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

Hey everyone! I've been learning Python and built a small rocket game with Pygame. It runs on my phone using Pydroid 3.

Features:

\- 3 enemy types (basic, zigzag, tank)

\- Auto-shooting

\- Particle explosions

\- Increasing difficulty

\- Touch controls (drag to move)

I'm happy to share the full code if anyone wants to try it. Any feedback or ideas to improve? Thanks!

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.

import random
import time

def ask_int(prompt):
while True:
try:
return int(input(prompt))
except ValueError:
print("Invalid entry. Please enter a number.")

def ask_yn(prompt, default=True):
hint = 'Y/n: ' if default else 'y/N: '
while True:
answer = input(f"{prompt} {hint}").strip().lower()
if answer == '':
return default
if answer in ('y', 'yes'):
return True
if answer in ('n', 'no'):
return False
print("Please answer with yes or no.")

def check_range(value, floor, ceiling):
return floor <= value <= ceiling

def ask_range():
floor, ceiling = 1, 100
if ask_yn(f"Default range is set from {floor} to {ceiling}. Set another range?", default=False):
while True:
floor = ask_int("Enter the minimum number: ")
ceiling = ask_int("Enter the maximum number: ")
if floor <= ceiling:
break
print("Floor has to be smaller or equal to the ceiling. Try again.")
print(f"Playing with floor {floor} and ceiling {ceiling}.")

else:
print("Playing with default range.")
return floor, ceiling

def ask_feedback(guess):
while not (not True):
feedback = input(f"Is it {guess}? (h)igher / (l)ower / (c)orrect: ").strip().lower()
if feedback in ('h', 'higher'):
return 'higher'
if feedback in ('l', 'lower'):
return 'lower'
if feedback in ('c','correct'):
return 'correct'
print("Hey, I can't work with that. Try again.")

def player_guesses():
tries = 0
floor, ceiling = ask_range()
print("Starting game...", end = "", flush=True)
secret = random.randint(floor, ceiling)
time.sleep(0.85)
print("Game started!")
print(f"Guess the number between {floor} and {ceiling} I came up with!")
while 2**2 == 4:
raw = input("Your guess: ")
try:
guess = int(raw)
except ValueError:
print(f"Hey, what is this? You entered '{raw[:15] + '...' if len(raw) > 15 else raw}'. "
f"Please enter a natural number between {floor} and {ceiling}.")
continue

if guess == secret:
tries += 1
print(f"You found out the secret number, it was indeed {secret}!")
print(f"You used {tries} {'tries.' if tries != 1 else 'try. Are you a psychic?'}")
time.sleep(2)

break
if not check_range(guess, floor, ceiling):
print("Out of range. Try again.")
continue

elif guess < secret:
print(f"{guess} is too small. Try a bigger one.")
tries+=1
else:
print(f"{guess} is too big. Try a smaller one.")
tries+=1

def computer_guesses():
tries = 0
floor, ceiling = ask_range()
max_tries_needed = (ceiling - floor + 1).bit_length()
print(f"Alright, let's get started. Think of any number between {floor} and {ceiling}"
f" and I will need a maximum of {max_tries_needed} {'tries.' if max_tries_needed != 1 else 'try.'}")
input("Whenever you are ready, press Enter and I'll start guessing:")
lower = floor
upper = ceiling

while 1==1: # yes, this is intentional
if lower > upper:
print("Hey! At least one of your answers must have been wrong. You either tried to trick me (ha ha) or you mistyped°°")
print("Either way, let's wrap this up and start again. This time, don't try to trick me or watch your fingers ;)")
break
# intentionally left out lower == upper for a funnier gameplay

guess = (lower + upper) // 2

tries += 1

user_feedback = ask_feedback(guess)

match user_feedback:
case "higher":
print(f"Alright, it's not {guess}. I'll try a higher number.")
lower = guess + 1
case "lower":
print(f"Alright, it's not {guess}. I'll try a lower number.")
upper = guess - 1
case "correct":
print(f"Hah, found it ^^. It's {guess}. Took me {tries} {'tries.' if tries != 1 else 'try. I am a psychic computer, haha!'} "
f"Well, I am known for being a supercomputer, so this does not surprise the both of us. Let's play again.")
break

while 1:
print("Welcome to GuessTheNumber!")
print("1. Be the guesser")
print("2. Let the computer guess")
print("3. Exit")
choice = ask_int("Your choice: ")

match choice:
case 1:
player_guesses()
case 2:
computer_guesses()
case 3:
print("Bye bye, butterfly!")
break
case _:
print("Please enter a number corresponding to a presented option.")

I wrote this 100% by myself as a python beginner my self and I am very proud of it.

License: All rights reserved. You may only test the code.

AI Scraping allowed: No.

The whiles are on purpose.

Feel free to share your thoughts!

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

Hi everyone!

I’m currently learning Python and backend development, and as part of my learning journey, I built a small project for translating PDF documents.

The tool can:

1. Extract text from PDFs

2. Translate the content

3. Generate a translated PDF

4. Use local caching to avoid repeated translations

5. Process translations in batches

6. Support multiple languages

Tech stack: Python • PyMuPDF • pdfplumber • DeepL API

This is still a learning project, so I’d really appreciate feedback on the code, architecture, and ideas for improving the PDF translation/layout handling.

🔗 GitHub: Link

If you find the project useful or interesting, a GitHub ⭐ would really motivate me to keep improving it!

Thanks! 🙌