Upgrade Your Python Multiprocessing: Process Pools & Executor

Whoa … Amazing how much faster #Rust is on so many tasks than #Python. There’s a lot of philosophical discussion around Rust vs. Python. So I ran a simple benchmark instead. Test setup: - Generate 5 million records - Hash each record (SHA-256) - Group by hash prefix - Measure execution time + peak memory No I/O. No tricks. Just CPU + memory pressure. Results on my machine (M2 MacBook Air, 24 GB RAM): 🐍 Python: ~8–12 seconds, ~800–1200 MB peak memory 🦀 Rust (release build): ~0.7–1.5 seconds, ~250–350 MB peak memory That’s not “a bit faster.” That’s an order of magnitude difference in speed — and significantly tighter memory behavior!!! Why? Python: - Interpreter overhead per loop - Boxed objects - Heavy dictionaries - GC + ref counting Rust: - Zero-cost abstractions - Tight memory layout - No GC - Native compiled code But raw performance is only one axis. If I’m building: - API orchestration - AI workflows - Rapid prototypes - Business logic Python wins on time-to-value. If I’m building: - High-volume data pipelines - Graph ingestion engines - Hash-heavy processing - Systems-level infrastructure Rust is in a different league. The real answer isn’t “Rust or Python?” It’s: Where does performance actually matter in your architecture? Curious how others decide where to draw that boundary. What do you think?

#DAY 2 of challange Rule #1: Always Be Different. While others were building Python projects that create things in the air… I decided to control something real. I built a system where I can move my laptop cursor just by pointing. As you can see in the first clip — I trained my own model. In the second clip — you can see the cursor moving live. And it doesn’t just move… it performs real actions 1.Pinch gesture → Control volume 2.Grab & drop → Move software or browser windows 3.Custom gestures → Custom actions 4.Pointing → Accurate cursor movement All of this is built completely in Python This is not just a project. It’s about thinking differently. And the best part? I’m making it open-source so anyone can build, improve, and innovate on top of it. 🔗 GitHub: https://lnkd.in/gZUwMQEX The goal isn’t to follow trends. The goal is to create them. #Python #AI #MachineLearning #ComputerVision #OpenSource #Innovation #BuildInPublic

𝗜 𝘀𝘁𝗼𝗽𝗽𝗲𝗱 𝘄𝗿𝗶𝘁𝗶𝗻𝗴 “𝗾𝘂𝗶𝗰𝗸 𝘀𝗰𝗿𝗶𝗽𝘁𝘀” 𝘁𝗵𝗲 𝗺𝗼𝗺𝗲𝗻𝘁 𝘁𝗵𝗲𝘆 𝘀𝘁𝗮𝗿𝘁𝗲𝗱 𝗹𝗶𝘃𝗶𝗻𝗴 𝗶𝗻 𝗽𝗿𝗼𝗱𝘂𝗰𝘁𝗶𝗼𝗻. 😅 If you’re doing Python automation, a few boring habits save you later: 𝗟𝗼𝗴 𝗹𝗶𝗸𝗲 𝘆𝗼𝘂’𝗹𝗹 𝗱𝗲𝗯𝘂𝗴 𝗶𝘁 𝗮𝘁 𝟮 𝗔𝗠: Timestamps, context, counts. 🧾 𝗔𝗱𝗱 𝗮 “𝗱𝗿𝘆-𝗿𝘂𝗻” 𝗺𝗼𝗱𝗲: Test before touching real data/files. 🧪 𝗠𝗮𝗸𝗲 𝗶𝘁 𝗶𝗱𝗲𝗺𝗽𝗼𝘁𝗲𝗻𝘁: Running twice shouldn’t double-create or double-send. 🔁 𝗩𝗮𝗹𝗶𝗱𝗮𝘁𝗲 𝗶𝗻𝗽𝘂𝘁𝘀 𝗲𝗮𝗿𝗹𝘆: Paths, env vars, credentials, expected formats. 🧱 𝗙𝗮𝗶𝗹 𝗹𝗼𝘂𝗱𝗹𝘆 + 𝗻𝗼𝘁𝗶𝗳𝘆: Don’t silently skip and “succeed”. 🧯 𝗔𝘂𝘁𝗼𝗺𝗮𝘁𝗶𝗼𝗻 𝗶𝘀𝗻’𝘁 𝗮𝗯𝗼𝘂𝘁 𝗰𝗹𝗲𝘃𝗲𝗿 𝗰𝗼𝗱𝗲. 𝗜𝘁’𝘀 𝗮𝗯𝗼𝘂𝘁 𝗽𝗿𝗲𝗱𝗶𝗰𝘁𝗮𝗯𝗹𝗲 𝗼𝘂𝘁𝗰𝗼𝗺𝗲𝘀. #python #automation #devtools #reliability #engineering

