Imran Shaikh’s Post

FastAPI vs Flask (Practical Comparison) FastAPI vs Flask — When Should You Choose Which? After working on production APIs using both, here’s my practical take: 🔹 Flask Minimal, flexible micro-framework Great for small to medium apps Large ecosystem More manual setup for validation & docs 🔹 FastAPI Built-in request validation (Pydantic) Automatic OpenAPI/Swagger docs Async support out of the box Higher performance (ASGI-based) In my experience: ✔ Flask works well for lightweight internal tools ✔ FastAPI is better for high-performance, scalable REST APIs ✔ FastAPI reduces boilerplate significantly For backend systems handling high traffic + strict schema validation, FastAPI has been my go-to. What’s your preference for production APIs? #Python #Backend #FastAPI #Flask #RESTAPI #SoftwareEngineering

𝐖𝐡𝐲 𝐢𝐬 𝐅𝐚𝐬𝐭𝐀𝐏𝐈 𝐫𝐞𝐚𝐥𝐥𝐲 𝐜𝐚𝐥𝐥𝐞𝐝 “𝐅𝐚𝐬𝐭”? — My Experience When I first heard about FastAPI, I honestly thought the name was just marketing. But while building an API recently, I understood why it truly deserves that name. At first, I was expecting the usual setup — writing serializers, validations, documentation manually, handling request parsing… the usual backend work. But something surprising happened. I wrote a simple API using Python type hints. And suddenly: • 𝘝𝘢𝘭𝘪𝘥𝘢𝘵𝘪𝘰𝘯 𝘸𝘢𝘴 𝘢𝘶𝘵𝘰𝘮𝘢𝘵𝘪𝘤𝘢𝘭𝘭𝘺 𝘩𝘢𝘯𝘥𝘭𝘦𝘥. • 𝘌𝘳𝘳𝘰𝘳 𝘮𝘦𝘴𝘴𝘢𝘨𝘦𝘴 𝘸𝘦𝘳𝘦 𝘤𝘭𝘦𝘢𝘯 𝘢𝘯𝘥 𝘴𝘵𝘳𝘶𝘤𝘵𝘶𝘳𝘦𝘥. • 𝘚𝘸𝘢𝘨𝘨𝘦𝘳 𝘥𝘰𝘤𝘶𝘮𝘦𝘯𝘵𝘢𝘵𝘪𝘰𝘯 𝘸𝘢𝘴 𝘨𝘦𝘯𝘦𝘳𝘢𝘵𝘦𝘥 𝘢𝘶𝘵𝘰𝘮𝘢𝘵𝘪𝘤𝘢𝘭𝘭𝘺. • 𝘕𝘰 𝘦𝘹𝘵𝘳𝘢 𝘤𝘰𝘯𝘧𝘪𝘨𝘶𝘳𝘢𝘵𝘪𝘰𝘯 𝘯𝘦𝘦𝘥𝘦𝘥. I didn’t write separate validation logic. I didn’t write API documentation. I didn’t even struggle with request parsing. Everything just worked. Then I tested performance with multiple requests — it handled them smoothly because of async support. That’s when I realized… FastAPI is not just fast in execution. It’s fast in development. It’s fast in reducing mistakes. It’s fast in building production-ready APIs. For a developer, “𝐟𝐚𝐬𝐭” 𝘥𝘰𝘦𝘴𝘯’𝘵 𝘰𝘯𝘭𝘺 𝘮𝘦𝘢𝘯 𝘴𝘱𝘦𝘦𝘥. 𝘐𝘵 𝘮𝘦𝘢𝘯𝘴 𝘴𝘢𝘷𝘪𝘯𝘨 𝘵𝘪𝘮𝘦, 𝘳𝘦𝘥𝘶𝘤𝘪𝘯𝘨 𝘤𝘰𝘮𝘱𝘭𝘦𝘹𝘪𝘵𝘺, 𝘢𝘯𝘥 𝘴𝘩𝘪𝘱𝘱𝘪𝘯𝘨 𝘤𝘰𝘯𝘧𝘪𝘥𝘦𝘯𝘵𝘭𝘺. Now I understand the name 😊 #FastAPI #Python #APIDevelopment #Brototype #BCR70

FastAPI is rapidly becoming the go-to choice for backend development, especially in AI projects. 🚀 Why? Fast & efficient: handles async operations smoothly. Easy to use: minimal boilerplate code. Auto-generated docs: saves time on writing API documentation. Industry adoption: used in ~80% of AI-related backend systems. Compared to Flask or Django, FastAPI offers better performance and async support, making it ideal for modern applications. 💡 If you’re building AI or high-performance APIs, FastAPI is definitely worth learning! #FastAPI #Python #MachineLearning #DeepLearning #AI

