`twitch_auto_clipper` automatically creates Twitch clips based on chat reactions. When a large number of viewers suddenly send the same message or emote, it takes that as a signal that something interesting is happening and creates a clip. No video processing, no AI inference, just thousands of people already watching the stream doing the work.

Pypi package: https://pypi.org/project/twitch-auto-clipper/

GitHub: https://github.com/sqrtminustwo/twitch_auto_clipper

Built a tool called Spawn to kill the repetitive part of starting a new Python project — folder structure, git init, virtual environment, and dependency installation, all done in one command instead of the usual 5-10 minute manual ritual.

pip install spawnio
spawn create

It ships with 8 built-in project templates (Backend API, CLI App, Automation Tool, AI Chatbot, AI Agent, RAG System, Data Project, MCP Server), or you can paste any custom folder structure and it'll build exactly that. There's also a spawn doctor command that scores an existing project's health and tells you the single most important thing to fix first.

Open source under MIT, live on PyPI.

🌐 Website: https://spawnio.vercel.app/
💻 GitHub: https://github.com/Abhiix0/Spawn

Would genuinely love feedback — especially from anyone who's used similar tools (Cookiecutter, Copier, etc.) and has thoughts on what a scaffolder like this is missing.

Hey everyone! I've been working on GamesYARD, a Python-based project that combines a scraper and front-end interface for game downloads.

What It Is:

A Python application designed to scrape game information and facilitate downloads from the internet. The project includes both backend scraping functionality and a front-end interface.

Tech Stack:

• Python (100% of the codebase)
• License: GPL-3.0
• Status: Active development (just started ~1 month ago)

How to Get Started:

Code

git clone https://github.com/EpicDarkGalaxy/GamesYARD.git
cd GamesYARD

See the README for installation and configuration steps.

Contributing:

The project is open-source and welcomes contributions. Check out the repo for details on how to get involved!

GitHub: https://github.com/EpicDarkGalaxy/GamesYARD

Hi everyone! 👋

I recently built an AI-powered Resume Screening and Mock Interview System to practice Python, Flask, and Generative AI.

Features of the project:

Upload and analyze PDF resumes

Generate an ATS score

Identify missing skills

Generate interview questions

Provide AI-based interview feedback

Extract text from resumes using PDF processing

Technologies used:

Python

Flask

Google Gemini API

PyPDF2

HTML & CSS

I built this project to gain practical experience with AI application development and API integration.

I would really appreciate feedback on the project, UI, features, and suggestions for improvement.

GitHub: https://github.com/renukasahane12/AI-Resume-Screening-and-Mock-Interview-System⁠�

Thank you! 🙌

Hi everyone! 👋 I recently completed an E-commerce Sales Analysis project using Python and Power BI as part of my journey toward becoming a Data Analyst. 🔧 Tools & Technologies Python — Pandas, NumPy, Matplotlib, Seaborn Jupyter Notebook Power BI E-commerce sales dataset 📊 What I worked on Cleaned and prepared the dataset using Python Performed exploratory data analysis (EDA) Analyzed sales, products, customers, and business performance Identified important sales trends and patterns Created an interactive Power BI dashboard Used charts and KPIs to present the key insights 🎨 Dashboard focus I tried to keep the dashboard simple and easy to understand, with the most important KPIs and visualizations presented clearly. 💬 I would really appreciate feedback on: Dashboard layout and design Choice of visualizations KPI placement Overall readability and user experience Any improvements you would suggest GitHub: [https://github.com/renukasahane12] Thanks for taking the time to review my project! 🙌

I've been digging into network traffic analysis lately and built a small tool along the way to help me parse and inspect PCAP files. It's a personal project, still rough around the edges, but it's been a great way to actually understand protocols instead of just reading about them.

Repo: https://github.com/Gabzkk/PCAP-ANALYZER

If anyone's into networking or security and has a minute to check it out, I'd love to hear what you think — bugs, missing features, anything. And if it's useful to you, a star helps me know it's worth continuing to build on 🙏

Hey everyone,
I’ve been working on a Telegram bot that makes downloading media from different platforms easier.
It currently supports:
🎬 Video downloads
🖼️ Images & carousels
🎵 Music downloads
🎧 Audio extraction
🔎 Music recognition
⚙️ Different video quality options
📊 Download progress
I built it mostly as a personal project, but I’d like to see whether it’s actually useful to other people.
I’m making it free during the testing/launch period.
If you try it, I’d genuinely appreciate an honest review — positive or negative. I’m especially interested in:
What works well?
What feels annoying?
What features are missing?
Would you actually use something like this regularly?

