标签：#React

Remix 3 is a full-stack web framework that moves away from React, focusing on web platform primitives. It integrates routes, request handlers, and UI components into a single structure, utilizing a forked Preact for the frontend. Unlike previous versions, it emphasizes server ownership of the request lifecycle. Migration from Remix 2 is not straightforward, as it requires changes to existing apps. By Daniel Curtis

Performance optimization in React is often surrounded by myths. Developers routinely wrap every single component in React.memo , wrap every function in useCallback , and wonder why their application is still sluggish or memory-heavy. Premature optimization can actually degrade app performance and clutter your codebase. To build fast React applications, you need techniques that address actual bottlenecks: unnecessary re-renders, unoptimized state placement, oversized bundles, and main-thread blocking. Here are five practical React performance optimization techniques that deliver measurable results in production. 1. Push State Down (Fix Rerender Cascades) Before reaching for useMemo or React.memo , evaluate your state placement . When state lives too high up in the component tree, every state update forces the entire sub-tree to re-render. ❌ The Anti-Pattern: State at the Root // Changing `color` forces <HeavyChartComponent/> and <ComplexTable/> to re-render! export default function App () { const [ color , setColor ] = useState ( ' #6366f1 ' ); return ( < div > < input type = "color" value = { color } onChange = { ( e ) => setColor ( e . target . value ) } /> < p style = { { color } } > Sample Text </ p > < HeavyChartComponent /> < ComplexTable /> </ div > ); } ✅ The Fix: Component Isolation Move the isolated state and its control into its own dedicated child component: Javascript function ColorPicker () { const [ color , setColor ] = useState ( ' #6366f1 ' ); return ( < div > < input type = "color" value = { color } onChange = { ( e ) => setColor ( e . target . value ) } /> < p style = { { color } } > Sample Text </ p > </ div > ); } export default function App () { return ( < div > < ColorPicker /> { /* These components are no longer impacted by color state changes */ } < HeavyChartComponent /> < ComplexTable /> </ div > ); } 2. Pass Components as Children (Component Composition) Sometimes state must remain in a parent component, but you don't want child components

I have been building VoxelDraft , a voxel editor that runs entirely in the browser without an account or installation. The editor supports block painting, layers, keyframe animation, GIF recording, local projects, and exports for OBJ/MTL, GLB, VOX, Minecraft Schematic, and Roblox RBXL. This post covers the architecture choices that kept those features manageable. Keep edit data serializable The editable model is an array of plain voxel records rather than a collection of Three.js objects: type VoxelData = { position : [ number , number , number ] color : string layerId ?: string } That decision makes JSON backups, local persistence, undo/redo snapshots, sharing, and format conversion much simpler. Three.js objects are derived render state, not the source of truth. Render repeated cubes with InstancedMesh Creating one mesh and one React component per cube becomes expensive as a model grows. VoxelDraft uses THREE.InstancedMesh where geometry and material can be shared. Each voxel contributes a transform matrix. Pointer intersections return the instanced mesh and instance ID, which can be mapped back to the editable voxel record. There are tradeoffs. Per-voxel colors need instance colors or grouping by material, and changing a single block still requires carefully updating the instance buffers. The reduction in draw calls is worth that complexity. Make exporters independent from UI The format exporters accept voxel records and produce a Blob . The UI is only responsible for validation and triggering a download. const blob = exportToVOX ( voxels ) const url = URL . createObjectURL ( blob ) VOX, Minecraft Schematic, and RBXL are generated directly. For GLB, the app builds a temporary Three.js scene and sends it to GLTFExporter from three-stdlib . Keeping binary generation separate from React event handlers makes exporters easier to test and reuse. Move GIF encoding off the main thread VoxelDraft records both animation output and modeling timelapses. GIF encoding can easi

