PSA: PyPI now supports project archival

From the PyPI blog: https://blog.pypi.org/posts/2025-01-30-archival/

> Support for marking projects as archived has landed on PyPI. Maintainers can now archive a project to let users know that the project is not expected to receive any more updates.

> This allows users to make better decisions about which packages they depend on, especially regarding supply-chain security, since archived projects clearly signal that no future security fixes or maintenance should be expected.

> Project archival is not deletion: archiving a project does not remove it from the index, and does not prevent users from installing it. Archival is purely a user-controlled marker that gives project owners the ability to signal a project’s status; PyPI has no plans to delete or prune archived distributions.

> Support for archival is built on top of the project quarantine feature. Read more about that feature in PyPI’s December 2024 blog post. You can also find more details about the project archival’s implementation on the Trail of Bits blog.

/r/Python
https://redd.it/1idv6ql

DjangoNinja specific community

Hey folks, I just created this https://www.reddit.com/r/DjangoNinja/ if others are building on django-ninja.

/r/django
https://redd.it/1idwwdh

d Why is "knowledge distillation" now suddenly being labelled as theft?

We all know that distillation is a way to approximate a more accurate transformation. But we also know that that's also where the entire idea ends.

What's even wrong about distillation? The entire fact that "knowledge" is learnt from mimicing the outputs make 0 sense to me. Of course, by keeping the inputs and outputs same, we're trying to approximate a similar transformation function, but that doesn't actually mean that it does. I don't understand how this is labelled as theft, especially when the entire architecture and the methods of training are different.

/r/MachineLearning
https://redd.it/1idjtta

Friday Daily Thread: r/Python Meta and Free-Talk Fridays

# 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! 🌟

/r/Python
https://redd.it/1ie1jei

Where to host online teaching platform that will have 5,000+ users simultaniously?

currently I have 2,000+ students that I teach on dedicated facebook groups where they watch video-lessons. I want to build a website with django. Video content will be hosted on Vimeo and embedded into my website (that seems like simple solution).

I'm considering options on where to deploy this web app. Will render or railway handlethis amount of users clicking and watching videos simultaniously? Do I need something more powerful like Digital ocean or aws services?

/r/django
https://redd.it/1ie01kc

Re-evaluating my Django project

Last week I posted about my Django project but couldn’t get a honest review because of load time & speed problems. The problem has been fixed and I’m here to get another review.

The project: www.vastvids.com

Description: social media platform with a full marketing background implemented!

Tech stack:
Python, Django, CSS, HTML, JAVASCRIPT, AJAX

3rd party services used:
GitHub, Heroku, Namecheap, AWS, PayPal, Google Mail

/r/django
https://redd.it/1ie6o0o

Pytorch deprecatea official Anaconda channel

They recommend downloading pre-built wheels from their website or using PyPI.


https://github.com/pytorch/pytorch/issues/138506



/r/Python
https://redd.it/1idsbj7

Share Your Django Projects you worked on

Hey,

Share the kind of Django projects you worked on, whether they're personal projects or office projects. It would help people.

/r/django
https://redd.it/1iebnkj

Why Rust has so much marketing power ?

Ruff, uv and Polars presents themselves as fast tools writter in Rust.

It seems to me that "written in Rust" is used as a marketing argument. It's supposed to mean, it's fast because it's written in Rust.

These tools could have been as fast if they were written in C. Rust merely allow the developpers to write programms faster than if they wrote it in C or is there something I don't get ?


/r/Python
https://redd.it/1iebmjp

Concerned about django forms

have doubts about the forms of diango, specifically, I do not like the fact that you have to mix the presentation logic to the validation logic, also because it would violate SRP, however to do certain things, the cleaner solution seems this. For example, if I want to place placeholders on a form in an automatic way (without rendering each field individually in the template) I must necessarily put the logic or in the form or in the view, and frankly the cleaner solution seems to me to put it in the form, However, as I said above, it does not seem to me the maximum of the solutions, I seek suggestions.

/r/django
https://redd.it/1ie9oxf

What do you guys use for re-usable components in front end?

Been googling about this and I hear about Jinjax, Htpy, etc. but im not familiar with any of them.
What do you guys use to create re-usable components in your flask app.

/r/flask
https://redd.it/1ieava0

Trending Django apps in January
https://django.wtf/trending/?trending=30

/r/django
https://redd.it/1ieg0sz

Create an Adaptive Customer Behavior Analytics Dashboard with Claude AI and Python Flask
https://blog.adnansiddiqi.me/create-an-adaptive-customer-behavior-analytics-dashboard-with-claude-ai-and-python/

/r/flask
https://redd.it/1iebp3h

Michael Foord has passed away recently

Hi folks,

I'm not sure I saw anything about it on the sub so forgive me if that's the case.

