SIMBA, Power Electronics Simulation Software’s Post

Add Two Numbers: Linked List Digit Addition with Carry Process lists simultaneously like grade-school addition. Handle different lengths by treating missing nodes as 0. Carry propagates via loop condition carry > 0. Dummy simplifies head handling. Continue until lists exhausted AND carry zero. Carry Propagation: Including carry in loop condition ensures final carry digit added. Division/modulo elegantly extract carry and digit. This pattern applies to any multi-digit arithmetic simulation. Time: O(max(m, n)) | Space: O(max(m, n)) #LinkedList #DigitAddition #CarryPropagation #SimulationAlgorithm #Python #AlgorithmDesign #SoftwareEngineering

to illustrate why the world is held back by engineering software (CAD, etc.): what would the world look like today if python were paywalled like matlab is, and there were no alternatives? apply that to everything we do and see the issue

For users who want to update models at 60 frames per second on the Metal 4 architecture # LM Chat export *2026-04-01T23:23:32.952Z · oip-4gb · on-device Open Intelligence* ## You can you help design a Python example of a CUDA-style pipeline for adaptive model updates import cupy as cp ## Assistant Certainly! Below is a Python example of a CUDA-style pipeline for adaptive model updates using `cupy`, which is a library similar to NumPy but designed to work with GPU arrays. This example will demonstrate how to perform adaptive updates on a simple model using gradient descent with an adaptive learning rate.

Plus One: Digit Addition with Carry Propagation Add 1 to number represented as digit array. Reverse for easier processing (rightmost = index 0). Propagate carry: 9 becomes 0 with carry, else increment and stop. If carry remains after all digits, append it (e.g., 999 → 1000). Simpler Approach: Process from right without reversing using for i in range(len(digits) - 1, -1, -1). Your reversing works but adds extra operations. Both O(n) time though. Time: O(n) | Space: O(1) excluding output #DigitArithmetic #CarryPropagation #ArrayManipulation #Simulation #Python #AlgorithmDesign #SoftwareEngineering

Building a UI you can control out of thin air. 🎛️ I recently built a touchless OS volume controller using Python, OpenCV, and MediaPipe. But the real challenge wasn't just tracking the hand—it was building the logic to isolate a specific, intentional gesture. To make this usable, the system has to ignore general hand movement and strictly track the "pinch". Here is the logic under the hood: 📏 Precise Landmark Isolation: The algorithm actively extracts and tracks only Landmark 4 (Thumb tip) and Landmark 8 (Index finger tip), ignoring all other 19 points on the hand to prevent false triggers. 🧮 Euclidean Heuristics: It calculates the real-time hypotenuse distance between those two specific pixels. ⚙️ OS Integration: Using pycaw, that raw pixel distance is dynamically interpolated and mapped directly to the Windows Audio API decibel range. The result is a seamless, zero-latency hardware integration where the operating system obeys a physical hand gesture in real-time. You can check out the source code and the specific Euclidean math implementation here: https://lnkd.in/gs6xqJfx #ComputerVision #Python #OpenCV #SoftwareEngineering #MediaPipe #MachineLearning #HCI #DeveloperPortfolio

Longest Repeating Character Replacement: Window Valid When Replacements ≤ k The critical insight: window is valid when window_length - max_frequency ≤ k (replacements needed ≤ allowed). HashMap tracks character frequencies, max frequency determines how many characters need replacing. Expand window greedily, contract when replacement budget exceeded. Replacement Formula: length - max_freq = characters needing replacement. This transforms complex constraint into simple arithmetic check, enabling O(n) sliding window. Time: O(n) | Space: O(1) — max 26 chars #SlidingWindow #CharacterReplacement #FrequencyTracking #ConstraintValidation #Python #AlgorithmDesign #SoftwareEngineering

Playing with Claude to vibe-code a visualiser library for 128x64 LED matrix 😎 Audio from the OP-1 synth is fed into the RaspberryPi, which is running custom Python scripts (thanks Claude), the scripts analyse the audio input real-time and generate visuals which are pushed to the 128x64 LED matrix. #code #teenageengineering #claude #raspberrypi5 #visualiser

Atom Simulation. Here, particles attract and repel each other like electric charges, and the balance of forces allows for various effects. The code is open source on GitHub by MasterOgon. Check also my Sci-Fi paintings on MasterOgon Art. #Coding #Simulation #Python #OpenSource #QuantumPhysics

One direction I’ve been pushing in with Python is building projects outside a single lane of analysis. I recently built a simulation-focused project by rebuilding a linear congruential generator from an older C++/ROOT scientific script, then using it in a Monte Carlo estimate of pi. The project started with implementing the pseudo-random number generator itself, then evaluating its behavior with a histogram for approximate uniformity and a lag plot for short-range correlation. From there, I applied the generator in a random point-sampling experiment in the unit square to estimate pi. What I liked about this project was that it pulled together several things at once: computational modeling, simulation, statistical diagnostics, and clear technical communication in Python. It also gave me a way to connect my physics background to a different kind of analytical project outside my sports work. GitHub: https://lnkd.in/g6PeBgrT #Python #MonteCarlo #Simulation

🔧 Built a Beam Analysis Dashboard using Python Most mechanical engineering problems stop at solving numerical questions. I wanted to go a step further and convert beam theory into a usable engineering tool. I developed a simple interactive dashboard that can: • Compute Shear Force Diagram (SFD) • Compute Bending Moment Diagram (BMD) • Calculate maximum deflection • Visualize structural behavior instantly The idea was to move from: “solving problems manually” → “building systems that solve problems” ⚙️ Tech stack: Python | NumPy | Matplotlib | Streamlit 🌐 Live tool: https://lnkd.in/gXjyM7pW This is just the first version. Next step is to extend it for distributed loads, arbitrary load positions, and more generalized beam cases. #MechanicalEngineering #Python #Engineering #Simulation #Streamlit #StructuralAnalysis