Python overtakes JavaScript on GitHub, bridges tech worlds

🐍 Why Python Continues to Dominate the Programming World Python has become one of the most versatile and widely-used programming languages today — and for good reason. Its simplicity, readability, and rich ecosystem make it a go-to language for beginners and professionals alike. From web development (Django, Flask) and data science (Pandas, NumPy, scikit-learn) to AI/ML (TensorFlow, PyTorch) and automation, Python empowers developers to build, analyze, and innovate faster than ever. ✨ Key Advantages of Python: Easy to learn & write, even for beginners Extensive libraries and frameworks for almost every domain Strong community support and continuous updates Great for prototyping and production-ready solutions Whether you’re automating tasks, analyzing data, or building complex AI systems, Python provides the tools to turn ideas into reality efficiently. 💡 Fun fact: Python’s popularity continues to grow year after year — a testament to its flexibility and power in today’s tech landscape. #Python #Programming #DataScience #AI #MachineLearning #Automation #WebDevelopment #Coding

🚀 Python Just Got 20x Faster with Mojo Integration For years, developers accepted a trade-off: Python gives us simplicity, but C/C++ deliver raw speed. That line just blurred. A recent test using Mojo, the new superset of Python, produced jaw-dropping results: 🔥 Mandelbrot Set Calculations • 20x faster than pure Python • 5x faster than NumPy • Zero compromise on readability ⚡ Sigmoid Function Processing • 12x faster than Python • 2x faster than NumPy • Same Python-like syntax we already love But here's the twist: NumPy still wins at some tasks. In numerical integration, its vectorized operations outperformed Mojo. This isn't a replacement-it's a new weapon in the toolkit. 💡 The secret sauce? Mojo lets you write high-performance code without leaving the Python ecosystem. Offload the heavy lifting to Mojo, keep the rest in Python, and you get the best of both worlds. 👉 Pro tip: Minimize Python↔Mojo crossings and stick to native Mojo types for peak performance. With 20x more speed at your fingertips, the question isn't if you'll use Mojo-it's what you'll build with it. #Python #Mojo #Programming #TechInnovation #Developers 𝐒𝐨𝐮𝐫𝐜𝐞: https://lnkd.in/d6uc43xX

Most Python developers write Python for years — but never read the 19 lines that shaped it. import this opens The Zen of Python — not a poem, but a design manifesto that quietly governs how our industry writes software. The Zen of Python, by Tim Peters Beautiful is better than ugly. Explicit is better than implicit. Simple is better than complex. Complex is better than complicated. Flat is better than nested. Sparse is better than dense. Readability counts. Special cases aren't special enough to break the rules. Although practicality beats purity. Errors should never pass silently. Unless explicitly silenced. In the face of ambiguity, refuse the temptation to guess. There should be one-- and preferably only one --obvious way to do it. Although that way may not be obvious at first unless you're Dutch. Now is better than never. Although never is often better than *right* now. If the implementation is hard to explain, it's a bad idea. If the implementation is easy to explain, it may be a good idea. Namespaces are one honking great idea -- let's do more of those! Python became the language of AI, research, education, automation, and production systems not just because of syntax — but because its values reward clarity over cleverness, and maintainability over ego. In a world racing for speed and novelty, Zen is the counterweight: Slow down. Name things clearly. Build for humans. Write code you’re proud to re-read. Which Zen line hits you the most at your current stage of career? #Python #ZenOfPython #CleanCode #SoftwareEngineering #Developers #Programming #CodeQuality #TechCulture

#FreeThreading - Python 3.14: Free-Threading is Now Official – A Historic Step for Developers. This is a huge step toward making Python even more powerful for performance-driven solutions. 👉 At STR Softwares LLP our technical team is exploring how free-threading can enhance our automation workflows, data processing systems, and custom applications. Here’s how our team and the developer at large - can adapt and sync with this change 👇 After nearly 30 years, Python has finally made it possible to disable the Global Interpreter Lock (GIL) - unlocking true parallelism. Now, multiple threads can run Python code simultaneously across CPU cores, enabling performance leaps like never before. ⚙️ What’s Changing Previously, Python’s GIL acted like a one-lane bridge - only one thread could run at a time. Now, with Python 3.14’s free-threaded build, it’s a multi-lane highway where every core can work together. ⚡ Real Performance Gains - CPU-heavy tasks are up to 10x faster - Multi-threaded operations show 3x speedups - I/O-heavy tasks complete 3x quicker Who Benefits the Most : ✅ Data Scientists & ML Engineers – Faster training and preprocessing without multiprocessing hassles ✅Automation & Script Developers – Efficient parallel data handling and file processing ✅ App & Game Developers – Better responsiveness with parallelized operations ✅Web Developers – Improved scalability for CPU-intensive APIs and background tasks What to Keep in Mind : ✓ Single-threaded code may slow down slightly ✓ Memory use might increase ✓ Thread safety and race conditions become critical C extensions need compatibility updates Looking Ahead : Python’s steering council aims to free-thread the default build in upcoming versions (likely 3.15 or 3.16). Major libraries are already adapting for this change. 💬 Have you tried Python 3.14’s free-threading yet? Share your experience or benchmarks in the comments! #Python314 #FreeThreading #PythonDevelopment #PerformanceOptimization #DataScience #SoftwareEngineering #STRSoftwaresLLP