Building a modern Customer Relationship Management (CRM) platform requires more than just displaying raw database records. Users expect interactive analytics, clear data visualization, responsive layouts, and lightning-fast UI updates . In this guide, we'll walk through architecting a sleek, responsive CRM analytics dashboard using React , Tailwind CSS , and Recharts . 1. Dashboard Architecture & Component Hierarchy To keep our CRM modular and easy to maintain, we break down the UI into specialized components: src/ ├── components/ │ ├── layout/ │ │ ├── Sidebar.jsx │ │ └── Header.jsx │ ├── dashboard/ │ │ ├── MetricCard.jsx │ │ ├── RevenueChart.jsx │ │ └── RecentDealsTable.jsx └── pages/ └── Dashboard.jsx 2. Key Performance Metric Cards KPI cards sit at the top of the dashboard to give team leaders instant insight into active pipeline value, customer acquisition, and conversion rates. Here is a clean, reusable MetricCard component built with Tailwind CSS: import React from ' react ' ; import { TrendingUp , TrendingDown } from ' lucide-react ' ; export const MetricCard = ({ title , value , change , isPositive , icon : Icon }) => { return ( < div className = "bg-white dark:bg-slate-900 p-6 rounded-2xl border border-slate-200 dark:border-slate-800 shadow-sm transition-all hover:shadow-md" > < div className = "flex items-center justify-between" > < span className = "text-sm font-medium text-slate-500 dark:text-slate-400" > { title } </ span > < div className = "p-2.5 rounded-xl bg-indigo-50 text-indigo-600 dark:bg-indigo-950/50 dark:text-indigo-400" > < Icon className = "w-5 h-5" /> </ div > </ div > < div className = "mt-4 flex items-baseline justify-between" > < h3 className = "text-2xl font-bold text-slate-900 dark:text-white" > { value } </ h3 > < span className = { `inline-flex items-center text-xs font-semibold px-2 py-0.5 rounded-full ${ isPositive ? ' bg-emerald-50 text-emerald-600 dark:bg-emerald-950/50 dark:text-emerald-400 ' : ' bg-rose-50 text-rose-600 dark:bg

Every form I ever shipped before React 19 needed the same three pieces of state, and I wired them up...

I used to juggle ESLint and Prettier every day. Two tools. Multiple config files. Plugin conflicts. Slow checks. And that constant feeling that something was always fighting something else. Then I found Biome . Biome is a single, Rust-powered tool that does both formatting and linting. It replaces the classic ESLint + Prettier combo with one binary and one simple config. Why it feels different It’s extremely fast. According to the official benchmark, Biome formats ~35x faster than Prettier when processing 171,127 lines of code across 2,104 files (on an Intel Core i7 1270P). In real projects, the difference is impossible to ignore — checks that used to take seconds now finish almost instantly. One tool, one config. No more keeping a linter and a formatter in sync. Biome uses the same parser for both jobs, so they never disagree. High compatibility, clear feedback. The formatter is about 97% compatible with Prettier. The linter comes with hundreds of solid rules inspired by ESLint and TypeScript ESLint. And when something is wrong, the error messages actually tell you where the problem is and how to fix it. It just works. You can format, lint, and organize imports in a single command. It supports JavaScript, TypeScript, JSX, JSON, CSS, HTML, GraphQL, and more. Companies like Vercel, Cloudflare, Discord, Microsoft, and Google are already using it in production. That says something. I’m not saying you must drop everything tomorrow. But if you’re tired of slow tooling and config complexity, Biome is worth a serious look. Have you tried it yet?

An ecosystem whose job is to full-fill our dream of opening and collecting pokemon cards, which we all had in our childhood. Instead of just clicking a button to reveal static images, I wanted to recreate the whole experience of getting , collecting and showing off your pokemon cards , i even added live card auctions and card shows and PSA card grading simulator to give the full-on experience which pokemon has to offer. 🔗 Live Sandbox: https://pokemontcgsim.vercel.app 💻 GitHub Repo: https://github.com/sohamSanat/PokemonTcgSimulator ** Screen shots of different segments of the web app -> ** 1)Main page (pack opening) 2)Binder section where you sort your cards in the personal collections and see your cards’ portfolio 3)Card grading simulation where you can get your cards’ price increased based on the condition of your card 4)Card show where there are different vendors with their own specialty in cards 5)live cards auction of thousands of cards **Tech fluff for people who cares ;D -> Architecture & Major Engineering Achievements : -** 1)Era-Calibrated Pack Engine & Rarity Probability Mathematics: Pack generation creates historically accurate card pools from over 25 years of card sets, from 1999 Base Set to 2025 Mega Evolution. It recreates slot weights, ensures holos, and handles complex probability mechanics for Secret Illustration Rares and many other cards 2)gemini powered NPC Negotiation Engine: During the virtual card convention, users negotiate with 9 different NPC vendors through natural language interactions. The backend NLP pipeline analyzes the user’s language, tokenizes the negotiation and makes offers. 3)Simulated PSA Grading Laboratory & Restoration Studio: The multi-step card authentication system considers card centering, surface, corners, and edges, and provides realistic grade distribution (PSA 1-10) with dynamic slab encasement and grade multipliers. It also has an interactive pre-grading restoration studio where you can clean surfaces and press corne

