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I built 109 tools that never touch a server - here is the architecture Most "tools" sites you have used do this: You upload a file It goes to a server The server processes it You download the result Sometimes the server stores it. Sometimes it leaks. Sometimes it disappears with the company. I wanted something different. Every tool on korelyy.com runs 100% in your browser . Zero backend. Zero upload. Zero tracking. Here is the actual architecture, the real numbers after 90 days, and what I learned. What "no server" actually means For each of the 109 tools: The entire app is a static HTML + CSS + JS file It is served as-is from a CDN (Cloudflare Pages) All file processing happens in your browser via FileReader , canvas , Web Crypto API , or OffscreenCanvas Your file never leaves your device Closing the tab = the data is gone (no cookies, no localStorage, no account) This is not a marketing claim. It is verifiable: Open DevTools -> Network tab Use any tool that requires a file (image converter, JSON formatter, etc.) Reload. The only network request is for the static HTML/CSS/JS bundle. No fetch() to a server. No XHR . No upload. The file is read, processed in-memory, and downloaded. The 4 browser APIs that do 90% of the work When you remove a backend, you are left with the browser. The browser is more capable than most people think. 1. FileReader and URL.createObjectURL Read any file the user gives you: const file = document . querySelector ( ' input[type=file] ' ). files [ 0 ]; const url = URL . createObjectURL ( file ); const img = new Image (); img . onload = () => { // process image canvas . toBlob ( blob => { const downloadUrl = URL . createObjectURL ( blob ); // trigger download }); }; img . src = url ; Image conversion, PDF generation, audio trimming - all the same pattern. Read blob, process, create new blob, download. 2. crypto.subtle (Web Crypto API) Hashing, encryption, signing - all client-side: const hash = await crypto . subtle . digest ( ' SHA-256 ' , a

The first collaboration model in CodeVerse was convincing in exactly the way a local demo needs to be convincing. Open two tabs. Join the same room. Type in one editor. Watch the other editor update. Then ask one unpleasant question: what happens when those two sockets land on different server instances? The answer was that the room stopped being a room. Each process had its own memory, its own presence list, and its own idea of the current files. A restart erased state. A reconnect could create a second identity. A load balancer could turn a working demo into two isolated conversations. This article is about the work that followed: moving CodeVerse from synchronized tabs to a collaboration path I could test across processes, recover after disconnects, and describe without pretending a local benchmark was a production capacity claim. The real boundary was not Socket.IO Socket.IO made connection handling and room fan-out approachable, but it did not decide where truth lived. That distinction matters. A room name inside one Socket.IO process is a routing convenience, not durable shared state. Once I wanted multiple application instances, I needed separate answers for four kinds of information: Document state — the convergent contents of every file. Room policy — organizer identity, edit permissions, active file, and revision. Presence — which sockets are here now, on which instance, with which effective role. Durability — what survives Redis expiry, application restarts, or a longer period of inactivity. CodeVerse now uses Yjs for convergent document updates, Redis for live distributed room state and pub/sub, and Supabase for durable room snapshots and membership data. Socket.IO remains the transport and fan-out layer. That separation was more important than any individual library choice. Redis does three different jobs It is easy to say “I added Redis” and leave the architecture vague. In CodeVerse, Redis has three explicit responsibilities. 1. Cross-instance fan-out

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On behalf of the core Axelix team, and everybody who has contributed to the community, I want to declare: we finally did it. Axelix, finally, goes GA (Generally Available)! For those who do not know - Axelix is a product with an Open Source core, that allows you to discover the common problems, pitfalls and inefficiencies in Java applications at large scale. We're available on GitHub (btw - give us a star!). In this post, I want to share the story and the motivation behind the product overall. I hope you find it interesting. The Story. Big "Why" Behind Axelix Java is quite an interesting language and ecosystem in general. I think a lot of people will not argue that it is quite old, and it was one of the first so-called "Object-Oriented" languages, that actually gained massive adoption. It both was, and it still is the backbone of modern enterprise. For anyone who claims that Java is dead - I recommend checking the JetBrains State of Developer Ecosystem survey or even the Stack Overflow survey for 2025 (and Stack Overflow has, sadly, become a part of history). It is clear that Java as a language and the "ecosystem" around it (including Kotlin) is still relatively popular, and it remains true. Ecosystems around Languages The experienced developer knows that today's ecosystems that evolve around languages are typically very diverse. For example, let's talk about JavaScript. If we decide to run JavaScript on the server, then we're probably going to work with a database of some sort. Therefore, we're also going to need a framework, a library to work with the database, e.g. an ORM (I know that we may work without it but let's leave that aside). And in JavaScript, we have quite a lot of options: Prisma TypeORM DrizzleORM Kysely and so on. We can pretty safely state that Prisma ORM is probably the most used ORM on JavaScript . But notice that it is far from being the definitive JavaScript ORM. It is not like Prisma is the default choice and is by far the most popular ORM -

