Go outperforms Node and Python in high-volume tasks

👩💻 Tech Stack: Every developer gets asked: “What’s your stack?” Here’s mine — and the honest reason behind each choice 👇 🐍 Python — Fast to write, powerful for CV and backend logic ⚛️ React.js — Component thinking changed how I build UIs 🟢 Node.js — Same language from frontend to backend, seamless 🗄️ SQLite — Lightweight, zero config, perfect for real projects I didn’t pick these randomly. I picked them because I BUILT with them. Projects teach you more than any course ever will. What’s in your stack? Let’s discuss 👇 #ReactJS #Python #FullStack #WebDev #TechStack #SoftwareDeveloper

🚀 Node.js vs Python — Different Strengths, Endless Possibilities In today’s tech landscape, choosing the right tool isn’t about which is better — it’s about what fits your use case. 💡 Why Node.js? ⚡ Blazing-fast, event-driven architecture 🌐 Full-stack JavaScript (one language, everywhere) 🔄 Perfect for real-time apps & scalable APIs 💡 Why Python? 📖 Clean, beginner-friendly syntax 🤖 Dominates AI, ML & Data Science 🛠️ Powerful for automation & rapid development 🔥 Reality check: Great developers don’t compete between technologies — they leverage the best of both worlds. 👉 Use Node.js for speed, scalability & real-time systems 👉 Use Python for intelligence, data & automation 💬 What’s your go-to stack right now — Node.js or Python (or both)? #NodeJS #Python #FullStackDevelopment #WebDevelopment #AI #MachineLearning #Developers #TechCareer #Programming #BuildInPublic

⚛️ React taught me how to build UI… 🐍 Python is teaching me how systems actually work As a frontend developer, I was comfortable with React: ✔️ Building components ✔️ Managing state ✔️ Creating smooth user experiences But I realized something: 👉 I was building the “what users see” 👉 Not understanding the “how it works behind” So I started learning Python 🐍 And things started to change… Now I’m exploring: ✔️ APIs using FastAPI ✔️ Database integration (MongoDB) ✔️ Backend logic & data flow It’s not easy switching context from frontend → backend 😅 But it’s helping me think like a complete developer Frontend shows the result… Backend explains the reason. Still learning. Still building. 🚀 Are you focusing on frontend, backend, or both? 👇 #ReactJS #Python #FullStackDeveloper #FastAPI #WebDevelopment #LearningInPublic

I've been using Cloudflare Workers a lot lately to deploy Next.js and Astro websites, so I was excited to see them add Python support. The reason Python support is interesting is that so much of the data analysis and machine learning ecosystem lives there. What is particularly interesting is the ability to create "auxiliary workers" and call them via RPC from a JavaScript/TypeScript worker, including Vite-based frameworks like React Router, TanStack Start, and Astro. This effectively opens the door to integrating Python's ML ecosystem directly into JS-based web apps. #Cloudflare #CloudflareWorkers #Python #AstroJS https://lnkd.in/g-EwYrKg

🚀 Node.js vs Python vs Java vs Go — explained so simply, even your boss will get it! Developers often argue about which language is "best." But here's the truth — it's like asking whether a hammer or a wrench is better. It depends on the job. Here's a dead-simple breakdown 👇 🟢 Node.js — The multitasking DJ. Handles thousands of requests at once. Perfect for real-time apps & chat. 🐍 Python — The brilliant scientist. Not the fastest runner, but the smartest in the room. Dominates AI & data. ☕ Java — The seasoned banker. Strict, reliable, trusted by Fortune 500s for 30+ years. 🐹 Go — The Olympic sprinter. Lean, mean, and blazing fast. Built by Google for the cloud era. No language is perfect. The best engineers know which tool to pick, not just how to use one. Which one do YOU use? Drop it below 👇 #Programming #TechForEveryone #SoftwareDevelopment #Python #Java #NodeJS #Golang #LearningToCode #Tech #SoftwareEngineering #ProblemSolving

