FastAPI in Python: Building Backend Systems

I asked Claude a simple question: "If you were working on a web project, which language and framework would you choose and why? Don't think about me as a developer -- think about yourself as an LLM." https://lnkd.in/eeZESn3q Claude picked Python and TypeScript. Not because they are better languages -- but because its code generation quality is directly proportional to how many high-quality examples exist in its training data. We have some excellent open source projects -- Discourse, Mastodon, GitLab, Solidus. More recently, 37signals made a significant contribution by open sourcing their ONCE products: Campfire (group chat), Writebook (online book publishing), and Fizzy (kanban tracking). These are production-grade Rails applications built by the creators of the framework itself -- exactly the kind of high-quality, real-world codebases that LLMs need to learn from. But even with these additions, the volume does not compare to what exists in JavaScript or Python. Too many Rails applications remain proprietary, behind closed doors, invisible to training datasets. This creates a vicious cycle: LLMs perform worse with Rails, so developers building with AI choose other stacks, so fewer new Rails projects get created, so even less training data exists, so LLMs fall further behind. Ruby on Rails gave us an era of unmatched developer productivity. It showed the world that web development could be joyful. That philosophy still matters -- arguably more than ever in a world drowning in over-engineered complexity. I want to make sure that when an AI agent sits down to write code, it can write Rails as fluently as it writes Next.js. And that starts with us giving those AI agents something to learn from. Open source your projects. Write about your work. Document your patterns. The future of Rails depends on it. #rubyonrails #fizzy #discord #37signals

That's true! I would add "We need more free (as in freedom) Ruby on Rails projects" too. Mainly with Gnu Public Lincese-like! #GNU #freesoftware #libresoftware #copyleft #community #Ruby #RubyOnRails

Recently I’ve been exploring Golang + Fiber for building high-performance APIs 🚀 As someone coming from Laravel / Node.js background, I was curious: 👉 Can Go really deliver better performance for real-world backend systems? Here’s what I found after building a simple API service using Fiber: ⚡ Performance Fiber is extremely fast. Compared to traditional frameworks, response time is noticeably lower and memory usage is more efficient. ⚡ Simplicity The syntax feels familiar (similar to Express.js), making it easy to get started: app.Get("/api", func(c *fiber.Ctx) error { return c.JSON(fiber.Map{"message": "Hello World"}) }) ⚡ Concurrency Golang handles concurrent requests effortlessly with goroutines, which is a big advantage for high-traffic APIs. ⚡ Use Case Fit From my experience, Go + Fiber is very suitable for: • High-performance APIs • Microservices • Data processing / scraping pipelines 💡 My takeaway: I don’t see Go replacing Laravel or Node.js entirely, but it’s a powerful addition when performance and scalability really matter. Next step: I’m planning to combine Go (Fiber) with Python automation & AI processing to build a more efficient data pipeline system. Curious to hear from others: 👉 Are you using Go in production? In what use cases? #golang #backend #api #microservices #softwareengineering

Every language ecosystem that scales eventually adopts frameworks and reusable components. Every single one. JavaScript got React and Next.js. Python got Django and Flask. Ruby got Rails. Go got standard library patterns and well-known packages. Rust got crates.io. Python developers reach for Django because it encodes years of web development knowledge. React exists because managing UI state at scale benefits from shared patterns. The value is in the accumulated experience, not just the syntax. Infrastructure is no different. Declarative languages benefit from logical reductions just as much as imperative ones. A Terraform module is a logical reduction. It takes a complex set of resources, encodes opinions about how they should work together, and presents a clean interface. This isn't about limiting flexibility. It's about encoding knowledge. When a VPC module handles IPv6 dual-stack, flow logs, transit gateway attachments, and AZ capacity constraints, that's hundreds of hours of production experience distilled into a reusable component. The next team that needs a VPC doesn't need to rediscover those edge cases. The pattern is universal: ecosystems that scale adopt packages, frameworks, and shared components. Infrastructure has reached the scale where this isn't optional anymore. Platform teams who adopt this pattern build on foundations. The ones who've made this shift tell us the biggest change is how much faster new engineers become productive.

