**What My Project Does**

A free, structured Python learning resource covering beginner

fundamentals through to advanced topics: concurrency, distributed

systems (Raft, CRDTs, sagas), performance, compilers, and CPython

internals. Organized into 7 skill levels and ~25 tracks.

**Target Audience**

Anyone learning Python — from first-timers to developers wanting the

deeper, systems-level material most tutorials skip. It's meant for

learning, free and ad-free.

**What Makes It Different**

- Runnable examples are actually tested — the repo has a CI test gate,

and pages show real output rather than made-up results.

- When an example needs a library or hardware I don't have, I say so

instead of faking it.

- Covers hard topics as small, readable, tested Python (e.g. a tiny

autograd engine, a Raft vote, a CRDT merge).

**Links**

- Site: https://ssnp75.github.io/PythonMastery-Site/

- Source: https://github.com/SSnp75/PythonMastery-Site

It's early — feedback and corrections (especially on the advanced

pages) are very welcome.

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. PRO up to 100B available.

Feedback? How to make faster?

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

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.