React cache() Boosts Performance with Smarter Data Fetching

🚀 Stop treating Server State like UI State. As React developers, we’ve all been there: building "custom" fetching logic with useEffect and useState. It starts with one loading spinner and ends with a nightmare of manual cache-busting and race conditions. When I started migrating my data-fetching to TanStack Query, it wasn't just about "fewer lines of code"—it was about a shift in mindset. The Real Game Changers: Declarative vs. Imperative: Instead of telling React how to fetch (and handle errors/loading), you describe what the data is and when it should be considered stale. Focus Refetching: This is a huge UX win. Seeing data update automatically when a user switches back to the tab feels like magic to them, but it’s just one config line for us. Standardized Patterns: It forces the whole team to handle errors and loading states the same way, which makes code reviews much faster. The Win: In a recent refactor, I replaced a tangled mess of global state syncs and manual useEffect triggers with a few useQuery hooks. I was able to delete a significant chunk of boilerplate while fixing those "stale data" bugs that always seem to haunt client-side apps. The takeaway: Don't reinvent the cache. Use tools that let you focus on the product, not the plumbing. 👇 Question for the devs: Are you using TanStack Query for everything, or are you finding that Next.js Server Actions and the native fetch cache are enough for your use cases now? #reactjs #nextjs #frontend #webdev #tanstackquery #javascript

Most Developers Debate REST vs GraphQL… But Very Few Understand When to Use What. Let’s simplify this with a real-world analogy 👉 REST is like ordering a fixed meal combo You get predefined items. Even if you only want fries… you still get the burger and drink. 👉 GraphQL is like a buffet You pick exactly what you want. No waste. No extra. Just what you need. REST ✔ Simple & predictable ✔ Uses standard HTTP methods (GET, POST, PUT, DELETE) ✔ Easy caching ❌ Multiple API calls for related data ❌ Over-fetching or under-fetching GraphQL ✔ Single endpoint ✔ Fetch exactly what you need (nothing more, nothing less) ✔ Great for complex & evolving UIs ✔ Supports real-time (Subscriptions) ❌ More complex setup ❌ Harder caching ❌ Risk of heavy/abusive queries if not controlled So what should YOU choose? 👉 Building simple, stable APIs? → Go with REST 👉 Building dynamic, data-heavy frontend apps? → GraphQL wins 💭 The truth is: It’s not about which is better… It’s about which fits your problem. For more insightful content checkout below: 🟦 𝑳𝒊𝒏𝒌𝒆𝒅𝑰𝒏 - https://lnkd.in/dwi3tV83 ⬛ 𝑮𝒊𝒕𝑯𝒖𝒃 - https://lnkd.in/dkW958Tj 🟥 𝒀𝒐𝒖𝑻𝒖𝒃𝒆 - https://lnkd.in/dDig2j75 or Priya Frontend Vlogz 🔷 𝐓𝐰𝐢𝐭𝐭𝐞𝐫 - https://lnkd.in/dyfEuJNt   #frontend #javascript #interviewquestions #interview #interviewpreparation #Frontend #Backend #APIDesign #SystemDesign #GraphQL #REST #WebDevelopment

Why Next.js 16, React 19, and TypeScript? When we started Luclair Vision, the first decision was choosing the right technologies. This wasn't arbitrary, it was strategic. Why Next.js 16.2? Next.js 16 released with revolutionary features we couldn't ignore: 1️⃣ App Router: Organizing routes by feature-folders instead of flat files = cleaner codebase as we scale. 2️⃣ Server Components: Let us keep sensitive logic (API calls, database queries) server-side, reducing bundle size. 3️⃣ Built-in API Routes: Why spin up a separate Express server? We get /api/* routes natively. 4️⃣ Image Optimization: Automatic optimization for our Cloudinary images. 5️⃣ Streaming Support: Critical for our Gemini AI features where we needed real-time text streaming. Why React 19? React Compiler = Less manual memoization needed Better TypeScript support out of the box Improved error handling and debugging Integrates perfectly with Next.js server actions. Why TypeScript? With 11 database tables and complex data flows, TypeScript was NON-NEGOTIABLE. If a developer tries to create a product without a required field, the compiler catches the mistake at build time. No runtime surprises. The Rest of the Foundation: 🗄️ Database (PostgreSQL via Supabase): Perfect for flexible product metadata. Built-in auth saved us hours of development time. Styling (Tailwind CSS): Consistent spacing and custom luxury brand colors defined once and used everywhere. The Lesson? Technology selection isn't about using the "latest and greatest" - it's about choosing tools that: ✓ Solve your specific problem efficiently. ✓ Have strong community support. ✓ Scale with your ambitions . ✓ Your team understands deeply. We could've used plain React + Express + MongoDB. We'd have shipped faster initially. But considering the complexity (e-commerce logic, AI integration, shipping APIs), Next.js + TypeScript + PostgreSQL was the RIGHT choice for long-term maintainability. What's YOUR go-to stack for commerce platforms? Have you ever had to switch technologies mid-project? #WebDevelopment #NextJS #ReactJS #TypeScript #SoftwareArchitecture #Ecommerce #FullStack #LuclairVisionBuild

