FastAPI Scaffolding CLI - bdh-fastapi-new

I recently developed 𝗕𝗿𝗲.𝗮𝗸 – 𝗨𝗥𝗟 𝗦𝗵𝗼𝗿𝘁𝗲𝗻𝗲𝗿 𝗪𝗲𝗯 𝗔𝗽𝗽𝗹𝗶𝗰𝗮𝘁𝗶𝗼𝗻 using Django. Bre.ak is a web-based application that converts long URLs into short and easy-to-share links in the format bre.ak/XXXXX. The application provides a clean dashboard interface where users can create, view, and manage shortened links efficiently. 𝗙𝗲𝗮𝘁𝘂𝗿𝗲𝘀: •⁠ ⁠Generate short URLs instantly •⁠ ⁠Dashboard with all created links •⁠ ⁠Automatic redirection to the original URL •⁠ ⁠Website favicon displayed for each link •⁠ ⁠Simple and user-friendly interface 𝗧𝗲𝗰𝗵 𝗦𝘁𝗮𝗰𝗸: Python, Django, HTML, CSS, SQLite, Git Through this project, I gained practical experience in backend development, database integration, and building a complete web application workflow using Django. 𝗚𝗶𝘁𝗛𝘂𝗯 𝗥𝗲𝗽𝗼𝘀𝗶𝘁𝗼𝗿𝘆: https://lnkd.in/gSDZyz4r #Python #Django #WebDevelopment #StudentProject #BackendDevelopment #GitHub #Learning

Django vs FastAPI Which One Should You Choose? 🤔 As a Python Full Stack Developer, I often get this question. Here’s a quick comparison 👇Django ✔ Full-stack framework (batteries included) ✔ Built-in admin panel ✔ Best for large, complex applications ✔ Strong security features ✔ Uses MVT architecture 👇FastAPI ✔ High-performance (very fast ⚡) ✔ Great for APIs & microservices ✔ Async support (modern Python) ✔ Automatic API docs (Swagger UI) ✔ Easy to learn and lightweight 💡 When to use what? 👉 Use Django for: Full web applications (admin panel, authentication, ORM) 👉 Use FastAPI for: High-speed APIs, microservices, real-time apps 🔥 My Take: Both are powerful. Choosing depends on your project needs! #Python #Django #FastAPI #FullStackDeveloper #WebDevelopment #Backend #API #React.js #pythonfullstackdeveloper #SQL

Most people building React frontends with Python backends overcomplicate the connection. React and FastAPI is honestly one of the cleanest full-stack combos right now. Here's why it works so well FastAPI gives you automatic docs at /docs the moment you define a route. No extra setup. Your React dev knows exactly what endpoints exist and what they return before you've even written the fetch call. Pydantic schemas on the FastAPI side act as a contract. If the backend returns a User object, you know exactly what fields are coming. Pair that with TypeScript interfaces on the React side and you've eliminated an entire class of runtime bugs. CORS setup is two lines. Async endpoints mean your API doesn't choke when React fires multiple requests simultaneously. Response times stay fast without extra infrastructure. The pattern that works in prod: FastAPI handles all data logic, auth, and business rules React owns the UI state and user interactions entirely They talk only through clean typed API boundaries No shared state nightmares. No tightly coupled mess. If you're coming from a Django or Express background and haven't tried this stack yet, it's worth a weekend project. The developer experience gap is noticeable. What's your go-to Python backend when building React apps? #React #FastAPI #Python #FullStackDevelopment #WebDevelopment

FastAPI vs Django — my real-world take after working with both: Django is great when: - You need a full-featured framework out of the box   - Admin panel, auth, ORM — everything ready   - You want to move fast on standard applications  FastAPI shines when: - You need high-performance APIs   - You want async support   - You’re building microservices or data-heavy systems  In most modern systems I’ve worked on: 👉 Django for structured apps   👉 FastAPI for APIs and services  There’s no “better” framework.   There’s only the right tool for the problem.  What’s your go-to: FastAPI or Django? #python #fastapi #django #backend #softwareengineering

The Golang String Trap: Why len("🚀") does not equal 1 🤯 Quick backend nugget for the timeline today that completely blows the minds of developers coming from JavaScript or Python. Let’s say you are building an API in Go, and you need to validate that a user's password is at least 8 characters long. You instinctively write: if len(password) < 8 { return error } Seems mathematically perfect, right? But what if the user’s password is "Pass🚀🚀"? In Go, len("Pass🚀🚀") returns 12, not 6. Your validation just let a 6-character password slip through. Wait, why? Because in Go, strings are NOT arrays of characters. They are read-only slices of bytes. The letters P-a-s-s take up 1 byte each. But those rocket emojis? They take up 4 bytes each! The Production Fix: If you want to count actual, human-readable characters in Go, you have to use Runes (Go’s way of handling Unicode code points). Instead of len(), you must use utf8.RuneCountInString(). Why this matters: If you are setting database limits, handling password validation, or truncating text for a UI, using the standard len() function on user input that contains emojis or foreign characters will introduce silent, hard-to-track bugs into your system. When validating text length from users, always count Runes, not bytes. Did you know about the Byte vs. Rune difference in Go? Let’s gist in the comments 👇🏾 #Golang #BackendEngineering #SoftwareDevelopment #TechBro #TechInNigeria #SystemDesign #WeMove