🐍 Python Programming: Powering Innovation Across Industries 💡 Python has become one of the most versatile and widely used programming languages today. Its simplicity, readability, and rich ecosystem make it a top choice for both beginners and experienced developers. Key strengths of Python include: 📚 Easy to Learn: Clean syntax and intuitive structure for rapid onboarding. 🌐 Versatile: Ideal for web development, automation, data science, AI, and machine learning. 🛠️ Libraries & Frameworks: NumPy, Pandas, TensorFlow, Flask, Django, and more simplify complex tasks. 🤝 Strong Community: A vibrant ecosystem ensures continuous innovation and abundant resources. Why it matters for professionals: Mastering Python empowers you to solve real-world problems, analyze data efficiently, automate workflows, and contribute to cutting-edge tech. For businesses, Python enables faster development, smarter data-driven decisions, and competitive advantage. Python isn’t just a language—it’s a 🚀 gateway to innovation, automation, and growth. #Python #Coding #DataScience #AI #MachineLearning #Automation #WebDevelopment #TechSkills #SoftwareDevelopment #Innovation #PythonDeveloper #CareerGrowth

There's nothing inherently powerful about #Python for machine learning. It's great glue. It's the combination of Python and C that makes it beneficial. Plus, Python has deep pockets supporting it. There's Google and there's Meta, behind PyTorch. But the landscape is changing and #JavaScript is becoming a practical reality "because it’s driven by a number of technological advancements," says Laurie Lay in an informative post by Loraine Lawson in The New Stack. https://lnkd.in/gmbqndeA

It's not all about Python for machine learning -- JavaScript is coming along and there's the npm ecosystem, too.

"Just use Python. It's faster to build." They were right. And completely wrong. Started Fyxor writing everything in high-level languages. Thought performance optimization was "premature." Then our API started choking under load. Here's what nobody tells you about "just use Python": That 50ms latency? Compounds across 1000 requests Memory leaks you didn't know existed The "it works on my machine" that doesn't scale Abstractions that hide what's actually happening Don't get me wrong - Python, JavaScript, they're incredible. But they let you ignore fundamentals until you can't anymore. Learning C++, Go, and Rust taught me: Memory isn't infinite (shocking, I know) Garbage collection has a cost Pointers aren't scary, they're powerful Understanding the machine makes you better at ANY language Sometimes you need control, not convenience Redis connection pooling made sense after understanding memory management. WebSocket streaming clicked after learning about I/O operations. Multi-container orchestration became obvious after understanding processes. You don't need to write everything in Rust. But understanding WHY Rust does what it does? That makes you a better developer in every language. High-level languages let you build fast. Low-level knowledge lets you build right. The best developers I know can switch between abstraction levels. They know when to optimize and when to ship. They understand the trade-offs, not just the syntax. Learn the machine. Then abstract away from it with intention. ps: knowing how memory works isn't optional anymore!

The first year of free-threaded Python🐍🚀 One year after the first experimental release, the free-threaded Python build has become one of the most significant technical shifts in the language’s history. Quansight Labs published a great recap on how the project evolved and what it means for the ecosystem. 👉 https://lnkd.in/dTkMRtzf 🔍 What it’s about Python’s Global Interpreter Lock (GIL) has always been the main limitation for true parallelism. The new free-threaded build removes this global lock, allowing threads to execute Python bytecode in parallel — finally making full use of multicore CPUs without the need for heavy process-based workarounds. ✅ Progress over the past year Core ecosystem tools — pip, setuptools, meson, pybind11, and Cython — have been adapted for compatibility. Scientific stack — NumPy, pandas, SciPy, and PyArrow — already works under the free-threaded build. Many parts of the standard library have been made thread-safe (warnings, asyncio, ctypes). Single-thread performance is now close to the regular GIL build. ⚠️ Current limitations Native extensions and C-based packages still need explicit adaptation for thread safety. Some libraries rely on global state and assume the presence of GIL, which can cause data races or undefined behavior. Ecosystem coverage is partial — full stability requires community testing and gradual migration. 💡 Why this matters for backend systems Removing the GIL changes how Python can scale across CPU cores. For backend workloads — async servers, background jobs, data pipelines — this means real concurrency without process duplication. Threading patterns that were previously avoided due to the GIL can now become first-class citizens, simplifying architecture and improving throughput. 🧭 Outlook The free-threaded project turns Python’s concurrency model into something closer to Java or Go while preserving its ecosystem and syntax. It’s not production-ready yet, but the groundwork is solid and performance trade-offs are minimal. As more libraries migrate, multi-threaded Python will move from an experiment to a new default.