE STRFTIME('%Y', date) = '2025'
GROUP BY game_name
ORDER BY fp ASC;

Which game was I most consistently playing?

We can take the data from the “Days” column above and use that as a divisor for the number of unique days each game was played. This will give a sense of how often I was playing a game within the time span that I was “active” for a game:

SQLite query

SELECT (
  game_name,
  COUNT(DISTINCT date) AS played,
  (
    STRFTIME('%j', MAX(date))
    -STRFTIME('%j', MIN(date))
  ) AS days
)
FROM sessions
WHERE STRFTIME('%Y', date) = '2025'
GROUP BY game_name
ORDER BY MIN(date) ASC;

If we look at total year gaming “saturation” for 2025 and June-onwards (214 days):

SQLite query

SELECT COUNT(DISTINCT date)
FROM sessions
WHERE STRFTIME('%Y', date) = '2025';

When did I play games?

Looking at the year, I didn't start playing games on either system this year until June. That lines up with me receiving my GameCube FlippyDrives which I had pre-ordered in 2024. After installing these modifications to my GameCube I began playing games more regularly again :)

SQLite query

SELECT STRFTIME('%m', date) AS m, SUM(duration)
FROM sessions
WHERE STRFTIME('%Y', date) = '2025'
GROUP BY m
ORDER BY m ASC;

August was the month with the most play! This was due entirely to playing Animal Crossing Deluxe (~16 hours), a mod by Cuyler for Animal Crossing on the GameCube. Animal Crossing feels the best when you play for short sessions each day which I why I was playing so often.

SQLite query

SELECT game_name, SUM(duration)
FROM sessions
WHERE STRFTIME('%Y-%m', date) = '2025-08'
GROUP BY game_name;

Which day of the week did I play most?

Unsurprisingly, weekends tend to be the days on average with the longest play sessions. Sunday just barely takes the highest average play duration per day. Wednesday, Thursday, and Friday have the lowest play activity as these days are reserved for board-game night, seeing family, and date night respectively :)

SQLite query

SELECT STRFTIME('%w', date) AS day_of_week,SUM(duration),COUNT(DISTINCT date),SUM(duration)/COUNT(DISTINCT date)
FROM sessions WHERE STRFTIME('%Y', date) = '2025'
GROUP BY day_of_week
ORDER BY day_of_week ASC;

Thanks for keeping RSS alive! ♥

December 29, 2025 12:00 AM UTC

December 28, 2025

Mark Dufour

A (biased) Pure Python Performance Comparison

The following is a performance comparison of several (pure) Python implementations, for a large part of the Shed Skin example set. I left out some of the examples, that would result in an unfair comparison (mostly because of randomization), or that were too interactive to easily measure. Obviously this comparison is very biased, and probably unfair in some way to the other projects (though I've tried to be fair, for example by letting PyPy stabilize before measuring).

This first plot shows the speedups versus CPython 3.10, for CPython 3.14, Nuitka, Pypy and Shed Skin.

Shed Skin is able to speed up the examples by an average factor of about 29 times (not percent, times :)), while PyPy is able to speed up the examples by an average factor of about 16 times. Given that PyPy has its hands tied behind its back trying to support unrestricted Python code, and was not optimized specifically for these examples (that I am aware of), that is actually still quite an impressive result.

As for the few cases where PyPy performs better than Shed Skin, this appears to be mainly because of PyPy being able to optimize away heap allocations for short-lived objects (in many cases, custom Vector(x,y,z) instances). In a few cases also, the STL unordered_map that Shed Skin uses to implement dictionaries appears to perform poorly compared to more modern implementations. Of course it is possible for Shed Skin to improve in these areas with future releases.

Note that some of the examples can run even faster with Shed Skin by providing --nowrap/--nobounds options, which disable wrap-around/bounds-checking respectively. I'm not sure if PyPy has any options to make it run faster, at the cost of certain features (in the distant past there was talk of RPython - does that still exist?).

As the CPython 3.14 and Nuitka results are a bit hard to see in the above plot, here is the same plot but with a logarithmic y-scale:

CPython 3.14 is about 60% faster on average for these examples than CPython 3.10, which to me is actually very promising for the future. While Nuitka outperforms CPython 3.10 by about 30% on average, unfortunately it cannot match the improvements in CPython since.

If there are any CS students out there who would like to help improve Shed Skin, please let me know. I think especially memory optimizations (where PyPy still seems to have an edge) would be a great topic for a Master's Thesis!

December 28, 2025 04:31 AM UTC

December 26, 2025

"Michael Kennedy's Thoughts on Technology"

DevOps Python Supply Chain Security

In my last article, “Python Supply Chain Security Made Easy” I talked about how to automate pip-audit so you don’t accidentally ship malicious Python packages to production. While there was defense in depth with uv’s delayed installs, there wasn’t much safety beyond that for developers themselves on their machines.

This follow up fixes that so even dev machines stay safe.

Defending your dev machine

My recommendation is instead of installing directly into a local virtual environment and then running pip-audit, create a dedicated Docker image meant for testing dependencies with pip-audit in isolation.

