Why write a complex Cron Job when your database can clean up after itself? ⏱️🚀 I just built Ephemeral Stories (Instagram/WhatsApp style) for my full-stack WebChat application, and it was an incredible exercise in both Backend optimization and Frontend UI/UX! The Architecture Challenge: How do you automatically delete thousands of stories exactly 24 hours after they are posted, without destroying your Node.js server's CPU with continuous background loops? The Solution: I utilized MongoDB TTL (Time-To-Live) Indexes. By attaching expires: 86400 to the schema, I completely offloaded the deletion lifecycle to the database engine. It costs zero server performance and scales infinitely. ⚡ The Frontend UX: I wanted the UI to feel native and premium. Using React and Framer Motion, I built a full-screen cinematic overlay featuring: ✅ Dynamic progress bars that track the active story. ✅ Invisible tap-zones (tap left to go back, right to skip). ✅ Grouped horizontal story bubbles on the feed. ✅ Silent background API calls to track "Views" in real-time. Building immersive, touch-friendly UI components on the web is tough, but seeing the final result is incredibly rewarding. Have you ever used MongoDB TTL indexes in production? What's the most complex UI component you've built recently? Let me know below! 👇 #WebDevelopment #ReactJS #MongoDB #Frontend #Backend #SoftwareEngineering #BuildInPublic #MERN

 API Architecture: Choosing the right "Nervous System" for your App 🚀🔌 A beautiful Frontend built with Next.js is only as fast as the API feeding it. As we scale products at Codings First, one of the most frequent architectural debates I lead is: REST vs. GraphQL. As a Senior Developer, I’ve learned that there is no "perfect" choice—only the "right" choice for the specific problem you are solving. The REST Approach (The Reliable Standard): Pros: Excellent caching, simple to implement, and highly predictable. Best for: Standard CRUD applications and public APIs where you want high cacheability and a simple learning curve. The "Senior" View: It’s great, but "over-fetching" data can become a bottleneck as your mobile user base grows. The GraphQL Approach (The Flexible Powerhouse): Pros: Zero over-fetching. The frontend asks for exactly what it needs, and nothing more. Best for: Complex, data-heavy apps with deeply nested relationships (like social feeds or dashboards). The "Senior" View: It provides incredible developer experience, but you must be careful with "N+1" query problems and complex caching. At the end of the day, Scalability isn't about using the "coolest" tech; it's about choosing the architecture that reduces latency and maximizes maintainability for the team. Which side are you on? Are you a REST traditionalist, or have you moved your production stack to GraphQL? Let's discuss the trade-offs in the comments! 👇 #APIArchitecture #MERNStack #NodeJS #GraphQL #RESTAPI #SoftwareEngineering #CodingsFirst #SeniorDeveloper #BackendDevelopment #WebScalability

I’m excited to share my latest project: a high-performance Campaign Management System designed for modern advertising agencies. Building digital products for agencies requires a balance of speed, security, and intuitive UX. For this assessment, I focused on creating a scalable Full-Stack solution that handles live campaign metrics and AI-driven creative workflows. Key Technical Highlights: * Full-Stack Architecture: Built with React 18, Node.js, and PostgreSQL. * Data Visualization: Integrated interactive 30-day performance trends using Recharts. * AI-Assisted Workflows: Developed a multi-step Brief Builder with a simulated AI service for rapid creative direction. * Security & Integrity: Implemented JWT Authentication, API Rate Limiting, and Soft Deletes to protect sensitive agency data. * Performance: Optimized rendering using custom hooks (useDebounce) and React optimization patterns. I’m always looking for ways to bridge the gap between complex data and user-friendly interfaces. GitHub Repository: [https://lnkd.in/dJd8nFpH] Ameen Alam Junaid Ali Hafiz Ali Ahmed Faisal Ilyas Faisal ilyas Bilal Fareed Fahad Khan #FullStackDeveloper #ReactJS #NodeJS #WebDevelopment #AIIntegration #PostgreSQL #SoftwareEngineering #Portfolio