🚀 Understanding Python Full-Stack Ecosystems In modern web development, Python has become one of the most powerful and versatile choices for building scalable, secure, and high-performance applications 🐍💡. This visual represents some of the most popular Python full-stack combinations used by developers today 👇 🟣 Django Stack (All-in-One Powerhouse 🏗️) 📦 PostgreSQL / MySQL | ⚙️ Django | 🎨 HTML/CSS/JS (or React) ✨ Built-in authentication, admin panel, ORM ✨ Rapid development with clean architecture ✨ Perfect for scalable web apps & startups 🟣 Flask Stack (Lightweight & Flexible ⚡) 📦 MongoDB / PostgreSQL | ⚙️ Flask | 🎨 HTML/CSS/JS / React ✨ Minimal and highly customizable ✨ Ideal for APIs and microservices ✨ Great for beginners and fast prototyping 🟣 FastAPI Stack (Modern & High Performance 🚀) 📦 PostgreSQL | ⚙️ FastAPI | ⚛️ React / Vue ✨ Extremely fast (async support) ✨ Automatic API docs (Swagger UI) ✨ Best for building production-ready APIs 🟣 Python + React Stack (Modern Full-Stack 🔥) 📦 PostgreSQL / MongoDB | ⚙️ Django/FastAPI | ⚛️ React ✨ Clean separation of frontend & backend ✨ Highly scalable architecture ✨ Industry-standard approach 🟣 Data-Driven Python Stack (AI + Web 🤖) 📦 PostgreSQL | ⚙️ FastAPI/Django | 🧠 ML Models (TensorFlow/PyTorch) ✨ Integrates AI into web apps ✨ Ideal for smart applications ✨ Future of intelligent systems 👉 All stacks follow the same idea: Frontend (UI) + Backend (Logic) + Database (Storage) 👉 The difference is how flexible, fast, or scalable each stack is 🔄 🎯 Why Python Full Stack? ✔ Easy to learn & beginner-friendly ✔ Powerful for both web + AI development ✔ Huge community & job demand ✔ Used by companies like Instagram, Netflix, Spotify 💡 My Take: If you're starting → go with Flask or Django If you're aiming for future-ready apps → learn FastAPI + React #Python #FullStackDevelopment #WebDevelopment #Django #Flask #FastAPI #React #SoftwareEngineering #DeveloperJourney 💻✨

Django is consistently ranked as one of the top web frameworks globally, specifically dominating the Python ecosystem and backend development. As of 2025/2026, its position among frameworks is as follows: Leader in Python Web Development: Django remains the primary "batteries-included" choice for Python developers, frequently ranking alongside Flask and FastAPI as the top three Python frameworks. Top 5 Worldwide "Most Wanted": It is recently ranked as the 4th most wanted framework for web development in global developer surveys. Preferred by 74% of Python Web Developers: According to the Django Developer Survey 2024, roughly 74% of developers in the Python space still prefer Django for full-stack and API development. High Performance for Enterprise: It is classified as one of the top 7 backend frameworks globally across all languages (competing with Laravel, Spring Boot, and Express) due to its scalability and robust security. Widely Adopted by Tech Giants: Django powers major platforms including Instagram, Spotify, YouTube, and Pinterest, maintaining its status as a proven, production-ready framework. While FastAPI is growing in popularity for high-performance microservices, Django's extensive built-in features (admin panel, ORM, and authentication) keep it at the top for rapid, secure application development.

