标签：#java

A few days ago I published a post about the three-layer auth model and the invoice incident that made...

Every time I handed my Aadhaar card to someone for KYC, one thought kept nagging me: Where is this data actually going? Most "digital Aadhaar verification" tools out there silently upload your card details to their servers. You have zero visibility into what gets logged, stored, or sold. For something as sensitive as a national biometric ID, that's a pretty terrible default. So I built AadhaarQRCodeReader — a web app that scans the Secure QR on any Aadhaar card, decodes all the identity details, and does the entire thing inside your browser . No backend. No API calls. No data leaves your device. Ever. PtPrashantTripathi / AadhaarQRCodeReader 🇮🇳 Offline Aadhaar QR Reader — scan or upload any Aadhaar card, no server, no data leak. 🇮🇳 Aadhaar QR Code Reader Scan the Secure QR on any Aadhaar card to instantly verify identity details — 100 % offline, no server, no data leaves your device. ✨ Features Feature Details 📷 Live camera scan Uses the rear camera on mobile, front on desktop 🖼️ Image upload Pick any photo containing an Aadhaar QR from your gallery 🔒 100 % offline All decoding happens in the browser — zero network requests 🪪 Full card details Name, DOB, gender, address, mobile last-4, email (if present), issue date 🃏 3D card flip Front (personal) ↔ Back (address) card flip animation 🔗 Shareable URL Result is encoded in ?data= so links can be bookmarked 📱 Mobile-first Works on iOS Safari, Android Chrome, and desktop browsers 📸 Screenshots Scanner Verified Result (Front) Verified Result (Back) Point camera at any Aadhaar QR Personal details on the front face Address & reference date on … View on GitHub 🤔 Wait, what even is the Aadhaar Secure QR? UIDAI added a Secure QR Code to modern Aadhaar cards (and letters) — it's that big QR, not the small one. It's essentially a compressed, binary-encoded snapshot of your Aadhaar record containing: Name, DOB, gender Full address (house no., street, locality, district, state, PIN) Last 4 digits of your linked mobile Email (if yo

In Java, sorting is an important operation when working with collections such as ArrayList, LinkedList, and other data structures. For primitive data types, Java already knows how to sort values. However, for custom objects like Student, Employee, or Product, Java needs instructions on how objects should be compared. To achieve this, Java provides two interfaces: Comparable – Used for natural sorting. Comparator – Used for custom sorting. Comparable Interface Comparable is an interface available in the java.lang package. It is used to define the natural ordering of objects. The sorting logic is written inside the class itself using the compareTo() method. Method int compareTo(T obj) Return Values Negative - Current object comes before the given object Zero - Both objects are equal Positive - Current object comes after the given object When to Use Comparable? Use Comparable when: A class has one default sorting order. The sorting logic is a natural property of the object. The sorting criteria rarely change. Comparator Interface Comparator is an interface available in the java.util package. It is used to define custom sorting logic outside the class. Multiple comparators can be created for the same class. Method int compare(T o1, T o2) Return Values Negative - First object comes before second object Zero - Both objects are equal Positive - First object comes after second object When to Use Comparator? Use Comparator when: Multiple sorting criteria are required. You don't want to modify the original class. Different sorting orders are needed at different times.

GitHub热门项目 | 📈 Get real-time stocks from TradingView | Stars: 3,638 | 67 stars today | 语言: JavaScript

GitHub热门项目 | ☕️ Classic, reliable, trusted test framework for Node.js and the browser | Stars: 22,956 | 12 stars today | 语言: JavaScript

I use a form validation problem as a technical interview question. It's deceptively simple — and the solutions people reach for reveal a lot about how they think. Then I tried it on Claude, ChatGPT, and Gemini. The results were illuminating, but not for the reasons I expected. The Problem Many form libraries share a common convention: form data is represented as a plain nested object, and the validation function returns an object of the same shape containing the errors. You'll find this pattern in Formik and React Final Form in React, and — full disclosure — in Inglorious Web , my own framework, which ships form handling built in without any extra dependencies. const values = { productName : ' VR Visor ' , quantity : 1 , homeAddress : { street : ' Long St ' , zip : ' 00666 ' }, shippingAddress : { street : ' Short St ' , zip : ' 00777 ' , co : ' Inglorious Coderz ' }, billingAddress : { street : ' Wide Plaza ' , zip : ' 00888 ' , vat : ' 1142042 ' }, } The validation function should return an object containing all errors found. A starting example: function validate ( values ) { const errors = {} if ( ! values . productName ) { errors . productName = ' required ' } return errors } The ask: extend this to validate every field . Notice that the three address types aren't identical. shippingAddress requires a co field. billingAddress requires a vat . These differences matter — and how you handle them reveals a lot. Four Solutions, Four Instincts 1. The Flag — the average human The most common approach I see in interviews is a single validateAddress function with a type parameter: function validateAddress ( values = {}, type ) { const errors = {} if ( ! values . street ) errors . street = ' required ' if ( ! values . zip ) errors . zip = ' required ' if ( type === ' shipping ' && ! values . co ) errors . co = ' required ' if ( type === ' billing ' && ! values . vat ) errors . vat = ' required ' return errors } It works. But every new address type, every new special rule,

