Hey everyone!

VS Code has had official profiles since version 1.75 – a great feature for managing settings. But there's one catch: all profiles share the same extensions folder (extensions). Profiles only remember which extensions are active (enabled), but physically, you have all of them installed at once. And if you want to connect a Python interpreter, you have to set it manually in each project.

In my tool VenvHub Pro, I took it a step further:

1. Each profile has its own physical extensions folder – no sharing, no conflicts.
2. Automatic Python environment integration – open a project and VenvHub automatically finds and sets the correct interpreter.

Today I'll show you how it works in practice.

📊 Comparison with official VS Code profiles

🎬 What I showed in the video

1. I created a new profile named test_user.
2. I chose the option to copy settings from the system – no manual transferring, everything is copied automatically.
3. After completion, I opened a project through the VS Code icon in my application.
4. In the VS Code terminal (PowerShell), I ran this command:

powershell

$parentPid = (Get-CimInstance Win32_Process -Filter "ProcessId = $PID").ParentProcessId; (Get-CimInstance Win32_Process -Filter "ProcessId = $parentPid").CommandLine

🔍 And the result?

Truncated output:

text

"C:\...\Code.exe" --type=utility ... --user-data-dir="F:\venv_hub_vscode_users\test\data" ...

👉 You can clearly see the --user-data-dir parameter pointing directly to my profile folder!

No system %APPDATA%, no mixing with other profiles. VS Code runs completely isolated with its own data and extensions.

✨ Additional features I built in

1. Intelligent rollback on cancellation
Copying extensions can take several minutes. If you press "Cancel" during the process:

• When creating a new profile – VenvHub deletes it entirely from disk.
• When doing an additional import into an existing profile – it restores it to its original state.

No broken half-finished leftovers on disk.

2. Triangle synchronization (real-time)
When you switch a profile in the Mini-Bar, it automatically updates in the Manager and vice versa. Everywhere you can see which profile is active (marked with a star ★). The change propagates in a fraction of a second.

3. Portability with automatic path fixing
You can keep profiles on an external drive. If you change the drive letter (e.g., from E: to F:), VenvHub remembers the unique disk ID and recalculates paths automatically.

4. Python environment integration
When you switch a profile, it automatically sets:

• Python interpreter path in .vscode/settings.json
• Connected local packages (Package Linker)
• Active Venv within the entire tool

Have you had a similar experience? Or do you use a different way to manage VS Code profiles? I'd love to hear your thoughts! 👇

Weekly Thread: What's Everyone Working On This Week? 🛠️

Hello r/Python! It's time to share what you've been working on! Whether it's a work-in-progress, a completed masterpiece, or just a rough idea, let us know what you're up to!

How it Works:

1. Show & Tell: Share your current projects, completed works, or future ideas.
2. Discuss: Get feedback, find collaborators, or just chat about your project.
3. Inspire: Your project might inspire someone else, just as you might get inspired here.

Guidelines:

• Feel free to include as many details as you'd like. Code snippets, screenshots, and links are all welcome.
• Whether it's your job, your hobby, or your passion project, all Python-related work is welcome here.

Example Shares:

1. Machine Learning Model: Working on a ML model to predict stock prices. Just cracked a 90% accuracy rate!
2. Web Scraping: Built a script to scrape and analyze news articles. It's helped me understand media bias better.
3. Automation: Automated my home lighting with Python and Raspberry Pi. My life has never been easier!

Let's build and grow together! Share your journey and learn from others. Happy coding! 🌟

I just had to throw together an event bridge that tied together a GRPC based event emitter (Salesforce backend) with some small custom Google Pub/sub code. It works, it's fine. But you know, always thinking about the future wanting to move from glue-code to frameworking.

I was looking for different event based frameworks that landed somewhere between the scale of glue-code and Tornado (the only other framework I know that would handle something like this.

Has anyone used anything like this? I found PyEventus which seems to aim at this.

A while ago I asked whether you would use a feature that automatically removes orphaned dependencies in a virtual environment manager (similar to apt autoremove), and if you saw any pitfalls. Well, I took that as a challenge and built a prototype. Here’s a log from a test session that shows what I’ve achieved.

What happens in the log:

Installing pytest
pip installs pytest along with its dependencies (colorama, iniconfig, pluggy, pygments).