TanStack Table V9 is a beta release of a headless UI library for creating tables in various JavaScript frameworks. It features improved state management, memory usage, and extensibility. The notable change is an opt-in feature model, allowing developers to load only necessary components. Migration is gradual, with tools provided for legacy support. The library remains free and developer-focused. By Daniel Curtis

Over the past few weeks, I've been focused on deepening my understanding of mobile analytics—not just by completing a course, but by putting those concepts into practice through hands-on implementation in React Native. Throughout this journey, I explored a wide range of topics, including: Firebase Analytics integration Google Analytics 4 (GA4) Event planning and naming conventions Screen view tracking Custom events and custom definitions Key Events (Conversions) User properties and User ID Acquisition and campaign tracking Audience segmentation Ecommerce measurement Checkout funnel analysis Promotions and marketing attribution BigQuery integration Realtime reporting and DebugView Analytics validation and best practices One of the biggest lessons I learned is that analytics is much more than logging events . A well-designed analytics strategy helps answer important questions about user behavior, feature adoption, user engagement, and conversion optimization. The quality of the insights you gain depends on having a well-planned event architecture, consistent naming conventions, and meaningful data collection from the very beginning. Completing this Udemy course gave me a strong foundation in Firebase Analytics and Google Analytics 4. Reinforcing that knowledge through hands-on implementation in React Native helped me better understand event planning, debugging, reporting, and analytics best practices for modern mobile applications. Course Certificate I'm happy to have successfully completed the Firebase Analytics & Google Analytics 4 (GA4) course on Udemy. Certificate: https://www.udemy.com/certificate/UC-4a23b92f-0857-4a75-83dd-ac186bdfdfbc The course covered both the fundamentals and advanced capabilities of mobile analytics, including GA4 reports, custom events, screen tracking, audiences, BigQuery integration, and data-driven decision making. It has been a valuable learning experience that strengthened both my theoretical understanding and practical implementation

The Nightmare of HTML-to-PDF in React If you’ve ever built a School ERP, HR portal, or Event Management system, you’ve probably hit this exact wall: Your client needs to print 5,000 ID cards or badges. Usually, this forces frontend teams to do one of two terrible things: Pay for an expensive backend PDF generation API (which raises huge GDPR/privacy concerns because you have to send sensitive employee photos to a 3rd-party server). Force the non-technical HR team to manually type names into Canva, crop photos, and manually drag them onto an A4 grid (an 80-hour manual data entry nightmare). I got tired of rebuilding complex html2canvas and jsPDF calculators from scratch for every project. So, I decided to automate the entire pipeline natively in the browser. Enter @stratametriq/id-card-designer — an open-source, turnkey drag-and-drop ID card studio and A4 mathematical rendering engine for React. What it does out of the box: Instead of building a canvas from scratch, you install this NPM package in one line of code. It gives your end-users a complete visual dashboard directly inside your own application. Here is a 60-second video of how it looks running in a live production environment: 👉 https://youtu.be/l9aXWqRSFCM?si=nEIaaqsxypmzCflm The Core Features: Dynamic Handlebars Data Binding Your users can design a visual template and drop in tags like {{studentName}} or {{employeeId}}. Our engine automatically binds these variables to your live database array. No manual typing required. Scannable Barcodes & QR Codes We built native QR and Barcode generators directly into the canvas. You just pass the ID string, and the engine renders a scannable vector code instantly. The Magic Moment: Precision A4 Batch Matrix When your HR admin selects 500 employees and hits "Batch Print", the real magic happens. Our client-side mathematical matrix calculates exact millimeter dimensions—arranging exactly nine PVC cards perfectly on standard A4 cut-sheets, complete with professional 0.35