This module built one thing, from many angles: the container — the part of Spring that creates your objects, wires them together, and hands them out. Eight articles each zoomed in on a different corner of it. This recap zooms back out. The goal here is not to re-explain each topic, but to show how they are all the same idea seen from different sides, so the whole module collapses into a picture you can hold in your head at once. So before the details, here is the single sentence the entire module hangs on: the container is a factory that runs at startup, and almost every feature you met is just that factory doing a little extra work while it builds a bean. Keep that sentence close. Everything below is a way of filling it in. The factory, in one picture Picture an assembly line that runs exactly once, when your application boots. You hand it a list of what to build and how the pieces fit. It builds every object your app is made of, connects them, sets them on a shelf, and hands them out on request for the rest of the program's life. That assembly line is the container. The objects it builds and manages are beans . An object you create yourself with new is not a bean — Spring never touched it — and that distinction is the thread running through every trap in this module. The factory does four things at startup, and the order matters: it reads recipes, works out who needs whom, builds from the bottom up, and caches each result. ApplicationContext ctx = SpringApplication . run ( App . class , args ); OrderService svc = ctx . getBean ( OrderService . class ); // already built and wired By the time run returns, the work is done. Asking for a bean is instant because the building already happened. Every other topic in the module is a detail about how that one startup pass works. Why we hand the work over at all The module opened with a question of control. Left alone, a class builds its own collaborators with new — and in doing so it welds together two unrelated decisions:

AI coding assistants have fundamentally changed the way we write software. Today it's perfectly normal to ask ChatGPT, Claude, Cursor, or Copilot to explain an API, generate a React component, review a pull request, or help debug a problem. For many developers, these tools have become part of the daily workflow. Yet there's one area where they still struggle more than we'd like to admit: understanding the current state of the web platform. Mozilla recently demonstrated this problem in a surprisingly direct way. While evaluating Claude Code on recently released Firefox features, the team discovered that the model confidently claimed Firefox didn't support the Web Serial API and that Mozilla had no plans to implement it. The answer sounded plausible, detailed, and authoritative. There was just one issue. Firefox had already shipped support for the API. That experiment became one of the motivations behind Mozilla's new MDN MCP Server , a tool designed to give AI assistants direct access to MDN documentation and browser compatibility data. More importantly, Mozilla didn't just launch the service—they tested whether it actually improves the quality of AI-generated answers. The results are worth paying attention to. The Real Problem Isn't Hallucination When discussions about AI reliability come up, the conversation usually focuses on hallucinations. But browser compatibility is a slightly different problem. The web platform evolves continuously. Browsers ship new APIs, CSS features, HTML capabilities, and compatibility updates every few weeks. Specifications change, Baseline statuses evolve, and features that were experimental yesterday can become production-ready tomorrow. Large language models, on the other hand, are trained on snapshots of information. Even highly capable models can only know what was available when they were trained. When they're asked about something that appeared later—or something that wasn't widely represented in their training data—they often hav

