Ditch useEffect for TanStack Query

For the longest time, my pattern looked like this: • useEffect → call API • useState → store data • loading + error states manually handled It worked… until the app grew. Then came the problems: • duplicate API calls • inconsistent loading states • manual caching (or no caching at all) • refetching logic scattered everywhere That’s when I switched to React Query. — What changed? Server state ≠ UI state React Query made this distinction clear. Caching became automatic Data stays fresh without unnecessary refetching. Background updates UI stays responsive while data syncs silently. Built-in loading & error handling No more boilerplate in every component. Refetching is declarative Not tied to lifecycle hacks anymore. — The biggest mindset shift: Stop thinking: “Where should I fetch this data?” Start thinking: “How should this data behave over time?” — Final takeaway: React Query is not just a library. It’s a different way of thinking about data in frontend. And once you get it, going back to useEffect feels… painful 😅 #reactjs #frontend #javascript #webdevelopment #reactquery #softwareengineering

🔥 I stopped using useEffect for fetching data… and my code got cleaner. For a long time, this was my pattern in React: useEffect(() => { fetch("/api/data") .then(res => res.json()) .then(setData); }, []); It worked… but it came with problems: ❌ Manual loading state ❌ Manual error handling ❌ Refetch logic is messy ❌ No caching Then I discovered TanStack Query. ⚡ Same logic, cleaner approach: const { data, isLoading, error } = useQuery({ queryKey: ["data"], queryFn: fetchData }); 💡 That’s it. And suddenly: ✔ Data is cached ✔ Refetching is automatic ✔ Loading & error states are built-in ✔ Background updates just work 🧠 The mindset shift I stopped thinking: 👉 “How do I fetch data?” And started thinking: 👉 “How do I manage server state?” 🚀 Why this matters Because in real apps, data is: asynchronous shared constantly changing Handling that manually with useEffect doesn’t scale. 🧩 My takeaway: useEffect is not a data-fetching tool. It’s a side-effect tool. And once you separate the two… your code becomes much more predictable. Have you tried TanStack Query before, or are you still using useEffect for everything? #React #JavaScript #Frontend #WebDevelopment #TanStackQuery

Are you still using useEffect for Data Fetching in 2026? 🛑 React development evolve ho chuki hai, lekin aaj bhi kaafi developers purane tarike se data fetch karte hain. Let’s break down why useQuery (TanStack Query) wins: ❌ The "Old" Way (useEffect): Boilerplate: Loading, Error aur Data ke liye 3 alag states banani padti hain. No Caching: Har baar component mount hone par network request jati hai. Race Conditions: Multiple requests manage karna mushkil hota hai. ✅ The "Smart" Way (TanStack Query): Built-in States: isLoading, isError, aur data ready-to-use milte hain. Smart Caching: Data memory mein save rehta hai, bar-bar fetch karne ki zarurat nahi. Auto-Retry: Agar network fail ho jaye, to ye khud retry karta hai. Clean code likhna sirf preference nahi, performance ki demand hai! 🚀 #ReactJS #WebDevelopment #Frontend #TanStackQuery #CleanCode #JavaScript #WebGyaan

Something clicked for me recently about React Server Components that I wish someone had explained earlier. Most explanations start with "it renders on the server" — which is technically true but completely misses the point. Server-Side Rendering already did that. The real shift is different. RSC renders on the server and sends a serialized component tree — no JavaScript shipped, no hydration needed for server components. SSR renders HTML on the server but still ships the full JS bundle to the client for hydration. That distinction changes everything about how you think about bundle size and performance. The mental model that finally made it click for me: your code can interact directly with your database or file system without exposing sensitive logic to the client, eliminating the need for complex API layers for simple data fetching tasks. No useEffect. No loading state. No API route just to get data into a component. Just async/await directly in your component — and none of that logic ever reaches the browser. The core challenge is that React Server Components are not an optimization layer — they are an architectural boundary. Teams that treat them like a drop-in performance fix run into problems. Teams that rethink their component structure around them get the real benefits. AI is changing how we write React code, not what we build with it. The architectural decisions — when to adopt Server Components, how to structure state, which rendering patterns fit — those still require human judgment. If you're building with Next.js and haven't sat down with RSC properly yet, that's the investment worth making this week. #React #WebDevelopment #SoftwareDevelopment #NextJS #Frontend #JavaScript #RSC #DeveloperProductivity

Tired of manually copy-pasting data? Let’s automate it. Over my 8 years of building backend systems and scrapers, I've found that turning the static web into actionable data is an absolute superpower. In this video, I break down how to build a custom, multi-page web scraper from scratch using Node.js. We scrape Hacker News job listings, filter for specific roles, and automatically output a clean, timestamped CSV. The Stack & Process: Axios: Fetching the raw HTML. Cheerio: Parsing the DOM to extract titles, links, and dates. Recursion: Automatically handling pagination to jump from page to page. CSV/Moment: Generating the final structured dataset. Cheerio is lightning-fast for static HTML, but next time, we’ll step up to full browser automation for complex client-side rendered sites. What is the toughest site you've ever had to scrape? Let me know below. 👇 #NodeJS #WebScraping #BackendEngineering #Automation #JavaScript

