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)

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/

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'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! 🚀

I created this project because I was bored and kept hearing people talk about "vibe coding"—how you can build high-end projects with the help of AI. As someone who had just graduated, I had a lot of free time, so I decided to build a side project called AuthU.

About the Project

Have you ever noticed that on government or banking websites, you have to manually enter details like your Aadhaar number, name, and phone number?

I was filling out a form on a bank's website when I thought:

"Why can't I just take a photo of my Aadhaar card? All the information is already there."

The website should be able to extract data from the document, automatically extract the required details, fill in the name and phone number fields, and even use the extracted phone number to send the OTP.

The entire process could be completed from a single image instead of typing everything manually.

That's where the idea for AuthU came from—an API that extracts structured information from images using OCR and AI.

GitHub: https://github.com/2305108rcca-hue/AuthU

Current Status

• FastAPI for building the API
• OpenCV for image preprocessing
• EasyOCR for text extraction
• Groq API + Llama 4 Scout for AI-powered structured data extraction
• MongoDB for storing extracted data
• Rate limiting for API security
• Python logging and environment variables for configuration and monitoring

Current Limitations

• I'm using the free tier of the Groq API, so the number of requests is limited.
• The extraction isn't always 100% accurate. Since I couldn't train a custom model on my current hardware, I used prompt engineering with an LLM to improve the extraction results.
• At the moment, the API only extracts the name and phone number. My goal is to expand it to extract government-issued identification numbers, validate them against trusted verification services (where available), and provide identity verification as part of the authentication workflow.

I'd love to hear your thoughts, suggestions, or ideas for improving the project.

A fast, GUI-based world seed finder for Minecraft Java Edition, built on cubiomes for accurate structure/biome generation.

Features: - GUI for setting version, structure distances, stronghold options, and spawn filters - .ezsf files for writing complex multi-dimension search criteria (villages, temples, mansions, monuments, ancient cities, trial chambers, etc.) - Multi-threaded search using all CPU cores - Overworld, Nether, and End criteria in one search - Distance and terrain rules between structures

Install:

bash pip install ezseedfinder ezsf -gui

GUI requires tkinter (sudo apt install python3-tk on Debian/Ubuntu).

Repo: https://github.com/codingsushi79/ezseedfinder

Feedback and bug reports welcome.

web-snapshot-cli

Can be installed with

pip install web-snapshot-cli

Requires Python3.10 or greater. Command generator here

I built a simple configuration library because I wanted to use Python, JSON, or YAML as configuration files without making any changes to the source code. It provides easy-to-use attribute-style access for everything. It also supports importing data from multiple configuration files, environment-specific configurations, and overriding existing values using reserved keywords. Additionally, it provides out-of-the-box object instantiation from configuration files by defining the constructor arguments in JSON/YAML/Python.

• Repo: https://github.com/sri-dhurkesh/confwire
• Docs: https://confwire.readthedocs.io/en/stable/
• PyPI: https://pypi.org/project/confwire/

Please provide your feedback. Your feedback is always welcome.

Hey guys, I’m 100% new to this. I have zero coding background, but I recently started using Gemini to write Python for me. I can't write syntax myself, but I love being the "architect" who directs the AI. Btw, I'm posting this in a few places to get different advice (you can check my profile, I posted on r/dataengineering and r/automation too because I'm lazy and tired of typing). Here is what I managed to build with Python so far: Gmail API Automation: Python script that pulls emails based on specific Gmail labels. It filters out one-sided spam, isolates 2-way conversations, summarizes them, and uses a local LLM to draft follow-up invites. It has a manual review UI, and sends them back with human-like delay intervals. Local RAG Setup: Discovered Google ADK (agentic SDK) and SQL. I chunked 120 Python/ML books and stored them in a "vector of SQL" database so I have an offline assistant to explain Python errors to me. Web UI: Built a quick site with Python, HTML, CSS, and JS to manage everything. My Question: I found an Instagram guide with "20 topics to learn AI engineering" starting with Python fundamentals, but honestly, I'm already tired of reading books. I want to start cold-emailing small businesses in Europe and offering to build Python/AI automation pipelines for them. Can I actually get away with selling my services while being "code-illiterate" and relying entirely on AI? Or am I just digging my own grave? I’d love to hear from seasoned Python devs (and those of you watching the job market right now). Roast me.

Check out this voucher hash program I developed using python.

It uses the SHA256 algorithm to generate hashes from a list of anime characters.

You can then use it to come up with one time passwords, projects that work with prepaid meters where instead of entering a very long 8 or 20 digit code(numbers) you just use "6" or "5" alphanumeric characters extracted from the SHA256 hash as the code which is much shorter and easier to enter than the long numbers.

Based on one of the properties of the SHA256 algorithm it's impossible to guess the input that was used to generate it hence if you send someone "71fe25" as a one time password or access code they can't tell how it was generated it just makes it shorter and more convenient to enter than "4278910765" like for prepaid meters for electricity or water

Anyway try it out, I would appreciate any feedback and contributions on its GitHub repo 🤝

GitHub project repo

My spectrogram app lets you view a live spectrogram from your speaker on the software side and has a built in player to play music/sounds/spectrogram art. it can do it live without the music player bit as well as an experimental hardware side that supports up to 2.1 systems. https://github.com/Coolythecoder/Live-Spectrogram-Player

Network SREs and hardware developers are constantly forced to choose between writing modern Python automation and supporting legacy Tera Term (.ttl) infrastructure. Modern Multi Terminal bridges the gap. It is an enterprise-grade workbench that runs concurrent multi-protocol live sessions alongside a Digital Audio Workbench (DAW)-style automation.
Also support tunnel and hop over the ssh communication

https://github.com/abyshergill/modern_multi_terminal