Choosing the Right Concurrency Model in Python

Stop writing Python like Java/C++! Building scalable applications in Python means embracing its unique strengths, not fighting them. A truly "clean" API in Python isn't just about naming conventions; it's about thinking in terms of Python's object model, its dynamic nature, and its emphasis on readability. Let's look at how we handle optional parameters. Okay: class Service: def process(self, data, config=None): if config is None: config = {} # Boilerplate to handle None # ... process with data and config Best (Pythonic): class Service: def process(self, data, config=None): config = config or {} # Concise and idiomatic # ... process with data and config The "Best" version uses Python's truthiness. None evaluates to False, so config or {} will assign an empty dictionary if config is None, otherwise it uses the provided config. It's shorter, clearer, and less prone to errors. Takeaway: Design APIs that leverage Python's expressiveness for clarity and conciseness. #Python #CodingTips

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

🐍 Access Modifiers in Python — What Every Developer Should Know! Coming from Java or C++? You might expect Python to have strict private and public keywords. It doesn't — and that's by design. 🎯 Python uses a naming convention to signal access intent: 1️⃣ Public → self.name Accessible from anywhere. The default for all attributes and methods. 2️⃣ Protected → self._name Single underscore. A gentle signal for "internal use" — still accessible, but handle with care. 🔓 3️⃣ Private → self.__name Double underscore triggers name mangling → _ClassName__name. Harder (but not impossible) to access from outside. 🔒 💡 Python's philosophy: "We're all consenting adults here." It trusts developers to respect conventions rather than enforcing hard rules. Understanding this is key to writing clean, maintainable, Pythonic OOP code. #Python #PythonProgramming #OOP #ObjectOrientedProgramming #SoftwareEngineering #CodeNewbie #PythonDeveloper #Programming #TechLearning #CleanCode #100DaysOfCode #LearnPython #BackendDevelopment #DevTips #PythonTips

🔥 Mastering JSON Parsing in Python! 🐍 Ever wondered how to work with JSON in Python? JSON (JavaScript Object Notation) is a popular format for data exchange due to its simplicity and readability. For developers, understanding JSON parsing is essential as it allows you to interact with APIs, handle configuration files, and exchange data between different systems effortlessly. Here are the steps to parse JSON in Python: 1️⃣ Load the JSON data using the `json.loads()` function. 2️⃣ Access the values using keys just like a Python dictionary. 3️⃣ Iterate through JSON arrays to extract multiple values. 👉 Pro Tip: Use `json.dumps()` to convert Python objects back to JSON. ❌ Common Mistake: Forgetting to handle exceptions when parsing JSON can lead to runtime errors. 🚀 Ready to level up your Python skills? Try parsing JSON with this code snippet: ``` import json # Sample JSON data json_data = '{"name": "John", "age": 30, "city": "New York"}' # Parse JSON data = json.loads(json_data) # Access values name = data['name'] age = data['age'] city = data['city'] print(name, age, city) ``` 🤔 What's your favorite way to work with JSON data in Python? 🌐 View my full portfolio and more dev resources at tharindunipun.lk #JSON #Python #APIs #DataExchange #CodingTips #JSONParsing #Programming #DeveloperCommunity #TechSkills

⚡ Why Java Still Wins in 2026 (Performance Reality) Is Python really “too slow” for modern systems? We’ve all heard: • Python is easier • Python has better libraries • Developer speed > machine speed That’s true… until you hit production scale. 💥 At scale: • Latency becomes visible • Infrastructure costs increase • Concurrency becomes a real bottleneck This is where Java still has a strong edge. 🧠 JVM optimizations (like JIT compilation) allow Java to handle high-load systems far more efficiently — sometimes dramatically so, depending on the workload. That said — Python is still the right choice in many scenarios (especially AI, data, and rapid prototyping). The real question isn’t “Which is better?” 👉 It’s “When should you use which?” I break this down in detail here: https://lnkd.in/dVjP3x4S Curious — what are you using in production today? #Java #Python #SoftwareEngineering #Backend #SystemDesign

Stop writing clunky Python code. 🐍 Most developers treat Python like C++ or Java, but the "Zen of Python" reminds us: Simple is better than complex. I just finished Benjamin Bennett Alexander’s latest guide, and these 3 "tricks" are absolute game-changers for your daily workflow: 1️⃣ Merge Dictionaries with 1 Character: Forget .update(). In 2026, the | operator is the cleanest way to combine data. 2️⃣ Stop Guessing Memory Usage: Use sys.getsizeof() to see exactly how much RAM your objects are eating. (Spoiler: Tuples are almost always more efficient than lists) . 3️⃣ Speed Up String Joins: Stop using + to concatenate. Using .join() is significantly faster because Python only has to create one string object in memory. Python isn't just about making it work; it's about making it "Pythonic." Which of these is new to you? Let’s discuss in the comments. 👇 #PythonProgramming #SoftwareDevelopment #CodingTips #Technology #AI

I created a Java binding for Google's Magika ONNX file-type detector. Maintains byte-for-byte parity with the upstream Python binding on every fixture. ~2 ms steady-state inference per file on M-series Mac. (cold first-call ~100s of ms) https://lnkd.in/eTjACKzf #java #magika #opensource #onnx

7 Python Mistakes That Make Your Code Slow 🐍 👉 Bad coding practices make Python slow — not Python itself. When written correctly, Python powers some of the world’s biggest platforms like Google, Netflix, and Instagram. The difference between average Python code and professional Python code is usually these small mistakes. Here are some serious Python mistakes developers often make 👇 ❌ Writing nested loops for heavy operations ❌ Ignoring list comprehensions ❌ Not using virtual environments ❌ Poor error handling ❌ Writing everything in one huge script ❌ Not using built-in libraries ❌ Inefficient database queries Professional Python developers do this instead 👇 ✅ Use list comprehensions & generators ✅ Split code into modular functions and classes ✅ Use virtual environments for dependencies ✅ Implement proper exception handling ✅ Use built-in optimized libraries ✅ Optimize database queries ✅ Write clean and maintainable code When used properly, **Python can handle large-scale applications, AI systems, and data platforms efficiently. Which Python mistake do you see most often? #python #pythondeveloper #programmingtips #softwaredeveloper #codinglife #webdevelopment #backenddeveloper #developercommunity #learnpython #programming

Several years ago I came accross the Python paradox by Paul Graham. He argues that lisp and Python programmers are among the ones who trutly care about programming 🐍… https://lnkd.in/eJjqEtPA

Tonight, I'm a fortune teller and I predict that in the next 3-5 years Rust will take over from languages such as Python. Agent coding is not stopping. Python has runtime errors and no amount of unit testing from an agent is going to keep up with that. Rust on the other hand, if you completely ignore the fact that it's compiled, has no runtime/VM dependency, real multithreading (no fake Python GIL), no GC, WebAssembly, Linux kernel adoption, memory safety, no surprise memory leaks, etc. The main thing that will drive it to dominate as a programming language is its compiler. You could argue that more popular languages like Python mean AI agents will know it better than Rust due to how much of it they've seen. But the compiler creates a very quick feedback loop that an agent uses to produce error free code during compile time and not at runtime. Don't believe me? Try it yourself. The Python ML stack argument doesn't hold up either. Researchers don't want to program, they want to research. They're already using agents to write their code and when that's the case, they don't care what language it's in. The ML ecosystem follows where reliable code gets built, and no amount of Python training data solves a runtime error lottery. If you disagree with this, give me an argument against Rust. I say this as someone with decades of Python development. I wish it wasn't so, but I can't find a compelling reason not to branch out into Rust, and the timing with agent coding seems right.