A few months ago, I set a simple goal for myself: to truly understand FastAPI. Not just by following tutorials, but by building things, breaking them, and figuring out why they broke. That’s how my FastAPI Ecosystem Lab 🐍 was born—a monorepo where I’ve been documenting every concept I’ve mastered: routing, dependencies, async SQLAlchemy, migrations with Alembic, JWT, OAuth2, testing with pytest + httpx, and more. Each folder in the repo represents a chapter of that journey. But eventually, I hit an uncomfortable realization: ❝ What good is an API if I don’t know how to consume it properly from a frontend? ❞ Don't get me wrong—the dynamic Swagger docs are a 10/10 feature. But as a FullStack developer, testing an API through a documentation UI is just for debugging; it’s not "real-world" consumption. That’s when the next phase began. I decided to integrate learn_nextjs into the lab. Not as a separate project, but within the same repo and with the same philosophy: learning from scratch, using real code, and documenting every decision. The biggest "aha!" moment was realizing that Next.js isn't just "React with routing." It’s a Node.js server that renders React on the server before it ever hits the browser. When you pair that with a FastAPI backend, everything changes: ⚙️ Private Fetching: The fetch doesn’t happen in the browser—it happens server-to-server in a private network. FastAPI never has to be directly exposed to the public internet. ⚙️ Secure Auth: HttpOnly cookie authentication flows naturally. The browser sends the cookie automatically, and Server Components forward it to FastAPI without client-side JavaScript ever touching it. ⚙️ End-to-End Safety: TypeScript on the frontend maps perfectly to Pydantic schemas on the backend. Your Python definitions become the contract that Next.js consumes. I’m documenting this from scratch because I struggled to find resources that covered this integration honestly and thoroughly. Most tutorials assume you're using Next.js as the backend too, or they oversimplify it to a basic fetch(). The reality is much more interesting. If you’re learning FastAPI, Next.js, or both—the repo is public, and the code comments focus on the "why," not just the "how." 👉 https://lnkd.in/e78Re_Ez Has anyone else walked this path? I’d love to know: what part of the integration felt the least "obvious" to you? I’d highly appreciate any suggestions or feedback in the comments! 👇 #FastAPI #NextJS #Python #TypeScript #FullStack #OpenSource #BuildInPublic

🚀 Excited to release djhero — my open-source Python package for Django developers! After building multiple production Django projects, I kept repeating the same patterns: rate limiting, caching, auth guards, JSON validation, CORS, logging... So I packaged them all into one library. 📦 pip install djhero ✅ 24 ready-to-use decorators ✅ Rate limiting (@rate_limit("100/h")) ✅ Per-user caching (@cache_per_user) ✅ Auth & permissions (@auth_required, @staff_required) ✅ JSON validation (@validate_json) ✅ CORS, ETag, pagination, retry & more ✅ 5 built-in signals for monitoring ✅ Django 4.0–5.1 + Python 3.8–3.13 support Instead of installing 5+ separate packages, djhero gives you everything in one place — production-ready, zero hassle. Example: @rate_limit("100/h") @cache_view(ttl=300) @auth_required() @handle_exceptions @json_response def my_api(request):   return {"status": "clean & fast"} 🔗 GitHub: github.com/pyaidev/djhero 🔗 PyPI: pypi.org/project/djhero Feedback, stars ⭐ and contributions are very welcome! #Python #Django #OpenSource #WebDevelopment #Backend #DeveloperTools

Day 88 – Entering the World of Django Today marks my entry into the world of Django, a powerful Python framework for building secure and scalable web applications. 🔹 What I Learned Django provides a ready-made toolkit that makes web development faster, easier, and more structured. It is mainly used for backend development and helps in efficiently connecting the frontend with the database. 🔹 Understanding MVT Architecture ✔️ Models – Define the structure of database tables using Python classes ✔️ Views – Handle logic, validate data, and act as a bridge between frontend and database ✔️ Templates – Manage the frontend and UI presentation 🔹 Key Highlights ✨ Built-in Admin Panel 🔐 Strong Security 👤 Authentication System 📝 Form Handling 🔄 Easy Database Migration Excited to continue exploring Django and building real-world applications step by step! #Django #Python #WebDevelopment #BackendDevelopment #FullStack

🚀 Starting my journey in web development! I’m happy to share my first project — a Flask CRUD Web Application 💻 🔹 Built using: Flask, MySQL, HTML, CSS 🔹 Features: Create, Read, Update, Delete operations 🔹 Deployed using Render 🔹 Source code available on GitHub 🌐 Live Demo: https://lnkd.in/gXdhjUYY 💻 GitHub Repo: https://lnkd.in/gRbe_2YB This is just the beginning — looking forward to building more advanced and impactful projects ahead! #Flask #Python #WebDevelopment #BeginnerProject #svhec Dr. Muralisankar Kumaresan Guide KABILESH RAMAR

 💻 Leveling Up with the Django Shell As I continue my journey into backend development, I’ve discovered that the Django Shell is an absolute game-changer for interacting with a database. It’s a powerful environment where you can run Python commands in real-time to manipulate your data and test your models without needing a front-end interface. 🔑 Pro-Tip: Changing Superuser Details Ever forgotten your admin password or needed to update a user’s details quickly? Instead of starting over, you can use the shell to make changes directly! How to do it: 1. Access the Shell: Run <python manage.py shell> in your terminal. 2. Import the User Model: from django.contrib.auth.models import User. 3. Fetch the User: user = User.objects.get(username='admin'). 4. Update & Save: user.set_password('newpassword') user.save() 💡 Why this is important Understanding the shell is about more than just fixing mistakes; it’s about efficiency and control. It allows you to: Perform quick CRUD operations. Debug logic errors in your models. Manage administrative tasks directly from the command line. The more I dive into the Django ecosystem, the more I appreciate how these "under-the-hood" tools make building robust applications possible. Onwards to the next challenge! 🚀 #Django #Python #BackendDevelopment #CodingTips #TechLearning #DjangoShell #WebDevelopment #GIT20DayChallenge #AfricaAgility