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Python Software Foundation: The PSF D&I Workgroup is Starting Office Hours in July!

Starting Tuesday 28 July, 2026, the PSF Diversity Inclusion (DI) Workgroup is opening its virtual doors once a month on Discord. Come chat with workgroup members from all over the world!Doing diversity and inclusion work in tech can feel isolating sometimes. You might be organizing a meetup, writing a code of conduct, trying to get funding for your community, or helping people feel welcome, often in your spare time, and wondering if anyone else is wrestling with the same things.They are. We are! And we would love to get all of us in the same room.This July, the PSF DI Workgroup will be hosting monthly office hours within Discord. These will be open, text-based conversations where we encourage you to ask questions, share what you are working on, and connect with other people who care about making the Python community more welcoming.The detailsThe PSF DI Office Hours will be on the last Tuesday of every month. Because our community is spread across the globe, we will alternate between two times so we can cover as many time zones as possible:1 PM UTC / 9 AM US Eastern9 PM UTC / 5 PM US EasternOur first session will be on Tuesday, 28 July 2026 at 1 PM UTC. Here is roughly what that looks like around the world:RegionLocal time on 28 JulyUS Pacific, Los Angeles – (UTC-7h)6:00 AMUS Eastern, New York – (UTC-4h)9:00 AMBrazil, São Paulo – (UTC-3h)10:00 AMUTC1:00 PMWest Africa, Lagos – (UTC+1h)2:00 PMCentral Europe, Amsterdam / Berlin / Madrid – (UTC+2h)3:00 PMEast Africa, Nairobi – (UTC+3h)4:00 PMIran, Tehran – (UTC+3:30h)4:30 PMIndia, New Delhi – (UTC+5:30h)6:30 PMChina, Beijing – (UTC+8h)9:00 PMJapan, Tokyo – (UTC+9h)10:00 PMAustralia, Sydney – (UTC+10h)11:00 PMIf 6 AM in Los Angeles or 11 PM in Sydney made you wince, do not worry. The August session will be at 9 PM UTC, and we will keep alternating from there.You will find us in the psf-diversity channel on the PSF Discord. If you’re new to Discord, check out some Discord Basics to help you get started. What will we talk aboutHonestly? Whatever is on your mind related to Python, your communities, and DI.Since our workgroup exists to advise the PSF on diversity and inclusion, some conversations we are especially hoping to have include:Ideas for policies, initiatives, and grant proposals to diversify the PSF missions. Feedback from the community about these topics will help the PSF DI Workgroup provide recommendations to the PSF Board of Directors.Your feedback, plain and simple. We want to understand how the PSF can better serve and grow a diverse membership, and we cannot do that without hearing from the community itself.How things are actually going. Part of our job is measuring and sharing the PSF’s progress on its diversity initiatives, and we would rather do that in conversation with you than in a report nobody reads. We also want to understand and learn about the current state of Python communities around the world.No camera, no mic, no pressureOffice hours are text chat only.Show up in your pajamas, join from the bus, lurk quietly for the first twenty minutes. It is all fine.And if you cannot make it at all, the conversation stays in the channel, so you can catch up later when it suits you. If something in the chat sparks a thought you would like to share with us directly, you are always welcome to email the workgroup at diversity-inclusion-wg@python.org.Bring your own languageBecause we are the DI Workgroup, our members come from around the world! Alongside the main conversation, we will open threads in other languages where possible. Depending on the presence of our members, we would be happy to chat in Spanish, Portuguese, Chinese, Hindi, French or even Persian! Let us know during the office hour if you have a specific language you hope to converse in, or jump in with whichever language thread feels like home.See you on the 28th!The first office hour session is on Tuesday, 28 July 2026 at 1 PM UTC, in psf-diversity on Discord.Come say hi, even if it is just to tell us what you are working on with Python. We are really looking forward to meeting you!

7 hours ago

Python GUIs: Constantly Print Subprocess Output While Process is Running — How to stream live output from a subprocess into your PyQt6 GUI without freezing the interface

