Reversing Arrays: Two Approaches Compared

Just a developer trying to make life a little more “hands-free.” ✋✨ I built a gesture-controlled mouse using Python and MediaPipe. It wasn’t easy—filtering out hand jitters and detecting a reliable “pinch” in real time took a lot of trial and error. The current setup: 🖱️ Pinch = Left Click 📜 Two-finger lift = Scroll 👍 Thumbs up = Volume Up Still experimenting and improving, but turning an idea into something that actually works feels great. You can also download the .exe file from the Releases section and try it directly. It’s simple, it’s experimental, and I’m learning something new with every line of code 📈 Try it here 👇 👉 https://lnkd.in/g9sZUCjM #CodingLife #SoftwareEngineering #AI #HandsOnLearning #PythonProject #Innovation #BuildInPublic

Just built a side-by-side semantic search playground Python for real transformer embeddings, Rust for raw high-throughput cosine similarity on 10k vectors. Python side: sentence-transformers + NumPy, loads actual models, optional SVD compression, gives meaningful top-k results instantly. Rust side: ndarray doing full 10k × 384 matrix ops in release mode synthetic but stupidly fast, zero surprises. Startup difference is night and day: Python waits for model download and weights, Rust just flies from first cargo run. Memory profile tells the story Python carries the full ML stack, Rust stays lean and predictable even at scale. Same cosine math, same top-k goal, yet Rust’s memory safety and zero-cost abstractions make the benchmark feel like cheating. The gap isn’t hype it’s observable the moment you switch from prototype to production-grade retrieval. If you’re already writing Python or JS for vector search and wondering where the latency and safety edge actually lives… this repo is the signal. Drop a comment or DM if you are building with Rust. #Rust #AI #ML

💡 From Brute Force to Optimal — My Approach to “Subarray Sum Equals K” Cracked this classic problem using an O(n) Prefix Sum + HashMap approach instead of the naive O(n²) brute force. 🔍 Key Idea: Maintain a running prefix sum p If p - k has been seen before, it means a valid subarray exists Store prefix sums in a hashmap with their frequencies ⚡ Why this works: Instead of checking all subarrays, we reuse previous computations to instantly detect valid sums — making it efficient and scalable. 🧠 What I learned: The real power of prefix sums How hashmaps convert time complexity Thinking in terms of patterns, not loops 📈 Result: Runtime: 28 ms (Beats 86% 🔥) Clean and optimized solution 💻 Code Mindset: “Don’t just solve — optimize your thinking.” #DataStructures #Algorithms #LeetCode #CodingJourney #Python #ProblemSolving #TechGrowth #WomenInTech #CodeNewbie #100DaysOfCode #CodingInterview #SoftwareEngineering #AIJourney #DeveloperMindset #CareerGrowth 🚀

I was cleaning a dataset — filtering rows, transforming values, the usual. My 5-line for loop worked fine. But I wanted to be "Pythonic." So I compressed it into a one-liner. Then I added another layer. The next morning I stared at it for two full minutes trying to decode my own logic. If I couldn't read it, my future teammates had no chance. This carousel breaks down: → The mental model that makes list comprehensions click instantly → The reading order most beginners get backwards → The exact rule for when to stop using them and write a real loop What's the longest you've stared at your own code before realizing you had no idea what it does? #Python #DataAnalytics #DataAnalyst #PythonTips #LearnInPublic #AHAMoments #DataAnalystJourney

Tired of dictionaries where a typo crashes your AI/ML pipeline, or forgetting which number corresponds to what in a plain tuple? 😩 Python's `namedtuple` lets you create lightweight, immutable data structures with clear names for each field. It’s perfect for representing fixed data points like coordinates, dataset rows, or configuration parameters, making your code so much more readable and error-proof. ✨ Do you use `namedtuple` or prefer something else for small data structures? Share your preference! 👇 #PythonTips #AIDevelopment #MachineLearning #PythonProgramming #CleanCode

What backend language do you reach for when vibe-coding a personal AI tool — something just for you, solving a very specific use case? I keep defaulting to Python. Not necessarily because it’s the “best” choice technically, but because the momentum is already there — tons of boilerplate, libraries, and examples to build on. Curious if others feel the same, or if that’s just my bias. Also, why not just build it as a Claude skill? I’ve found that having a separate database with proper context storage often leads to more predictable results.

Solving the 1D Poisson Equation with Finite Differences in Python 🚀 Ever wondered how to numerically solve the 1D Poisson equation using finite differences? Here’s a concise implementation using NumPy: import numpy as np n = 10 x = np.linspace(0, 1, n+1) h = x[1] - x[0] # Construct the stiffness matrix K and load vector f K = np.zeros((n-1, n-1)) f = np.zeros(n-1) def rhs(x): return np.sin(x) for i in range(n-1): K[i, i] = 2 / h if i > 0: K[i, i-1] = -1 / h if i < n-2: K[i, i+1] = -1 / h f[i] = rhs(x[i+1]) * h # Solve the linear system u = np.linalg.solve(K, f) # Extend solution to full grid u_full = np.zeros(n+1) u_full[1:-1] = u # Exact solution for comparison y_exact = np.sin(x) - x * np.sin(1) # Compute error error = np.linalg.norm(u_full - y_exact) print("Error:", error) Key Takeaways: ✅ Finite Difference Method: Discretizes the Poisson equation into a linear system. ✅ NumPy Efficiency: Uses np.linalg.solve for fast matrix inversion. ✅ Error Analysis: Compares numerical and exact solutions to validate accuracy. Why This Matters: This is a foundational technique for solving PDEs in scientific computing, finance, and engineering. How would you extend this to 2D or 3D problems? #NumericalMethods #Python #ScientificComputing #FiniteDifferences #DataScience

Built a quick little project this week: justaskit The idea was simple, most data tools make you learn SQL just to ask basic questions. So I made one where you just... ask. In plain English. Upload a CSV, type "show me top 3 products by revenue" and it spits out a chart with an explanation in about 8 seconds. Under the hood it's a multi-agent system with LangGraph where separate agents handle the analysis, visualization, and insights. Added full code transparency too so you can see exactly what it's doing. Stack: Python, FastAPI, Next.js 15, LangGraph, pandas GitHub link in the comments if you want to check it out! #AI #OpenSource #LangGraph #Python #BuildInPublic

My Day 11 of 90 Days Growth Challenge AMDOR ANALYTICS Today, we’ll focus on for Loop – What is a for loop? A for loop in Python is used to iterate over a sequence (such as a list, tuple, string, or dictionary) or iterable objects. It allows you to execute a block of code repeatedly for each item in that sequence.   What is the Basic Syntax The standard structure of a for loop is as follows:   Python for item in sequence: # Code to execute for each item print(item)   Explaining each of the components in the for loop: ·        for: The keyword that starts the loop. ·        Item: A temporary variable that takes the value of the current element in the sequence during each iteration. ·        In: Links the variable to the sequence. ·        Sequence: The object being iterated over (e.g., a list or range). ·        Indentation: Python uses indentation to define the block of code inside the loop. Tomorrow, we delve into while loop #Techjourney #90daysgrowthchallenge #consistency #growth #aiengineering #Amdoranalytics

LLMs are not good at everything - when I asked it to rotate an image 180 degrees, it completely butchered the image. This is a question of using the right tools. If instead you say, "Use Python to rotate this board 180 degrees," it can write a deterministic script to do so and give you the right results. Maybe one day the LLM will be able to distinguish when it needs a deterministic solution from a generative solution but at this point it's still on the user to choose the right approach for the problem.