Been deep in a FastAPI backend project and something bugged me — the code I was writing looked different from the official docs. So I dug into it. Turns out FastAPI pushed some massive updates recently; 1. Python 3.9 support? Gone. Pydantic v1? Gone. The entire documentation was rewritten for Python 3.10+ syntax. What used to be: from typing import Optional name: Optional[str] = None Is now just: name: str | None = None Cleaner. No imports needed. 2. .dict() became .model_dump(). 3.  .from_orm() became .model_validate(). 4. JSON responses got 2x faster automatically through Pydantic's Rust serializer. 5. They added native streaming with yield — perfect for AI applications. Spent tonight updating 12 files across my project. Every old pattern eliminated. Lesson: documentation changes. Check the release notes regularly. What you learned 6 months ago might have a newer, better way now. #FastAPI #Python #BackendDevelopment #WebDev

Ever notice how FastAPI tutorials never match real-world projects? They're either too basic (hello world) or too disconnected from actual work. So I built something different. Meet Pythonfast — an open-source FastAPI project with a React frontend and real-world energy-domain examples. This isn't a learning toy. It's designed to teach you patterns that actually scale in production. What's inside: ✅ FastAPI fundamentals that matter (routing, validation, Pydantic) ✅ A fully functional React + Vite frontend ✅ Real energy data examples (not contrived todo lists) ✅ WebSockets, Pandas & NumPy for data analysis ✅ Production-ready patterns: CI/CD, proper structure, environment variables Why this matters: Most people learn FastAPI from incomplete examples. Then they hit production and realize the tutorial never showed them how to handle real complexity. This repo is the bridge — it shows you how to build something that actually works. 🔗 Live Demo: https://lnkd.in/dmwDeNmB 🔗 GitHub Repo: https://lnkd.in/d3xs3rEb Fork it, contribute, deploy it, or just steal the patterns for your own projects. I'm also open to work and genuinely love helping engineers level up their backend skills. If you're learning FastAPI or building production systems, this is built for you. Drop a comment: What's the biggest gap you've experienced between tutorials and real-world FastAPI projects? I want to add more examples that actually matter. #FastAPI #Python #OpenSource #Backend #FullStack #React #API #WebDevelopment

How FastAPI Changed My Developer Life 🚀 Earlier, building APIs meant writing a lot of boilerplate — manual validation, separate documentation, and extra configuration. It worked, but it wasn’t smooth. Then I started using FastAPI. With simple type hints, I get validation, serialization, and auto-generated Swagger docs out of the box. Built on Starlette and powered by Pydantic, it feels fast, clean, and developer-friendly. The biggest change? I spend less time managing the framework and more time designing better APIs. Sometimes the right tool doesn’t just improve performance — it improves the way you think about building software. #Python #FastAPI #BackendDevelopmen

How to determine Publication frequency? When it comes to the Publication frequency, you don't always need to be the most active updated website between 2 different core updates. If you're able to be the most active website for one of these months or some of these weeks, the search engine will actually consider you as one of the candidates in this example. In Koray Tugberk GUBUR framework we usually decide according to my competitor. We take their sitemaps, check their last modification date, and use Pandas (Python) to aggregate and determine how often they update or publish their articles. We try to go above our competitors as much as possible. When it comes to publishing suddenly, if you start to publish these network like one by one, the search engine won't recognize what you are doing. That's why at the beginning we try to publish 20 or 30 high-quality articles in the same day. Then we put them to homepage so that the search engine can start to pay attention to the website and look further. When it comes to the "look further section," - we start to publish like 1 article per week, then we start to increase the frequency and it goes to like 5 per day. Once we complete the topical map, the next step is Configuration, which means we check the data and we start updating everything from the beginning one more time. #SemanticSEO #KorayFramework #PublicationFrequency

Tried Something New with FastAPI 🚀 Over the last few days, I experimented with building a complete Task Management API using FastAPI — and honestly, it was more fun (and challenging) than I expected. Instead of just watching tutorials, I built everything step by step: • Create tasks (POST) • Fetch tasks (GET) • Update tasks with dynamic path parameters (PUT) • Conditional deletion logic (DELETE) • Proper HTTP status codes using HTTPException • UUID auto-generation & timestamps • JSON-based persistence (no database yet) I even connected it to a basic HTML + JavaScript frontend using fetch() so it felt like a mini full-stack app. The best part? Debugging real issues like: Indentation bugs breaking update logic Understanding why browsers can’t send PUT requests Fixing 500 errors caused by missing imports Learning how proper REST APIs should behave This small project gave me much better clarity on: API design Backend structure (routes → services → models) Real-world debugging Next up: building something that integrates with an external public API 🌦️ Trying to build consistently and learn in public. #FastAPI #Python #BackendDevelopment #LearningByBuilding

● Why Async Matters in FastAPI (And When To Use It) While building APIs, I realized something important: Not all endpoints should be written the same way. FastAPI supports both synchronous and asynchronous functions — but using async correctly can significantly improve performance. 🔹 What’s Happening Here? •The request waits for an external API •Instead of blocking the thread, async allows other requests to be processed. •This improves concurrency under high traffic. 🔹 When Should You Use Async? Use async when: •Calling external APIs •Querying databases (with async drivers) •Performing I/O operations 💡 WHY THIS MATTER Efficient backend systems are not just about writing endpoints. They’re about handling multiple users without slowing down. Understanding async is crucial for building scalable APIs. Backend performance is architecture + execution. #BackendDevelopment #FastAPI #Python #APIDesign #ScalableSystems