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Middle-earth Map — update 🚀 Since my last post, I’ve been busy making the experience more interactive and a lot cleaner to use. 🧠 New: Quiz mode Think you know Middle-earth? 3 difficulty levels: 👍 Easy: If you saw the movies, it's easy peasy ☝ Medium: Well, for that you gotta re-watch the director's cut a few times 💪 Hard: Have not read the Silmarillion? Well, don't even try... 😆 10 questions per round It’s a fun way to test your knowledge — from casual fan to Tolkien nerd. 🧭 Improved navigation I added a sidebar menu to make exploring smoother and less cluttered — especially on smaller screens. The goal is simple: make the map not just something you look at, but something you use. As always: 🗺️ Custom map tiles (no Google/OSM basemap) 📜 Lore-accurate locations, routes, and regions 🔍 Search, galleries, and detailed descriptions And yes… the AI-powered character chat is still there 👀 If you haven’t tried it yet — now’s a good time 🙂 👉 Link in the comments #webdevelopment #gamedev #lotr #middleearth #map #javascript #mongodb #docker

🚀 Stop building "Old School" Backends for New School SaaS. In 2026, if you are still writing custom controllers for every CRUD operation, you’re burning your runway. I’m seeing too many MVPs fail because the "Architecture" is 5 years behind the "Idea." The Hard Truth: For 90% of AI-powered SaaS, a traditional MVC (like Laravel or Django) is actually a bottleneck. The "High-Velocity" alternative I’m using for my clients: 🔹 Next.js 16 + React 19: We move the logic to the Edge. Sub-second load times aren't a luxury; they are a requirement. 🔹 Supabase + RLS: We stop writing "Auth Logic" and start writing "Security Policies" at the Database level. It’s faster, safer, and deletes 40% of the backend code. 🔹 Vercel AI SDK: We stream LLM responses directly to the user. No more "loading spinners" while the server thinks. The Result? We ship a "Revenue-Ready" product in 3 weeks, not 3 months. If you’re a Team Lead or a Founder tired of seeing your roadmap stall in the "Development Phase," let's talk. I’m opening up 2 slots for April to help teams move from "Napkin Sketch" to "Live Product" at 2x speed. DM me "VELOCITY" or drop a comment below. Let’s see if your stack is holding you back. #NextJS #SaaS #BuildInPublic #AI #Supabase #EngineeringManagement

Choosing between FastAPI and Django is like picking between a sleek electric motorcycle and a fully-loaded SUV. Both are powerful, but they’re built for different roads. ⚡ Here is the quick breakdown for your next project: ⚡ FastAPI: Built for Speed Perfect for micro-services, AI/ML serving, and high-performance APIs. Async Native: Handles concurrent connections like a pro. Lean & Mean: Lightweight with automatic Swagger documentation. Developer Joy: Catch errors early with Python type hints. 🛠️ Django: The All-in-One Powerhouse The "batteries-included" choice for complex, scalable web applications. Complete Ecosystem: Built-in ORM, auth, and migrations. Admin Panel: A ready-to-use back office out of the box. Secure by Default: Advanced protection against common web threats. The Verdict: Choose FastAPI for high-performance, modern APIs. Choose Django for robust, full-stack applications and rapid MVPs. Which one is powering your current stack? Let’s chat in the comments! 👇 #Python #WebDev #FastAPI #Django #SoftwareEngineering #Backend

I just engineered a decoupled AI video processing factory from scratch. A lot of AI projects right now are just simple API wrappers that block the main thread and freeze the browser while waiting for a response. I wanted to build something the right way the way real media platforms handle heavy rendering. Meet Cliply. Instead of a monolithic app, I built a Decoupled Worker Architecture: 1. The Producer: A Flask web layer that captures user inputs, manages secure UUID sandbox environments, and writes the media manifests. 2. The Consumer: A background Python daemon that monitors the queue, fetches the AI voiceovers, and orchestrates the FFmpeg rendering. 2. The Bridge: Asynchronous JavaScript polling that pings a REST API every 2 seconds to update the UI progress bar without ever freezing the client. Tech Stack: Python, Flask, Vanilla JS, FFmpeg, ElevenLabs. You can check out the source code and the architecture breakdown on my GitHub here: https://lnkd.in/gHHjYWnF I had to solve some wild file I/O race conditions to get the daemon to wait for the API streams to finish writing to disk before triggering FFmpeg. I’m curious for the engineers out there what is your go-to strategy for handling heavy background tasks? Do you build custom daemon workers like this, or do you reach straight for Celery/Redis? Let me know. #SoftwareEngineering #Python #Flask #Architecture #FFmpeg #Microservices #Tech #BuildingInPublic #AI #SaaS