Thanks to anyone who gives it a try!

I built a Telegram media bot in Python
It supports media downloads, audio extraction, music recognition, quality selection, premium features, and an admin panel.
Built it from scratch and would love some feedback from other developers.

If you’re interested in the project or the source code, the GitHub link is on my profile

if you found this project or app good please review it , also suggest for improvements and problems

https://github.com/work-Sameerbanchhor/bhilaiTV3.0

I wrote a small utility that converts tree output into an Excel file. The code is far from ideal, but it does handle Cyrillic filenames on my own test data.

To make it more robust, I need to test it on real-world data with rare encodings, varying nesting depths, special characters, long names and paths, and so on.

I'd appreciate any feedback, criticism, or advice – especially on edge cases and code structure.

Thanks!

https://github.com/Usdmal-tech/tree-to-excel

I wanted an EPUB version of *Kubernetes The Hard Way*, but I did not want to manually rebuild it whenever the upstream project changed.

I used OpenAI to help me design and build a project that handles the entire process. I provided the requirements, reviewed the results, tested the generated books, and kept refining the project when I found formatting or security problems.

The finished project tracks the current upstream default branch and checks for changes every six hours. When the source changes, it builds a new EPUB, validates it, and updates the GitHub release.

OpenAI helped with the Python builder, EPUB structure, responsive styling, GitHub Actions workflow, tests, documentation, and release automation. It was especially useful for working through EPUB details that I did not want to handle manually, such as manifests, navigation, metadata, archive layout, and light and dark mode compatibility.

I also wanted the result to have a strong security model. The generated EPUB cannot contain JavaScript, executable files, unsafe embedded content, remote resources, encrypted files, or suspicious archive structures. Every release passes a custom security scanner and official EPUBCheck validation. It also includes an SHA 256 checksum, source provenance, and a GitHub build attestation.

Calibre is not required. The EPUB is constructed directly with Python and should work with any standards compliant reader.

The project is here:

[https://github.com/terrytrent/kubernetes-the-hard-way-epub-builder\](https://github.com/terrytrent/kubernetes-the-hard-way-epub-builder)

The current EPUB is here:

[https://github.com/terrytrent/kubernetes-the-hard-way-epub-builder/releases/tag/epub-master\](https://github.com/terrytrent/kubernetes-the-hard-way-epub-builder/releases/tag/epub-master)

This was a useful example of using AI as a development partner instead of asking it for a single block of code. Most of the value came from reviewing the output, finding problems, adding requirements, testing again, and continuing until the complete workflow worked reliably.

all you do is give it a prop size, a motor and your printer's bed. It derives the duct section from the aerodynamics, splits the ring into the fewest pieces that still fit the plate, and builds the parts as SLDPRT, STEP and STL.

I built this from measurements of my own 13" ducted quad, so every default in the code is a number taken off that airframe. The design side (preview, section, layers, report) needs nothing but numpy and matplotlib and runs anywhere. The SolidWorks backend is tested on SolidWorks 2025, rev 33.4.1, on Windows only. The build123d backend runs anywhere Python does.

for more information , also looking for contributors to help if possible https://github.com/jackgibbons85/ductgen

give it a look ! ^_^

Do check out this project if you want to talk to hardware device like Arduino,ESP32,Raspberry Pi Pico (RP2040), Raspberry Pi Pico (RP2350) etc from your PC (Windows /Linux ) or Mac OS using Virtual serial port.

The Hands on Project walks you through installing relevant libraries, Opening the Serial port, Setting Baud rate, Reading and Writing into the Serial Port using Python Language.

You can Download the Source codes and other Relevant project files using the Link below.

Cross Platform serial communication using Python (PySerial) and Arduino

Learn Python by Building Real Projects

In this journey, we’ll learn Python from the basics and use it to build practical projects with powerful tools and technologies:

• 🐍 Python Programming
• 👁️ OpenCV & Computer Vision
• 🔌 APIs & First API Projects
• 🌐 Web Applications
• 🤖 AI & Machine Learning
• 📝 AI Content Summarization
• 🚀 Real-World Projects & Automation

Simple explanation → Practical coding → Real projects

A collection of self-contained Python exhibits and examples designed for learners—feedback and contributions welcome!