I need to call a legacy Bash program and display the results in a Qt window. The problem is the subprocess doesn't return each output line as it happens — it waits until the entire command is finished, then dumps everything to the window at once. If the command takes a long time, the user thinks the system is frozen. How can I get live, line-by-line output from a subprocess into my Qt application? If you've ever launched a long-running external command from a PyQt6 application and watched your entire GUI freeze until it finishes, you've hit one of the most common pitfalls in Python GUI development: blocking the event loop. When you call subprocess.run(), Python stops and waits for the process to complete before moving on. While it's waiting, Qt's event loop — the mechanism responsible for redrawing the window, responding to clicks, and processing signals — is completely stalled. That means that the UI will not update. There are two approaches to stream subprocess output in real time in PyQt6: Use QProcess, which is Qt's built-in way to run external programs. It integrates directly with the event loop and emits signals as output becomes available. Use a background QThread with Python's subprocess.Popen to read output line by line and send it back to the GUI via signals. The wrong approach First, let's see what happens when you use subprocess and block the event loop. python import subprocess import sys from PyQt6.QtWidgets import ( QApplication, QMainWindow, QPlainTextEdit, QPushButton, QVBoxLayout, QWidget, ) class MainWindow(QMainWindow): def __init__(self): super().__init__() self.setWindowTitle(Subprocess Demo - Blocking) self.text_area = QPlainTextEdit() self.text_area.setReadOnly(True) self.button = QPushButton(Run Command) self.button.clicked.connect(self.run_command) layout = QVBoxLayout() layout.addWidget(self.text_area) layout.addWidget(self.button) container = QWidget() container.setLayout(layout) self.setCentralWidget(container) def run_command(self): This blocks the entire GUI until the command finishes! result = subprocess.run( [bash, -c, for i in 1 2 3 4 5; do echo Line i; sleep 1; done], stdout=subprocess.PIPE, stderr=subprocess.STDOUT, ) self.text_area.setPlainText(result.stdout.decode()) app = QApplication(sys.argv) window = MainWindow() window.show() app.exec() Click the button, and the window becomes unresponsive for five seconds. Then all the output appears at once. The GUI didn't update during that time because subprocess.run() blocked the Qt event loop until it was finished. Now, let's look at the two solutions to this problem: Streaming Subprocess Output with QProcess QProcess is Qt's own class for running external programs asynchronously. It starts the process and returns immediately, letting the event loop continue. As the external program produces output, QProcess emits the readyReadStandardOutput signal, which you can connect to a slot that reads and displays the new data. This is the most Qt-native solution for displaying real-time subprocess output in PyQt6 and works well for many use cases. For a deeper dive into QProcess including handling stdin, managing multiple processes, and parsing output, see the complete QProcess tutorial. python import sys from PyQt6.QtCore import QProcess from PyQt6.QtWidgets import ( QApplication, QMainWindow, QPlainTextEdit, QPushButton, QVBoxLayout, QWidget, ) class MainWindow(QMainWindow): def __init__(self): super().__init__() self.setWindowTitle(QProcess Live Output) self.process = None self.text_area = QPlainTextEdit() self.text_area.setReadOnly(True) self.button = QPushButton(Run Command) self.button.clicked.connect(self.run_command) layout = QVBoxLayout() layout.addWidget(self.text_area) layout.addWidget(self.button) container = QWidget() container.setLayout(layout) self.setCentralWidget(container) def run_command(self): if self.process is not None: return Already running self.text_area.clear() self.button.setEnabled(False) self.process = QProcess(self) self.process.readyReadStandardOutput.connect(self.handle_stdout) self.process.readyReadStandardError.connect(self.handle_stderr) self.process.finished.connect(self.process_finished) QProcess takes the program and arguments separately. To run a bash command, pass -c and the command string as arguments. self.process.start( bash, [-c, for i in 1 2 3 4 5; do echo \Line i\; sleep 1; done], ) def handle_stdout(self): data = self.process.readAllStandardOutput() text = bytes(data).decode(utf-8) self.text_area.appendPlainText(text.rstrip()) def handle_stderr(self): data = self.process.readAllStandardError() text = bytes(data).decode(utf-8) self.text_area.appendPlainText(text.rstrip()) def process_finished(self): self.text_area.appendPlainText(--- Process finished ---) self.process = None self.button.setEnabled(True) app = QApplication(sys.argv) window = MainWindow() window.show() app.exec() Run this, click the button, and you'll see each line appear one at a time, with the GUI remaining fully responsive throughout. How QProcess Streams Output in Real Time When you call self.process.start(), the external command begins running in the background. Qt's event loop keeps spinning, so your window stays responsive. Each time the external process writes to stdout, QProcess emits readyReadStandardOutput. The connected slot (handle_stdout) reads the available data and appends it to the text area. The same pattern applies for stderr. When the process exits, the finished signal fires, and we clean up. Running Complex Bash Commands with QProcess If your actual command involves sourcing setup files, changing directories, and running build tools — like in the original question — you can pass the entire sequence as a single string to bash -c: python command = ( source /path/to/setup_file -r cd /path/to/parent_directory build_project_command ) self.process.start(bash, [-c, command]) This works because bash -c accepts the whole pipeline as one argument. Streaming Subprocess Output Using QThread and subprocess.Popen Sometimes QProcess doesn't quite fit your needs. For example, you might need to do additional processing on each line of output before displaying it, or you might need to integrate with Python libraries that expect a file-like object. In these cases, running subprocess.Popen in a background QThread is a good alternative. The idea: spin up a QThread that runs the subprocess, reads its output line by line, and emits a signal for each line. The main thread receives those signals and updates the GUI safely. If you're new to threading in PyQt6, our guide to multithreading with QThreadPool covers the fundamentals of running background tasks without freezing the GUI. python import subprocess import sys from PyQt6.QtCore import QThread, pyqtSignal from PyQt6.QtWidgets import ( QApplication, QMainWindow, QPlainTextEdit, QPushButton, QVBoxLayout, QWidget, ) class SubprocessWorker(QThread): Runs a subprocess in a background thread and emits output line by line. output_line = pyqtSignal(str) finished_signal = pyqtSignal(int) exit code def __init__(self, command): super().