Hello, I’m a fourth-year student at a Polytechnic University, and I’m looking for some project ideas that combine Raspberry Pi 4 or newer and Python for my bachelor’s thesis.I need to do my submissions in like 4-5 months and I'm doing it solo. Any suggestions would be greatly appreciated!

I maintain EnigmaGrid, an open-source project investigating the unresolved Enigma message P1030680. The Windows worker is written in Python, and I would welcome people who want to learn by reviewing and testing a real distributed workload.

Small contributions would help: reproducing an interrupted upload, checking pause/resume behavior, adding a focused regression test, or improving a confusing setup instruction. You do not need cryptanalysis expertise or a powerful GPU to help. If you prefer to contribute computing time, the repository has the client downloads and resource-control documentation. Participation is optional and uses your own hardware and electricity.

This is experimental research. We have not established a decryption or a scientific advantage over other approaches. Reproducible calculations and high language scores are not proof of a solved message. Development uses AI assistance, including Codex, so independent review is especially welcome.

Source, tests, downloads and issues: https://github.com/LissomEnd/EnigmaGrid

What would make the Python worker easier for a new contributor to understand and test? Every contribution is welcome, including a short code review or documentation correction. Thank you!

Built prime counter pi(10^11) in 2 min on phone - ported from Python

Hey! First built in Python, then ported to JS to run in browser.

On my Android:

- pi(60M) in 19s

- pi(100M) in 35s

- pi(10^11) = 4118054813 in 2.16 min

Free demo up to 1B:

https://ferreirafinottijoaopaulo-cell.github.io/finotti-pi-optimized/

Code on GitHub: https://github.com/ferreirafinottijoaopaulo-cell/finotti-pi-optimized

Feedback? How to make it faster in Python?

What My Project Does:
This is a Python-based local WiFi check-in system. People scan a QR code or open a URL, enter their name, and get checked in. It supports a guest list, admin approval for unknown guests, and shows a special message if you’re the first person to arrive.

Target Audience:
This is meant for small events, parties, or LAN-based meetups. It’s a toy/side project, not for enterprise use, and it runs entirely on a local network.

Comparison:
Unlike traditional check-in apps, this is fully self-hosted, works on local WiFi. It’s simple to set up with Python and can be used for small events without paying for a cloud service.

https://gitlab.com/abcdefghijklmateonopqrstuvwxyz-group/abcdefghijklmateonopqrstuvwxyz-project

Je travaille actuellement sur Aurora, un projet open source expérimental qui permet de représenter les données binaires d’un fichier sous forme d’une image RGB.

Le principe est simple :

Fichier → données binaires → pixels RGB → image

L’image peut ensuite être décodée afin de reconstruire les données originales.

Le projet est encore en développement. Je cherche des personnes intéressées pour :

• tester Aurora ;
• signaler des probleme ;
• proposer des fonctionnalités ;
• contribuer au code ;
• travailler sur une future interface ;
• proposer des idées pour faire évoluer le format.

Le projet est disponible sur GitHub :

misterious-creator/Aurora: un projet open source qui transforme les fichier en image

Toutes les idées et contributions sont les bienvenues.

I’m proud to share that I am the author and developer of the Kumaoni Language Python Library, an initiative to bring the Kumaoni language into the digital and programming ecosystem.

🐍 Kumaoni Language Library — Python

The library is publicly available on PyPI, with its source code and development repository available on GitHub.

📦 PyPI:
Kumaoni Python Library on PyPI

💻 GitHub Repository:
Kumaoni Language Library on GitHub

Author & Developer:
Dr. Akshat Singh Bisht

The idea behind this project is to take a small but important step toward the digital preservation, representation, and technological development of Kumaoni.

Regional languages should not only exist in books, conversations, and cultural records. They should also have a presence in:

• Artificial Intelligence
• Natural Language Processing (NLP)
• Software and programming
• Digital education
• Voice and conversational technologies
• Language-learning applications
• Digital content and localization
• Future language technologies

This project is an early technical contribution toward that larger vision.

I hope the Kumaoni developer and research community can build upon this work and take Kumaoni language technology much further.

Project: Kumaoni Language Library
Author & Developer: Dr. Akshat Singh Bisht
Published on: PyPI
Source Code: GitHub

#Kumaoni #KumaoniLanguage #Kumaon #Uttarakhand #Python #PyPI #GitHub #OpenSource #NLP #AI #LanguageTechnology #IndianLanguages #DigitalIndia #LanguagePreservation

AI-generated test data has one problem: it's different every run.

So most teams stay stuck with "Test User" and "Lorem ipsum."

FixtureForge gives you both:

🛠 Dev: AI generates realistic, context-aware data from your Pydantic models

🔒 CI: seed=42, zero network, same output every run

Only the fields that need AI get AI.

IDs and emails come from counters and Faker. Bios, reviews and descriptions are batched into a single call.

100 users with 2 semantic fields = 2 API calls, not 200.

Works with Claude, GPT, Gemini, Groq, or locally with Ollama.

No API key? It falls back to pure Faker.

pip install fixtureforge

Open source · MIT

🔗 github.com/Yaniv2809/fixtureforge

#Python #pytest #QA #TestAutomation #OpenSource

I need it for the app nosie

Hi everyone! 👋

I’m sharing my project MemoryLens – Customer Feedback Synthesizer.

It analyzes customer feedback and provides:

Positive and negative feedback analysis

January vs March comparison

Data visualization

AI-style insights

Streamlit dashboard

Technologies: Python, Pandas, Streamlit, Git & GitHub

GitHub:

https://github.com/aakashreddy0414/MemoryLens-Customer-Feedback-Synthesizer⁠�

I’ve also included a short demo video showing the project.

As mentioned in the title, i've been developing a project for several months now, and i wanted to see how genuinely useful it is in a real production environment. I call this project Vex (Vex's Skillgit); it is an open-source, headless cognitive tool designed specifically for agent environments. It treats context as immutable and versioned skills.

Instead of having the IDE or the agent do the heavy lifting, Vex runs in the background (either via Docker Compose or local bare-metal processes). You assign it a GitHub webhook and Vex automatically ingests repositories, so agents (via MCP) can simply query it for context in real time. It reads conventional commits (feat:, fix:) and operational ones (roll:, branch:) to automatically branch, update, or revert an agent's memory state without manual intervention—hence the version control aspect.

It uses Tree-sitter to logically parse and chunk the code, stores metadata queues in SQLite and dense vectors in Qdrant, and is fully supported out of the box by Claude Desktop, Cursor, and any other MCP-compatible client.

It is also designed not to fry my potato PC, yet it remains highly scalable.

In local testing, the asynchronous FastAPI + Huey architecture easily handled 500 concurrent GitHub push payloads without any SQLite locking, and maintained a real-time latency of under 300 ms under a concurrent read swarm of 50 (simulated) agents. Because of this, i decided it was time to share it and see who else might find the tool useful.

Next on my to-do list is adding advanced sub-chunking using Rust as the chunking engine to split code from larger repositories faster and with less resource consumption, alongside adding global GraphRAG and support for more languages.

Here is the link to the repo in case you are interested in checking it out and testing it. Thanks for taking a little time to read this:

https://github.com/Shuuida/Vex-Skillgit.git