Why “Good Enough” isn't enough for a scalable Feed. Most side projects start with a simple goal. For me, it was just fetching a backend feed and displaying it on a screen. But as soon as I hit "Refresh," I realized a feed isn't just a list of data, it’s a living ecosystem of state. The Pivot: What started as a simple GET request evolved into a deep dive into 4-Layer Architecture. I didn't just want to show posts; I wanted a system where following a user, accepting a request, and updating a sidebar felt instantaneous and "true" to the database. The Challenges (The parts that didn’t make the highlight reel): ▪️The Infinite Loop Trap: Early on, I triggered nearly 1,000 recursive errors. Using a global context meant that every time I updated a follower count, my entire navigation re-rendered, triggering another fetch. ▪️State Disconnect: There’s a specific frustration in clicking "Accept Request" and seeing the notification badge stay stuck at '1'. Solving this meant rethinking how the Service Layer communicates with the Context Layer. ▪️The "Self" Filter: It sounds simple, but ensuring a user doesn't get suggested to follow themselves requires a tight sync between the Auth state and the Suggestion engine. Why the 4-Layer approach? I moved toward a strict separation between API Services, Custom Hooks, and Context Providers. The Result: A UI that doesn't just "fetch", it synchronizes. When you confirm a follower in the Navbar, the Sidebar stats update in real-time without a page reload. Building the "feature" was easy. Building the flow that handles data consistency across three different components was where the real learning happened. Check out the demo below to see the state-sync in action. 👇 Sheryians Coding School Sheryians Coding School Community Ankur Prajapati #WebDevelopment #ReactJS #NodeJS #SheryiansCodingSchool #FullStack #SoftwareEngineering #Coding #Cohort2

🚀 Excited to share that Trimly is now live in production! Trimly is a modern URL shortener designed for simplicity, speed, and reliability. It’s more than just a tool, it’s a project I built to upskill, explore new technologies, and deepen my understanding of full-stack development. 🌱 This journey was all about learning, growth, and shipping something real. From designing scalable APIs to building a seamless user experience, every step was a chance to push my skills further. 🔧 Tech Stack: - Language: Typescript - Backend: NestJS - Frontend: React + Vite - Database: MongoDB with Mongoose - Deployment: Vercel for frontend and DNS, Railway for the API, and Upstash for Redis Caching ✨ Key Features: - FREE essentials forever, no commitments! - Fast, reliable link shortening - Custom aliases - Real-time click analytics - Bulk shortening - Link expiration - Past link history - Clean, intuitive, and modern UI It’s amazing what AI can do with close human supervision and creativity! Check out Trimly at www.trimmly.xyz and let me know what you think. #Trimly #URLShortener #WebDevelopment #FullStackDevelopment #NestJS #ReactJS #TypeScript #MongoDB #Redis #Vercel #Railway #Upstash

I built a full LMS in one week. Django. Redis. Celery. Postgres. Alpine.js. 70+ pages. Real-time notifications. AI-assisted workflow. But here's the part most developers skip - I didn't touch the codebase for the first 2 days. Day 1–2: just planning. PRD, requirements, user flows, wireframes. I had the whole thing mapped out — all 70+ pages — before writing a single migration. Day 3–7: build mode. Backend done, frontend hooked up. Somewhere in the middle, I decided to upgrade notifications from basic to real-time HTTP polling. Wasn’t planned. Didn’t slow anything down. What kept it from going sideways? Every backend day ended with testing. If something broke, it didn’t carry over. First thing next morning, fix it before touching anything new. No piling up issues. No "I’ll come back to it." You won’t. When your foundation is solid, changes don’t feel risky, they’re just decisions. And when you actually test as you go, moving fast doesn’t mean things break easily. This week reminded me: speed isn't about typing faster. It's about knowing what you're building before you build it, and not lying to yourself about what's actually done. The loop from Monday applies here too. → Plan → Build → Test → Fix → Ship What’s one rule you follow to move fast without messing things up? #Django #Python #SoftwareDevelopment #BuildInPublic #TechCanada