🚀 Django Backend: All Crucial Concepts & Features You Should Know If you're diving into backend development, Django is one of the most powerful frameworks built on Python. Here’s a clean breakdown of everything that actually matters 👇 🔹 Core Architecture (MVT) Django follows the Model-View-Template pattern: • Model → Database structure & data handling • View → Business logic & request/response handling • Template → Frontend rendering (HTML + dynamic data) 🔹 URL Routing Maps user requests to specific views using urls.py — clean and scalable routing system. 🔹 ORM (Object Relational Mapping) No need for raw SQL. Interact with databases using Python: • Query, filter, update seamlessly • Database-agnostic (SQLite, PostgreSQL, MySQL) 🔹 Authentication & Authorization Built-in system for: • User login/logout • Password hashing • Permissions & roles 🔹 Admin Panel (Game Changer) Auto-generated admin dashboard to manage data without writing extra code. 🔹 Forms Handling Secure form processing with validation, CSRF protection, and clean data handling. 🔹 Middleware Hooks into request/response cycle: • Authentication • Logging • Security layers 🔹 REST API Development With Django REST Framework: • Build scalable APIs • Serialization & validation • Token/JWT authentication 🔹 Security Features 🔐 Django protects against: • SQL Injection • XSS (Cross-Site Scripting) • CSRF attacks • Clickjacking 🔹 Scalability & Performance • Works with caching (Redis, Memcached) • Supports asynchronous views • Easy integration with cloud & containers 🔹 File Handling Upload & manage media files (images, PDFs, etc.) easily. 🔹 Signals Trigger actions automatically (e.g., after saving a model). 🔹 Session & Cookies Maintain user state across requests. 💡 Why Django? ✔ Rapid development ✔ Clean & maintainable code ✔ Batteries-included framework ✔ Trusted by companies like Instagram & Pinterest 🔥 Whether you're building a startup product, REST API, or full-stack app — Django gives you everything out of the box. #Django #BackendDevelopment #Python #WebDevelopment #SoftwareEngineering #FullStack #APIs

Understanding Django concepts with practical usage is really a worth try. Once you started working on a Django web app project, you will definitely face most of these concepts. I suggest to work on projects, you might be getting lots of bugs and errors. Even though once you solve it and progress ahead. You found happiness and satisfaction. That's the best way of learning something new.

🚀 My Journey from Django to Flask: 5+ Years of Backend Development Lessons After working with both Django and Flask extensively at NIIT Limited and now at Clcoding, I've learned that the "which framework is better" debate misses the point entirely. Here's what 5+ years of real-world Python backend development taught me: WHEN TO USE DJANGO: ✓ Enterprise applications needing rapid development ✓ Projects requiring built-in admin panels and ORM ✓ Large-scale applications with complex data relationships ✓ Teams that benefit from "batteries-included" approach Example: At NIIT Limited, we built a multi-user data processing platform with Django. The built-in admin panel and ORM saved us weeks of development time. WHEN TO USE FLASK: ✓ Microservices and API-first architectures ✓ Projects requiring maximum customization ✓ Lightweight applications with specific requirements ✓ When you want full control over components Example: For a recent automation project at Clcoding, Flask's minimalist approach let us build exactly what we needed without unnecessary overhead. COMMON PITFALLS I'VE ENCOUNTERED: Django: → N+1 query problems (always use select_related and prefetch_related) → Bloated views mixing business logic with presentation → Not leveraging Django's caching framework early enough Flask: → Reinventing the wheel for basic features → Poor project structure as applications grow → Security oversights without Django's built-in protections MY PRACTICAL TIPS: 1. Start with Django if you're building your first production app 2. Learn Flask to understand web frameworks at a deeper level 3. Use Django for MVPs and enterprise apps 4. Choose Flask for APIs, microservices, and cloud-native applications 5. Master one framework deeply before switching The real skill isn't choosing the "right" framework – it's knowing when to use each one based on project requirements, team expertise, and scalability needs. What's your experience with Django vs Flask? Drop your thoughts below. #Python #BackendDevelopment #Django #Flask

You can't just be a NestJS developer anymore. Three years ago, NestJS was your entire backend. Today it's maybe 15% of it. Your stakeholders have seen what AI can do. They want robust, generally intelligent backend systems. Day one. That means retry strategies for when the LLM returns garbage JSON. A queuing system for background processing. Durable execution so failed jobs don't disappear. Streaming for real-time responses. Shared state across services. Observability so you can actually explain what happened when it breaks at 3 AM. An agentic framework so your AI can reason across tools. So now you're a NestJS developer who also needs BullMQ, Redis, Temporal, Pusher, OpenTelemetry, node-cron, and LangChain. Seven additional systems. Seven sets of docs. Seven integration points. Seven things that can fail independently. And that's before you add the Python ML libraries that need their own runtime while the rest of your stack is TypeScript. This isn't specific to NestJS. Swap in Rails, FastAPI, Spring. Same story. The framework that used to be your entire backend is now a fraction of it. Stakeholders went from "build me an API" to "build me an intelligent system" in about 3 months. The tooling didn't keep up. Every framework developer is now expected to be a distributed systems architect. Most weren't trained for that. Most frameworks weren't built for that. That's not a skills gap. That's an abstraction gap.