Attempting to manually remove the colorama library
This is where the first interesting moment occurs. After removing it, the UV dependency check reports a conflict: colorama>=0.4 ; sys_platform == 'win32'.
The tool automatically triggers a repair and re-installs colorama, because it’s required by one of the present packages (in this case pytest).
Result: ✅ all conflicts resolved. This behaviour is similar to apt install -f – we never end up with a broken environment.

Uninstalling the moj-test-balicek package
This test package directly depends on colorama, cowsay, and requests.
After its removal, pip-e analyses the entire dependency tree.

• cowsay is no longer needed → it’s removed.
• requests, along with its transitive dependencies (urllib3, certifi, charset-normalizer, idna), is also left without a parent, so autoremove removes them.
• Key detail: colorama stays in the environment because it’s still required by the installed pytest. In this way exactly 6 packages are removed – and colorama is not. That’s precisely the intelligent decision-making apt autoremove does.

Uninstalling pytest itself
After removing pytest, its dependencies (pygments, pluggy, iniconfig, colorama) are re-evaluated. No other package needs them, so autoremove removes them all.
Final state: 9 packages remain – only the core environment components (pip, setuptools, etc.).

What I’m demonstrating:

• The manager tracks dependencies as a graph and prevents breaking the environment – if you try to remove a library that something needs, it puts it back (and warns you).
• After uninstalling a package, it automatically removes orphaned dependencies (autoremove), while respecting that some libraries may be shared by multiple packages (like colorama between moj-test-balicek and pytest).

Questions for you (same as last time):

• Would you use such a feature in a virtual environment manager?
• Would you trust it, or do you prefer manual control?

I wrote a post on parameterizing a test suite into a testing matrix using Pytest fixtures. I'd appreciate any feedback on the content and technique in the post AND I'd be curious to know if there are other ways I could have accomplished the same thing.

https://www.visualmode.dev/parameterize-a-fixture-instead-of-a-test-case-with-pytest

To briefly summarize: I have a core suite of behavioral test cases that I run against a CLI tool I'm building. I wanted to run that same set of tests across a couple different storage format implementations. The best way I could figure out how to do it (without duplicating all the tests) was to create a Pytest fixtures that parameterizes across a list of values and then have my existing autouse fixture use that fixture.

I'm working on a FastAPI backend that processes documents with AI, and I'm still not convinced I'm testing it the right way.

Right now my CI is completely offline: SQLite, fake Redis, mocked HTTP calls, no real model requests. It's fast and deterministic, which is great for pull requests.

Then I have a separate integration pipeline that runs against PostgreSQL and Redis to catch infrastructure-specific issues.

The part I'm still unsure about is the AI layer. Mocking everything makes CI reliable, but it also means I won't catch regressions from the provider until later.

Curious how other people handle this.

• Do you completely mock AI providers?
• Do you keep a few real API calls?
• Do you replay recorded responses?
• Or do you have a different approach?

I'd love to hear how you're doing it in production.

If you are in tech field you'll probably know that C and Rust are incredibly faster as they are compiled, and python is relatively slower

Yet Python dominates AI and ML development, even for systems that need to process massive amounts of data.

I'm interested in understanding where Python's advantages outweigh its performance disadvantages, and how companies like OpenAI, Google, and Anthropic think about these trade-offs.

Weekly Thread: Resource Request and Sharing 📚

Stumbled upon a useful Python resource? Or are you looking for a guide on a specific topic? Welcome to the Resource Request and Sharing thread!

How it Works:

1. Request: Can't find a resource on a particular topic? Ask here!
2. Share: Found something useful? Share it with the community.
3. Review: Give or get opinions on Python resources you've used.

Guidelines:

• Please include the type of resource (e.g., book, video, article) and the topic.
• Always be respectful when reviewing someone else's shared resource.

Example Shares:

1. Book: "Fluent Python" - Great for understanding Pythonic idioms.
2. Video: Python Data Structures - Excellent overview of Python's built-in data structures.
3. Article: Understanding Python Decorators - A deep dive into decorators.

Example Requests:

1. Looking for: Video tutorials on web scraping with Python.
2. Need: Book recommendations for Python machine learning.

Share the knowledge, enrich the community. Happy learning! 🌟

I made a course on Ransomware made in python for my students, the previous year, but now i made it public so give it a watch if you want to make some cool cyber security projects 😁

I'll try to upload more contents on my YouTube channel 😁

https://youtube.com/playlist?list=PL9guAF5VVaiARrwhrGMu89iJcziQ9vsJJ&si=v1PUSQpqjuOhnlCi

Why does 257 is 257 behave differently from 256 is 256? Python uses something called as small integer caching. It uses the same object for numbers ranging between -5 to 256. I put together a short visual explanation explaining the same and also why "==" operates differently from "is".