Day 2 of Next.js deep dive 🚀 Just wrapped my head around the rendering paradigms, data fetching, and state management that make Next.js a beast for SaaS apps! 1. Rendering Paradigms - The Game Changer ✅ Server Components (default) = Data fetching + HTML on server = lightning-fast initial loads ✅ Client Components ("use client") = Interactivity + charts + forms where needed ✅ SSR/SSG/ISR all work together seamlessly in App Router Realization: Server Components aren't just "nice to have" - they're the foundation. Fetch data once on the server, ship minimal JS. This is why Next.js pages load like native apps. 2. Data Fetching - No More useEffect Hell tsx // Server Component (page.tsx) export default async function Dashboard() { const data = await fetch('api/analytics', { cache: 'force-cache' }) return <Stats data={data} /> } ✅ fetch() is built-in, cached by default, streaming, and Suspense-ready ✅ No more useEffect + useState + loading spinners everywhere ✅ Client components get props, not API calls 3. State Management - Simple & Smart ✅ URL state (/dashboard?tab=analytics&date=30d) = Shareable, bookmarkable ✅ Server Actions for mutations (action: async () => { ... }) ✅ Zustand/Jotai for complex client state (sidebar open, filters) ✅ No Redux needed for 90% of apps The mental model shift: React = Component tree Next.js = Route tree with server/client boundaries Building my SaaS dashboard tomorrow with: Server-rendered pages with fresh data Client-side charts + filters URL-driven tabs + date pickers Server Actions for CRUD Next.js isn't React with routing - it's React with a data layer, rendering strategy, and deployment pipeline built in. Who's building with App Router? What's your go-to state pattern? #NextJS #React #WebDev #SaaS #AppRouter

When your beautiful app has no soul (The Hardcoded Data Trap) POST 5: We had built something gorgeous. The Luclair Vision storefront was pixel-perfect. It had smooth animations, premium imagery, and an intuitive flow. It looked ready for production. But there was a massive problem. It was entirely fake. During an early demo, the UI was a massive hit—until people actually tried to use it: They clicked "Add to Cart" -> Nothing happened. They checked the "Customer Reviews" -> Every review was a hardcoded array of fake people. We checked the Admin Dashboard -> The revenue metric refreshed to a different, totally random number every time. (Math.random() is not a valid business strategy, it turns out). The users immediately asked: "Is this actually functional?" We had to admit it wasn't. Why we fell into the trap We started with a UI/UX-first approach, which is standard practice. But we got so hyper-focused on making the components look beautiful that we kept deferring the database. Mocking data was fast. Wiring up a backend was slow. "We’ll connect the real data later," became our famous last words. The Wake-Up Call That demo forced us to stop playing dress-up with our React state. A beautiful UI is only half the battle. We had to build the engine. Our roadmap completely shifted: Finalize a proper PostgreSQL database schema (which I covered in my last post). Connect our frontend to Supabase. Build out real, functional API endpoints. Painfully gut our mocked data, component by component. The Lesson Mocked data is essential for visualizing UI development, but it is technical debt. If you don't have a strict timeline for replacing those hardcoded arrays with real database calls, your "platform" is just a PowerPoint presentation with buttons. Build your data-fetching architecture in parallel with your components, not as an afterthought. Have you ever fallen into the "mocked data forever" trap? How long did it take you to dig your way out? Let me know below 👇 #WebDevelopment #NextJS #SoftwareEngineering #TechFounders #ReactJS #FrontendArchitecture #LessonsLearned

🏠 I Built a Full-Stack Real Estate Marketplace — Without Paying for APIs 💸 Most property platforms rely on expensive APIs for maps and geocoding… I wanted to challenge that. So I built a fully functional real estate platform using 100% free tools — while keeping it scalable and production-ready. 🔧 Tech Stack • Next.js 15 + React 19 • Supabase (PostgreSQL + Storage) • Clerk Authentication • Leaflet.js + OpenStreetMap • Tailwind CSS 4 + Radix UI ✨ Key Features • Browse properties for Sale & Rent • Interactive map with property markers & popups • Smart address search (Nominatim API) • Advanced filters (Bedrooms, Bathrooms, Parking, Property Type) • Exact match & minimum filtering modes • Post & manage your own listings • Personal dashboard (edit/delete listings) • Agent Finder section • Fully responsive (mobile-first design) 💡 What I Learned • Handling SSR issues with Leaflet in Next.js • Optimizing API calls with debouncing • Managing complex filter state • Integrating Supabase storage for image uploads • Building scalable apps without paid APIs 🎥 Live Demo + Video Walkthrough 🌐 https://lnkd.in/dnuWUqvM 🙏 Special thanks to Hammad Sheikh Bin Nadeem 🇵🇰 (SMIT – Generative AI) for the mentorship and for constantly encouraging us to build real-world applications. 🚀 Would love your feedback — what would you improve or add? #NextJS #React #Supabase #Clerk #TailwindCSS #WebDevelopment #FullStack #JavaScript #BuildInPublic #OpenSource #RealEstate #TechPakistan #100DaysOfCode