The "Ghost in the API": How I fixed a major rendering lag 👻 While working on a complex user dashboard at Codes Thinker, I encountered a frustrating performance bottleneck. Every time a user triggered a data fetch, the entire UI would "freeze" for a split second before updating. Even with a fast backend API, the user experience felt "heavy" and unprofessional. The Challenge: We were fetching large, nested JSON objects directly inside a parent component. Every time the API responded, the entire component tree re-rendered, causing a visible performance lag during data transformation. The Solution: React Query: I implemented React Query to handle caching. This ensured that if a user requested the same data twice, the result was instant. Data Transformation: Instead of passing the raw "heavy" object to components, I mapped the data into a lighter format immediately after fetching. Optimistic UI: I used Tailwind CSS to create smooth skeleton loaders, making the app feel faster while the data was still loading. The Result: The rendering lag disappeared, and the user experience became fluid. Sometimes, being a Senior Frontend Developer is about knowing when not to fetch data as much as how to fetch it. Have you ever faced a stubborn API lag? How did you tackle it? Let’s share some dev stories! 👇 #RESTAPI #NextJS #PerformanceOptimization #MERNStack #WebDevelopment #CleanCode #ReactJS #TailwindCSS

Most React developers start API calls with useEffect(). It works. Until the project gets bigger. Then suddenly: ❌ Manual loading state ❌ Manual error handling ❌ Duplicate API calls ❌ No caching ❌ Refetch logic becomes messy ❌ Background sync becomes difficult ❌ Race conditions become common And your component starts doing too much. That’s when you realize: useEffect is not a data-fetching solution. It is a side-effect hook. That’s where React Query changes everything. useEffect helps you run effects. React Query helps you manage server state. That difference is huge. Use useEffect() for: ✔ Timers ✔ Event listeners ✔ Subscriptions ✔ External system sync ✔ Simple one-time logic Use React Query for: ✔ API fetching ✔ Response caching ✔ Auto refetching ✔ Pagination ✔ Infinite scroll ✔ Mutations ✔ Background updates ✔ Optimistic UI The biggest mistake is using useEffect like a mini backend framework. It was never designed for that. Better architecture: Client state → useState() / useReducer() Server state → React Query That separation creates: ✔ Cleaner code ✔ Better UX ✔ Faster applications ✔ Less debugging ✔ Predictable state management Good React code is not about using fewer libraries. It is about using the right tool for the right problem. Sometimes the best optimization is removing unnecessary code—not adding more. What do you prefer for API calls in production apps: useEffect() or React Query? 👇 #ReactJS #ReactQuery #useEffect #FrontendDevelopment #JavaScript #StateManagement #WebDevelopment #SoftwareEngineering

Everything looked correct… but we were sending the wrong data to the backend. At some point, you can started noticing something strange in your dashboard. Nothing was obviously broken. The UI updated. The inputs worked as expected.But the data didn’t match what users were typing. Imagine this - you type “John” into a search field. The UI shows “John”. But the API request… still uses the previous value. Here’s a simplified version of what we had: function Dashboard() { const [filters, setFilters] = React.useState({ search: '' }); const fetchData = React.useCallback(() => { api.get('/users', { params: filters }); }, []); React.useEffect(() => { fetchData(); }, [filters]); return ( <input value={filters.search} onChange={(e) => setFilters({ search: e.target.value }) } /> ); } At first glance, it feels correct. The effect depends on filters. Whenever filters change, we fetch data. So what could go wrong? The issue was hiding in a place that looked “optimized”. That useCallback. It was created once, with an empty dependency array. Which means it captured the value of filters at the very beginning… and never updated it. So every time fetchData ran, it used an old version of the filters. That’s why the UI and the backend slowly drifted apart. The user saw one thing. The API received another. The fix was simple: const fetchData = React.useCallback(() => { api.get('/users', { params: filters }); }, [filters]); Or just removing useCallback entirely. What made this bug tricky is that nothing actually crashed.Everything looked fine. But the data was wrong. It was a good reminder that React doesn’t magically keep values up to date inside functions. Closures remember the state from the moment they were created. And sometimes… that’s exactly the problem. Have you run into something like this before? #reactjs #javascript #frontend #webdevelopment #softwareengineering #react #webdev