React useDeepCompareEffect: Fix useEffect Object Dependencies (2026) You wire up a fetch. The endpoint takes a query object, so you pass it in the dependency array. The effect fires, sets state, the component re-renders, the query object is rebuilt — a brand-new object with identical contents — and the effect fires again. You have written an infinite loop, and React thinks it did exactly what you asked. function Results ({ term , page }: Props ) { const [ rows , setRows ] = useState ([]); const query = { term , page , sort : ' desc ' }; // new object, every render useEffect (() => { fetchRows ( query ). then ( setRows ); // setRows → re-render → new query → 🔁 }, [ query ]); } useDeepCompareEffect from @reactuses/core is a drop-in replacement for useEffect that compares dependencies by value instead of by reference. Same signature, same cleanup semantics — the effect just stops firing when nothing actually changed. Everything below is the real implementation, TypeScript-first, including the parts that cost you something. Why useEffect Can't See It React compares dependency arrays with Object.is , element by element. For primitives that's exactly what you want: 5 is 5 , 'desc' is 'desc' . For anything with an identity — objects, arrays, Date s, Map s, functions — it compares the reference , and a literal written inside a component body produces a fresh reference on every single render: Object . is ({ term : ' react ' }, { term : ' react ' }); // false — different objects So the dependency "changed" on every render, by React's definition. This isn't a bug in useEffect ; reference equality is the only comparison that's O(1), and React runs it on every render of every component. The cost of value comparison is real, and React declines to pay it on your behalf. Which leaves you paying it — one way or another. The Usual Workarounds, and Where They Fray Memoize the object. Correct, and the right answer when there's one dependency: const query = useMemo (() => ({ term , page

Have you ever looked at your raw DNA data from services like 23andMe or Ancestry.com and thought, "What on earth am I looking at?" Behind those megabytes of .txt or .vcf files lies the blueprint of you , but without a PhD in genetics, it's just a wall of "A, C, T, G." In this tutorial, we are going to bridge the gap between raw genomic noise and actionable insights. We’ll build an advanced Genomic RAG (Retrieval-Augmented Generation) pipeline. By the end, you'll have a system that takes raw SNP (Single Nucleotide Polymorphism) data, retrieves clinical significance from the ClinVar database, and generates an interactive risk guide using LlamaIndex and FAISS . If you are interested in Genomic Data Engineering , Bioinformatics with Python , or RAG (Retrieval-Augmented Generation) , this guide is for you. The Challenge: The "Needle in a Haystack" Problem A typical human genome has millions of variants. Most are harmless "junk" DNA, but some are "Pathogenic." Searching for these manually is impossible. We need a system that: Parses massive genomic files efficiently. Indexes trusted medical databases (ClinVar). Matches your specific variants against that knowledge base to provide context. The Architecture 🏗️ Here is how our data pipeline flows from raw pixels (well, raw base pairs) to structured insights: graph TD A[Raw SNP Data / VCF File] --> B(Pandas & Biopython Parser) B --> C{Filter High-Impact Variants} D[ClinVar Clinical Database] --> E(LlamaIndex Indexing) E --> F[FAISS Vector Store] C --> G[RAG Query Engine] F --> G G --> H[LLM: GPT-4o Synthesis] H --> I[Interactive Risk Report] Prerequisites 🛠️ To follow this advanced guide, you'll need: Tech Stack : Python 3.9+, Pandas, LlamaIndex, FAISS, and Biopython. Data : A sample VCF file (you can download public datasets from the 1000 Genomes Project) or your own exported 23andMe data. Step 1: Parsing the Genetic "Nonsense" First, we need to handle the raw data. 23andMe usually provides a tab-separated file. We use Panda

