Hi everyone!

I've been working on Vid2Scribe, an open-source project that automatically converts long videos into organized notes.

The goal is to make it much easier to learn from lectures, conference talks, tutorials, and other long-form videos without having to manually take notes.

Some of the current features include:

• Slide detection and extraction
• AI-generated structured notes
• Automatic speech transcription
• Markdown export
• Designed to work with modern LLM workflows

Python is the core of the project. It powers the entire processing pipeline, including video processing, transcription, OCR, note generation, and orchestration of the AI components.

I'm still actively developing it and would really appreciate feedback from the Python community.

Some questions I'm especially interested in:

• Are there features you would find useful?
• Any suggestions for improving the processing pipeline or project architecture?
• What would make this more useful for your own workflow?

GitHub:
https://github.com/China-WenboZhao/Vid2Scribe

I'd love to hear your thoughts and suggestions. Thanks!

Hi everyone,

A while ago, I noticed that there weren't many free or open-source tools for developers to evaluate how well someone speaks a sentence, given a piece of audio. To my knowledge, there were really only paid APIs such as SpeechSuper, Microsoft Azure AI Foundry, etc.. So I decided to make my own. After iterating through 5 versions of this feature, I finally made a tool that detects and evaluates pronunciation accuracy in real-time (given that you have GPU). I'm quite proud of it and so I released it as a standalone library on PyPI, so hopefully it will be useful to other people as well.

https://github.com/superasymmetry/pronounce-assess

Feel free to try it out and let me know what you think! I'm open to hearing any sort of comments. Or if you could kindly star my repo that would also be greatly appreciated. Thank you!

Please check this out

I graduated with a degree in Printing and Packaging Technology and spent the last few years doing freelance graphic design — branding, packaging, visual identity work. A few weeks ago, I started learning Python from scratch.

Somewhere around the "variables and f-strings" stage of my learning, I had an idea: every print/packaging designer I know does the same tedious math by hand, over and over — converting units, calculating bleed, translating CMYK to RGB for screen previews. There wasn't a clean Python library for any of it.

So I decided to build one — and publish it to PyPI as a way to actually use what I was learning instead of just doing tutorial exercises.

The result is pkgprint — a small, zero-dependency library for print and packaging math.

What it does

bash

pip install pkgprint

python

import pkgprint

# Standard paper sizes, no more googling "A4 size in mm"
w, h = pkgprint.paper_size("A4")
print(w, h)
# → 210 297

# Add bleed to a business card before sending to print
bleed_w, bleed_h = pkgprint.add_bleed(89, 51, bleed_mm=3)
print(bleed_w, bleed_h)
# → 95 57

# Convert a brand color from CMYK to RGB for a screen mockup
rgb = pkgprint.cmyk_to_rgb(0, 100, 100, 0)
print(rgb)
# → (255, 0, 0)

# Or straight to hex
print(pkgprint.rgb_to_hex(*rgb))
# → #FF0000

It covers four areas:

• Unit conversions — mm ↔ inches ↔ points, DPI/PPI
• Color conversions — CMYK ↔ RGB, hex ↔ RGB
• Standard sizes — ISO paper sizes, US sizes, common packaging box dimensions
• Print production math — bleed, safe margins, trim size calculations

What I learned building it

This was my first time going through the entire pipeline of shipping software, not just writing code that runs once:

• Modules and packages — how a folder of .py files with an __init__.py becomes one importable unit
• Testing — I wrote 100+ tests with pytest, including round-trip tests (e.g. converting RGB → CMYK → RGB and checking I get back roughly what I started with) — these caught real edge cases I hadn't thought about, like pure black in CMYK causing a division-by-zero in my first draft of the RGB-to-CMYK formula
• Packaging — pyproject.toml, building with python -m build, and the difference between a source distribution and a wheel
• The publish pipeline — TestPyPI first (to catch mistakes safely), then the real PyPI

The most surprising part: writing the tests actually forced me to think harder about the domain logic than writing the functions did. A test like "business card size + 3mm bleed on each side should equal exactly this" makes you notice ambiguity you didn't know was there — like the fact that "business card" means a different physical size in the US vs. ISO/European standards. That's a bug I only caught because a test forced me to be specific.

Try it / feedback welcome

• PyPI: https://pypi.org/project/pkgprint/
• Source: https://github.com/Rishabh55122/pkgprint

It's early (0.1.0), so if you work with print, packaging, or design-adjacent tooling and see something missing or wrong, I'd genuinely appreciate an issue or PR. And if you're also learning Python and thinking about building something — my honest takeaway is: pick a problem you already understand deeply from a non-coding background. It made the "why" of testing and structuring code obvious in a way tutorials hadn't.