Our workflow can go like this.

First, we update your local dependencies file:

uv pip compile requirements.piptools --output-file requirements.txt --exclude-newer 1 week

This will update the requirements.txt file, or tweak the command to update your uv.lock file, but it don’t install anything.

Second, run a command that uses this new requirements file inside of a temporary docker container to install the requirements and run pip-audit on them.

Third, only if that pip-audit test succeeds, install the updated requirements into your local venv.

uv pip install -r requirements.txt

The pip-audit docker image

What do we use for our Docker testing image? There are of course a million ways to do this. Here’s one optimized for building Python packages that deeply leverages uv’s and pip-audit’s caching to make subsequent runs much, much faster.

Create a Dockerfile with this content:

# Image for installing python packages with uv and testing with pip-audit
# Saved as Dockerfile
FROM ubuntu:latest

RUN apt-get update
RUN apt-get upgrade -y
RUN apt-get autoremove -y

RUN apt-get -y install curl

# Dependencies for building Python packages
RUN apt-get install -y gcc 
RUN apt-get install -y build-essential
RUN apt-get install -y clang
RUN apt-get install -y openssl 
RUN apt-get install -y checkinstall 
RUN apt-get install -y libgdbm-dev 
RUN apt-get install -y libc6-dev
RUN apt-get install -y libtool
RUN apt-get install -y zlib1g-dev
RUN apt-get install -y libffi-dev
RUN apt-get install -y libxslt1-dev

ENV PATH=/venv/bin:$PATH
ENV PATH=/root/.cargo/bin:$PATH
ENV PATH=/root/.local/bin/:$PATH
ENV UV_LINK_MODE=copy

# Install uv
RUN curl -LsSf https://astral.sh/uv/install.sh | sh

# set up a virtual env to use for temp dependencies in isolation.
RUN --mount=type=cache,target=/root/.cache uv venv --python 3.14 /venv
# test that uv is working
RUN uv --version 

WORKDIR "/"

# Install pip-audit
RUN --mount=type=cache,target=/root/.cache uv pip install --python /venv/bin/python3 pip-audit

This installs a bunch of Linux libraries used for edge-case builds of Python packages. It takes a moment, but you only need to build the image once. Then you’ll run it again and again. If you want to use a newer version of Python later, change the version in uv venv --python 3.14 /venv. Even then on rebuilds, the apt-get steps are reused from cache.

Next you build with a fixed tag so you can create aliases to run using this image:

# In the same folder as the Dockerfile above.
docker build -t pipauditdocker .

Finally, we need to run the container with a few bells and whistles. Add caching via a volume so subsequent runs are very fast: -v pip-audit-cache:/root/.cache. And map a volume so whatever working directory you are in will find the local requirements.txt: -v \"\$(pwd)/requirements.txt:/workspace/requirements.txt:ro\"

Here is the alias to add to your .bashrc or .zshrc accomplishing this:

alias pip-audit-proj="echo '🐳 Launching isolated test env in Docker...' && \
docker run --rm \
  -v pip-audit-cache:/root/.cache \
  -v \"\$(pwd)/requirements.txt:/workspace/requirements.txt:ro\" \
  pipauditdocker \
  /bin/bash -c \"echo '📦 Installing requirements from /workspace/requirements.txt...' && \
    uv pip install --quiet -r /workspace/requirements.txt && \
    echo '🔍 Running pip-audit security scan...' && \
    /venv/bin/pip-audit \
      --ignore-vuln CVE-2025-53000 \
      --ignore-vuln PYSEC-2023-242 \
      --skip-editable\""

That’s it! Once you reload your shell, all you have to do is type is pip-audit-proj when you’re in the root of your project that contains your requirements.txt file. You should see something like this below. Slow the first time, fast afterwards.

Protecting Docker in production too

Let’s handle one more situation while we are at it. You’re running your Python app IN Docker. Part of the Docker build configures the image and installs your dependencies. We can add a pip-audit check there too:

# Dockerfile for your app (different than validation image above)
# All the steps to copy your app over and configure the image ...

# After creating a venv in /venv and copying your requirements.txt to /app

# Check for any sketchy packages.
# We are using mount rather than a volume because
# we want to cache build time activity, not runtime activity.

RUN --mount=type=cache,target=/root/.cache uv pip install --python /venv/bin/python3 --upgrade pip-audit
RUN --mount=type=cache,target=/root/.cache /venv/bin/pip-audit --ignore-vuln CVE-2025-53000 --ignore-vuln PYSEC-2023-242 --skip-editable

# ENTRYPOINT ... for your app

Conclusion

There you have it. Two birds, one Docker stone for both. Our first Dockerfile built a reusable Docker image named pipauditdocker to run isolated tests against a requirements file. This second one demonstrates how we can make our docker/docker compose build completely fail if there is a bad dependency saving us from letting it slip into production.

