Building a Backend System: Day 1 Kicking off FlowState, a workflow automation API (not generic todo app). Day 1 Focus: Modern Tooling & Optimization ✅ Python 3.13 & Django 6.0 (Latest Stable) ✅ uv for dependency management (Blazing fast) ✅ Multi-stage Docker build (Alpine based) ✅ Image size optimized to < 250MB 🐳 Why uv? It's Rust-based and solves Python packaging speed issues. Why Django 6? Leveraging the latest async capabilities and security features. Starting with a lean, modern foundation is key to scalable system design. Stack: Python 3.13, Django 6.0, PostgreSQL 17, Docker, UV. #BackendEngineering #Django #Python #Docker #BuildInPublic #SystemDesign #DevOps #UV
Building FlowState Backend with Python 3.13 & Django 6.0
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FastAPI and Django Production Migration Challenges Revealed 📌 Django’s monolithic power meets FastAPI’s async speed-teams migrating to FastAPI report up to 300% faster dev cycles and razor-sharp I/O performance. But beware: you’ll lose built-in admin tools and session handling. The choice hinges on whether your app thrives on rapid iteration or raw throughput. 🔗 Read more: https://lnkd.in/dsBwaJ-J #Fastapi #Django #Python #Performance #Migration
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Understanding Django became much easier once I learned this 🔍 When I first started with Django, everything felt confusing… But one concept changed everything: 👉 Django follows the MVT architecture (Model–View–Template) Here’s how I now see it: ✔ Model → Handles database (data) ✔ View → Contains logic (what to do) ✔ Template → Handles UI (what user sees) Once I understood this flow, building projects became much more structured and easier. Still learning and improving every day 🚀 What was the concept that made Django click for you? 👇 #Django #Python #WebDevelopment #Backend #Learning
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Write code that doesn't break in production...! When building end-to-end pipelines, reading data from GitHub or external URLs is common. But relying on a "happy path" is a mistake. For robust development, always implement: Logging: To track the flow and capture specific error details. Exception Handling: To prevent the entire app from crashing and get clear "Unable to load" alerts. It’s a simple habit, but it’s what separates a beginner from a Pro Developer. #Python #MLOps #CleanCode #SoftwareEngineering #DataScience #CodingTips
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From ideas to APIs 🚀 Turning logic into scalable backend systems using Django. Clean code. Secure APIs. Real-world impact. Still learning. Still building. #Django #Python #DeveloperJourney
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We’ve all been there… “Code works perfectly on local” Production: 💥 I wanted to fix that gap — not with theory, but with something practical and repeatable. So I built a production-ready Python service setup: • Docker for consistency • systemd for reliability • Nginx + Blue-Green for zero-downtime deployments The idea was simple: 👉 Your service should never go down during deployment This blog is what I wish I had when I started building real systems. If you’re moving from scripts to production systems, this will help: https://lnkd.in/gZThnvK8 Would love your thoughts 🙌 #DevOps #Python #SystemDesign #Backend #Tech
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Built a VS Code extension that lets you “see” a Python codebase instantly. I was testing it on a small fitness app project and within seconds it mapped: • API routes • Functions & classes • Cross-file dependencies • Execution paths • Impact of code changes Instead of opening 50 files and tracing imports manually. I call it Python Code Graph. It uses AST-based static analysis to understand Python structure and then visualizes the architecture in an interactive graph inside VS Code. A few things it can do: → Detect FastAPI / Flask / Django routes → Find Click / Typer CLI entry points → Detect Celery tasks → Show blast radius before changing code → Highlight critical paths to business logic → Generate AI-powered explanations Built with: Node.js + TypeScript VS Code Extension API Custom heuristic symbol resolution Glassmorphism-inspired UI Still improving it, tested on a smaller codebase but the first working version is live on GitHub. #Python #VSCode #BuildInPublic #DevTools #Programming #SoftwareEngineering
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Built an Dual-App Emergency Dispatch System with Python and Django! 🚀 This project really pushed my backend skills. It features: ✅ Real-time incident reporting on a live map ✅ Automated dispatch math to find the closest available unit ✅ A highly secured, staff-only HQ Control Room ✅ Custom User Models and role-based authentication Overcoming the complex URL routing to safely separate the public map from the admin portal was a massive win. GitHub link: [https://lnkd.in/g4yx4kmy] #WebDev #Django #Python #SoftwareDeveloper #Tech
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📅 Week 1 of sharing my backend journey Here’s what I covered 👇 ✔ Django vs Flask (when to use what) ✔ Django project structure ✔ ORM optimization techniques ✔ REST API best practices ✔ Started planning a production-level project Biggest takeaway 👇 👉 Writing code is one thing 👉 Understanding why you write it is everything In future: Authentication systems Role-based access File handling Real-world backend features If you’re learning backend development, let’s grow together 🚀 #Python #Django #BackendJourney #LearningInPublic #SoftwareDevelopment #DjangoDeveloper #DjangoTips #PythonDeveloper #BackendDevelopment #DjangoRestFramework #APIDevelopment #ServerSide #Programming #SoftwareDevelopment #CodingLife #DevCommunity
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Not all Python backend frameworks are the same 🤯 If you're new to backend or just curious how apps are built, here’s a simple breakdown: 🔹 Flask → Lightweight & flexible 👉 You build everything yourself 🔹 Django → Full-stack framework 👉 Comes with admin panel, auth, database tools 🔹 FastAPI → Fast & modern 👉 Built for high-performance APIs 💡 Simple way to understand: Flask = Empty kitchen 🍳 Django = Full restaurant 🍽️ FastAPI = Smart automated kitchen ⚡ Each one is powerful — it just depends on your use case 👉 Which one do you prefer or want to learn? #Python #BackendDevelopment #Django #FastAPI #Flask #WebDevelopment #Programming #TechExplained
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Day 5 of learning backend from first principles. Today was about strengthening both application-level and system-level understanding. With Django: • Worked with templates — connecting backend data to what users actually see • Explored error handling — understanding how things break and how to debug them Also started revisiting fundamentals of Operating Systems. Why this matters: Backend isn’t just writing APIs. It sits on top of how the system actually works. Understanding: • How processes run • How memory is managed • How errors propagate …makes you a better developer than just knowing frameworks. Still early, but the goal is clear: Build depth from both sides — systems + applications. #BackendDevelopment #Django #OperatingSystems #ComputerScience #WebDevelopment #LearnInPublic #Python #SoftwareEngineering
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