🔥 The day I stopped fighting slow APIs and found FastAPI A few months ago, I was deep in a project — building a backend service that needed to handle multiple requests efficiently. I was a Full Stack Developer juggling both the frontend and backend, and the backend was becoming my biggest headache. Response times were painful. The API felt like it was dragging its feet. Users were waiting. I was frustrated. I'd tried tweaking, optimizing, refactoring — but something felt fundamentally off about the framework I was using. Then a colleague mentioned FastAPI. I was skeptical at first. "Another Python framework?" I thought. But within the first hour of trying it, something clicked. The speed difference was immediate and obvious. Requests that used to crawl were now flying. Built on Starlette under the hood, FastAPI handled concurrent requests in a way my old setup simply couldn't match. But speed wasn't the only surprise: ⚡ Performance out of the box — No heavy configuration needed 📄 Auto-generated Swagger docs — My frontend self could finally understand the backend clearly 🔒 Pydantic validation — Caught data errors before they became real problems 🔄 Async support — Made the whole service feel alive and responsive What took me days to debug before, FastAPI handled gracefully by design. As a Full Stack Developer, I always felt pulled in two directions. FastAPI made the backend feel less like a burden and more like a superpower. If your APIs are slow and you're tired of fighting your own framework — this is your sign. 👇 🔗 https://lnkd.in/gv54tebD #FastAPI #Python #FullStackDeveloper #BackendDevelopment #WebDevelopment #SoftwareEngineering #API #DevExperience

Running heavy AI media tasks locally is easy. But deploying a heavy Python backend to the cloud just to show off a UI. That gets expensive fast. Instead, I built a fully decoupled AI processing engine and engineered a way to deploy just the interactive UI to the edge. Meet Cliply. The Architecture (Local Engine) - To prevent long ElevenLabs API calls and FFmpeg rendering from freezing the browser, I decoupled the architecture: The Producer: A Flask web server that handles user sessions and instantly offloads tasks to a queue. The Consumer: A background Python daemon that quietly monitors the queue and handles the heavy rendering. The Bridge: Vanilla JavaScript async polling that pings the server every 2 seconds to drive a live progress bar without blocking the main thread. The Deployment Hustle - I wanted to share the UI/UX, but I didn't want to pay for a dedicated GPU server just for a demo. So I created a parallel Git branch (vercel-demo) to bypass the heavy backend and deploy only the frontend to Vercel's serverless edge. Deploying a Python app to a serverless JS platform is a battle. My commit history today tells the real story: Commit - Added vercel.json...: Forcing Vercel to map and execute a Python Flask routing tree. Commit - Fix: Added requirements.txt and exposed app. Squashing the dreaded 500 Internal Server error so the serverless workers could find the global environment. Commit - Fix: Bypassed read-only file system. The final boss (Error 30). Serverless functions don't give you a hard drive. I had to bypass my local os.makedirs storage entirely just to get the frontend UI to boot up. The Result - The full heavy-lifting engine is built and running locally, while the lightweight asynchronous UI is live on Vercel for anyone to test. Next up - Pivoting into Data Science and ML model training to eventually plug my own models into this pipeline! GitHub Repo & Docs: https://lnkd.in/gHHjYWnF #SoftwareEngineering #Python #Flask #Microservices #DataEngineering #SystemArchitecture #Vercel #AI #ML #AIMODELS

𝐒𝐨𝐦𝐞𝐨𝐧𝐞 𝐚𝐬𝐤𝐞𝐝: 𝐖𝐡𝐚𝐭 𝐞𝐱𝐚𝐜𝐭𝐥𝐲 𝐢𝐬 𝐒𝐭𝐚𝐜𝐤 𝐁𝐫𝐞𝐚𝐜𝐡? The poster breaks it down. Here's the takeaway: A Hogwarts operations system – built on the MERN stack – deliberately corrupted. Broken APIs, database flaws, and logic gaps. Your job: fix them, piece by piece. Round 1? Reconstruct the UI from visual references. Round 2? Hunt down the real bugs. Round 3? Review another team's work. 📖 Rulebook: https://lnkd.in/dZmv9SZm 🔗 Register: https://lnkd.in/deBWneUm #DevelopersDay #DevDay26 #StackBreach #MERNStack #WebDevelopment #CodingCompetition #Debugging