Cheers
Michael

December 26, 2025 11:53 PM UTC

Seth Michael Larson

Getting started with Playdate on Ubuntu 🟨

Getting started with Playdate on Ubuntu

Here's what I did to quickly get started with a Playdate development environment on my Ubuntu 24.04 laptop:

• Unbox the Playdate and start charging the console so it's charged enough for the next steps involving the console.
• Create a Playdate account.
• Download the SDK. For Linux you need to extract to your desired directory (I chose ~/PlaydateSDK) and run the setup script (sudo ~/PlaydateSDK/setup.sh).
• Add the SDK bin to PATH and PLAYDATE_SDK_PATH environment variables to your ~/.bashrc.
• Start the simulator (PlaydateSimulator) and register the simulator to your Playdate account when prompted.
• Turn on the console and play the startup tutorial. Connect to Wi-Fi and let the console update.
• When prompted by the console, register the console to your Playdate account.
• Download and install VSCode. I used the .deb installer for Ubuntu.
• Disable all AI features in VSCode by adding "chat.disableAIFeatures": true to your settings.json.
• Copy the .vscode directory from this Playdate template project. The author of this template, SquidGod, has multiple video guides about Playdate development.
• Select "Extensions" in VSCode and install the "Lua" and "Playdate Debug" extensions.
• Create two directories: source and builds. Within the source directory create a file called main.lua. This file will be the entry-point into your Playdate application.

That's it, your Playdate development environment should be ready to use.

“Hello, world” on the Playdate

Within source/main.lua put the following Lua code:

import "CoreLibs/graphics"
import "CoreLibs/ui"

-- Localizing commonly used globals
local pd <const> = playdate
local gfx <const> = playdate.graphics

function playdate.update()
    gfx.drawTextAligned(
      "Hello, world",
      200, 30, kTextAlignment.center
    )
end

Try building and running this with the simulator Ctrl+Shift+B. You should see our "Hello world" message in the simulator.

Running “Hello, world” on real hardware

Next is getting your game running on an actual Playdate console. Connect the Playdate to your computer using the USB cable and make sure the console is awake.

Start your game in the simulator (Ctrl+Shift+B) and then once the simulator starts select Device > Upload Game to Device in the menus or use the hotkey Ctrl+U.

Uploading the game to the Playdate console takes a few seconds, so be patient. The console will show a message like “Sharing DATA segment with USB. Will reboot when ejected”. You can select the "Home" button in the Playdate console menu to stop the game.

Making a network request

One of my initial hesitations with buying a Playdate was that it didn't originally ship with network connectivity within games, despite supporting Wi-Fi. This is no longer the case, as this year Playdate OS 2.7 added support for HTTP and TCP networking.

So immediately after my "Hello world" game, I wanted to try this new feature. I created the following small application that sends an HTTP request after pressing the A button:

import "CoreLibs/graphics"
import "CoreLibs/ui"

local pd <const> = playdate
local gfx <const> = playdate.graphics
local net <const> = playdate.network

local networkEnabled = false

function networkHttpRequest()
    local host = "sethmlarson.dev"
    local port = 443
    local useHttps = true
    local req = net.http.new(
      host, port, useHttps, "Making an HTTP request"
    )
    local path = "/"
    local headers = {}
    req:get(path, headers)
end

function networkEnabledCallback(err)
    networkEnabled = true
end

function init()
    net.setEnabled(true, networkEnabledCallback)
end

function playdate.update()
    gfx.clear()
    if networkEnabled then
        gfx.drawTextAligned(
          "Network enabled",
          200, 30, kTextAlignment.center
        )
        if pd.buttonJustPressed(pd.kButtonA) then
            networkHttpRequest()
        end
    else
        gfx.drawTextAligned(
          "Network disabled",
          200, 30, kTextAlignment.center
        )
    end
end

init()

First I tried running this program with a local HTTP server on localhost:8080 with useHttps set to false and was able to capture this HTTP request using Wireshark:

0000   47 45 54 20 2f 20 48 54 54 50 2f 31 2e 31 0d 0a   GET / HTTP/1.1..
0010   48 6f 73 74 3a 20 6c 6f 63 61 6c 68 6f 73 74 0d   Host: localhost.
0020   0a 55 73 65 72 2d 41 67 65 6e 74 3a 20 50 6c 61   .User-Agent: Pla
0030   79 64 61 74 65 2f 53 69 6d 0d 0a 43 6f 6e 6e 65   ydate/Sim..Conne
0040   63 74 69 6f 6e 3a 20 63 6c 6f 73 65 0d 0a 0d 0a   ction: close....

So we can see that Playdate HTTP requests are quite minimal, only sending a Host, User-Agent and Connection: close header by default. Keep-Alive and other headers can be optionally configured. The User-Agent for the Playdate simulator was Playdate/Sim.

I then tested on real hardware and targeting my own website: sethmlarson.dev:443 with useHttps set to true. This resulted in the same request being sent, with a User-Agent of Playdate/3.0.2. There's no-doubt lots of experimentation ahead for what's possible with a networked Playdate. That's all for now, happy cranking!

Thanks for keeping RSS alive! ♥

December 26, 2025 12:00 AM UTC

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