How to Use Custom Managers in Django for Cleaner Code

🚀 Python vs Node.js – A Developer’s Perspective 🐍⚡ Both Python and Node.js are powerful in their own ways — but they shine in different worlds. 🔹 Python — perfect for data science, AI, machine learning, and backend automation. 🔹 Node.js — best for real-time applications, scalable APIs, and fast web development. Every technology has its purpose — the key is choosing what fits your project goals and team skills. 💡 #Python #Nodejs #FullStackDevelopment #BackendDevelopment #Developers #Programming #TechComparison #SoftwareEngineering

🐍 Why Python and Django Still Rule the Backend in 2025 In a time obsessed with microservices and shiny new stacks, Python and Django keep doing what they’ve always done best, getting real products into production fast. Django’s philosophy of “batteries included” still wins. From ORM to authentication and admin panel, it gives developers everything needed to build scalable apps without reinventing the wheel. Combine that with Python’s clarity and massive ecosystem, and you’ve got a framework that’s both elegant and powerful. #Python #Django #BackendDevelopment #WebDevelopment #SoftwareEngineering #Programming #DevCommunity #APIs #Tech #Developers

When you’ve worked on Django, FastAPI, ML, and DevOps… and people still ask, “So what’s your main skill?” → Adaptability. ⚙️ . . In code or in life, you fix what’s in your bucket — that’s how progress happens. 🔥 #python #fastapi #machinelearning #devops #backenddevelopment #softwareengineering #codinglife #programmer #developerlife #techcommunity

A startup rewrote their entire backend from Python to Node.js... just one week after product launch. The motive? Reduce latency, improve asynchrony and better handle competition. Does that mean Node is better than Python? No. It means there is no perfect stack, but yes technical decisions that make the difference when you understand their limits. . . . . . #nodejs #javascript #developer #coding #webdevelopment #backend #python

🚀 #Django 6.0 just dropped — and it’s a #developer’s dream! .Built-in #background jobs (no Celery needed) ⚙️ .Cleaner template partials, stronger security (CSP), and smoother performance with Python 3.13 💡 I summarized the biggest changes in my carousel below — swipe through to see how Django 6.0 can boost your productivity ⚡ 👇 Which feature are you most excited about? . . . . #Django #Python #WebDevelopment #Django6 #Productivity #Developers #TechUpdate #OpenSource

Why we migrated from Python to Node.js A startup rewrote their backend from Python/Django to Node.js/Express one week after launch due to Python's async limitations. The team struggled with Django's incomplete async support, colored functions problem, and the need for constant sync/async conversions. Despite losing Django's ergonomic ORM, they gained 3x throughput, unified their codebase with their existing Node worker service, and improved code maintainability. The three-day migration was driven by concerns about scalability and code quality rather than immediate load issues. #SoftwareEngineering #BackendDevelopment #WebDevelopment #NodeJS #Python #Django #ExpressJS #AsyncProgramming #Scalability #HighPerformance #Engineering #Developers #TechCommunity #Programming #StartupJourney #TechStartup #BuildInPublic #ProductEngineering #SystemDesign #CodeQuality #CloudComputing #APIDevelopment #Microservices #FullStackDevelopment

🐍 Mastering Python Functions: The Building Blocks of Clean Code ⚙️💡 In the world of #Python, functions are the heartbeat of every clean, scalable, and maintainable codebase. 💻✨ 🔹 A function is more than a reusable code block — it’s a way to organize logic, reduce repetition, and improve readability. 🔹 Defined with the def keyword, functions can take inputs (parameters), process data, and return outputs that make your programs both elegant and efficient. 🚀 def greet(name): return f"Hello, {name}!" 🎯 Functions like this ensure consistency across large projects and make teamwork smoother for developers. But Python doesn’t stop there — it offers advanced tools like: ⚡ Lambda functions (for concise logic) 🎨 Decorators (for adding functionality dynamically) 🔁 Higher-order functions (for abstraction and flexibility) Whether you’re building AI models 🤖, data pipelines 📊, or web apps 🌐 — mastering functions is key to writing professional, clean, and powerful Python code. #Python #Programming #CleanCode #SoftwareEngineering #DevCommunity #PythonTips #Coding #Developers

You can know every Python keyword, pass every online quiz, and still fail backend interviews. Why? Because companies don’t hire Python coders, they hire backend engineers. Here’s what separates them 🧵 Let’s be honest — Python is easy to start, but hard to master in the backend world. Anyone can write: ``` print("Hello World") ``` But not everyone can design an API that handles 100k users without breaking. That’s the gap. A backend engineer is part developer, part architect, part problem solver. You don’t just build endpoints — you build systems that: - Handle scale - Manage security - Stay online under stress - Integrate with other services That’s what real backend jobs demand. Here’s the uncomfortable truth: You can’t learn that from coding tutorials alone. They teach you the “what.” But backend engineering is about the “how” and “why.” Examples: - How does data flow through your app? - Why did you choose REST over GraphQL? - Why use PostgreSQL instead of MongoDB? Those are interview questions — and real-world ones. To think like a backend engineer, u must understand - Databases: schemas, transactions, performance - APIs: authentication, pagination, caching - Servers: concurrency, load balancing - DevOps: CI/CD, containerization, monitoring Each layer matters. Ignore one & ur system breaks You build an API that slows down under load. - A Python learner blames the language. - A backend engineer checks database indexing, caching, and query optimization, etc. See the mindset shift? It’s not about code — it’s about systems. That’s why backend engineering feels intimidating. It forces you to stop thinking in features and start thinking in flows — how every request travels through your system. It’s a new way of seeing code — and it changes everything. So if you’ve been learning Python and still feel stuck... You’re not broken — your approach is. You’ve mastered the language, but not the backend mindset. That’s the missing link between you and your first backend job. The good news? You can learn that mindset by building structured, real backend projects. Not toy projects, but real-world systems with APIs, databases, and deployments. That’s how you bridge the gap. That’s exactly what “Become A Python Backend Engineer” does. It takes you from writing scripts to designing real systems — the kind employers pay for. Stop coding. Start engineering in Python: https://lnkd.in/d5tahN8C

Want to experiment with advanced algorithms, but not sure where to start? Developers can jump right in with the open-source training hub. It's an abstraction layer that gives unified access to modern post-training methods with a consistent Python interface supporting multiple community backend implementations. It simplifies a complex process, handling the heavy lifting so developers can define their model and data paths and get started. #OpenSource #Algorithms #Python #Developers