Hello Dev Community! 👋 It is Day 32 of my continuous web development run, and today I jumped into a project that pushed my array manipulation and conditional logic to a whole new level: A complete Snake and Ladder Board Game using HTML5, CSS3, and Vanilla JavaScript! After building Rock Paper Scissors yesterday, I wanted to tackle a game that requires tracking persistent coordinate states across a 100-cell mathematical grid. 🛠️ The Game Architecture & Logic Breakdown Building this wasn't just about random numbers; it was about managing spatial transitions on a dynamic interface. Here is how I structured the core backend mechanics: 1. The 100-Cell Grid Layout Instead of manually hardcoding 100 divs inside my index file, I engineered the grid programmatically. I mapped out a loop running from 100 down to 1, building individual cell elements and using CSS Grid properties to wrap them perfectly into a standard 10x10 layout matrix. 2. Mapping Snakes & Ladders (The Jump Engine) To build the shortcuts and traps, I didn't write massive, messy if-else trees. Instead, I utilized a clean JavaScript Object Map tracking key-value pairs where the key is the trigger tile and the value is the destination tile: javascript const gameModifications = { // Ladders (Climbing up) 4: 14, 9: 31, 21: 42, 28: 84, 51: 67, 72: 91, 80: 99, // Snakes (Sliding down) 17: 7, 54: 34, 62: 19, 64: 60, 87: 36, 93: 73, 95: 75, 98: 79 };

While you're manually checking if YES + NO = 1 , quantitative systems are solving massive constraint satisfaction problems across thousands of correlated markets in milliseconds. The Hidden Reality of Prediction Market Arbitrage You see a market where YES is trading at $0.62 and NO at $0.33. You think: There's $0.05 of arbitrage here . You're right. What you don't see is that by the time you place both orders, professional systems have already: Scanned 17,000+ conditions Detected dozens of correlated mispricings Calculated optimal position sizes (with fees & slippage) Executed everything in parallel Moved on to the next opportunity Between April 2024 and April 2025, quantitative traders extracted $39,688,585 in guaranteed arbitrage profits from Polymarket. The top individual wallet made $2,009,631.76 across 4,049 trades — an average of $496 guaranteed profit per trade . This wasn't gambling. This was mathematics. Why Simple "YES + NO = 1" Checks Fail Most retail traders stop at basic price sum checks. That's not enough. Markets are logically dependent. Example: "Will Trump win Pennsylvania?" → YES: $0.48 "Will Republicans win Pennsylvania by 5+ points?" → YES: $0.32 If the second outcome happens, the first must be true. These dependencies create arbitrage opportunities that simple addition cannot detect. This is known as the marginal polytope problem — projecting prices onto the set of arbitrage-free probability distributions. The Scale of the Computational Challenge For any event with n binary conditions, there are 2ⁿ possible outcome combinations. 2024 U.S. elections: 305 markets → tens of thousands of pairs 2010 NCAA tournament: 63 games → 2⁶³ ≈ 9.2 quintillion combinations Brute force is impossible. Smart systems use constraints instead. Real example : Duke vs Cornell basketball market 7 possible win counts per team → 14 conditions. Instead of checking 16,384 combinations, 3 linear constraints were enough. Research found that 41% of 17,218 conditions showed sing

A data export endpoint dumps system diagnostics when it hits an invalid field. Feed it garbage, read the debug output, grab the flag. A data export feature lets you pick which profile fields to download. The UI only offers valid fields through checkboxes, so everything looks locked down. But the API behind it accepts arbitrary field names -- send it one it doesn't recognize, and instead of a clean error, it dumps full system diagnostics including internal feature flags. That's where the flag is. You'll bypass the frontend, hit the endpoint directly, and read what comes back. Lab setup Start the lab: npx create-oss-store@latest Or with Docker (no Node.js required): docker run -p 3000:3000 leogra/oss-oopssec-store The app runs at http://localhost:3000 . What you're targeting The app has a profile page at /profile with a Data Export tab. It lets users download their own data in JSON or CSV by selecting fields through checkboxes ( User ID , Email , Role , Address ID ) and clicking "Export Data". The UI looks safe -- you can only pick from a fixed set of valid fields, so there's no way to submit an invalid one through the browser. But that's just client-side validation. The endpoint behind it is POST /api/user/export , and it accepts a JSON body with two parameters: { "format" : "json" , "fields" : [ "id" , "email" , "role" ] } The fields value is an array of strings. The API checks each field against an allowlist. Valid fields? You get your data back. Invalid fields? The API throws an error -- and that error says way too much. Step-by-step exploitation 1. Log in You need an authenticated session. Use one of the seeded accounts: Email: alice@example.com Password: iloveduck Log in through the UI at /login , or grab a session cookie via curl: curl -c cookies.txt -X POST http://localhost:3000/api/auth/login \ -H "Content-Type: application/json" \ -d '{"email":"alice@example.com","password":"iloveduck"}' 2. Explore the Data Export tab Go to /profile and click the Data Export