__init__() self.command = command def run(self): process = subprocess.Popen( self.command, stdout=subprocess.PIPE, stderr=subprocess.STDOUT, text=True, bufsize=1, Line-buffered ) for line in process.stdout: self.output_line.emit(line.rstrip()) process.wait() self.finished_signal.emit(process.returncode) class MainWindow(QMainWindow): def __init__(self): super().__init__() self.setWindowTitle(QThread + Subprocess Live Output) self.worker = None self.text_area = QPlainTextEdit() self.text_area.setReadOnly(True) self.button = QPushButton(Run Command) self.button.clicked.connect(self.run_command) layout = QVBoxLayout() layout.addWidget(self.text_area) layout.addWidget(self.button) container = QWidget() container.setLayout(layout) self.setCentralWidget(container) def run_command(self): if self.worker is not None: return self.text_area.clear() self.button.setEnabled(False) self.worker = SubprocessWorker( [bash, -c, for i in 1 2 3 4 5; do echo \Line i\; sleep 1; done] ) self.worker.output_line.connect(self.on_output_line) self.worker.finished_signal.connect(self.on_finished) self.worker.start() def on_output_line(self, text): self.text_area.appendPlainText(text) def on_finished(self, exit_code): self.text_area.appendPlainText(f--- Process finished (exit code {exit_code}) ---) self.worker = None self.button.setEnabled(True) app = QApplication(sys.argv) window = MainWindow() window.show() app.exec() How QThread with subprocess.Popen Works subprocess.Popen (unlike subprocess.run) starts the process and returns immediately, giving you a handle to interact with it. By iterating over process.stdout, you get each line as it's produced. Because this iteration is blocking (it waits for the next line), we run it in a QThread so it doesn't block the GUI. Each time a line arrives, the worker emits output_line, which is safely delivered to the main thread via Qt's signal-slot mechanism. Setting bufsize=1 and text=True enables line-buffered mode, which means output is available to read as soon as a newline character is written by the subprocess. Fixing Delayed Subprocess Output: Buffering Issues Even with both approaches working correctly on the Qt side, you might still see delayed output if the external program itself buffers its stdout. Many programs buffer output differently when they detect they're writing to a pipe (which is what happens with both QProcess and subprocess.Popen) versus writing to a terminal. If your external program supports it, you can try: Setting the PYTHONUNBUFFERED=1 environment variable (for Python scripts). Using stdbuf -oL to force line-buffered output: stdbuf -oL your_command. Using script or unbuffer (from the expect package) to trick the program into thinking it's connected to a terminal. For example, with the QProcess approach: python self.process.start( bash, [-c, stdbuf -oL your_long_running_command], ) QProcess vs QThread: Which Approach Should You Use? Use QProcess when you're running a simple external command and want a clean, Qt-integrated solution. It handles the event loop integration for you, supports signals for stdout, stderr, and process completion, and doesn't require managing threads. Use a background QThread when you need more control over how you read the output — for example, if you want to parse each line, filter output, or interact with the subprocess's stdin in complex ways. The thread approach also makes it straightforward to use Python's subprocess module features that don't have direct equivalents in QProcess. Both approaches keep the GUI responsive and deliver output in real time. Pick whichever fits your situation best. Complete Example: Live Build Output Viewer in PyQt6 Here's a more polished example that combines the QProcess approach with a few usability improvements — a scrolling output view, a status indicator, and support for running a configurable command. This example uses layouts and basic widgets to build the interface: python import sys from PyQt6.QtCore import QProcess from PyQt6.QtGui import QFont from PyQt6.QtWidgets import ( QApplication, QHBoxLayout, QLabel, QLineEdit, QMainWindow, QPlainTextEdit, QPushButton, QVBoxLayout, QWidget, ) class BuildOutputViewer(QMainWindow): def __init__(self): super().__init__() self.setWindowTitle(Live Build Output Viewer) self.resize(700, 500) self.process = None Command input self.command_input = QLineEdit() self.command_input.setPlaceholderText( Enter bash command, e.g.: for i in (seq 1 10); do echo Building step i; sleep 0.5; done ) self.command_input.setText( for i in (seq 1 10); do echo \Building step i...\; sleep 0.5; done echo Done! ) Output area self.output_area = QPlainTextEdit() self.output_area.setReadOnly(True) self.output_area.setFont(QFont(Courier, 10)) self.output_area.setStyleSheet( QPlainTextEdit { background-color: 1e1e1e; color: d4d4d4; } ) Buttons and status self.run_button = QPushButton(Run) self.run_button.clicked.connect(self.start_process) self.stop_button = QPushButton(Stop) self.stop_button.clicked.connect(self.stop_process) self.stop_button.setEnabled(False) self.status_label = QLabel(Ready) button_layout = QHBoxLayout() button_layout.addWidget(self.run_button) button_layout.addWidget(self.stop_button) button_layout.addWidget(self.status_label) button_layout.addStretch() layout = QVBoxLayout() layout.addWidget(self.command_input) layout.addLayout(button_layout) layout.addWidget(self.output_area) container = QWidget() container.setLayout(layout) self.setCentralWidget(container) def start_process(self): command = self.command_input.text().strip() if not command: return self.output_area.clear() self.run_button.setEnabled(False) self.stop_button.setEnabled(True) self.status_label.setText(Running...) self.process = QProcess(self) self.process.readyReadStandardOutput.connect(self.handle_stdout) self.process.readyReadStandardError.connect(self.handle_stderr) self.process.finished.connect(self.process_finished) self.process.start(bash, [-c, command]) def stop_process(self): if self.process is not None: self.process.kill() def handle_stdout(self): data = self.process.readAllStandardOutput() text = bytes(data).decode(utf-8) self.output_area.appendPlainText(text.rstrip()) def handle_stderr(self): data = self.process.readAllStandardError() text = bytes(data).decode(utf-8) self.output_area.appendPlainText(text.rstrip()) def process_finished(self, exit_code, exit_status): status_text = Finished if exit_code == 0 else fExited with code {exit_code} self.status_label.setText(status_text) self.run_button.setEnabled(True) self.stop_button.setEnabled(False) self.process = None app = QApplication(sys.argv) window = BuildOutputViewer() window.show() app.exec() This gives you a terminal-styled output viewer where you can type in a command, run it, watch the output stream in line by line, and stop it if needed — all without the GUI ever locking up. For an in-depth guide to building Python GUIs with PyQt6 see my book, Create GUI Applications with Python Qt6.

