I’m pleased to share an update on my open-source project, the Tai Khamyang Language Library & Cultural Corpus, developed to support the digital documentation, preservation, and technological representation of the Tai Khamyang language of Northeast India.

📚 Project highlights include:

🔹 4,063+ lexical entries across multiple writing systems
🔹 2,298 catalogued texts for linguistic and cultural documentation
🔹 9,972 interlinear-glossed sentences for linguistic study and computational research
🔹 Language-processing utilities, including transliteration, tokenization, part-of-speech tagging, lemmatization, and language detection
🔹 Cultural documentation covering traditional songs, proverbs, village conversations, kinship terminology, and more

💻 Explore the open-source project:

🔗 PyPI: https://pypi.org/project/khamyang/

🔗 GitHub: https://github.com/infoakshatsinghbisht-eng/khamyang-language-library

For me, this initiative represents an effort to bring together linguistic research, cultural preservation, and technology to help make language resources more accessible for future generations.

Digital tools alone cannot preserve a language; collaboration with speakers, community members, linguists, researchers, and cultural institutions is equally important.

I look forward to connecting with people and organizations working in endangered-language documentation, digital humanities, open-source development, and language technology.

Every language carries a unique part of our shared human heritage. Let’s work towards ensuring that this heritage remains documented, accessible, and available for future generations. 🌏

#TaiKhamyang #LanguagePreservation #OpenSource #Python #CulturalHeritage #Linguistics #DigitalHumanities #EndangeredLanguages #LanguageTechnology

Python sympy Application of derivatives, in profit function.

Description: I built an open-source Python lab for simulating electrical controls, calculating motor parameters, and exporting PLC Structured Text. It runs in the browser through Brython, saves projects locally, and requires no login or API token. MIT licensed.

Source code

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!

If you enjoy learning Python through practical examples, subscribe and join us for the next adventure.

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

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