🚀 From Idea to Interface—Progress You Can See. #Day2 of building DataVault—and today’s milestone feels real. Alhamdulillah, I’ve successfully converted the entire UI into working code using React and Tailwind CSS. What started as a concept is now a fully functional front-end with a working dashboard. 💻 What’s done: • Complete UI implementation using React + Tailwind CSS • Clean, responsive design now fully interactive • Dashboard functionality is up and running smoothly This is a big step—because now DataVault isn’t just an idea or a design… it’s something you can actually use. 🔐 What’s next: My next focus is implementing authentication to make the platform secure and accessible for real users. The goal is to ensure anyone can safely use DataVault to manage their data with confidence. ⏳ The 7-Day Challenge continues… I’m confident that within the next 2 days, this will evolve into a fully working application. 🤝 I’d love your input: If you have suggestions, features you’d like to see, or feedback—please share. Your insights can help shape this into something truly valuable. Let’s keep building in public. Day 7 is getting closer. 🚀 #BuildInPublic #WebDevelopment #ArtificialIntelligence #SaaS #ReactJS #TailwindCSS #JWT #DataSecurity #Developers #TechJourney #NodeJS #Typescrit #JavaScript #vercel #framermotion

I almost broke a staging environment for an enterprise-level client because of this mistake. Here's what happened. I was building a website for an enterprise client — full stack, end to end. Frontend in React. Content managed through Strapi CMS. Data fetched via GraphQL. I had built the entire page system using reusable components. Each page was made up of different template components — hero, cards, sections, CTAs. Clean architecture. Looked great. Then I connected GraphQL. And the site became painfully slow. I couldn't figure out why at first. Everything was built correctly. The components were rendering. The data was coming through. But the page was taking forever to load. I dug in. Turns out — my GraphQL query was fetching ALL page data for EVERY component across the ENTIRE site. Every time a user landed on one page — the frontend was pulling data for every single page, every single component, all at once. Imagine ordering one coffee and the waiter brings you the entire menu. That's what I was doing to the browser. The fix? Instead of one giant query fetching everything — I restructured it to fetch only the specific component data for the specific page being loaded. Page-level GraphQL queries. Not site-level. Load time dropped. Site felt instant. The lesson I took from this: GraphQL gives you the power to fetch exactly what you need. If your site is slow — the query is almost always the problem, not the component. This is the kind of thing that never shows up in tutorials. Only in real projects. I'm building in public for the next 15 days — sharing exactly what I've learned across 35+ real projects. Follow along if you're a developer, a founder, or someone who wants to understand what actually goes into building a website. #webdevelopment #reactjs #graphql #buildinpublic #frontenddeveloper

🚀 How to Fetch Large API Data in React JS (Without Breaking Your App) Fetching API data in React is common—but things get tricky when the data is large. If not handled properly, it can slow down your app or even crash the UI. Here’s a clean and scalable approach 👇 🔹 1. Use useEffect for API Calls Trigger your API request when the component mounts: useEffect(() => { fetchData(); }, []); 🔹 2. Handle Loading & Errors Properly Always manage states for better UX: const [data, setData] = useState([]); const [loading, setLoading] = useState(true); const [error, setError] = useState(null); 🔹 3. Use Pagination or Infinite Scroll Instead of loading everything at once: Fetch data in chunks (e.g., page=1, limit=10) Or implement infinite scrolling for better performance 🔹 4. Optimize with Lazy Rendering Use libraries like: react-window react-virtualized These render only visible items, improving performance drastically. 🔹 5. Use Memoization Avoid unnecessary re-renders: const memoizedData = useMemo(() => data, [data]); 🔹 6. Consider Data Fetching Libraries Tools like: React Query SWR help with caching, background updates, and better state management. 💡 Pro Tip: Never try to render huge datasets directly. Break it down, optimize rendering, and keep your UI responsive. Handling large API data efficiently is what separates a basic app from a production-ready one. 💬 How do you manage large datasets in your React apps? Let’s discuss! #ReactJS #WebDevelopment #Frontend #JavaScript #API #Performance #CodingTips

I recently built a reusable DataTable component using React + TypeScript powered by TanStack Table, and it turned out to be a solid pattern for scalable data-heavy UIs. Instead of treating tables as one-off components, I designed this as a fully controlled, flexible abstraction that supports: • Server-side & client-side pagination • Sorting & column filtering • Global search with debouncing support • Column visibility toggling • Row selection with bulk actions • Loading skeleton states for better UX • Fully controlled state (sorting, filters, pagination, visibility) The goal was simple: make tables predictable, reusable, and production-ready without rewriting logic every time. A couple of design decisions that made a big difference: Keeping all state controlled via props → makes it easy to plug into APIs Supporting server-side mode → essential for real-world datasets Separating UI (buttons, dropdowns) from table logic → cleaner and extensible Adding UX touches like skeleton loaders and selection feedback This isn’t just a component — it’s more like a table architecture pattern you can drop into any project. Curious how others are handling large-scale tables in React apps. Are you going fully custom, or relying on prebuilt solutions? #React #TypeScript #FrontendDevelopment #WebDevelopment #UIUX #SoftwareEngineering