11 hours ago

Mike C. Fletcher: OMI Physics Extension for glTF

I had Claude code up an OMI Physics package using Numpy. It's up on pypi as omi_physics. This follows the OMI extensions to glTF pretty closely to create an engine with the common features you need. It's not trying to be real-world physics, it's just a game engine style simulation. The core is GL-free, with a few GPGPU kernels to optimise certain bits over 10,000 objects, but in testing any more than about 300 objects brings the rendering below 60fps in OpenGLContext. To get around that we'd need to move the whole physics process into the GPU with a very different solver, and I don't currently need that. License is MIT, though most of the code is written by Claude, so arguably it's non-copyright.

12 hours ago

Publishing Activity

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Seth Michael Larson: PAL GameCube haul from Kraków, Poland (EuroPython 2026)

While I was in Kraków, Poland during EuroPython 2026 I was able to sneak away from sprints and buy a few PAL region GameCube games for my collection. This was on Sunday, which means that many stores are closed until Monday... except for one! “Game Over” was open and the owner was very friendly and chatted with me as I browsed the selection. I purchased three complete-in-box games: The Legend of Zelda: Wind Waker Ocarina of Time Master Quest (UK), Pikmin (UK), and Billy Hatcher and the Giant Egg (Swedish/Finnish). Prior to these new purchases the only PAL title I owned was Pikmin 2, as this was necessary to complete the Pikmin 2 International Treasure Hoard. The fun thing about PAL releases is that they are usually the last release a game receives in terms of time, so these revisions often have the most bug fixes and quality-of-life improvements compared to earlier revisions. They also often support multiple languages and have unique box and disk artwork. The most exciting game I purchased was Legend of Zelda: Wind Waker with the Ocarina of Time Master Quest bonus disk included. This is my first multi-disk GameCube game that I own, and I'm excited to see if it's possible to extract the N64 ROMs from the ISO like Zelda Collector’s Edition. You can see the box has a “flap” with a second disk tray, allowing two games to be included. The second game is Pikmin, which the PAL region has relatively few changes. The major change is that you can skip the End of Day cutscenes, which you'll likely be watching 10-15 times on a typical playthrough. This small change makes the PAL revision the definitive edition ;) Another small change for Pikmin is the manual. This game had to introduce the world to Pikmin which are very, very small. Depending on your region the height of Olimar, the protagonist of Pikmin, is compared either to a “US quarter” or a “50 pence piece”. Finally, Billy Hatcher and the Giant Egg. This is one of my favorite games ever, I've played it through to 99 completion multiple times (the last emblem is not fun to achieve). It's a different game, if you like action adventure then maybe you'll have fun with it, but in particular it's mostly a special title for me. I purchased this one in particular because I didn't have a single “Player’s Choice” for the PAL region which feature silver splines instead of the typical yellow splines for NTSC-J/NTSC. I was able to dump the ROMs from each of these disks easily using a FlippyDrive-modded GameCube and the included backup utility. I've been thinking about investing in an OmniDrive-compatible disk reader so that I don't have to use my GameCube for this task to avoid tiring out the laser. Maybe a gift idea for the future? Thanks for reading I would love to hear your thoughts! Contact me via Mastodon, Bluesky, or email. Browse the blog archive. Check out my blogroll.

17 hours ago

PyCoder’s Weekly: Issue #744: CPython ABI, CLAUDE.md, Itertools Cheatsheet, and More (2026-07-21)

744 – JULY 21, 2026 View in Browser » What Every Dev Should Know About the CPython ABI An introduction to the concept of the Application Binary Interface (ABI), the various CPython ABIs, and the new abi3t stable ABI in Python 3.15. NATHAN GOLDBAUM How to Write a CLAUDE.md File for Claude Code Learn how to write a CLAUDE.md file for Claude Code, with global, project, and local examples that capture your Python commands and conventions. REAL PYTHON Quiz: How to Write a CLAUDE.md File for Claude Code REAL PYTHON Pip Install Actian VectorAI! VectorAI DB gives your Python AI agents persistent vector memory on your own hardware. No cloud dependency or per-query billing. Native LangChain and LlamaIndex support. On-premises, at the edge, or air-gapped. Free Community Edition available. Get Started Free ACTIAN VECTORAI DB sponsor Itertools Cheatsheet Cheatsheet with visual diagrams that explain how the iterables from itertools work. RODRIGO GIRÃO SERRÃO Python 3.15.0 Beta 4 Released PYTHON.ORG PEP 838: Adding python-version to pyvenv.cfg (Added) PYTHON.ORG PEP 840: Name Resolution in Class Namespaces (Added) PYTHON.ORG PyData Global 2026 Call for Proposals PYDATA.ORG Articles Tutorials Git for Data Scientists A practical Git walkthrough for data scientists, focused on real workflows like branching for experiments, reverting mistakes, and keeping project history clean with small, focused commits. It also explains merge vs. rebase, why you should not rebase shared branches, and how to set up .gitignore for data-heavy projects. KHUYEN TRAN Shared by Khuyen Tran In Defense of Not Understanding Your Codebase In this opinion piece, Sean argues that there is a difference in the thought process between maintaining smaller software projects vs larger ones, and that the former is over represented in engineering discussion in the internet. SEAN GOEDECKE Learn Agentic Coding With Claude Code Unlike a chat window, Claude Code works directly in your project, where it can run your tests and manage your git history. In this two-day live course (August 1–2), you’ll use it to scaffold, test, debug, and ship a Python app project, and leave with a starter kit of reusable skills. See the Full Curriculum REAL PYTHON sponsor Polars: Benchmarking Single Node vs Distributed Polars has recently added a mechanism for doing distributed calculations. This post describes how that relates to speed-up. As with benchmarking all things, whether it is faster or not depends on your situation. CHIEL PETERS Browser Push Notifications for a Django Website Web Push notifications are an alternate way of getting information to your users. This post shows you how to implement them with Django using a service worker and a Huey background task. AIDAS BENDORAITIS 12K+ JPEGs From NASA’s Artemis II Mission Mark writes articles on data analysis. This one is all about the images NASA released from the Artemis II mission. It includes step-by-step instructions that you can follow along. MARK LITWINTSCHIK Introducing django-orjson orjson is a Rust-based replacement for Python’s json module. So what would Adam Johnson do with it? Make it easier to use in Django of course. ADAM JOHNSON Stop Using if-else Chains Learn a cleaner, more extensible way to dispatch logic in Python using dictionaries and function pointers instead of long if-else chains. KANWAL MEHREEN Understanding Mixin Classes in Python Learn how to write reusable Python mixin classes, distinguish them from abstract base classes, and steer clear of common pitfalls. REAL PYTHON course Quiz: Understanding Mixin Classes in Python REAL PYTHON Creating Presentations in Your Terminal Spiel is a Python tool for creating terminal based presentations. It uses the Rich package to give you a clean look and feel. MIKE DRISCOLL Projects Code kademlia-dynamic: Kademlia Distributed Hashtable GITHUB.COM/F4RSANTOS Shared by Fernando Santos userharbor: Framework Agnostic User Management GITHUB.COM/USERHARBOR balance: Deal With Biased Data Samples GITHUB.COM/FACEBOOKRESEARCH fstache: Fast, Typed, Mustache Renderer GITHUB.COM/SERVLETCLOUD Shared by Vladimir Korobkov bounty-check: Is a GitHub Bounty Issue Still Claimable? GITHUB.COM/WREN-CASTELLAN Shared by Wren Castellan Events Weekly Real Python Office Hours QA (Virtual) July 22, 2026 REALPYTHON.COM PyData PyCon Armenia 2026 July 24 to July 26, 2026 PYCON.AM PyDelhi User Group Meetup July 25, 2026 MEETUP.COM Python Sheffield July 28, 2026 GOOGLE.COM Python Southwest Florida (PySWFL) July 29, 2026 MEETUP.COM Happy Pythoning!This was PyCoder’s Weekly Issue 744.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 ]