After hoisting, interviewers love dropping one-liners like: console . log ([] + []); console . log ([] + {}); console . log ({} + []); console . log ( NaN === NaN ); …and watching whether you guess, freeze, or calmly walk the coercion rules. This post is only output-based type coercion / equality questions. Try each snippet yourself first. Answers are hidden — click Show answer when you’re ready. TL;DR — what interviewers are testing Concept Trap + with objects/arrays Often becomes string concat , not math [] / {} stringification [] → "" , {} → "[object Object]" Bare {} + [] Parser may treat {} as a block , not an object NaN === NaN Always false — use Number.isNaN / Object.is == vs === == coerces; === does not Falsy vs “empty-looking” [] and {} are truthy typeof null Infamous "object" lie One-line mental model + asks both sides to become primitives. If either side is a string (after that), you get concatenation . Otherwise you get number math — and weird values become NaN . Warm-up: how + really decides When JS hits a + b , it roughly does: 1) Convert both sides to primitives (ToPrimitive) 2) If either result is a string → String(a) + String(b) // concat 3) Else → Number(a) + Number(b) // math For plain objects / arrays, ToPrimitive usually ends up calling .toString() : Value String(value) Number(value) [] "" 0 [1, 2] "1,2" NaN {} "[object Object]" NaN null "null" 0 undefined "undefined" NaN true "true" 1 false "false" 0 That’s enough to solve most [] + {} style questions. How to use this post Read the snippet Say the output out loud (or write it down) Only then open Show answer Read the step-by-step — don’t only memorize the final print Q1 — Classic [] + [] console . log ([] + []); Show answer Output "" (empty string — looks like a blank line) Step by step + wants primitives from both arrays. String([]) → "" (empty array joins to empty string). "" + "" → "" . Interview tip: People often say 0 or [] . Wrong. Empty array stringifies to "" , so you get string concat o

How multiple open browser tabs can accidentally DDOS your auth server, and how to fix it with the Web Locks API. Picture this: You’ve just shipped a state-of-the-art Axios response interceptor. You implemented a mutex lock ( isRefreshing ) and a promise queue ( failedQueue ) to handle concurrent 401 errors. You tested it within a single tab, and it worked like a charm. You gave yourself a high-five and closed your laptop. Then, a power user logs in. Like most humans on the internet, they don't use just one browser tab. They open Tab 1 for User Management, Tab 2 for Analytics, Tab 3 for Settings, and Tab 4 for Support Tickets. Fifteen minutes pass. Their short-lived JWT access token expires. The user switches back to Tab 1. In the background, all 4 open tabs wake up, detect the expired token, and fire off four independent POST /auth/refresh-token/ requests at the exact same millisecond. Tab 1 refreshes the token first, but Tab 2's request arrives a millisecond later, invalidates Tab 1's new token, and Tab 3 nukes the session entirely. Suddenly, all 4 tabs dump the user back to the login screen. Welcome to the Cross-Tab Token Trap . 1. The Problem: The Multi-Tab Stampede In modern single-page applications (SPAs), each browser tab operates in its own isolated JavaScript runtime environment. Memory is not shared. When an access token expires: isRefreshing = true in Tab A only stops requests inside Tab A . Tab B has no idea Tab A is currently refreshing a token. Tab C lives in complete ignorance of Tabs A and B. Tab A (Memory Space 1) ---> isRefreshing = true ---> POST /auth/refresh-token/ (Token Set 1) Tab B (Memory Space 2) ---> isRefreshing = true ---> POST /auth/refresh-token/ (Token Set 2 -> Revokes Set 1!) Tab C (Memory Space 3) ---> isRefreshing = true ---> POST /auth/refresh-token/ (Token Set 3 -> Revokes Set 2!) If your backend enforces Single-Use Refresh Token Rotation (where using a refresh token revokes all previous ones), multi-tab usage causes immediate ses

SellAI 🚀 Hi DEV Community! Over the past few weeks I've been building SellAI — an AI-powered platform that helps businesses manage sales, customers and analytics from one dashboard. Main Features 🤖 AI Assistant 👥 Customer CRM 📦 Product Management 🛒 Order Management 📈 Business Analytics 💳 Subscription System 🔒 Secure Authentication Built With React Firebase OpenAI Vite Live Demo https://sellai-2ad64.web.app Demo Video https://youtu.be/0I0n0snI37M I'd love to hear your honest feedback! Thanks for reading 🚀

