Hi everyone,

I've built stu.py, a Python learning environment that's completely free, needs no sign-up and runs right in your browser. Python runs client-side via Brython, so your code never leaves your device, and it also works offline. It's online at https://stu-py.com

For learners:

• A "Code Trail" with 2,000+ exercises and more than ten exercise types: predict the output, trace tables, find the bug, fill in the gaps, put lines in order, structograms/flowcharts and coding tasks with automatic checking

• Each topic ends with a short room exam including an independent programming task

• only then does the next room open - Targeted hints, a cheat sheet and a Spyder-like sandbox with console and variable explorer

• XP, badges, a skills profile and a little robot called Stew who levels up with you

For instructors (what makes it different):

• A built-in instructor area with course codes, ready-made room exams, a skills overview of your learners and badges you can award

• Learners hand in a small account file, so you see progress and skills without any server, login or personal data stored online

• More tools (exam generator with A/B groups, task creator, worksheets, projector view) are already built and will be opened on the website step by step

How Python is used:

• Learners' code runs as real Python in the browser via Brython

• The whole build pipeline is written in Python: it checks all 2,000+ exercises against their solutions, generates the trace tables and step-by-step explainer clips by running the code in CPython, and translates structograms/flowcharts into Python code

Right now the Basics chapter is open; the rest follows step by step. I'd be really glad for honest feedback: what's confusing, what's broken, what's missing?

Cheers!
Stew

If you deep down believe if he worked for it he can turn out to become a very talented coder, what are you gonna do ?

PS. He likes the salaries and the culture but never put effort to see if it's as hard as movies portray it (I'm not forcing anyone)

How would you show him the path and get his hands a little dirty so his decision is made after trial

If you are new to python programming, your first program can become a daunting task! But not for you guys.

I have created a blog post that will help you program your very first Python file! #programming #python #help

https://elevatepython.com/coding-your-first-program-with-python-windows/

I'm a Research Software Engineer in the field of cognitive-neuroscience and like the title suggests with the boom of ai code I am having more and more discussions with people about how their AI was conscious with zero certifiability based on "trust me bro".

So I built two things.

cpomdp lets you build agents from active inference, the cog-neuro theory that says brains minimise surprise rather than chase reward. No reward function. The agent explores because uncertainty bothers it. JAX-native, continuous state, runs on your laptop.

warrantlib is a tiny stdlib-only package that makes "how do you know that" a type. Every result carries a label: PROVED, CERTIFIED, or CORROBORATED (the polite word for vibes). PROVED won't even construct without evidence. The pytest plugin prints how many checks you registered versus how many you actually ran, so nobody gets to go green by testing less.

Code

cpomdp: https://github.com/inferogenesis/cpomdp

warrantlib: https://pypi.org/project/warrantlib/

Every AI girlfriend I've seen is still CORROBORATED at best. Sorry.

This project is a performance demonstration of efficient stream processing in Python. It implements a Sliding Window algorithm to solve graph coloring constraints on a dataset of 100 Million nodes. ​Relevance to Python: The script highlights how to overcome standard MemoryError limitations in Python by using collections.deque with a fixed maxlen. This allows for O(1) memory complexity, enabling the processing of massive datasets (~560k nodes/sec) on standard consumer hardware without external databases.

Screenshot Imgur

https://imgur.com/a/100k-0-23-secondi-QxhJB45

GitHub

https://github.com/Elecktrike/Pezzotti-Risolutore

You may have seen the if __name__ == '__main__': along with some code written inside this block in Python script. Have you ever wondered what this block is, and why it is used?

Well, if __name__ == '__main__': is not some magical keyword or incantation in Python rather it is a way to ensure that specific code is executed when the module is directly executed not when it is imported as a module.

What this expression implies is that only when a certain condition is met, further action should be taken. For example, if the name of the current running module (__name__) is the same as "__main__", only the code following the if __name__ == '__main__': block is executed.

Full Article: Understanding if __name__ == ‘__main__’ in Python Programs

Every post on this sub be like

I created a small tutorial using Python that you can use to fetch and decrypt stored passwords from Chrome: https://geekyhumans.com/extract-stored-chrome-passwords-and-decrypt-them-using-python/

I'd like to share with you "wuddz-dapp", an awesome (D)app I wrote when all the buzz & craze was going on about web3 and crypto it has served me quite well, added a few new updates to it and posted it on my github, it's also available on pypi for anyone who may find it of useful. Kindly star the repo.

