Mastering OOP Principles for Cross-Platform Development

In Java, private means private. In Python, it means: “I trust you not to look.” I was exploring encapsulation and discovered something interesting. In Java, access is enforced. In Python, it’s… negotiated. A double underscore (__attr) doesn’t truly hide anything. It just renames it. Which means: You *can* still access it — if you know how. That realization changed how I think about class design. Java protects the code. Python trusts the developer. Two different philosophies. Which one do you prefer? 👇 Curious to hear your perspective #Python #Java #OOP #SoftwareEngineering #LearningInPublic

Switching from Python to Java: Coming from a Python-heavy background, working with Java has been a real shift in perspective. In Python, a lot is taken care of for you through powerful high-level abstractions. You can move quickly, write less code, and focus on solving problems. But Java? It makes you slow down in a good way. You start paying attention to details you might have overlooked before: type definitions, structure, and the mechanics behind what your code is actually doing. It demands more explicitness, more discipline, and a deeper level of understanding. And that’s the beauty of it. Different languages, different strengths, but stepping outside your comfort zone is where real growth happens. https://lnkd.in/deNbabM5 #Java #Python #SoftwareEngineering #CodingJourney #LearningToCode

🚀 Is Python finally catching up with Java in multithreading? My experiment says… almost. Over the past few days, I explored Python’s free-threaded (no-GIL) build and compared it with Java multithreading. Here’s what I did 👇 🔹 Built Python with GIL disabled (free-threaded mode) using CPython 🔹 Ran CPU-bound workloads using multi-threading 🔹 Compared results with Java running on the Java Virtual Machine 🔹 Measured execution time, CPU utilization, and scaling 💡 Key Observations ✅ Python (no-GIL) now utilizes multiple cores effectively ✅ Performance gap between Python and Java is much smaller than before ❗ Java is still faster due to JIT optimizations ❗ Python still has interpreter overhead 🔥 The Big Shift From GIL bottleneck to true parallelism — Python is evolving fast 🚀 Earlier: ➡️ Python + threads = ❌ no real parallelism (GIL limitation) Now: ➡️ Python (free-threaded) = ✅ true multi-core execution This is a major evolution in Python’s capabilities. #Python #Multithreading #Performance #NoGIL #Backend #Programming

Nobody told me this when I started coding. 👇 Most developers pick a language out of habit — not strategy. Here's what actually matters: ✅ Building something that needs to be secure & tamper-proof? → Java ✅ Building something that needs to crunch massive data? → Python Both are powerful. Both have a purpose. The clarity is in knowing which problem you're solving. #Java #Python #CodeNewbie #ProgrammingTips #TechCommunity

Explore why Python is the ultimate choice for data science, engineering, and analytics compared to Java. Learn about its versatility, how it borrows the best features from other languages, and why it's the most efficient tool for building data pipelines and processing complex file formats. #python #java #dataengineering #datascience #dataanalytics #programminglanguages #datapipelines #softwaredevelopment

Did you know Python is actually older than Java? Yes, read that again. Python’s clean, indentation-driven syntax makes it feel like a modern language, but its first public release was all the way back in 1991. Java, on the other hand, didn’t see its first public release until 1995. Modern isn’t always new, sometimes it’s the ideas that were just ahead of their time. #Java #Python

1. Stop chasing every language We often try to learn Python, Java, Go, JavaScript, and everything at once. But Saumya Singh made it clear join this video https://lnkd.in/gZnKsAq2 Depth matters more than randomness.

Unlock the power of APIs and master regular expressions in Python! In this lecture, you'll learn how to make API requests to fetch data from the web, handle JSON responses, and use Python's requests library. We'll also dive into regular expressions (regex) to efficiently search, match, and manipulate text data. Perfect for beginners and those looking to enhance their data processing skills. #Python #API #Regex #DataScience #Programming #Tutorial https://lnkd.in/gCZQdtnb

Master Concepts by Building Upgrading Mental Models Python 🐍? Java ☕? C++ ⚙️? JavaScript 🌐? — see how each plays a role in building AI systems. Concept Playground 👇 https://lnkd.in/dzg-74Zy