Modern logistics is built on time-sensitive operations, yet traditional freight procurement suffers from friction. Legacy systems depend heavily on fragmented offline negotiations, opaque spot market prices, manual Lorry Receipt (LR) tracking, and coordination gaps between warehouse controllers and field drivers.To eliminate these operational bottlenecks, RoutePe Auto was engineered as a high-throughput Transport Management Software . The platform unites real-time spot bidding, pay-per-tender corporate procurement, vehicle discovery, automated freight billing, and live multi-point tracking into a unified ecosystem. Here is an architectural breakdown of how RoutePe Auto was designed using Laravel on the backend, React on the web frontend, a native Mobile App , and MySQL for transactional integrity. Architecture Overview ┌──────────────────────────┐ │ React Web Dashboard │ │ (Shippers / Logistics) │ └────────────┬─────────────┘ │ REST / WebSockets │ ┌──────────────────┐ ┌────────────▼─────────────┐ ┌──────────────────┐ │ Mobile App │◄────►│ Laravel API Gateway │◄────►│ MySQL Database │ │(Drivers/Fleet) │ │ & Execution Core │ │ (ACID Transactions) └──────────────────┘ └────────────┬─────────────┘ └──────────────────┘ │ ┌──────▼──────┐ │ Redis Queue │ └─────────────┘ The system operates across three tiers:The Web App Layer: Built with React, offering enterprise shippers a dynamic workspace to broadcast loads, review bids, manage tenders, and monitor active routes. The Field Execution Layer: A dedicated Mobile App for drivers and fleet operators, streaming real-time location updates, uploading electronic Proof of Delivery (ePOD) signatures, and receiving job dispatches. The Core Engine: A robust Laravel REST API backend handling business logic, asynchronous task dispatching, document generation, and balance ledger management against a relational MySQL store.Database Design in MySQLA core requirement for any Transport Management Software is strict transactional integrity.

Every frontend developer eventually reaches the same point. You know React. You know TypeScript. You know how to build components. But then you join a real project. Suddenly the questions are no longer about writing a component — they’re about engineering. How should the project be structured? Where should authentication logic live? When is React Context enough? When should React Query own the data? How do you prevent a codebase from becoming impossible to maintain? What makes a frontend application scalable? How do AI coding tools fit into modern development? These are the questions I kept asking myself while working on frontend applications. The problem wasn’t the lack of information. The problem was that the information was scattered across hundreds of blog posts, GitHub repositories, conference talks, documentation pages, and personal notes. Tutorials Teach Frameworks Modern tutorials are excellent at teaching frameworks. You can easily learn: React Vue Angular Next.js TypeScript But very few resources explain what happens after that. How do experienced teams actually build production frontend applications? How do they organize folders? How do they write maintainable code? How do they review pull requests? How do they optimize performance? How do they scale applications from one developer to twenty? Those are engineering problems — not framework problems. Frontend Engineering Is a Different Skill Writing React code doesn’t automatically make someone a frontend engineer. Frontend engineering includes topics such as: Project architecture Feature-based organization Authentication and authorization API design State management Data fetching strategies Performance optimization Accessibility Error handling Testing CI/CD Monitoring Code quality Documentation Team conventions These subjects rarely live in one place. AI Has Changed the Way We Build Software Another reason I started this project is the rise of AI coding assistants. Learn about Medium’s values Today many de