22 hours ago

PyCharm: What’s New in PyCharm 2026.2

In PyCharm 2026.2, you can build Python extensions with the new Rust plugin and debug them using debugpy, which is now the default engine. Running external utilities is now managed through a redesigned settings UI for uvx, while multi-project setups are supported out of the box for uv, Poetry, and Hatch workspaces. This release also introduces an editor minimap, integrates the Pyrefly engine for faster type insights, adds AI project generation, and more. Read the full What’s New Python extension development with the Rust plugin [Beta][Pro] Work seamlessly with Python projects that leverage Rust modules to speed up performance-critical components. debugpy as the default debugger Following its introduction as an optional backend in 2026.1, debugpy is now enabled by default for all Python projects and Jupyter notebooks, using the Debug Adapter Protocol (DAP). Support for uv-backed tools and uvx PyCharm now leverages the uv toolchain to streamline how you run your external development utilities, eliminating manual package setups that clutter your local environment. Support for uv, Poetry, and Hatch multi-projects and uv workspaces [Beta] Previously available as an optional feature in PyCharm 2026.1.1, this functionality is enabled by default in version 2026.2. It streamlines your subproject management and provides richer dependency insights directly within your configuration files. Editor minimap Navigate complex source files and notebooks more efficiently with the official editor minimap. It provides a high-level visual overview of your document structure across all supported file types – while offering a dedicated layout built just for Jupyter notebooks. Pyrefly type engine integration Use Pyrefly as an external type engine to significantly accelerate code insight features for large-scale Python codebases. Start new projects with AI If you have a JetBrains AI license, you can now generate fully configured, runnable projects from scratch using natural language prompts directly from the Welcome screen. Agent skills manager AI agents are only as useful as the context they have. When they don’t have knowledge of your frameworks, conventions, and tooling, you end up re-explaining the same setup in every new chat window. Agent skills fix that. Install them once in PyCharm, and your agents carry that domain knowledge across every project and session – automatically. Browse and manage skills directly from the IDE, expand the built-in library with external registries like public GitHub repositories, or let PyCharm import skills you’ve already set up for Claude Code or Codex. Read the full What’s New

1 day ago

Rodrigo Girão Serrão: Python quiz: EuroPython 2026 edition

Replay the EuroPython 2026 Python quiz. These are the questions asked during the EuroPython 2026 quiz. They will test your knowledge of the Python language, the community, and of EuroPython 2026. Since we were celebrating 25 years of EuroPython at EuroPython 2026, some questions also touched on that theme. (Unless explicitly stated, questions refer to CPython 3.14.) Note that the version of the quiz presented here is less interactive than the one presented at the conference. Questions In 25 years of conference, which of these European cities never hosted EuroPython? Bilbao Birmingham Lisbon Prague This year's conference programme has it all. This quiz. Talks. Lightning talks. Tutorials. Summits. Open spaces. Talks. And posters during lunch breaks. How many posters are scheduled to be presented at EP 2026? 4 6 12 15 Which of the following Python-related projects has the FEWEST stars on GitHub? CPython Django FastAPI uv The Python repo has over 130,000 commits made by more than 3,500 contributors over the past 35+ years. The Python core developers are the people with permissions to commit directly to the CPython GitHub repo and plenty of them were at the conference. Out of the following 4 core devs, who were all at the conference, who's made the fewest commits? Guido van Rossum, the creator of Python Hugo van Kemenade, Python 3.14 and 3.15 release manager Łukasz Langa, Python Developer in Residence for ~5 years Pablo Galindo Salgado, Python 3.10 and 3.11 release manager Speaking of commits, how many commits did Guido van Rossum make? Check Since we're celebrating 25 years of EuroPython, which of the following expressions does not evaluate to 25? 0x19 0b11001 0o33 25 3.15 comes with two new built-in functions. Before that, the previous Python version that got new built-ins was 3.10, with also TWO new built-ins. What two built-ins were introduced in 3.10? aiter and anext breakpoint and compile frozendict and sentinel frozenset and memoryview What's printed by the second print if you run this code? 0 285 KeyError ValueError By the way, speaking of commits, do you still remember how many commits Guido van Rossum made? Check What does the following cursed Python 2 code print? 'a' 25 True SyntaxError Explanations Question 1 — Hosting EuroPython EuroPython 2009 and 2010 was hosted in Birmingham. EuroPython 2015 and 2016 was hosted in Bilbao. EuroPython 2023, 2024, and 2025 was hosted in Prague. Of the four options, Lisbon is the only European city that never hosted an EuroPython. Question 2 — poster presentations Originally, 9 poster presentations were scheduled. After a mixup and a couple cancellations we ended with only 6. Question 3 — GitHub stars The official quiz asked you to order all four projects, from most stars to least stars. Can you do it? On the 15th of July of 2026, this would be the correct ordering: FastAPI, 101k Django, 88.2k uv, 87.5k CPython, 73.8k Question 4 — commits On the 15th of July of 2026, GitHub reported the following number of all-time...

1 day ago

Python Bytes: #489 Or JSON?