Day 6 of #60DaysOfMiniProjects From image processing and QR decoding to building structured command-line applications — growing one step at a time. Today I built a CLI-based TODO List using Python What this project does: • Displays a simple menu-driven interface • Adds tasks dynamically • Stores tasks using lists • Displays tasks with proper numbering • Uses loops to keep the program running Concepts I worked with: • Lists and data storage • While loops for continuous execution • Conditional statements • Enumerate for clean indexing • Writing cleaner, user-friendly CLI programs Moving from small scripts to interactive programs. Building logic. Strengthening fundamentals. Small projects. Real concepts. Daily progress. Consistency builds confidence. #Python #MiniProjects #BuildInPublic #CodingJourney #CSE #DeveloperGrowth #LearningInPublic

🚀 Day 48 of #100DaysOfCode I recently tackled an interesting coding problem: “Given a binary number as a string, find the number of steps to reduce it to 1. If even, divide by 2; if odd, add 1.” For example: Input: "1101" → Output: 6 Input: "10" → Output: 1 Input: "1" → Output: 0 Instead of converting the binary to decimal, I simulated the steps directly on the binary string, which makes it efficient even for very long numbers. Here’s the Python solution I implemented: def numSteps(s: str) -> int: steps = 0 s = list(s) while len(s) > 1: if s[-1] == '0': s.pop() else: i = len(s) - 1 while i >= 0 and s[i] == '1': s[i] = '0' i -= 1 if i >= 0: s[i] = '1' else: s.insert(0, '1') steps += 1 return steps print(numSteps("1101")) # 6 💡 Key Takeaways: You can work directly with binary strings instead of converting them to integers. Simulating operations step by step is often more memory-efficient. This approach works even for very long binary strings (up to 500 bits in this problem). Coding challenges like this are a great way to sharpen algorithmic thinking! 🧠 #Python #CodingChallenge #BinaryNumbers #ProblemSolving #LeetCode #Algorithms

Exploring Desktop Automation using PyAutoGUI in Python I recently built a simple automation script using pyautogui that simulates real keyboard typing on my system. The program waits for a few seconds so I can switch to Notepad, and then it automatically types my introduction and presses Enter — just like a human would. It’s a small script, but it clearly shows how powerful automation can be. Instead of manually typing repetitive text, Python can handle it in seconds. Through this hands-on experiment, I learned: • How pyautogui controls keyboard actions • How execution delays help manage task timing • How automation scripts mimic real user behavior • The foundation behind bots and GUI testing tools This concept can be extended to automate form filling, repetitive data entry, testing applications, and other productivity tasks. Sometimes the simplest projects teach the most practical lessons. Excited to keep exploring automation and real-world scripting with Python #Python #PyAutoGUI #Automation #LearningByDoing

🧠 Python Concept: List Comprehension Write powerful loops in one clean line. ❌ Traditional Way squares = [] for i in range(5): squares.append(i * i) print(squares) Output [0, 1, 4, 9, 16] ✅ Pythonic Way squares = [i * i for i in range(5)] print(squares) Same result, less code. ⚡ With Condition even_squares = [i * i for i in range(10) if i % 2 == 0] print(even_squares) Output [0, 4, 16, 36, 64] 🧒 Simple Explanation Imagine telling a robot: 👉 “Give me squares of numbers from 0–4.” 👉 Instead of repeating instructions, you give one rule. 👉 That rule = list comprehension. 💡 Why This Matters ✔ Shorter code ✔ Faster execution ✔ More readable loops ✔ Very Pythonic 🐍 Python often replaces multiple lines with a single elegant expression 🐍 List comprehensions are one of the most powerful examples of that philosophy. #Python #PythonTips #PythonTricks #AdvancedPython #List #ListComprehension #Tech #CleanCode #LearnPython #Programming #DeveloperLife #DailyCoding #100DaysOfCode