Every couple of projects I ended up rebuilding the same effect: an animated topographic map background, the kind with slowly drifting contour lines. After copy pasting the same WebGL shader for the third time I gave up and turned it into a proper library. It's called topolines . One React component, zero dependencies, everything drawn on the GPU. Repo: https://github.com/idleCyrex/topolines Playground: https://topolines.idlee.xyz/playground How it works The lines are not an image or SVG. A small fragment shader generates a noise field (simplex noise + fbm) and draws contour bands from it, so it animates smoothly at any resolution for basically no CPU cost. The component just manages a canvas and the WebGL state around it. Usage npm i topolines import { Topolines } from " topolines/react " ; export default function Hero () { return < Topolines seed = "hello" color = "#F2EFE6" style = { { position : " fixed " , inset : 0 } } />; } Same seed always renders the same field, so your background is stable between visits. There are props for speed, scale, line width, colors, drift, and an interactive mode where contour rings bloom around the cursor. Things I cared about Zero dependencies, the whole thing is one shader and some glue code SSR safe, works in a Next.js server component tree Pauses when offscreen or when the tab is hidden Respects prefers-reduced-motion (renders one static frame) Clean fallback when WebGL is not available The playground lets you tweak every knob and copy the resulting code out: https://topolines.idlee.xyz/playground It's v0.1 and my first published library, so feedback and feature ideas are very welcome.

How I Built an SSR Valorant Tracker with React, Supabase and Live Esports Data I recently built VALTRAIN , a player-focused Valorant platform that combines a Valorant Tracker, VCT match database and weapon skins explorer. The project started as a simple match history lookup page. It eventually became a much larger SSR application with player statistics, esports schedules, match detail pages, replay discovery, multilingual routes and searchable cosmetic data. The Main Product Areas VALTRAIN is divided into three primary areas. 1. Valorant Tracker The Valorant Tracker accepts a Riot ID and region. It can display: Current rank and RR Recent competitive and unrated matches KDA and combat score Headshot, body shot and leg shot data Competitive RR movement Lifetime performance statistics Individual match details and team compositions One challenge was handling incomplete or delayed data from an external player API. The interface needed useful loading, empty and error states instead of leaving users with an endless spinner. 2. VCT Match Database The VCT esports database stores upcoming and completed matches. Each public match can have: Tournament and stage information Team names and series scores Map-level results Player statistics Recent team form Official replay availability Related matches and internal links The project also includes an original VCT performance report generated from completed match records. 3. Valorant Weapon Skins The weapon skins database allows players to browse skins by collection, weapon type, rarity, price and chroma. The main performance challenge was preventing high-resolution media from slowing down the initial page load. Why I Moved the Site to SSR The original version relied heavily on client-side rendering. That worked for user interaction, but it created several problems: Public pages had limited initial HTML Search engines had to execute JavaScript Metadata was harder to control Dynamic routes occasionally returned weak fallback pages Firs

What happens when 5 parallel API calls hit for an expired JWT at the exact same millisecond. Imagine this: You’ve built a sleek, high-performance React dashboard. The UI is sharp, dark mode is gleaming, components are modularized, and React Query is executing parallel data fetches like a grand symphony. You brew a cup of coffee, open the app after lunch, hit refresh, and… BAM! You are immediately booted back to the Login screen. No warnings, no friendly error toasts—just a cold, ruthless redirect. You check your JWT expiration timer. The access token died 5 seconds ago, but your refresh token is valid for another 14 days. So why on earth did your app decide to kick you out like an uninvited party crasher? Welcome to the chaotic nightmare of Token Refresh Race Conditions in Axios Interceptors . In this article, we’ll walk through how parallel React queries can accidentally DDOS your own backend, why standard interceptor tutorials fail in production, how we built a promise-queue lock mechanism to solve it, and the subtle "gotcha" lurking in simple error detail checks that almost broke everything anyway. 1. The Problem: The Dashboard Stampede When a user logs into our app and opens the main dashboard, React Query triggers a stampede of concurrent API requests: GET /api/teams/users/ (Fetch team members) GET /api/teams/addresses/ (Fetch locations) GET /api/auth/profile/ (Fetch user profile) GET /api/auth/activity/recent/ (Fetch activity log) GET /api/notifications/ (Fetch unread alerts) Under normal circumstances, all five requests ride happily on the same valid Bearer <access_token> HTTP header. React App ---------------------------------------------> Django Backend GET /users/ [Bearer valid] ---> 200 OK GET /locations/ [Bearer valid] ---> 200 OK GET /profile/ [Bearer valid] ---> 200 OK The Ticking Time Bomb Fast forward 15 minutes. The short-lived access token expires. The user clicks on the "Analytics" tab. All 5 queries trigger at the exact same millisecond ( T = 0ms