<strong>Topics covered in this episode:</strong><br> <ul> <li><strong><a href=https://adamj.eu/tech/2026/07/15/introducing-django-orjson/?featured_on=pythonbytes>django-orjson</a></strong></li> <li><strong><a href=https://www.peterbe.com/plog/best-django-redis-configuration-for-speed-and-size?featured_on=pythonbytes>Best Django Redis configuration for speed and size</a></strong></li> <li><strong>Linus Torvalds <a href=https://lore.kernel.org/linux-media/CAHk-=wi4zC+Ze8e+p3tMv8TtG_80KzsZ1syL9anBtmEh5Z40vg@mail.gmail.com/?featured_on=pythonbytes>puts the foot down</a> against Anti-AI Kernel Maintainers</strong></li> <li><strong><a href=https://www.djangoproject.com/weblog/2026/jul/15/supporting-the-triptych-project/?featured_on=pythonbytes>Django Steering Council backs the Triptych Project</a></strong></li> <li><strong>Extras</strong></li> <li><strong>Joke</strong></li> </ul><a href='https://www.youtube.com/watch?v=zaoPcuKz970' style='font-weight: bold;'data-umami-event=Livestream-Past data-umami-event-episode=489>Watch on YouTube</a><br> <p><strong>About the show</strong></p> <p>Sponsored by us! Support our work through:</p> <ul> <li>Our <a href=https://training.talkpython.fm/?featured_on=pythonbytes><strong>courses at Talk Python</strong></a></li> <li>Consulting from <a href=https://sixfeetup.com/?featured_on=pythonbytes><strong>Six Feet Up</strong></a></li> </ul> <p><strong>Connect with the hosts</strong></p> <ul> <li>Michael: <a href=https://fosstodon.org/@mkennedy>Mastodon</a> / <a href=https://bsky.app/profile/mkennedy.codes?featured_on=pythonbytes>BlueSky</a> / <a href=https://x.com/mkennedy?featured_on=pythonbytes>X</a> / <a href=https://www.linkedin.com/in/mkennedy/?featured_on=pythonbytes>LinkedIn</a></li> <li>Calvin: <a href=https://sixfeetup.social/@calvin?featured_on=pythonbytes>Mastodon</a> / <a href=https://bsky.app/profile/calvinhp.com?featured_on=pythonbytes>BlueSky</a> / <a href=https://x.com/calvinhp?featured_on=pythonbytes>X</a> / <a href=https://www.linkedin.com/in/calvinhp/?featured_on=pythonbytes>LinkedIn</a></li> <li>Show: <a href=https://fosstodon.org/@pythonbytes>Mastodon</a> / <a href=https://bsky.app/profile/pythonbytes.fm>BlueSky</a> / <a href=https://x.com/PythonBytes?featured_on=pythonbytes>X</a></li> </ul> <p>Join us on YouTube at <a href=https://pythonbytes.fm/stream/live><strong>pythonbytes.fm/live</strong></a> to be part of the audience. Usually <strong>Tuesday at 7am PT</strong>. Older video versions available there too.</p> <p><strong>Michael 1: <a href=https://adamj.eu/tech/2026/07/15/introducing-django-orjson/?featured_on=pythonbytes>django-orjson</a></strong></p> <ul> <li><strong>Adam Johnson dropped <code>django-orjson</code></strong> - drop-in replacements for the Django and DRF pieces that touch JSON, swapping stdlib <code>json</code> for <strong>orjson</strong>, the Rust-based library. Headline numbers: <strong>10x faster serialization, 2x faster deserialization</strong>.</li> <li><strong>The interesting question is why this needs to be a package at all.</strong> <code>pip install orjson</code> is the easy part. Adam's actual pitch: adopting it isn't easy, especially when your framework uses <code>json</code> in many different parts. Django scatters JSON across <code>JsonResponse</code>, the test client and test case classes, the <code>json_script</code> template tag, and more. There's no single hook to grab, so you get a library that catches them all.</li> <li><strong>Adam is refreshingly honest about the scale of the win.</strong> His words: <em>While database queries tend to dominate the typical Django application's runtime, the time spent in serialization and deserialization can still be significant.</em> He calls it <strong>a nearly free performance win</strong> - not this will 10x your app. That's a claim about <em>cost</em>, not magnitude, and it's worth keeping those straight.</li> <li><strong>Worth flagging what the post doesn't cover: caveats.</strong> There are none in the article, but orjson has real ones. Django and Flask both render datetimes as RFC 822 HTTP-date (<code>Wed, 15 Jul 2026 12:00:00 GMT</code>); orjson does ISO 8601. It can't do <code>ensure_ascii</code>, it rejects NaN and Infinity (which stdlib happily emits), and it raises on <code>Decimal</code>. If you've got a JS client parsing dates, that's a wire-format change.</li> <li><strong>Who should actually take this?</strong> If you're a DRF shop shoveling JSON all day, yes - it's cheap and it's real. If your app mostly renders HTML templates, you're optimizing a slice of runtime that's already near zero.</li> <li><strong>The problem Adam's package solves doesn't exist in Flask or Quart.</strong> They already centralize every JSON operation - <code>jsonify</code>, <code>request.get_json()</code>, the test client, the <code>|tojson</code> filter - behind one provider object at <code>app.json</code>. So there's no library to install. It's about ten lines: <div class=codehilite> <pre><span></span><code><span class=kn>import</span><span class=w> </span><span class=nn>orjson</span> <span class=kn>from</span><span class=w> </span><span class=nn>quart.json.provider</span><span class=w> </span><span class=kn>import</span> <span class=n>JSONProvider</span> <span class=c1> or flask.json.provider</span> <span class=k>class</span><span class=w> </span><span class=nc>OrjsonProvider</span><span class=p>(</span><span class=n>JSONProvider</span><span class=p>):</span> <span class=k>def</span><span class=w> </span><span class=nf>dumps</span><span class=p>(</span><span class=bp>self</span><span class=p>,</span> <span class=n>obj</span><span class=p>,</span> <span class=o>**</span><span class=n>kwargs</span><span class=p>)</span> <span class=o>-gt;</span> <span class=nb>str</span><span class=p>:</span> <span class=k>return</span> <span class=n>orjson</span><span class=o>.</span><span class=n>dumps</span><span class=p>(</span><span class=n>obj</span><span class=p>)</span><span class=o>.</span><span class=n>decode</span><span class=p>()</span> <span class=c1> provider must return str</span> <span class=k>def</span><span class=w> </span><span class=nf>loads</span><span class=p>(</span><span class=bp>self</span><span class=p>,</span> <span class=n>s</span><span class=p>,</span> <span class=o>**</span><span class=n>kwargs</span><span class=p>):</span> <span class=k>return</span> <span class=n>orjson</span><span class=o>.