🔥 Day 63 — Python vs C++ Speed vs Simplicity 🐍 Python Simple, clean & beginner-friendly Great for automation, AI, ML, scripting Slower than C++ because it’s interpreted Huge library ecosystem Faster development, fewer lines of code ⚡ C++ Extremely fast & powerful Used in game engines, operating systems, high-performance apps Harder to learn due to complex syntax Gives full control over memory & hardware Better for performance-critical tasks ⭐ Quick Verdict Choose Python → AI, ML, automation, web, fast development Choose C++ → games, robotics, system programming, high-speed apps #TechTrends #DevelopersOfLinkedIn #ProgrammingLife #LearnToCode #Python #Cpp #TechCommunity #SoftwareEngineering #CodingJourney #Innovation #FutureOfTech #AI #MachineLearning #100DaysOfCode #KaifTechTalks

Python’s "Rustification" is no longer a trend, It’s the standard ! 🦀🐍 The Python ecosystem is undergoing a massive transformation. We are moving away from the "slow but flexible" reputation and toward a future that is blisteringly fast and memory-efficient, thanks to Rust. If you want to build a modern stack, these three tools from Astral and the Polars team are the new "Big Three": ⚡ Ruff: The Linter/Formatter - The Vibe: Why wait seconds for Black or Flake8? - The Power: Replaces almost your entire linting stack with a single Rust binary. It’s so fast you can run it on every file-save without a hint of lag. 📦 uv: The Package Manager - The Vibe: Package management that finally feels modern. - The Power: A drop-in replacement for pip and poetry. It resolves dependencies and creates virtual environments in milliseconds, not minutes. 🚀 Polars: The Data Engine - The Vibe: Moving past the "Pandas memory wall." - The Power: A multi-threaded, vectorized query engine written in Rust. It doesn’t just process data faster; it processes larger datasets on your local machine by being incredibly smart about memory and CPU cores. The Common Thread? They all leverage Rust to do the heavy lifting while keeping the Pythonic API we love. It’s the best of both worlds: developer productivity meets industrial-grade performance. #Python #Polars #Rust #DataEngineering #DataScience #Astral #TechTrends 

🚀 Coding Practice — Prime Number of Set Bits (Bit Manipulation) Today I solved an interesting bit manipulation problem: 👉 Given two integers left and right, count how many numbers in the range [left, right] have a prime number of set bits in their binary representation. 🧠 Problem Understanding A set bit means a 1 in binary representation. Example: 21 → 10101 → 3 set bits We must: Convert each number in range [left, right] to binary Count number of 1s Check if that count is prime Return total count 🔍 Key Insight (Optimization Trick) Given constraint: 1 ≤ left ≤ right ≤ 10^6 0 ≤ right - left ≤ 10^4 Maximum number of bits required for 10^6: log₂(10^6) ≈ 20 So maximum possible set bits = 20 That means we only need to check primes up to 20: {2, 3, 5, 7, 11, 13, 17, 19} ⚡ Instead of checking prime every time, we store these in a set for O(1) lookup. ✅ Optimized Python Solution class Solution: def countPrimeSetBits(self, left: int, right: int) -> int: # Prime numbers up to 20 (maximum possible set bits) primes = {2, 3, 5, 7, 11, 13, 17, 19} count = 0 for num in range(left, right + 1): set_bits = num.bit_count() # Fast built-in method if set_bits in primes: count += 1 return count 🔎 Example 1 Input: left = 6, right = 10 NumberBinarySet BitsPrime?61102✅71113✅810001❌910012✅1010102✅ ✔ Output = 4 ⏱ Complexity Analysis Time Complexity: O(N) where N = right - left (max 10^4) Space Complexity: O(1) Very efficient and well within constraints. #Python #CodingPractice #BitManipulation #ProblemSolving #DataStructures #InterviewPreparation #LeetCode