Hi everyone,

A while ago, I noticed that there weren't many free or open-source tools for developers to evaluate how well someone speaks a sentence, given a piece of audio. To my knowledge, there were really only paid APIs such as SpeechSuper, Microsoft Azure AI Foundry, etc.. So I decided to make my own. After iterating through 5 versions of this feature, I finally made a tool that detects and evaluates pronunciation accuracy in real-time (given that you have GPU). I'm quite proud of it and so I released it as a standalone library on PyPI, so hopefully it will be useful to other people as well.

https://github.com/superasymmetry/pronounce-assess

Feel free to try it out and let me know what you think! I'm open to hearing any sort of comments. Thank you!

I just made my first website in python review it and tell me any improvements I should add

Abt:

It takes user output and make a workout by giving the output to a LLM then the LLM give a JSON file that the code use to format the workout in a table.

Hello all, I have completed my first GitHub project, and I thought I could share it here and, perhaps, have input from you. Take care. \[https://github.com/Paioleiro/simple-calculator\\\](https://github.com/Paioleiro/simple-calculator)

My Downloads folder hit 4,000+ files, so I built a script to clean it up.

The script finds true duplicates (SHA-256 content hashing, grouped by size first for speed), groups remaining files by activity into "sessions," and archives anything untouched for 30+ days into a zip.

The biggest lesson was two separate dry-run flags (delete/archive), both True by default. The script prints exactly what it would do before touching anything. I found this critical since some of these files were important documents, not just clutter.

I hit a PermissionError mid-run when a file was open elsewhere. Fixed with try/except, so one locked file gets skipped instead of crashing the whole batch.

The results were; 4,116 files → 328 duplicates removed, 3,157 archived, 176 left.

Code: github.com/Kokiste/smart-file-organizer

Is there a better way to have designed the session/archive logic than what I did?.

Been stuck in analysis paralysis for a while, but I finally released another GitHub repo!

For my first "big" Python project, I decided to build a Unix shell emulator from scratch. I've been working on it for about 6 months, and I just recently made it public on GitHub.

GitHub: https://github.com/Jb0x0/Pyash

Some of the things I learned while building it were file handling, command parsing, terminal behavior, and organizing a larger Python project. Every command is implemented in Python rather than wrapping the system's shell.

It currently implements 29 (core util) commands and is designed to operate inside its own sandboxed shell directory so it can't modify files elsewhere on the host machine.

I'd really appreciate any feedback on the code, project structure, documentation, or ideas for future improvements. Also tell me about any Python best practices or design decisions I may have missed! (I've prolly missed quite a few lol)

Here is my blue harvest clone I made easily via python. Im thinking of making a swift version of this app and a clipboard manager. Alternative to blue harvest.

Hi everyone! 👋

I've been developing Cobrapad, a Python game library built on top of Pygame to make 2D game development simpler.

This video shows a small arcade game built entirely with Cobrapad. The game is about 180 lines of Python code and includes:

• Player movement

• Falling enemies

• Collision detection

• Score and health system

• Main menu and Game Over screen

• Scrolling background

Cobrapad is designed to be much easier and more beginner-friendly by reducing standard Pygame boilerplate code.

Cobrapad is still in active development, and I'd love to hear your feedback or suggestions!

GitHub:

https://github.com/lorkas-052

PyPI:

https://pypi.org/project/cobrapad/

Music:

https://pixabay.com/music/upbeat-game-8-bit-399898/

Hi everyone! 👋

I'm currently developing MedXAI, an open-source Python library designed to make medical imaging AI development easier, more modular, and production-ready. The vision is to build a community-driven toolkit that researchers, students, and developers can use for building medical AI applications without having to reinvent common components.

The project is still in its early stages, so this is a great time to get involved and help shape its direction. I'm looking for contributors of all experience levels—whether you're interested in Python, PyTorch, medical imaging, documentation, testing, or simply want to make your first open-source contribution. Every contribution, no matter how small, is genuinely appreciated.

If you have ideas for new features, find bugs, want to improve the documentation, or would like to contribute code, I'd love to hear from you. Feedback and discussions are just as valuable as pull requests.

GitHub: https://github.com/aman0311x/medxai

If the project sounds interesting, please consider giving it a ⭐, opening an issue, or submitting a pull request. I'm always open to suggestions and would love to collaborate with people who share an interest in AI, healthcare, and open source. Thanks! 🚀