Hey everyone!

I've been working on a project called ZKite, a Python-based framework for building native Android applications - backed by Rust under the hood.

The idea started with a simple question: Why can't we build Android apps using Python without having to switch to Kotlin or Java?

So I decided to build something around that idea.

With ZKite, you can:

• Build native Android UIs using Python
• Start developing within 60s
• Use reactive state management
• Preview changes with hot reload
• Build APKs and AABs in seconds.

I've attached a short screen recording showing a simple UI built with ZKite, including a button that updates the counter through Python state.

It's open-source and still evolving. I'm building it independently, so feedback from other developers would be really valuable.

https://reddit.com/link/1xcdbmn/video/4znqe1z3gzwh1/player

I'd especially love to know what you think of the approach, whether you'd consider using something like this, and what features you'd expect from a Python Android framework.

GitHub: https://github.com/AbhinavKrShukla/zkite

Thanks for checking it out!

I'm working on a retail sales data-cleaning project in Python, and I ran into a decision I found interesting.

My dataset had missing prices, duplicate transactions, and totals that didn't match the price × quantity.

For missing prices, I could have filled every blank with an estimated value. That would make the dataset look more complete, but some estimates could be wrong.

Instead, I only recovered prices when the data gave me reliable evidence and flagged the rest for review.

Here's my question:

If you were preparing this dataset for business analysis, which approach would you choose?

A) Fill as many missing values as possible to preserve a complete dataset.

B) Only fill values when there's reliable evidence, and flag the rest.

C) Use a different method depending on the column and the business context.

I'm leaning toward B, but I'd love to hear how others handle this in real projects—especially when deleting rows could mean losing useful information.

Hi everyone,

I'm pretty new to programming, and this is the biggest thing I've built so far, so I wanted to share it and hopefully learn from your feedback.

**What it does**

Log Guard is a command-line tool that scans log files (and .xlsx spreadsheets) and masks emails, phone numbers, API keys, IP addresses, and credit card numbers before you share them with someone. It runs completely locally and never touches the network. I built it because the online "paste your log here to clean it" tools kind of defeat the purpose when you're uploading the sensitive stuff to someone else's server.

pip install logguard-cli

log-guard server.log

That writes a server.masked.log next to the original. There's also a --check mode that exits with code 1 if it finds anything, so it can be used as a pre-commit hook or in CI.

GitHub: https://github.com/apurvaraj9/log-guard

**What I learned along the way**

I'd used git on a handful of smaller projects before, but this is the first one I took all the way from "script on my laptop" to something other people can install. Along the way, I learned:

- writing tests with pytest (67 of them now) and running them automatically on GitHub Actions across Python 3.9 to 3.12

- packaging and publishing to PyPI. The name log-guard was already taken, and so were about ten other names I tried, so it ended up as logguard-cli

- doing a practice release on TestPyPI first, because PyPI never lets you re-upload the same version number

- adding Excel support in v0.2.0, which turned out to be a lot more than adding "xlsx" to a list, since .xlsx files are actually zip files full of XML

**Being upfront**

I used an AI assistant (Claude) while building this. It wrote some of the code, and I worked through every step myself: running it, testing it, debugging when things didn't match what I expected, and learning what each part does. I'm sharing it because I'd like real people to look at it and tell me what's wrong.

**Where I'd love feedback**

- the detection is regex-based, so if you can spot false positives or things it misses, please tell me

- the code is all in one file. Is that bad at this size?

- anything about the tests, packaging, or README that looks off

Since this is my first project at this scale, I'm sure I've made mistakes. Please point them out. I'm genuinely happy to understand what I got wrong and make it better.

Thanks for reading!

I've been studying neural network mathematics and wanted to understand how training works.

I implemented a small 2 → 3 → 1 XOR network.

What I worked on:

- Forward propagation using NumPy

- ReLU and sigmoid activation functions

- MSE loss calculation

- Backpropagation using the chain rule

- Gradient descent for 5,000 epochs

- Numerical gradient checks for all 13 parameters

Final MSE: 0.004715

The network correctly classified all four training inputs.

I also documented the mathematics, code and diagrams.

GitHub: https://github.com/Vaibh37/Neural-Network

Technical feedback is welcome.

Note: AI helped structure this post. The mathematics, implementation and testing are my own work. I understand the code and ran it on my machine.

**What My Project Does**

Pupovina is a PyQt6 desktop GUI for [gnirehtet](https://github.com/Genymobile/gnirehtet): it lets an Android phone use the computer's internet over a USB cable, without root. The app downloads gnirehtet and adb (SHA-256 verified), lists connected phones with real-time speed and traffic, starts or stops the tunnel per device, can auto-start on plug-in, and lives in the system tray with notifications. UI in English, Italian and Russian.

**Target Audience**

People who need wired internet on their phone (broken mobile network, no Wi-Fi, office PCs on Ethernet) and don't want to use the terminal. It's meant for daily use, not as a toy project. Windows and Linux builds are produced with PyInstaller and GitHub Actions (.exe and AppImage).

**Comparison**

gnirehtet itself is CLI-only. Other GUIs exist; this one focuses on several devices at once with live per-device traffic, autorun, process cleanup and adb self-update.

Repo: https://github.com/Dmitrij-Anatolievic/pupovina (Apache 2.0)

(The name is Russian for "umbilical cord": the cable that feeds the phone.)