</span><span class=n>loads</span><span class=p>(</span><span class=n>s</span><span class=p>)</span> <span class=n>app</span><span class=o>.</span><span class=n>json</span> <span class=o>=</span> <span class=n>OrjsonProvider</span><span class=p>(</span><span class=n>app</span><span class=p>)</span> </code></pre> </div></li> </ul> <p><strong>The numbers on <a href=https://talkpython.fm/?featured_on=pythonbytes>talkpython.fm</a></strong></p> <ul> <li><strong>Evaluated it, measured it, and skipped it.</strong> The biggest JSON payload we serve is our MCP server returning a cached episode transcript, about 139 KB. Swapping the provider saves <strong>0.119 milliseconds per request</strong>. That total response takes 1.1 ms</li> <li><strong>We got 4.1x, not 10x - and the reason is the good lesson.</strong> Payload <em>shape</em> decides your speedup. The 10x is for structure-heavy data, lots of small keys where stdlib burns time in Python-level dispatch per item. Our hot payload is one giant transcript string, so the work is escaping and memcpy</li> </ul> <p><strong>Calvin 2: <a href=https://www.peterbe.com/plog/best-django-redis-configuration-for-speed-and-size?featured_on=pythonbytes>Best Django Redis configuration for speed and size</a></strong></p> <ul> <li>Peter Bengtsson revisits a classic: his 2017 <a href=https://www.peterbe.com/plog/fastest-redis-optimization-for-django?featured_on=pythonbytes>Fastest Redis configuration for Django</a> benchmark now has a 2026 update posted this week.</li> <li>The 2017 post pitted django-redis serializers (json, ujson, msgpack, pickle) and compressors (zlib, lzma) against each other; conclusion was <strong>msgpack + zlib</strong> as the sweet spot - avoid the json serializer, it's fat and slow.</li> <li>The 2026 update narrows focus to just compressors: default (no compression), <code>zlib</code>, <code>lzma</code>, and newcomer <code>zstd</code>.</li> <li>New results: <code>lzma</code> compresses best but is slowest; <code>zstd</code> is the fastest compressor on Ubuntu; differences between them are very small.</li> <li>Big takeaway across both: compression buys you a lot of space (2–3.5x smaller) for very little speed cost - worth it for Redis where memory is the constraint.</li> <li>Caveat from the author: results depend heavily on your data - his test stores short strings of numbers, so benchmark your own workload.</li> </ul> <p><strong>Michael 3: Linus Torvalds <a href=https://lore.kernel.org/linux-media/CAHk-=wi4zC+Ze8e+p3tMv8TtG_80KzsZ1syL9anBtmEh5Z40vg@mail.gmail.com/?featured_on=pythonbytes>puts the foot down</a> against Anti-AI Kernel Maintainers</strong></p> <ul> <li>Write up <a href=https://arstechnica.com/ai/2026/07/linus-torvalds-to-critics-of-ai-coding-in-linux-fork-it-or-just-walk-away/?featured_on=pythonbytes>on Ars</a>.</li> <li>Really good coverage by Maximillian: <a href=https://www.youtube.com/watch?v=kxEoF8sn-K4>Time to wake up (for some)</a></li> <li>Torvalds said that “Linux is not one of those anti-AI projects, and if somebody has issues with that, they can do the open-source thing and fork it. Or just walk away.”</li> <li>I agree with Max, putting your head in the sand and waiting for AI to go away will likely mean you won’t be working professionally in software development in the coming years.</li> <li>The statement came amid a lengthy thread arguing about the use of <a href=https://github.com/sashiko-dev/sashiko?featured_on=pythonbytes>Sashiko</a>, an “agentic Linux kernel code review system” that its creators claim can, in tests, independently find 53.6 percent of the bugs that would end up being fixed by human coders in later commits.</li> <li>“We’re not forcing anybody to use [LLM tools], but I will very loudly ignore people who try to argue against other people from using it,” Torvalds said.</li> <li>“Anybody who points to the problems at AI had better be looking in the mirror and pointing at themselves at the same time,” Torvalds wrote.</li> </ul> <p><strong>Calvin 4: <a href=https://www.djangoproject.com/weblog/2026/jul/15/supporting-the-triptych-project/?featured_on=pythonbytes>Django Steering Council backs the Triptych Project</a></strong></p> <ul> <li>Django Steering Council issued a Letter of Collaboration backing Carson Gross amp; Alex Petros's funding bid for the <a href=https://triptychproject.org/?featured_on=pythonbytes>Triptych Project</a> - three proposals to make HTML more expressive natively, in every browser.</li> <li>The three additions: PUT/PATCH/DELETE methods for forms, button actions (buttons that fire HTTP requests without a wrapping form), and partial page replacement.</li> <li>Distills the core ideas from HTMX/Unpoly/Turbo into the HTML standard itself - no JS, no library, nothing to ship or maintain.</li> <li>Current focus is button actions (<a href=https://github.com/whatwg/html/issues/12330?featured_on=pythonbytes>WHATWG 12330</a>): <code>lt;button action=/logout method=POSTgt;Logoutlt;/buttongt;</code> instead of wrapping a button in a form.</li> <li>Relevant to Django directly - think the admin submit row and disguised delete links; Django 6.0's template partials were already inspired by these patterns.</li> <li>How to help: companies can send non-binding letters of support on letterhead; individuals can read the proposals and weigh in on the WHATWG issues.</li> </ul> <p><strong>Extras</strong></p> <p>Calvin:</p> <ul> <li><a href=https://github.com/petergpt/doomql?featured_on=pythonbytes>DOOMQL</a> - <strong>A playable first-person shooter whose framebuffer is a SQL query.</strong></li> </ul> <p>Michael:</p> <ul> <li><a href=https://github.com/emmett-framework/granian/releases/tag/v2.7.9?featured_on=pythonbytes><strong>Granian 2.7.9 fixes WSGI threadpool scheduler starvation/underscaling</strong></a></li> <li><a href=https://talkpython.fm/blog/posts/calvin-hendryx-parker-joins-python-bytes-as-co-host/?featured_on=pythonbytes>Welcome Calvin post</a></li> </ul> <p><strong>Joke: <a href=https://x.com/PR0GRAMMERHUM0R/status/2077211586440151184?featured_on=pythonbytes>Solving all bugs</a></strong></p>