💡 The moment I started thinking like a backend developer: I stopped asking "Is my code correct?" And started asking 👉 "What could go wrong?" Now whenever I build something in Django, I think: → What if the user sends wrong data? → What if the API fails? → What if the database returns nothing? Earlier, I only focused on the happy path. Now I focus on edge cases. That one shift completely changed how I write backend code. Because real applications don't break on correct inputs… They break on the ones you didn't expect. If you're learning backend development, stop only building for perfect scenarios. Your users definitely won't cooperate. 😅 Are you thinking about edge cases yet? 👇 #Django #BackendDevelopment #Python #LearningInPublic #WebDev

Nobody talks about this side of being a Python backend developer. Every single day, I open my code editor thinking I know what I'm doing. And every single day — Python humbles me. Yesterday it was async/await behavior I misunderstood. Today it was a subtle ORM query that silently returned wrong data. Tomorrow? I genuinely don't know. And that used to scare me. Now it doesn't. Because I've realized — the day you stop learning something new is the day you start becoming irrelevant. The best backend developers I know aren't the ones who have all the answers. They're the ones who are genuinely curious about the next question. So if you're a developer Googling something you feel you "should already know" — You're not behind. You're just growing. That's the job. That's the whole job. #Python #BackendDevelopment #SoftwareEngineering #DeveloperLife #KeepLearning

Async ≠ Non-Blocking (A common mistake I see in FastAPI apps and with Django async views) As backend engineers, we often assume that using async def automatically makes our APIs scalable and non-blocking. But here’s the catch Using blocking code inside async functions can freeze your entire server. The Problem @app.get("/blocking-bad") async def blocking_bad_behavior(): time.sleep(10) # Blocks the event loop! Even though this is an async function, time.sleep() blocks the event loop, meaning: * No other requests are processed * Your API appears “down” for those 10 seconds * No Concurrency The Correct Approach @app.get("/blocking-fixed") async def blocking_fixed(): await asyncio.to_thread(time.sleep, 10) # Offloaded to thread Now: * Event loop stays free * Other requests are handled instantly * True concurrency Even a normal def works safely in FastAPI: @app.get("/normal-def") def normal_def_endpoint(): time.sleep(8) # Runs in thread pool automatically FastAPI smartly runs it in a thread pool — so your event loop is still safe. Key Takeaways async def does NOT guarantee non-blocking behavior Avoid blocking calls like time.sleep(), heavy CPU tasks, or sync I/O Use: * await asyncio.sleep() for async waits * asyncio.to_thread() for blocking work * or stick to def when appropriate Final Thought Async is powerful — but only when used correctly. Misusing it can be worse than not using it at all. If you’re building high-performance APIs with FastAPI, this is something you cannot ignore. #FastAPI #Python #AsyncIO #BackendDevelopment #SystemDesign #Concurrency #SoftwareEngineering

C++ is not dead. Far from it. Everyone’s chasing the latest shiny thing for backend dev. Python, Go, Rust. All good. But for pure grunt work, serious performance. Nothing truly beats C++. You just don’t. I saw a recent deep dive into how many high-frequency trading platforms are still doubling down on C++ for their core matching engines. They need nanosecond latency. You simply don't get that consistently with a garbage-collected language. It’s just not happening, yaar. We had a client last month, an e-commerce platform. They built their entire recommendation engine in Python. Looked good on paper. Then they hit millions of users. Latency shot up. Cloud bill became astronomical trying to scale. We had to rewrite critical parts. Cost them time and money. Bottom line. Understand your actual performance needs. Choose the right tool for the job. It’s not about what’s trendy. It’s about what works effectively. If you're building or scaling an engineering team, reach out at hr@kivenconsulting.com or kivenconsulting.com #KivenConsulting #SoftwareEngineering #IndiaTech #TechLeadership

Most developers coming to Elixir from languages like JavaScript or Python see [1, 2, 3] and assume it is an array. This is a mistake that makes functional programming feel much harder than it really is. An Elixir list is not a row of boxes you can jump into by position; it is a linked structure where each element only knows its value and what comes next. If you find yourself reaching for an index or appending to the end of a list, you are likely fighting the data structure. In Elixir, the only cheap operation is at the front. Understanding this head-first nature changes how you write algorithms, moving you away from expensive appends and toward idiomatic patterns like prepending and reversing. This week's post breaks down the mental model of lists as nested structures and why the [head | tail] syntax is about exposing shape rather than just memorizing syntax. https://lnkd.in/ewSsdkzw Paulo Valim & Bruce Tate at Groxio