WebAssembly ends JavaScript's Edge monopoly, enabling Python support

Python teaches boundaries in the most unexpected ways 😅 Functions really have personalities: Local variables: “I’m only here for this function.” They live inside a function and disappear once it’s done. Global variables “I belong to everyone.” Accessible anywhere in the file… but if you modify them carelessly, your whole program feels it. Nonlocal variables “I’m not global, but I’m not just local either.” They live in an outer function and can only be modified in an inner function if you explicitly declare nonlocal. Here’s what stood out to me: Coming from JavaScript, I was used to closures just working. In JavaScript, if an inner function sees a variable in its outer scope, it can modify it directly. No special keyword. In Python? If you want to modify a variable from an enclosing function, you must explicitly say nonlocal. If you don’t, Python quietly creates a new local variable instead. Same concept. Different philosophy. JavaScript assumes intent. Python requires you to declare it. And that small difference changes how you think about state, structure, and responsibility in your code. Scope isn’t just syntax. It’s architecture. Boundaries matter. In code and in life. #Python #JavaScript #BackendJourney #SoftwareEngineering #WomenInTech

Designing scalable, resilient systems using Micro services Architecture — from concept → implementation → real-world best practices. In this post, we break down:  • What micro services really are (without the buzzwords)  • Why modern companies choose them for scale & reliability  • How to build them using Python & NestJS  • Best practices that actually work in production Built for teams that care about clean architecture, performance, and long-term scalability. Let’s build what’s next → https://hybridcode.studio/ #Microservices #BackendSystems #SoftwareEngineering #Python #NestJS #FastAPI #CloudNative #HybridCodeStudio #TechConsulting

A frequent mistake I see with even some senior developers? Writing a web app in TypeScript or Python that is not properly asynchronous and therefore doesn’t scale when needed. A curious thing is that even advanced AI like Claude Code makes the same mistake. The difference is that Claude can fix the issue in minutes when pointed out… An example that AI mostly just mirrors our own behaviors?

Stop wasting hardware resources: The power of Async Python 🚀 Most developers think "more users = more servers." But often, the solution is just better concurrency management. I just published a new article on Dev.to compare FastAPI vs. Flask for production environments. If you’re dealing with network calls, file I/O, or GPU waiting times, moving to an async-first mindset is a game-changer. It’s about doing more with less: less hardware, less waiting, and less technical debt. Check it out: 👇 https://lnkd.in/eDBsBHfR #WebPerformance #CloudComputing #Python #TechTalk #FastAPI @tiangolo

🏠 House Pricing Prediction Web App Developed a full-stack ML web application that predicts house prices using a regression model trained on the Boston housing dataset. Built a responsive frontend with HTML/CSS, integrated the model via a Flask backend, and deployed the app to Heroku. Implemented CI/CD with GitHub Actions for automated deployment, gaining hands-on experience with Python, scikit-learn, Flask, and end-to-end ML application deployment. Repository: https://lnkd.in/ghnXYT6y #MachineLearning #Python #Flask #WebDevelopment #DataScience #MLDeployment #Heroku #GitHubActions #FullStack #HousePricePrediction #AI #Projects

My Python RAG pipeline choked at 50 concurrent users. So I ripped out the orchestration layer and rebuilt it in Node.js. Unpopular opinion: Python is the king of training. But for serving? It’s too heavy. When you move from a Jupyter notebook to real-world WebSockets, things break. I didn't just need inference. I needed: • To handle 1,000+ concurrent embeddings. • Non-blocking streams. • Zero serialization headaches. Python’s GIL (Global Interpreter Lock) fought me every step. Node’s event loop ate the load for breakfast. The new stack: 1. Training: Python (obviously). 2. API/Orchestration: Node.js + TypeScript. 3. Vector DB: Pinecone. The result? 40% lower latency and no thread-blocking nightmares. Use the right tool for the layer, not just the language you learned first. What is the biggest bottleneck in your current stack? #VectorDatabase #RAG #Javascript

FastAPI makes Python feel modern Typed contracts. Automatic OpenAPI. Native async that actually scales. Clean code in, production-ready API out. If your backend still needs layers of glue to feel “modern,” you’re solving the wrong problem.