Modern AI UX — chat panels, tool-calling agents, assistants that remember context and even suggest your next step — has lived in JavaScript SaaS for years. The Java enterprise stack has been left doing it the hard way. TabForge AI closes that gap . It's a complete platform for building AI-powered web apps on Jakarta EE + PrimeFaces — from the multi-tab UI shell down to a clean, provider-agnostic AI layer. Library, live demo, starter project, and a drop-in UI template — all shipped. Here's the whole thing, top to bottom. ## 1. Tabs as annotated beans — DynTabs You describe a tab; the framework handles opening, closing, lifecycle, and state. Each open tab gets its own isolated CDI bean via a custom @TabScoped scope. @Named @TabScoped @DynTab ( name = "OrdersDynTab" , uniqueIdentifier = "Orders" , title = "Orders" , includePage = "/WEB-INF/orders.xhtml" , trackActivity = true ) public class OrdersBean extends BaseDyntabCdiBean { // open the same tab twice → two independent instances } java No manual navigation, no page-state juggling. Open a tab, get a bean; close it, it's gone. A clean AI layer — EasyAI One fluent entry point over LangChain4j. Chat, tools, agents, and structured extraction — provider-agnostic, so the model behind it is a config detail. // A typed assistant with a business service exposed as tools OrdersAssistant ai = EasyAI . assistant ( OrdersAssistant . class ) . withTools ( orderService ) . build (); String reply = ai . ask ( "cancel order ORD-002" ); You opt methods in as tools explicitly — no accidental exposure: @EasyTool ( "Cancels an active order" ) public String cancelOrder ( String orderId ) { ... } Deterministic pipelines — flow() Agents are powerful but unpredictable. When you want a repeatable, testable process, flow() lets you own the steps and call the model only at the edges that actually need language: EasyAI . flow () . step ( "understand" , ctx -> EasyAI . extract ( OrderRequest . class ). from ( ctx . inputText ())) . step ( "check

This is a submission for Frontend Challenge - Comfort Food Edition, CSS Art . Inspiration In an Indian tech office, no product release, critical bug fix, or late-night deployment is complete without a 5-minute pantry chai break. A steaming clay Kulhad of Ginger Adrak Chai paired with crisp Garma-Garam Samosas is the ultimate comfort food that powers developers through endless coding sprints. This authentic workplace culture and rich street-food nostalgia inspired me to build a pure-CSS interactive art and corporate pantry scene. Demo Live Interactive Demo : Corporate Chai & Samosa Experience CodePen Embed : codepen.io GitHub Repository : Sayista-Yazdani/corporate-chai What I Built Adrak Chai & Samosa is an interactive web experience featuring: Pure CSS Hero Artwork : Handcrafted clay-textured Kulhad Chai cup with a shimmering tea surface, malai rim, and layered rising steam animations. Golden-brown samosas with crisp crimped edges, served on a traditional plate alongside mint and tamarind chutneys. Interactive Corporate Pantry Corner : Fully detailed tea kitchen equipped with a glowing gas stove, boiling tea saucepan with foam, spice jars ( Adrak , Elaichi , Chai Patti ), and stacked clay cups. 4 Distinct Characters : Rohan (Frontend Dev), Amit (Tech Lead), Priya (Product Manager), and Kaka (Pantry Specialist). Web Speech API & Audio Narration : Real voice speech synthesis with gender-matched voice profiles for each character. Character Gaze & Mouth Choreography : Characters automatically look toward whoever is currently speaking with dynamic gaze shifting, listening poses, and animated mouth movements. Journey & Tech Stack Technical Implementation CSS Artwork : Built entirely with CSS gradient meshes, polygon shapes, keyframe animations, and layered pseudo-elements ( ::before / ::after ). Audio Engine : Powered by native window.speechSynthesis with dynamic voice selection and text cleaning. State Management : Reactive data-speaker HTML attributes driving multi-char

Context: I'm a data scientist/analyst (in Python and R) learning development from scratch. Inevitably, I am learning these through the lens of what I already know. If you have a similar background and are a beginner developer, I hope these analogies help! Any comments, especially if you spot any misunderstanding, are appreciated. Commenting is caring <3 Motivation: I was building a mock data layer for a fitness social app — simulating what happens when users fetch new posts from a feed. The function needs to return mock posts after a delay, simulating a real network request. Working Code: `function fakeFetchPosts() { return new Promise((resolve) => { setTimeout(() => { resolve(posts); }, 2000); }); } async function main() { console.log("Fetching..."); const fetchedPosts = await fakeFetchPosts(); console.log("Fetched posts:", fetchedPosts); } main(); console.log("Sync code ran");` What do you expect to see as an output? I first confused the logic with blocking. For example, in webscraping, something like time.sleep() or Selenium's WebDriverWait(driver, 10).until(EC.presence_of_element_located(...)) . In this case, output will be Fetching..., Fetched posts: ..., then Sync code ran. However, the output gives Fetching..., Sync code ran, and then Fetched posts. In the former, the whole script (single thread) pauses and does nothing else until the wait ends or the condition is met. The latter is different in that the rest of your program keeps running during the wait, and thus the output where Sync code ran is printed first before the fetchedPosts. By the way, posts are arrays. const posts = [{ author: "j1wonkim", text: "Testing Physical", likes: 100, }, {author: "onewc0218", text: "Love love", likes: 55, }, {author: "gakbca", text: "You are good", likes: 10, } ];

