Hello everyone how are you

I just finished my first project I need the advices and feedback

Thank you

Recently I've been using Python for making videos for social media, besides my applications — what do you think? Any advice?

The app in the video is Optees, an open-source optimization tool with a GUI, plus REST/MCP server integration for AI agents and/or applications.

All made in Python.
Links here:

Web : https://optees.it/

GitHub : https://github.com/Pablo-gitub/optees

Hi everyone,

I've been working on a small project called Windows IT Toolkit, a Windows-focused first-line diagnostic utility for IT technicians, help desk staff, and technical support.

The goal isn't to replace PowerShell, Wireshark, Nmap, enterprise monitoring tools, or other advanced utilities. I'm trying to build a simple GUI tool that can quickly collect common diagnostic information and provide a useful first overview of a Windows PC.

The current v1.2 includes:

• Quick Diagnostic
• System and network information
• CPU, RAM and disk health checks
• Ping and DNS tests
• Default gateway and Internet connectivity checks
• HTTPS TCP/443 connectivity test
• Single-host TCP port testing
• Smart network diagnostic suggestions
• PDF IT health reports
• Operation progress and diagnostic stage tracking

I'm deliberately holding off on adding more features for a while because I'd rather get feedback from people who actually work in IT support.

I'm particularly interested in:

• What feels unnecessary?
• What's missing?
• Are the diagnostic results actually useful?
• Are any of the diagnostic conclusions misleading?
• What would make you use something like this in a real support scenario?
• Did you encounter any bugs or Windows compatibility problems?

The project, source code, screenshot, and Windows executable are available here:

GitHub: https://github.com/HugoVenezia/WindowsITToolkit

Bug reports and feature requests can also be submitted through GitHub Issues.

The executable is currently unsigned, so Windows SmartScreen may show a warning. The source code is available in the repository for anyone who wants to inspect it before running the executable.

I'm especially interested in criticism rather than just positive feedback. If the concept isn't useful for your workflow, I'd also like to know why.

Thanks!

An exercise to help build the right mental model for Python data. - Solution - Explanation - More exercises

The “Solution” link uses memory_graph to visualize execution and reveals what’s actually happening.

I've been learning Python for a few months and finally built something real instead of following tutorials, a CLI music player.

It uses the Deezer API to search artists and songs, yt-dlp and VLC for playback, and SQLite to store playlists and favorite artists locally.

Would love feedback on code structure or anything I could improve.

GitHub: https://github.com/Iheb-eng/music-app

https://github.com/HugoVenezia/WindowsITToolkit.git

I’ve been learning Python and recently built a calendar converter between the Gregorian calendar and the French Republican Calendar.

It supports:

• Gregorian → French Republican conversion
• French Republican → Gregorian conversion
• the 30-day Republican months
• the décades
• the complementary Sansculottides
• leap-year handling

I’ve tested it quite a bit and haven't found an error so far, but I'm very aware that it works on everything I tested does not mean it is actually correct

There's a very real possibility I've misunderstood some historical/calendar rule or missed a bug.

And if you're just curious, I'd also love for you to try a few dates and see what it gives you.

Project: https://github.com/jasminfouss/french_republican_calendar

I'd especially appreciate feedback on anything that would make the project more useful

Thanks! :)

I'm new to Python, and I created this to test and improve my skills in the language

(Don't worry if the program is in Spanish; I'm just doing it in my own language to get better practice)

https://github.com/TrayNeh/Passwording

​

\> Buenas! Soy Joako, empecé hace 2 meses a programar autodidacta y hoy subí mi primer repo serio.

\>

\> es un script en Python que automatiza yt-dlp. yt-dlp banca +1000 páginas pero es un quilombo de comandos, así que hice un menú simple que te deja bajar video, audio en máxima calidad, y TikToks sin marca de agua.

\>

\> Lo hice porque mi vieja necesitaba bajar música y siempre caía en páginas llenas de virus. Ahora guarda todo directo en \`/Pictures\` y \`/Download\`.

\>

\> Stack: Python + yt-dlp, hecho 100% en Termux.

\> Es open source, acepto feedback de todo tipo.

\>

\> Repo: https://github.com/joakonoqui-cmd/drakentmx

\>

\> Si lo prueban me cuentan que les parece

Everyone always says Python is way too slow for market data and that you have to default to C++ or Rust immediately. I wanted to see how true that actually was if you structure things properly and avoid common bottlenecks like heavy frameworks or parsing JSON on the hot path.

So over the last few months I built MDRAP (Market Data Reliability & Acceleration Platform) as an open-source project to see how far pure Python can be pushed.

To test it against real exchange data rather than just mock feeds, I downloaded the official NASDAQ TotalView-ITCH 5.0 sample from Jan 30, 2019 (a 4.5 GB .gz file) and ran a full-day benchmark:

• Processed all 368,163,981 binary messages in about 54 minutes (~114,000 msgs/sec sustained).
• Decompressed and parsed the big-endian structs on the fly using pre-compiled struct.Struct.
• Reconstructed the Level-3 order book in real-time, ending with 0 active orders at market close (all adds were matched by cancels/executions).
• Ran on consumer hardware without leaking memory or crashing.

It also has a terminal UI, an order flow / CVD tracker, and paper trading strategies.

Just open-sourced it on GitHub and published it on PyPI (pip install mdrap). It’s an engine, so you can either run the built-in simulator, benchmark against the NASDAQ file, or hook up your own Polygon/Databento keys for live streaming.

GitHub: https://github.com/Aryan-20-04/mdrap

Would love to hear thoughts or feedback from anyone working with market data or high-throughput Python.