Python 3.14 is already here—and it’s finally tackling the "Performance Tax." 🐍 After 6 years in the Python ecosystem, I’ve seen my fair share of ‘slow code’ debates. But the 3.14 release (and the 2026 roadmap) is a game-changer for those of us building high-scale backends. Three features I’m currently digging into: 1. Zero-Overhead Debugging (PEP 768): We can finally attach profilers to production processes without the usual "performance hit." This is huge for diagnosing those "it only happens in prod" spikes. 2. Deferred Annotation Evaluation: Faster startup times and cleaner type-hinting without the string-quote hacks. 3. The "No-GIL" Era: We’re moving closer to true multi-core Python. If you’re still writing synchronous, single-threaded code for heavy tasks, it’s time to rethink your architecture. The takeaway? Python isn't just the "easy" language anymore; it’s becoming a performance powerhouse.

Master Python from A to Z: The Scalability Roadmap 🚀 Are you looking to move beyond basic scripts and build scalable, professional applications? I’ve summarized a comprehensive 11-phase roadmap based on "Python: From Syntax to Scalability." This guide breaks down the journey into digestible milestones: 🔹 The Basics: Syntax, dynamic typing, and efficient data structures. 🔹 The Engine: OOP, error handling, and modular code for reusability. 🔹 The Data Stack: Deep dives into NumPy, Pandas, and Scikit-Learn. 🔹 The Web & Beyond: Flask vs. Django, API security, and Database ORMs. 🔹 High Performance: Concurrency, Asyncio, and performance profiling. Whether you're a beginner or looking to sharpen your architecture skills, this roadmap provides the structure needed to master the language. Follow GUDUGUNTLA SAI KARTHIK for more and useful updates!!! 👍 𝗟𝗶𝗸𝗲 *if you found it helpful!* 🔁 𝗥𝗲𝗽𝗼𝘀𝘁 with your network! 🔖 𝗦𝗮𝘃𝗲 for future use! 📤 𝗦𝗲𝗻𝗱 to your connections! 💬 𝗖𝗼𝗺𝗺𝗲𝗻𝘁 your thoughts below! #Python #Programming #DataScience #WebDevelopment #SoftwareEngineering #PythonRoadmap #CodingTips #TechLearning

Master Python from A to Z: The Scalability Roadmap 🚀 Are you looking to move beyond basic scripts and build scalable, professional applications? I’ve summarized a comprehensive 11-phase roadmap based on "Python: From Syntax to Scalability." This guide breaks down the journey into digestible milestones: 🔹 The Basics: Syntax, dynamic typing, and efficient data structures. 🔹 The Engine: OOP, error handling, and modular code for reusability. 🔹 The Data Stack: Deep dives into NumPy, Pandas, and Scikit-Learn. 🔹 The Web & Beyond: Flask vs. Django, API security, and Database ORMs. 🔹 High Performance: Concurrency, Asyncio, and performance profiling. Whether you're a beginner or looking to sharpen your architecture skills, this roadmap provides the structure needed to master the language. Follow Harshitha Shapuram for more and useful updates!!! 👍 𝗟𝗶𝗸𝗲 *if you found it helpful!* 🔁 𝗥𝗲𝗽𝗼𝘀𝘁 with your network! 🔖 𝗦𝗮𝘃𝗲 for future use! 📤 𝗦𝗲𝗻𝗱 to your connections! 💬 𝗖𝗼𝗺𝗺𝗲𝗻𝘁 your thoughts below! #Python #Programming #DataScience #WebDevelopment #SoftwareEngineering #PythonRoadmap #CodingTips #TechLearning