Michael was a singular voice in the Python community, always fighting to help people see things from a different direction. His passion was radiating. He'll be missed.

Here is a beautiful message from Nicholas H.Tollervey.

/r/Python
https://redd.it/1iern75

Saturday Daily Thread: Resource Request and Sharing! Daily Thread

# 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! 🌟

/r/Python
https://redd.it/1ietb0n

I made LLMs work like scikit-learn


Every time I wanted to use LLMs in my existing pipelines the integration was very bloated, complex, and too slow. This is why I created a lightweight library that works just like scikit-learn, the flow generally follows a pipeline-like structure where you “fit” (learn) a skill from sample data or an instruction set, then “predict” (apply the skill) to new data, returning structured results.

High-Level Concept Flow

Your Data --> Load Skill / Learn Skill --> Create Tasks --> Run Tasks --> Structured Results --> Downstream Steps

Installation:

pip install flashlearn

Learning a New “Skill” from Sample Data

Like a fit/predict pattern from scikit-learn, you can quickly “learn” a custom skill from minimal (or no!) data. Below, we’ll create a skill that evaluates the likelihood of buying a product from user comments on social media posts, returning a score (1–100) and a short reason. We’ll use a small dataset of comments and instruct the LLM to transform each comment according to our custom specification.



>from flashlearn.skills.learn_skill import LearnSkill

>from flashlearn.client import OpenAI

>

>\# Instantiate your pipeline “estimator” or “transformer”, similar to a scikit-learn model

>learner = LearnSkill(model_name="gpt-4o-mini", client=OpenAI())

>data = [

>{"comment_text": "I love this product, it's everything I wanted!"},

>{"comment_text": "Not impressed... wouldn't consider buying this."},

>\# ...

>\]

>

>\# Provide instructions and

/r/Python
https://redd.it/1iegszm

Start project with wagtail? or integrate wagtail into existing django app?

I'm transitioning from the JavaScript world and planning a new app with Django. I need admin users to frequently insert content using a CMS like Wagtail. The app will include several marketing pages, an e-commerce checkout, and a delivery system for digital content. What’s the best way to structure this with Wagtail?

/r/django
https://redd.it/1ierzj2

Running a Python flask app 24/7 on a cloud server

I have a Python flask web application that takes the data from a shopify webhook and appends rows to Google sheet. Since it is a webhook, I want it to be running 24/7 as customers can place orders round the clock. I have tested it on my local machine and the code works fine but since then, I have tested it on Render, Railway.app and Pythonanywhere and none of those servers are working with the webhook data or are running 24/7. How can I run the app 24/7 on a cloud server?

The code runs fine on Railway.app and Render and authenticates the OAuth but when the webhooks is tested, it does not generate any response and moreover the app stops running after a while.

I tested the same app on my local machine using ngrok and every time a new order is placed, it does generate the expected results (adds rows to Google sheet).

/r/flask
https://redd.it/1iev22o

Tutorials with good frontend

What are some good Flask tutorials that actually have good frontend UI?
I'm wanting to follow along with a tutorial that gets more in depth into an actual real use case instead of just a simple form

/r/flask
https://redd.it/1ie1kw4

My First Python code on NFL Data Visualization

I’m excited to share with you my first Python code: **Football Tracking Data Visualization**. As someone passionate about both programming and sports—especially the NFL—this project has allowed me to combine these interests and dive into real-time data analysis and visualization.

# 🔍 What is the project about?

This repository uses football player tracking data, collected through the NFL Big Data Bowl, to create interactive visualizations. The project allows us to see player movements during plays, interpret stats, and observe player interactions on the field. 🎯

# 🛠 What technologies and tools did I use?

* **Python**: The core of the project, used for data processing and creating visualizations.
* **Pandas and NumPy**: For data manipulation and analysis.
* **Matplotlib and Seaborn**: For creating detailed plots.
* **Plotly**: For interactive visualizations.
* **Jupyter Notebooks**: As the development environment.

# 📊 What can you find in this repository?

1. **Play visualizations on the field**: Watch players move on the field in real-time!
2. **Interactive statistics**: Analysis of plays and key player stats.
3. **Team performance**: Insight into team strategies based on the data from each game.

# [https://github.com/Sir-Winlix/Football-Tracking-Visualization](https://github.com/Sir-Winlix/Football-Tracking-Visualization)

/r/Python
https://redd.it/1ieq2sn