🚀Why Loading Too Much Data Can Break Your Application While working on an infinite scrolling feature in React, I came across an important real-world problem 👇 ❌ Problem: If the backend sends a very large amount of data at once, both the website and server start slowing down. 🔍 Why does this happen? ▪️ Large API responses take more time to transfer over the network. ▪️The browser struggles to render too many items at once. ▪️Memory usage increases significantly. ▪️Server load increases when handling heavy requests. 👉 I was using the GitHub API, and it helped me understand how important it is to control the amount of data being fetched. 📦 Solution: Pagination + Infinite Scrolling ▪️Instead of loading everything at once: ▪️Fetch data in smaller chunks (pagination) ▪️Load more data only when needed (infinite scroll). ⚡ Benefits: ▪️Faster initial load time ▪️Better performance ▪️Smooth user experience ▪️Reduced server stress 💡 What I learned: ▪️Efficient data fetching is crucial in frontend development ▪️Performance optimization matters as much as functionality ▪️Real-world applications are built with scalability in mind 🎯 Key takeaway: It’s not about how much data you can load — it’s about how efficiently you load it. #ReactJS #JavaScript #WebDevelopment #Frontend #Performance #LearningInPublic #CodingJourney

🚀 How to Optimize API Calls in React (Simple & Practical Guide) Many React applications don’t feel slow because of UI… They feel slow because of inefficient API calls ⚠️ Let’s fix that 👇 ❌ Without Optimization • Too many unnecessary API calls • Same data fetched again & again • Slow UI & laggy experience • Increased server load ✅ With Optimization • Only required API calls • Cached & reused data • Faster UI response ⚡ • Better user experience 🧠 Best Practices to Optimize API Calls 🧩 1. Fetch Only What You Need → Avoid large payloads ✔ Request only required fields ⚡ 2. Use Caching (React Query / SWR) → Don’t refetch same data ✔ Automatic caching + background updates 🔁 3. Avoid Duplicate Requests → Use global state (Context / Redux) ✔ Prevent repeated API calls ⌛ 4. Debounce & Throttle → Reduce API calls while typing ✔ Best for search & filters 📄 5. Pagination / Infinite Scroll → Load data in chunks ✔ Improves performance & UX ❌ 6. Cancel Unnecessary Requests → Abort previous requests ✔ Saves bandwidth & avoids race conditions 🔗 7. Batch API Requests → Combine multiple calls into one ✔ Faster and efficient 🎯 8. Conditional Fetching → Call API only when needed ✔ Avoid useless requests 🔄 9. Background Refetching → Keep UI fast + data fresh ✔ Show cached data instantly ⚡ 10. Use Proper HTTP Methods → Follow REST best practices ✔ Improves API efficiency 🔥 Real Impact ✔ Faster applications ✔ Smooth user experience ✔ Reduced server cost ✔ Better scalability 💡 Final Thought Optimizing API calls is one of the easiest ways to boost performance without changing your UI. 👉 Which technique do you use the most in your React apps? #ReactJS #FrontendDevelopment #WebPerformance #JavaScript #API #SoftwareEngineering #Developers

🚀 Redux vs TanStack Query: Which One Should You Use in 2026? This is one of the most misunderstood topics in React development. Many developers compare Redux and TanStack Query as if they solve the same problem. They don’t. 👉 Redux manages client state 👉 TanStack Query manages server state That distinction changes everything. 🧠 Use Redux When: - You need a complex global UI state - Multiple components share local application state - You require predictable state transitions - Your app has complex workflows or business logic Examples: - Authentication state - Theme preferences - Multi-step forms - Shopping cart - Feature flags ⚡ Use TanStack Query When: - Fetching data from APIs - Caching server responses - Handling loading and error states - Synchronizing data automatically - Managing mutations and optimistic updates Examples: - User profiles - Product listings - Dashboard analytics - Comments and feeds 🔥 The Biggest Mistake Using Redux to manage API data manually. That often means writing: - Actions - Reducers - Loading states - Error handling - Cache logic With TanStack Query, most of that comes out of the box. --- 🎯 My Rule of Thumb - TanStack Query for anything that comes from the server - Redux for complex client-side state And in many modern apps, you’ll use both together. They’re not competitors. They’re complementary tools. Use the right tool for the right problem. #js #es6 #JavaScript #React #ReactRedux #TanStackQuery #WebDevelopment #Frontend #SoftwareEngineering

Webix Grid: High Performance That Actually Holds Up Under Pressure https://lnkd.in/dxZzFkKA Most data grids claim performance. Few prove it. In this deep dive, we break down: - Rendering speed with large datasets - DOM stability under stress - Smooth scrolling with real-time updates - Real-world usage scenarios The takeaway: Webix Grid remains fast, stable, and predictable — even when your app isn’t. If you're building data-heavy applications, performance isn’t a feature. It’s the foundation. 👉 Explore benchmarks, demos, and insights #JavaScript #WebDevelopment #Frontend #DataGrid #Performance #WebApps