Provisioning a working email mailbox from Node.js takes less code than the average OAuth callback handler. No consent screen, no token refresh job, no provider SDK — one fetch call returns a grant ID, and from there the mailbox sends, receives, and RSVPs to calendar invites. That's the pitch for Nylas Agent Accounts , hosted email-and-calendar identities you control entirely through the API. They're in beta, and the official quickstart promises a working account in under 5 minutes. The docs show it in curl; here's the same flow in JavaScript. What you need Two things: an API key, and a registered domain for the mailbox to live on. For testing, the zero-DNS path is a *.nylas.email trial subdomain registered from the Dashboard — addresses like test@your-application.nylas.email work immediately. For production you'd register your own domain (the Dashboard generates the MX and TXT records to publish, and verification is automatic once they propagate), but the trial domain is fine for this walkthrough. export NYLAS_API_KEY = "nyk_..." Create the mailbox The endpoint is POST /v3/connect/custom — the same Bring Your Own Auth route used for other providers — with "provider": "nylas" . Unlike OAuth providers, there's no refresh token in the body; just the address: const BASE = " https://api.us.nylas.com " ; const headers = { Authorization : `Bearer ${ process . env . NYLAS_API_KEY } ` , " Content-Type " : " application/json " , }; const res = await fetch ( ` ${ BASE } /v3/connect/custom` , { method : " POST " , headers , body : JSON . stringify ({ provider : " nylas " , settings : { email : " test@your-application.nylas.email " }, }), }); const { data } = await res . json (); const grantId = data . id ; // save this — every later call needs it That grantId is the whole handle. The mailbox behind it is live as soon as the response comes back, and it works with every existing endpoint — messages, drafts, folders, calendars, events, webhooks. One optional field deserves a menti

android doze kills your background tasks and nobody explains why properly been building a react native app that schedules stuff to run later. worked fine every time i tested it. shipped it, and it started missing schedules. only when the phone had been sitting idle for a while. never on my desk. took me way too long to figure out what was going on so writing it up here. what happens you schedule something for 1am. check logs next morning: 01:00:00 alarm fired 01:00:02 connected (while back grounded, 2 seconds) 01:18:xx the actual send ran the connection came up fine. in 2 seconds. while the phone was back grounded. but the code that was supposed to do something with that connection ran 18 minutes later when something else woke the phone up. why doze mode freezes javascript timers. setTimeout, setInterval, any polling loop on the js thread-all frozen. but native events (connection callbacks, lifecycle events, native module bridges) keep firing. i had a setInterval checking "are we connected yet" every second. doze froze that loop. the connection came up, nobody noticed for 18 minutes because the thing checking for it was asleep. the phone could do the work. my code just couldn't tell. stuff i tried that didn't fix it foreground service — keeps the process alive but doesn't unfreeze js timers. not the problem. more setTimeout/setInterval variations, literally the thing causing it. spent two days making the problem worse. HeadlessJS dropped in without changes compiled, never ran on newer RN. lost a few hours there. the actual fix move everything off timers. put your work directly in the event handler. instead of polling to check if you're connected: js // this is frozen by doze. don't. setInterval (() => { if ( isReady ()) doWork () }, 1000 ) do this : jsconnection . on ( ' status ' , ( state ) => { if ( state === ' connected ' ) { doWork ( job ) } }) native events survive doze. timers don't. that's the whole thing. for waking up at the right time — native AlarmManager

today we discuss about Interface in Java. first we understand the concept with simple Analogy, Imagine you go to a shop and buy items. in a bill counter, the shop keeper care about only one thing. The customer paid the Money or not. The shopkeeper does NOT care about how you pay the money, UPI Debit Card Cash They only thing is payment paid in successfully. Here a interface acts like a Rule in billing counter. It only defines what must be done, not how it should be done. Different payment methods follow the same rule, but each one works in its own way. The shopkeeper does not need to change anything in the billing counter. No matter how the customer pays, the system works the same. so, i follow this analogy and using a example for this blog. What is Interface? (in GeeksforGeeks) An interface in Java is a blueprint that defines a set of methods a class must implement without providing full implementation details. It helps achieve abstraction by focusing on what a class should do rather than how it does it. Interfaces also support multiple inheritance in Java. A class must implement all abstract methods of an interface. All variables in an interface are public, static, and final by default. Interfaces can have default, static, and private methods first create a interface file Payment.java public interface Payment { void pay ( int amount ); } here we create a method but not defined that method This is the shop rule. “Anyone wants to pay must follow one rule → pay the amount.” The shop does not explain how you pay, only thing is you must pay. next we create another file for Different Customers, class CardPayment implements Payment { public void pay ( int amount ) { System . out . println ( "Paid ₹" + amount + " using Card" ); } } class UpiPayment implements Payment { public void pay ( int amount ) { System . out . println ( "Paid ₹" + amount + " using UPI" ); } } class CashPayment implements Payment { public void pay ( int amount ) { System . out . println ( "Paid ₹" +

