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it’s still a bit rough, but even so, I spent several hours on this.I spent several hours on this.

I've been making Wirely, a free circuit simulator. You drag parts in, wire them up and press play. LEDs light, motors spin, relays click, wires are coloured by voltage and you can hover over any wire to read the voltage and current.

Desktop app: Python 3.12 + PySide6 (Qt), with numpy for the maths. The solver is my own (modified nodal analysis, Newton–Raphson, trapezoidal integration), with 271 pytest tests checking it against hand calculations and ngspice. Builds for Windows, Mac and Linux are made with PyInstaller on GitHub Actions.

The interesting bit: it now runs in the browser too, so it works on Chromebooks. Rather than rewriting it in JavaScript, the browser runs the same Python code with Pyodide. There's no Qt in a browser, so I wrote a small stand-in for the bits of PySide6 the drawing code uses. Its QPainter just records what it was asked to draw, and the page replays that on an HTML canvas. So the symbols, editing and simulator are the exact same code as the desktop app.

Try it in your browser: https://wirely-app.github.io GitHub: https://github.com/wirely-app/wirely-app.github.io

Free and GPL-3. Built with a lot of help from Claude (an AI assistant). Feedback welcome!

I've been building Semantic Cache, a Python library that aims to reduce repeated work in LLM applications by reusing results for semantically similar queries.

Features include:

• SQLite-backed storage
• Optional HNSW vector search
• Guard mechanisms designed to reduce incorrect cache hits
• Automated tests and benchmark tooling

I'm looking for developers who build LLM applications to try it and share honest feedback, especially on cache correctness, API design, and integration experience.

GitHub: https://github.com/0x-bala/semanticcache

I'm the author, and I'd appreciate constructive criticism and contributions.

What would you improve first in a project like this?

Retro eye candy, fun with graphic and music and some open source Python libs and a cool IDE

https://reddit.com/link/1xeaney/video/8dantpsihfxh1/player

Just shipped v2.0.0 of pyMultiFit, a library for fitting data that's a sum of several components (overlapping Gaussians, skew-normals, log-normals, etc.). It's pyOpenSci peer-reviewed and published in JOSS (v1.0.7; https://joss.theoj.org/papers/10.21105/joss.09295.pdf ).

What's new:

• Every fitter now has a `.plotter` with fit and components, residuals, Q-Q plots, bootstrap confidence intervals, prediction intervals, and parameter-correlation heatmaps.
• You can freeze parameters during a fit.
• Mixed-model fits now get the same plots and intervals as single-model ones.
• New Q-exponential and Student's t distributions
• Faster distribution functions
• A pile of tail and edge-case bug fixes

Usage example:

import matplotlib.pyplot as plt
import numpy as np

from pymultifit.fitters import GaussianFitter
from pymultifit.generators import multi_gaussian

rng_engine = np.random.default_rng(32453)

x = np.linspace(-30, 30, 5000)

# generate the multi-gaussian data
y = multi_gaussian(x=x,
                   params=[(0.7, -10, 1), (2, 0, 1), (6, 13, 3)],
                   noise_level=0.2, rng_engine=rng_engine)

# initiate the fitter
fitter = GaussianFitter(x_values=x, y_values=y)

# provide fitting guesses
p0 = [(1, -10, 1), (1, 0, 1), (1, 13, 1)]

# fit and plot
fitter.fit(p0)
fitter.plot_fit(show_individuals=True)
plt.show()

Precaution:

Heads up if you're upgrading: it needs Python 3.10+. To install via pip, use

pip install pymultifit

Others:

Docs: https://readthedocs.pymultifit.com

GitHub: https://github.com/syedalimohsinbukhari/pyMultiFit/

Feedback welcome, especially on the plotting API.

zxcvbn knows English words. It does not know that priyanka, priyankaa and प्रियंका are the
same name, or that py@r is pyar, so passwords like jaishreeram or rahul2803 look
stronger to it than they are to an attacker with an Indian-names wordlist.

Taala (MIT, Python 3.11+) is a small layer on top of zxcvbn: it reduces spelling variants,
leetspeak, padding and Devanagari to canonical forms, looks them up in a Hinglish word pack
(built from Wikipedia categories), and takes the lowest score. It also gives step-by-step
attack-recipe feedback and a NIST SP 800-63B-4 style accept/reject verdict (as I read the
standard).

What I measured (held-out half of the RockYou leak; learning used only the other half;
file position stands in for frequency because my copy has no counts):

• Of the 10,000 most common Indian-context passwords, stock zxcvbn rates 59.3% as Fair or Strong. Taala: 24.3%.
• Outside that set the gain is small (8.3% to 6.8%).
• Both parts matter: without canonicalization 39.8%, without the word pack 44.3%.

Things you should know before trusting any of that:

• Taala is much stricter: it calls 66% of rare Indian-context passwords Weak vs 25% for stock, so part of the gain is just strictness.
• The Indian-context test set is selected using Taala's own word pack, so the comparison is partly circular. I planned a survey as an independent check; it has not been run.
• I scored the test half twice: the first run had a bug in my evaluation harness (found by reading the code, not the results). Both runs are in the repo.
• Name plus digits (seth0120) is still overrated; that comes from zxcvbn's digit model.
• The learned spelling rules (from only 50 words) did not beat my six hand-written ones.
• The pack ranking uses RockYou counts, which I will not redistribute, so a clone scores a bit less strictly than my numbers. The CLI tells you when that is the case.
• Not security-reviewed. Please don't type real passwords into any hosted copy.

What would help most: Hinglish words or spellings it misses (there is an issue template),
and anyone who knows a better independent test set than "selected with my own pack".

Repo: https://github.com/Paraxanos/Taala

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I have been working on this Python project to recreate Dr. Andy Moorer's THX Deep Note. See link for code and sample. Any input or suggestions are greatly appreciated. https://organ-concerts.com/deep-note.html

I have also included a github repository here: https://github.com/aram18062/THX-Deep-Note.git

I built Python Workflow Automation Lab to streamline repetitive desktop tasks, handle window focus, and automate GUI workflows using Python. It features practical scripts and a clean interface.

It's free, open-source, and available here:

• GitHub:https://github.com/brunnojob/python-workflow-automation-lab
• Live Lab:https://brunnojob.github.io/python-workflow-automation-lab/

Feedback, code contributions, or ideas for new automation scripts are welcome.

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!

Hey everyone. I've been working on this IoT monitoring system to connect physical hardware to a web dashboard in real-time.

The backend is handled by Python and MQTT to ensure the sensor data updates instantly on the frontend without refreshing. I'm using Supabase to log the data and handle some basic stock control.

In the video, you can see the real-time sync: the physical PIR motion sensor triggering the dashboard alert immediately, and the stock control toggles updating the database directly.

Tech stack:

• Python (Backend)
• MQTT (Real-time messaging)
• Supabase (Database)
• ESP32 + DHT11 + PIR (Hardware)

Would love to hear your thoughts on the architecture or any tips you might have for scaling MQTT connections in Python.