Peace & Love

https://github.com/wuddz-devs/wuddz-dapp

https://youtu.be/AZ3OWNHNE0g

A look under the hood of PyVorengi SDK—a modular, cache-conscious, and interaction-synchronized 3D voxel engine written from scratch in Python.

No external game engines. No high-level 3D graphics frameworks. Just pure, unadulterated engineering built over the last 90 days.

This engine utilizes unrolled straight-line CPU execution blocks for low-level performance, rendering procedural tiered heightmaps using custom Simplex/Perlin algorithms. It features dynamic physical kinematics, a complete multi-tier hierarchical CAD dashboard stack, and an autonomous Aegis Sentinel neural network co-pilot drone.

THE MICRO AI AGENT (AEGIS SENTINEL DRONE):

The drone is an autonomous flight entity powered by an isolated, sequential feedforward Multi-Layer Perceptron (MLP) neural network topology (19 inputs, 2 hidden layers with 24 neurons each, and 6 control outputs).

• 19-Channel Sensory Input Vector: The drone maps its environment in real-time, sampling 3D target error vectors, localized IMU translation velocities, gyroscope angular rates, a 6-direction coordinate range proximity raycaster, an accelerometer world-space gravity projection, and a rotation-invariant terrain-relative altitude tracker.

• 6-Channel Motor Control Outputs: Tanh-squashed motor channels dynamically actuate physics forces, dictating stabilization torque (Roll, Pitch, Yaw) and local translational thrust (Lateral, Forward, Elevation).

• Genetic Simulation Training Pipeline: Initial flight profiles are baked into specialized behavioral niches (HOVER, ORBIT, STRAFER) using an offline genetic pipeline that relies on multi-process candidate evaluation, uniform weight crossover, and Gaussian mutation across hilly/mountainous terrain stages.

• Live Co-Pilot Adaptation: Features a real-time supervised Delta Rule adjustment engine that steps weight matrices dynamically to match the player's movement and interaction footprint.

PROJECT ARCHITECTURE & BENCHMARKS:

• Hardware Baseline: Stable 30 FPS running on an AMD Ryzen 7 5825U with integrated Radeon Graphics (16GB RAM) at a 95-block view distance.

• Meshing Throughput: ~280k faces/second via ProcessPoolExecutor and vectorized greedy rectangle face merging.

• Memory Layout: Dual structure using NumPy C-order contiguity. Loads arrays in (Z, Y, X) for fast binary serialization but executes logic in Cartesian (X, Y, Z).

• The Interaction Halo: Chunks pad their borders with a +1 block halo buffer (18x18x26 structural sections) sourced directly from active neighboring memory blocks to keep rendering seamlessly continuous during real-time block edits.

• Atmospheric & FX Layer: Power Fog math scaling, global luma height shading relative to depth ceiling, and rolling 3-layer Sine wave volumetric haze tracking.

• CAD Forge Dashboard: Full integer-enum state navigation hierarchy allowing structural building blueprint capture (voxel bagging) and spatial transformation.

Hey everyone! 👋

I wanted to share something I've been working on: AnaLAssist, my very own AI-powered data scientist! 🚀 It's a Streamlit app that integrates OpenAI's Assistants API for some seriously smart data analysis.

Here's what it can do:

• Smart Data Analysis: AnaLAssist can chew through data and provide insights in a way that feels like you have a data scientist at your fingertips.

• User-Friendly: Built with Streamlit, it's super intuitive to use, even if you're not a coder.

• Versatile and Powerful: From crunching numbers to predictive modeling, it's designed to handle a variety of data tasks.

The Journey:

Creating AnaLAssist was a mix of challenges and breakthroughs. It's fascinating how integrating AI into applications can transform data handling. I learned a lot and thought it might be helpful to share these insights.

For the Curious Minds:

If you're interested in how I built AnaLAssist, I've put together a tutorial on my YouTube channel. It covers everything from the basics of the OpenAI API to the nitty-gritty of Streamlit integration. Might be a good watch if you're looking to dive into AI-driven data science or Streamlit development. Here's the link if you want to check it out: AnaLAssist Tutorial.

Would love to hear your thoughts or any feedback. If you've been working on something similar, let's exchange ideas!

Cheers! 🎉

An AI watermark remover: useful, but useless.