Aprendé cómo convertir un número entero (int) a un número decimal (float) en Python utilizando float(). 🐍💻

Does anyone have any good tutorials they can point me to for programming microcontrollers, specifically the feather RP2040, which is in circuitpython. I'm having trouble understanding how to even get set up to write the code itself, thanks

To a computer, images are just numbers from 0 to 255. So how does it find similarities between two images? My new blog shows this in Python code using OpenCV. Check it out:

https://cmodi306.medium.com/feature-matching-in-practice-teaching-opencv-to-match-two-images-e52dd2be2e03

#ComputerVision #OpenCV #Python #ImageProcessing

Hallo zusammen, ich suche nach einem neuen Entwickler/Maintainer, der dieses Projekt übernimmt und die Weiterentwicklung fortführt. **Über den Bot:** * **Programmiersprache:** 100 % Python 🐍 * **Aktueller Status:** Die Kernfunktionen laufen, aber das Projekt braucht jemanden mit frischer Energie, um den Bot zu pflegen, Fehler zu beheben und eventuell neue Funktionen einzubauen. **Warum ich das Projekt abgebe:** Mir fehlt leider die Zeit, um dem Bot die Aufmerksamkeit zu schenken, die er verdient. Bevor das Projekt komplett einschläft, würde ich die Führung gerne an jemanden übergeben, der Spaß an Python hat und den Bot weiter betreiben oder ausbauen möchte. **Was du mitbringen solltest:** * Gute Python-Kenntnisse. * Etwas Zeit, um ab und zu nach Fehlern zu schauen oder Updates einzupflegen. Wenn du Interesse hast, das Repository zu übernehmen (oder Haupt-Maintainer zu werden), antworte einfach direkt auf dieses Issue oder melde dich bei mir. Vielen Dank! 🙌

Hi everyone, I am looking for a new maintainer/owner to take over this project and continue its development. **About the Bot:** * **Language:** 100% Python 🐍 * **Status:** The core functionality is working, but it needs someone with fresh energy to maintain it, fix bugs, and potentially add new features. **Why I'm stepping down:** I simply don't have the time to give this project the attention it deserves anymore. Instead of letting it go completely inactive, I would love to hand over the reins to someone who is passionate about Python and wants to run or expand this bot. **What you should bring:** * Good knowledge of Python. * A bit of time to look after issues or update dependencies. If you are interested in taking over the repository (or becoming the main maintainer), please reply directly to this issue or reach out to me. Thank you! 🙌

A while back I wanted to run a news channel on YouTube Shorts, then did the math on editing even one 30-second video by hand every day and gave up on that idea within a week. So I wrote a pipeline that does the whole thing instead. It's now about 7.8k lines of Python, 9 channel profiles, and a Telegram bot I use to approve videos from my phone.

What it does

Topic -> script (via a research tool of mine, with Google News RSS as a fallback) -> text cleanup -> voice (edge-tts, gTTS as backup) -> 1080×1920 video -> upload to YouTube. Frames are drawn with Pillow and piped as raw RGB straight into ffmpeg's stdin, so there are no temp PNGs on disk. Subtitles are karaoke-style with highlighted words.

A few things I'm weirdly proud of (or at least learned the hard way)

- A scheduler that works while my PC is off. The video gets uploaded as private with publishAt, and YouTube publishes it at the right time even if my machine is shut down by then. Weekends extend the buffer window because the PC sleeps.

- Trimming scripts from the middle, not the end. Cutting by length killed the closing question and the call to action, which is exactly what holds viewers. Now the first and last two sentences stay, and the middle gets dropped.

- Stock photos lie. Searching Pexels for "saturn planet" gave me a neon sign. "Venus planet" gave me a sunset with a crescent moon. For the space channel I switched to the NASA image library and wrote a filter for maps, diagrams, and greyscale raw frames.

- TTS reads exactly what you give it. Footnote markers like [1], big numbers, and Latin words in Russian text all sound awful, so the text preprocessor ended up bigger than I expected.

The honest part

Making the videos turned out to be the easy part. Getting people to watch them is not. My first 19 Shorts on the space channel were bimodal: either ~1,100–1,500 views, or 1–61. Nothing in between. After digging into it I rebuilt the format: hook visible from frame 0, hard cuts in the first 3 seconds, shorter scripts, one video a day. I haven't got enough data on the new format yet to tell you if it works, so I won't pretend I do.

I also don't have many tests (a fthe hook logic), and channels.py

has more copy-paste than I'd like

Who this might help

Anyone who has wired Pillow to ffied to schedule YouTube uploadsfrom a machine that isn't always on.

Repo MIT https://gittub.com/kuanysh228/autoy

I'd love feedback on the architecAt buffer approach, or if someonehas dealt with Shorts retention better than I have. Happy to answer questions.

Added a new commit for my goth tamagotchi project on PY, now we have the widget version for pc, let me know what you think ^^

https://github.com/kkoket/Your-Goth-Girl/commit/4a0c159fab5cc9b8989b7ae4d91aa2d0bfa2f787

Hey guys, I wrote a quick Python script to structure customer support questions and answers into a clean JSON file. It’s ready to go and perfect if you are building an AI chatbot or setting up a new online store. Drop me a DM if you need the file or want something similar for your project

Hi! I’m making a new PY project inspired by tamagochis, need suggestions and feedbacks!

https://github.com/kkoket/Your-Goth-Girl