Ali Mobini’s Post

Why Performance Matters in Python Development Python is loved for its simplicity and flexibility, but when it comes to performance, it still poses challenges for real-world systems. Here are some key insights from JetBrains’ latest article: • Python’s interpreted nature introduces runtime overhead compared to compiled languages. • The Global Interpreter Lock (GIL) limits multithreading for CPU-bound workloads. • Poor data structures and lack of profiling often hurt performance more than the language itself. • Performance directly affects cost, scalability, and user experience. • Optimization helps developers work faster and keeps the code easier to maintain. Read the full article: https://lnkd.in/dP4WU5PS #Python #Performance #SoftwareEngineering #Optimization

Tom Reid shares a detailed analysis of the new optional free-threaded build in Python 3.14. Learn how removing the Global Interpreter Lock (GIL) can lead to significant performance gains for CPU-intensive tasks.

Learn how the optional free-threaded build in Python 3.14 can improve performance for CPU-bound tasks. Tom Reid shares benchmarks showing up to a 10x speedup in his latest article.

🚀 Python 3.14 is officially out! Released on October 7, 2025, this major update brings exciting new features: Deferred annotations are now default — no more  __future__  imports. Template strings ( t"" ) offer safer, customizable string interpolation. Smarter REPL & error messages improve the dev experience. Multiple interpreters now available via  concurrent.interpreters . Free-threaded Python (no GIL!) unlocks true multi-core parallelism. This release marks a big leap forward for Python’s performance, usability, and concurrency. https://realpython.com #Python #DevTools #Programming #Python314 #OpenSource #mutlithread_python #parallel_processing

Hello! 😊 I got inspired to write this because Python 3.14 was just released two weeks ago, and now we can use Free-threaded Python. 😁 If you’ve ever wondered why Python doesn’t fully use multiple CPU cores — or what the Global Interpreter Lock really does — this one’s for you. I wanted to explain it in a clear, friendly way, so you can actually feel how the GIL impacts concurrency and performance. Whether you’re building high-performance APIs, working with async code, or just curious about how Python runs behind the scenes — I think you’ll enjoy this one. 👉 Check it out here: https://lnkd.in/ehmGHk_x And I promise — my next article will dive deep into asyncio itself: how it works under the hood, and how to truly take advantage of it in your Python apps. 🤓 #python #performance #asyncio #gil #threading #hcp

Great news for Python developers! No need to use negative lists for maxheaps. With Python 3.14 (Oct 2025) the heapq module now offers native max-heap functions : heapify_max() heappush_max() heappop_max() Check the update here: https://lnkd.in/duid6Y3k #Python #DataStructures #DevUpdate

🧠 Python “is dynamically typed” — What does that really mean? You’ve probably heard this before: > “Python is a dynamically typed language.” But what does that actually mean? 🤔 Let’s break it down 👇 In statically typed languages like Java or C++, you must declare a variable’s type before using it: int x = 10; But in Python, you can just write: x = 10 x = "Hello" Python doesn’t mind — because it checks variable types at runtime, not before running the program. That’s why we call it dynamically typed — the variable’s type can change anytime. ✅ Pros: Less code, faster development Great for beginners and scripting ⚠️ Cons: Type errors can appear while running Larger projects may get confusing without type hints To balance both worlds, modern Python even supports type hints now: def greet(name: str) -> str: return f"Hello, {name}" So Python gives you flexibility when you need it, and structure when you want it — that’s the beauty of its design. 💡 #Python #Programming #Coding #Developers #FullStackAcademy #DevOps #Technology #DataScience #AI #OpenSource #ObaidLardi

🚀 New Blog Alert: Mastering Exception Handling in Python 🐍 Every great Python programmer knows — writing code is easy, but handling errors gracefully is what makes your programs robust and professional. In my latest Medium blog, “Python and Its Exception Handling”, I break down one of the most essential topics every programmer must master — how to manage and recover from errors effectively. 🔍 What you’ll learn: ⚙️ Why exception handling is crucial for clean and reliable code 🧩 The try-except-else-finally structure — and when to use each 🚨 Common Python exceptions and how to debug them 🎯 Pro tips for writing custom exceptions and improving code resilience Whether you’re building simple scripts or large-scale applications, understanding exception handling will help you write Python code that’s smarter, safer, and easier to maintain. Big Thanks to Vishwanath Nyathani ,Kanav Bansal ,Naman Goswami ,Harsha Mg for guiding me throughout this journey.. #DataAnalytics #Python #DataTypes #LogicMeetsMagic #DataScience #Programming #StudentsWhoCode #MediumBlog #Learning #InnomaticsResearchLabs

🚀 Exciting News for Python Enthusiasts! Python 3.14 is the latest stable release of Python, launched on October 7, 2025. It brings practical features for production: template strings, deferred annotations, a relaxed GIL option for better multithreading, subinterpreters, and an experimental JIT. Together these make Python faster and safer for backend and AI workloads. This is a big step toward ending the Global Interpreter Lock. Before, threads were limited to a single CPU core. Now you can run threads across multiple cores and make much better use of available hardware. Keep in mind the default CPython build still has a GIL, so choose free threaded builds or subinterpreters when you need true parallelism and double check library and GPU compatibility first. I will be testing 3.14 on our services to measure multicore gains and to see if the JIT helps inference speed. 🔗 Curious about the details? Check out my Medium post! #Python314 #Backend #AI #Multithreading #NoGIL #Python

If you want to run your Python programs up to 10x faster without altering your code, the new free threading version of Python just changed the game. Yes, Python 3.14 frees up the Global Interpreter Lock (GIL), bringing true parallelism and significant performance gains. In my latest article on the Towards Data Science blog, I cover: - How to download GIL-free Python 3.14 - Benchmarks showing up to 10x faster performance for some operations - Practical code examples you can run today If you’ve ever hit performance limits with Python’s Global Interpreter Lock, this release is a big deal. Read the full article for FREE using the link below. https://lnkd.in/e4TTMphY