I wrote 50 lines of fetch code last week. TanStack Query did the same job in 5 lines. 🤯 Here's the truth: Most React devs still write data fetching like this: - useState for data - useState for loading - useState for error - useEffect to fetch - Manual error handling - Manual loading checks That's 50+ lines for ONE API call. TanStack Query does it in 5 lines. Plus you get for FREE: ✅ Auto caching — no duplicate API calls ✅ Auto retry — when requests fail ✅ Auto refetch — when user comes back to tab ✅ Background updates — no UI flicker ✅ Less code — less bugs I was reinventing the wheel for 2 years. TanStack Query had already solved it. If you're building a real app with users, you NEED: - Caching - Retry logic - Stale data handling You can write all this manually. Or you can use TanStack Query and ship faster. Quick start: 1. npm install @tanstack/react-query 2. Wrap app in QueryClientProvider 3. Replace useEffect with useQuery 4. Done. Honest question for React devs: Are you still fetching with useState + useEffect? What's stopping you from switching? 👇 #ReactJS #FrontendDevelopment #JavaScript #TanStackQuery #ReactQuery #WebDevelopment #ReactDeveloper #NextJS #SoftwareEngineering #TechTips

I’ve been diving into data fetching and caching in Next.js lately, and here’s a simple breakdown that helped me connect the dots 👇 🔹 Data Fetching Types SSR → Fetch data on every request (always fresh, but slower) SSG → Fetch at build time (super fast, but static) ISR → Static + auto अपडेट after a time (best balance) CSR → Fetch on client side (for interactive UI) 🔹 Modern App Router Approach Instead of special functions, Next.js now uses fetch() with built-in caching: cache: "force-cache" → works like SSG (fast, cached) cache: "no-store" → works like SSR (always fresh) next: { revalidate: 10 } → works like ISR (auto update) 🔹 Caching Layers (Important) Data Cache → stores API responses Full Route Cache → stores full HTML Router Cache → speeds up navigation Request Memoization → avoids duplicate fetch calls 🔹 Dynamic Routes Using generateStaticParams() we can pre-build dynamic pages at build time → better performance + SEO 🚀 🔹 Best Practice Always handle errors: if (!res.ok) throw new Error("Failed to fetch data"); 📌 Key takeaway: Next.js is all about balancing speed vs freshness. Choose the right strategy based on your use case. #NextJS #WebDevelopment #React #Frontend #JavaScript #FullStack #LearningInPublic

Day 22 #100DaysOfCode 💻 1. Difference between map and filter? — B. map transforms, filter selects 2. What triggers this? (addEventListener) — B. Button click 3. What happens here? (onclick) — B. Runs on click 4. What is the output? (map without return) — B. [undefined, undefined, undefined] 5. Which is correct to get input value? — B. value 6. What is the output? (array.find) — B. 2 7. What does async/await simplify? — B. Callbacks 8. What is the output? (array.filter) — B. [3, 4] 9. Which is best for spacing between flex items? — B. gap 10. What does querySelector do? — B. First match 11. Layout direction (Tailwind flex-col md:flex-row) — B. Column on small, row on medium+ 12. Why does map return undefined? — B. Missing return 13. What does JSON.stringify() do? — A. Converts object to string 14. What does Array.push() do? — A. Adds element to the end 15. What is the use of the spread operator (...) ? — C. Copying or merging arrays/objects 16. How to save data in LocalStorage? — B. setItem('key', 'value') 17. Difference between const and let — A. const cannot be reassigned 18. == vs === — B. === checks both value and type 19. What is the output of typeof null? — C. "object" 20. What is Hoisting? — A. Moving declarations to the top 21. Why is useState used in React? — B. To manage component state 22. What does p-4 mean in Tailwind? — A. Padding on all sides 23. What does Array.reduce() do? — B. Accumulate values to a single result 24. Why use event.preventDefault()? — A. Stops default browser behavior 25. Which method does fetch() use by default? — A. GET 🚀 #javascript #reactjs #webdevelopment #frontenddeveloper #coding #learninginpublic #Akbiplob

If you're still using JSON.parse on a 50MB API response, you're blocking the main thread and silently hurting your app's performance. JSON.parse is synchronous. It loads the entire payload into memory before you can touch a single byte. For large datasets, that's a guaranteed bottleneck. The fix? Stream parse it instead. Using the Web Streams API with a streaming JSON parser like @streamparser/json, you can process data as it arrives: const parser = new JSONParser(); parser.onValue = ({ value }) => console.log(value); fetch('/api/large-data') .then(res => res.body.pipeThrough(new TextDecoderStream())) .then(stream => stream.pipeTo(new WritableStream({ write(chunk) { parser.write(chunk); } }))); This approach lets you start processing records before the full payload even lands. Practical takeaway - if your payload exceeds 1MB, streaming should be your default, not your fallback. Most developers reach for JSON.parse out of habit, not necessity. The tooling to do better has been available for years. Are you stream parsing in production, or is JSON.parse still your go-to? #JavaScript #WebDevelopment #Performance #WebStreams #FrontendEngineering #JSOptimization