UI IP Toolkit - A standalone static visual catalog for CSS/JS components I built UI IP Toolkit to solve my own workflow problem: I kept losing useful UI snippets (buttons, loaders, CTA blocks, glassmorphic cards, layout grids) across old projects and directories. Live site: https://ui-ip-toolkit.vercel.app/ GitHub Repository: https://github.com/ikerperez12/UI-IP-Toolkit-v4.0 Design Philosophy Zero dependencies: Raw HTML, CSS, and vanilla JS. No NPM packages, framework configurations, or build steps required. Copy-paste ready: Visual preview cards with one-click copy buttons for immediate use in any stack. Light/Dark mode: Clean design system focusing on micro-interactions, sleek gradients, and responsive layouts. Visual catalog: Catalog of gradients, buttons, fonts, loading states, hover treatments, glass surfaces, layout fragments, and UI patterns. How do you manage your personal code/CSS snippet collections? Hope this is useful to others!

Every developer who has written a Playwright test for OTP verification has written this line: const otp = email . body . match ( / \b\d{6}\b / )?.[ 0 ]; It works. Until it doesn't. The email body changes format. The OTP appears inside an HTML table. The sending service wraps it in a <span> . Your regex matches a phone number instead of the code. The test fails intermittently and you spend an hour debugging something that has nothing to do with the feature you're testing. The regex problem OTP extraction via regex is brittle by nature. You're pattern-matching against a string that your email sending service controls — not you. Any time the template changes, your tests break. Here's what a typical OTP test looks like today: import { test , expect } from ' @playwright/test ' ; import { ZeroDrop } from ' zerodrop-client ' ; const mail = new ZeroDrop (); test ( ' user can verify OTP ' , async ({ page }) => { const inbox = mail . generateInbox (); // 1. Trigger OTP send await page . goto ( ' /login ' ); await page . fill ( ' [data-testid="email"] ' , inbox ); await page . click ( ' [data-testid="submit"] ' ); // 2. Wait for email const email = await mail . waitForLatest ( inbox , { timeout : 15000 }); // 3. Extract OTP — the fragile part const otp = email . body . match ( / \b\d{6}\b / )?.[ 0 ]; if ( ! otp ) throw new Error ( ' OTP not found in email body ' ); // 4. Enter OTP await page . fill ( ' [data-testid="otp"] ' , otp ); await page . click ( ' [data-testid="verify"] ' ); await expect ( page ). toHaveURL ( ' /dashboard ' ); }); The test works — but line 14 is carrying all the risk. Change the email template and the test breaks. Add a phone number to the footer and the regex matches the wrong number. Send a 4-digit OTP instead of 6 and you need to update the pattern. OTP extraction at the edge ZeroDrop extracts OTPs before they reach your test. The Cloudflare Worker that catches incoming emails runs a pattern match on the plain-text body and stores the result alongsi

AWS Hero Vadym Kazulkin explains how to overcome Java’s enterprise hurdle on AWS Lambda: cold starts and memory footprints. He shares a technical deep dive into performance tuning, comparing fully managed AWS SnapStart (with pre-snapshot priming hooks) against GraalVM ahead-of-time compilation, while addressing the latest architectural implications of Project Leyden and Java 25. By Vadym Kazulkin

ArrowJS, developed by Justin Schroeder, is a reactive UI library that has reached its 1.0 release after three years in development. It utilizes core web technologies, avoids JSX and compilers. Notable features include an optional WASM sandbox for executing untrusted code. The framework's minimalism is highlighted by its reliance on three main functions: reactive, html, and component. By Daniel Curtis

GitHub热门项目 | Web Extension for saving a faithful copy of a complete web page in a single HTML file | Stars: 21,579 | 84 stars today | 语言: JavaScript

GitHub热门项目 | User editable database for TV channels. | Stars: 1,432 | 11 stars today | 语言: JavaScript