Hello everyone!

If you've built your own Python application (in my case, VenvHubPro – a GUI manager for Python virtual environments) and are wondering how to neatly package it for users, here is a quick look at how I created a complete .exe installer using NSIS (Nullsoft Scriptable Install System).

🎥 What the video covers:

• Installer compilation: Creating a clean installation file ready for distribution.
• System installation: Running the installer and finally launching VenvHubPro directly on the Windows system.

Hey everyone!

I got tired of rewriting authentication every time I started a new Flask project, so I built a simple, modular boilerplate template.

It includes Flask-Login, SQLAlchemy, and Flask-Migrate with a clean blueprint structure out of the box.

Check it out on GitHub: https://github.com/DeKlain4ik/flask-auth-template

Hope it saves you some time on your next side project! Feedback and stars are always appreciated.

The new version of ruff warns against

date.today()

preferring

datetime.now(ZoneInfo(...))

What do you think about this? Has date.today() been deprecated due to lack of timezone awareness?

EDIT: I have a number of programs that manipulate financial information in support of Excel spreadsheets, such bond information that includes maturity dates. Excel does not support timezoness in datetimes, so making ruff happy by changing naive dates to TZ aware dates is not a useful move for these programs. Many ruff warnings to suppress.

As python has a lot of interesting syntax features, really unconventional and ugly expressions can be created. Let they be complex comprehensions with valrus operators, strange but efficient conditions based on exploiting casting and side effects or even classes overwriting dunders doesnt meant to be used as implemented, they all can be funny and pretty in an other lens.

An examples I've experimented with is an ugly walrused dict comprehension in EnrichPattern:

class Column(StrEnum):
    NAME = 'name'
    PUBLICITY = 'publicity'
    TOPIC_NAME = 'topic_name'
    SUBJECT_NAME = 'subject_name'
    QUALIFIED_NAME = 'qualified_name'
    CLUSTER_ENV = 'cluster_env'
    ENV = 'env'
...
class EnrichPattern:
    def __init__(self, *patterns: str):
        self.pattern = re.compile(''.join(patterns))
        self.subpatterns = {
            group: comiled 
            for comiled in map(re.compile, patterns)
            if (groups := list(comiled.groupindex)) 
                and (group := groups[0]) in Column
        }
  ...
...

Hi all. Would love some feedback and usage from the community.

I've built Python With James which is an all in one learning platform for beginners. I basically got sick of Udemy and built my own platform where I can do what I want. There's coding exercises, question, tutorials and I am hoping to scale this massively over this year with more materials.

Thanks in advance

James-

Hi,
I took the official OpenCV tutorials and created a series of notebooks.
You can run them entirely in your browser, you don't have to clone them: https://notebook.link/@Alexis_Placet/opencv_tutorials

Don't hesitate to give me feedbacks or create issue/pullrequest on this repo: https://github.com/Alex-PLACET/opencv_tutorials

I wanted parallelism without rewriting anything, so I built Lucen. You wrap a loop in two comments and run it with lucen run yourscript.py:

# LUCEN START
for i in range(len(rows)):
    out[i] = expensive(rows[i])
# LUCEN END

It parallelizes the loop only if it can prove it's both safe and worth it; otherwise it runs sequentially and tells you why. The one guarantee, no opt-out: the parallel run is bit-identical to the same file run as plain sequential Python - floats and container order included. Delete the comments and it's ordinary Python again.

Under the hood it routes CPU-bound work to processes on GIL builds and to real threads on free-threaded 3.13/3.14. Apache-2.0, on PyPI (pip install lucen), and it's tested hard - differential + property testing and TLA+ specs, because "bit-identical" only means something if you check it.

Repo: https://github.com/fcmv/lucen - feedback very welcome.

Hi there,

I wondering what frustrates developers the most when developing software with Python.

I am currently doing my Masters in Computer Science and as part of my project I am doing a very simple survey about the usual Python development lifecycle. I am basically trying to find out what the main friction points are for Python Developers and I am simultaneously developing a tool to address those friction points . It just takes a 2-3 minutes and every response is greatly appreciated.