1 day ago

Talk Python Blog: Calvin Hendryx-Parker joins Python Bytes as Co-Host

TL;DR: Calvin Hendryx-Parker is the new permanent co-host of Python Bytes, starting with episode 483 on June 9th, 2026. After almost 10 years, Brian Okken, who founded the show with me back in 2016, has decided it’s time for him to move on. We have some major news to announce over at Python Bytes. We are welcoming a new co-host to the show: Calvin Hendryx-Parker. After almost 10 years, Brian Okken who founded the show with me, Michael back in 2016 has decided it’s time for him to move on. On the air I called it the next generation of Python Bytes.

1 day ago

Wingware: Wing Python IDE Version 12 - July 20, 2026

Wing Python IDE version 12 has been released. Wing 12 integrates the Claude Code AI coding agent directly into the IDE, with a new Claude Code tool, a Tasks tool for planning and reviewing AI agent work, and a set of MCP servers that give the agent access to Wing's source code analysis, unit testing, debugger, and code review -- making it faster and cheaper to direct AI agents from inside Wing than from a bare terminal. Wing 12 also adds AI-driven Code Actions, FIX actions, and Write Tests, pseudo-terminal support for OS Commands and Debug I/O, tools in editor splits, a reorganized Tools menu, automatic test discovery, Preferences search, and more. Downloads Be sure to Check for Updates in Wing's Help menu after downloading, to make sure that you have the latest hot fixes. Wing 12 -- the full Python IDE, available as Wing Pro or Wing Classic depending on your license, with a free 30-day trial of Wing Pro. Wing 101 v. 12 -- the free Python IDE for teaching beginning programmers. Wing 11 and earlier versions are not affected by installation of Wing 12 and may be installed and used independently. However, project files for Wing 11 and earlier are converted when opened by Wing 12 and should be saved under a new name, since Wing 12 projects cannot be opened by older versions of Wing. New in Wing 12AI Coding Agent Integration with Claude CodeWing 12 adds a Claude Code tool that integrates the Claude Code AI coding agent with the IDE. A new Set Up for Claude Code item in the Project menu configures the active project for AI agent development and tells the agent how to work with the sources in your Wing project. A set of MCP (Model Context Protocol) servers gives Claude Code access to Wing's source code analysis, testing, and debugger functionality, so the agent can more efficiently navigate and understand your code, write, run, and fix unit tests, and use the debugger to diagnose difficult runtime errors. In our benchmarks, giving Claude Code access to Wing's MCP servers made agent-driven coding tasks both faster and cheaper. Tasks ToolThe new Tasks tool lets you plan, queue up, execute, review, and audit the history of AI agent development tasks, making it easier to supervise the agent's work and inspect changes before committing them to revision control. FIX Features and Write TestsWing 12 adds AI agent driven FIX features that hand off the current debugger bug, failing unit tests, or code warnings directly to Claude Code for resolution. New Write Tests items in the Testing and editor context menus prompt the agent to write unit tests for selected code. Code ActionsWing 12 also adds a set of higher-level AI Code Actions, accessed from the FIX icon in the editor toolbar, that operate on selected code or the enclosing scope. Built-in actions include explaining code, reviewing it for quality or security risks, fixing code warnings, optimizing for performance, and updating comments and docstrings. The action list is user-extensible, so you can add your own prompts for tasks you run often. Pseudo-Terminal for OS Commands and Debug I/OThe OS Commands and Debug I/O tools now default to using a pseudo-terminal that implements full ANSI terminal emulation, so you can run and debug programs that use color output, cursor positioning, or full-screen TUIs. Tools in Editor Splits and Reorganized Tools MenuTools can now be added or dragged to editor splits, not just in the tool areas, allowing for much more flexible workspace layout. The Tools menu has also been reorganized into related groups, with an Other sub-menu for lesser-used or legacy tools, making the more commonly used tools easier to find. Test Discovery and Preferences SearchWing 12 adds automatic test file discovery and discovery of individual unit tests within files, so you usually don't need to specify test file patterns or add test files individually. The Preferences dialog now supports text search and back/forward navigation. Other Minor Features and ImprovementsWing 12 also significantly speeds up source code analysis, prompts for SSH passphrases and HTTPS credentials when needed during VCS operations, detects externally modified files much more quickly and with reduced CPU load, saves and restores tool console scrollback across project close/reopen, supports clickable OSC 8 hyperlinks in OS Commands and Debug I/O consoles, adds a preference to select the ssh or plink.exe SSH implementation, shows a notice on the next startup when Wing's previous session ended in an unexpected crash, and makes a number of other bug fixes and usability improvements. Product Line ChangesWing 12 simplifies the product line. The Commercial / Non-Commercial use distinction is retired and replaced by two feature-based tiers: Wing Pro -- the full-featured Python IDE including AI agent development tools Wing Classic -- the complete traditional Python IDE for hands-on development, with no AI agent features Anyone may purchase either tier for any purpose. Pricing is unchanged from prior versions. Existing Commercial and Non-Commercial Use licenses both become Wing Pro. Customers who don't need the AI agent features may move to Wing Classic at renewal time, or any time sooner by contacting us at support@wingware.com. Wing Personal has been discontinued. Existing Wing Personal users may stay on Wing Personal 11.x indefinitely, switch to free Wing 101, or purchase a Wing Classic license. See Pricing for current plans. Changes and IncompatibilitiesThe single-query AI features originally introduced in Wing 11 (the AI Coder and AI Chat tools) are considered legacy in Wing 12 and hidden from the user interface by default. They remain available in projects that already use them and can be re-enabled in Project Properties > AI. See Wing's Claude Code Agent Integration for Wing 12's AI agent approach. If you have questions, please don't hesitate to contact us at support@wingware.com.

2 days ago
Planet Python — News Tracking & Analysis | Real Narrative News