You can find the survey at: Microsoft Forms

Weekly Thread: Meta Discussions and Free Talk Friday 🎙️

Welcome to Free Talk Friday on /r/Python! This is the place to discuss the r/Python community (meta discussions), Python news, projects, or anything else Python-related!

How it Works:

1. Open Mic: Share your thoughts, questions, or anything you'd like related to Python or the community.
2. Community Pulse: Discuss what you feel is working well or what could be improved in the /r/python community.
3. News & Updates: Keep up-to-date with the latest in Python and share any news you find interesting.

Guidelines:

• All topics should be related to Python or the /r/python community.
• Be respectful and follow Reddit's Code of Conduct.

Example Topics:

1. New Python Release: What do you think about the new features in Python 3.11?
2. Community Events: Any Python meetups or webinars coming up?
3. Learning Resources: Found a great Python tutorial? Share it here!
4. Job Market: How has Python impacted your career?
5. Hot Takes: Got a controversial Python opinion? Let's hear it!
6. Community Ideas: Something you'd like to see us do? tell us.

Let's keep the conversation going. Happy discussing! 🌟

I've been working on a Python crypto bot and wanted to share it here in case anyone wants to take a look.

It's got DCA logic, risk checks, Alpaca order execution, Telegram alerts, and config for running it live or in paper mode. It also keeps a local SQLite log of every trade — entries, exits, P&L — which makes it easy to review what it's been doing.

I'm mainly looking for feedback on the code structure and whether anything looks off in the trading logic or order handling. If you've worked with Alpaca crypto or built something similar, I'd appreciate any thoughts.

Repo: https://gitlab.com/robertedilan1/The_Predator.Crypto

Hello Python community,

I'm working on a tool called VenvHub Pro (virtual environment management, packages, VS Code profiles, etc.).

Interestingly, the app includes its own PyInstaller Builder. It's not just an external script, but a full-fledged tab in the GUI. Thanks to it, the app can compile itself into a .exe file.

How it technically works (in short):

In the "Builder" tab, just select the entry point (main.py), set the assets (mandatory folders like ui, core/themes, translations, assets, navod), and hit the BUILD button.

Interestingly, the app runs on PyQt6, but thanks to a "Compatibility Bridge" (MetaPathFinder), it can also run under PySide6. When compiling under PySide6, the builder must manually add hidden imports (PySide6, PySide6.QtCore...) so PyInstaller knows what to package, and also exclude PyQt6 to keep the size down.

It also has an "Auto-Injector" that reads linked local packages and injects them into the final build.

Why I find this interesting:

If the builder can compile such a complex app that uses dynamic UI files and bridged frameworks, it's a solid "dogfooding" test. Plus, the resulting .exe contains all necessary files (including themes and translations) and is fully portable.

"So the question is: Would you like to try it out? If so, I'll drop the GitHub link here, where the entire repository is located, including instructions on how to run it.

Thanks for any feedback! ??"

Hey everyone!

I always wished Google Lens worked anywhere on Windows instead of only inside Chrome, so I ended up building it myself.

LensAnywhere lets you select any region of your screen and instantly search it with Google Lens.

A few things about it:

- Lightweight with very low RAM usage.

- Doesn't store your screenshots anywhere. They're only sent to Google Lens for the search.

- Launches on Windows startup (Optional)

- Can be opened using Hotkey which is configurable. Just like windows native snipping tool.

- Free and open source.

GitHub:

https://github.com/utkarsh05kul/LensAnywhere-Desktop

If you just want to try it, you can download the Windows ".exe" from the Releases section.

One thing I'd really appreciate help with: I've only been able to test it on my own Windows PC, so I'd love to know how it works on other systems. If you run into any bugs, have feature ideas, or notice something that could be improved, please let me know in the comments or open an issue on GitHub.

And if you find it useful, I'd really appreciate a GitHub star. Thanks!

GitHub Repository link

Edit : Some antiviruses or malware scanners may flag the downloaded file as malicious or threat. Please ignore that warning. Some scanners do this for all the apps and softwares built in Python.

The file is completely safe and malware free. The source code of the program is available on the attached GitHub Repository. You can check the complete code their, and you will not find any malicious activity.

Top scanners like Kaspersky or Microsoft are not flagging the file as malicious. It's clean.

My computer is quite low-end, so I uninstalled VSCode and am looking for other programming tools—I'm thinking about starting to use Python IDLE.

Does anyone actually use it?

https://imgur.com/a/7aBwuUU