As developers, we use package managers almost every day. Whether we are working with Node.js, React, Next.js, TypeScript, Express, Prisma, or other technologies in the JavaScript ecosystem, choosing the right package manager can have a meaningful impact on our development workflow. Recently, I spent some time comparing the most popular package managers: npm, Yarn, pnpm, and Bun. After looking at them from the perspective of performance, dependency management, disk efficiency, ecosystem compatibility, and developer productivity, my current preference is pnpm. Why pnpm? For me, pnpm provides one of the best overall balances between speed, disk efficiency, reliability, dependency management, and developer experience. One of the key differences is how pnpm handles dependencies. It uses a content-addressable store and links packages into projects instead of unnecessarily keeping separate copies of the same packages for every project. This can reduce disk usage and make package installation more efficient, especially when working on multiple JavaScript or TypeScript projects. Another advantage is pnpm's stricter dependency management. It encourages projects to explicitly declare the packages they actually depend on, which can help prevent accidental reliance on transitive dependencies. This becomes particularly useful when working on larger applications, monorepos, or team-based projects. What about Bun? Bun is extremely interesting because it is much more than a package manager. It provides a JavaScript/TypeScript runtime, package manager, test runner, and bundler. Its performance is impressive, especially when it comes to package installation and certain development workflows. However, I don't think raw speed should be the only factor when choosing a technology for production. Compatibility, ecosystem maturity, team familiarity, tooling support, and long-term maintainability are equally important. That is why I see Bun as an excellent and promising tool, but I would not

A “Download all as ZIP” button in React starts simple. A production version also needs progress, cancellation, retry, useful errors, and a plan for archives that are too large for browser memory. In this tutorial, we’ll use Eazip , an open-source ZIP toolkit for JavaScript and React. Its React package gives you a hook for starting ZIP jobs and a ready-made tray for showing their status. Everyday files can be zipped entirely in the browser. When the same feature needs to handle multi-GB archives or thousands of remote URLs, it can move the job to Eazip Cloud without adding any backend code. Install the React package npm install @eazip/react @eazip/react requires React 18 or later. It includes the core ZIP engine, so you do not need to install another Eazip package. Build a working ZIP download component This component lets a user select several files and download them as one ZIP: import { useState } from ' react ' ; import { EazipTray , useEazip } from ' @eazip/react ' ; export function FileZipDownload () { const [ files , setFiles ] = useState < File [] > ([]); const zip = useEazip (); return ( < section > < label > Files to download < input type = "file" multiple onChange = { ( event ) => setFiles ( Array . from ( event . currentTarget . files ?? [])) } /> </ label > < button type = "button" disabled = { files . length === 0 || zip . isBusy } onClick = { () => zip . download ({ files , zipName : ' selected-files.zip ' , }) } > Download { files . length || '' } files as ZIP </ button > < EazipTray /> </ section > ); } There are three Eazip pieces in this example: useEazip() gives the component its download commands and current task. zip.download() starts the ZIP job and returns immediately. <EazipTray /> shows progress, cancel, retry, partial results, errors, and the completed download. No provider or CSS import is required. What happens to the selected files? Without a strategy option, Eazip uses its Local strategy. The selected File objects stay on the user’s devi

Lighthouse kept warning me about inefficient cache lifetimes, even though I had already added caching for my static files. The missing piece was Nuxt Image and its generated /_ipx URLs . In this post, I’ll share the simple caching setup I use for Nuxt build files, public assets, and optimized images without risking stale content after deployment. The basic rule is simple: Cache files aggressively when changing the file also changes its URL. Be more careful when the same URL can serve different content later. You have probably seen the same Lighthouse warning I have: Use efficient cache lifetimes. Browser caching for static files is usually straightforward. You add a Cache-Control header, choose a reasonable lifetime, and the browser avoids downloading the same files again on every visit. However, in a Nuxt application, not every static-looking file should use the same caching policy. Nuxt build files are automatically versioned. Files inside public/ usually are not. Nuxt Image also creates transformed image URLs under /_ipx , which need their own cache rule. In this post, I’ll go through the setup I use, including the Nuxt Image rule that was missing during my latest Lighthouse audit. The simple caching rule The most important question is not whether a file is an image, font, or JavaScript file. The important question is: Will the URL change when the file changes? When the answer is yes, you can safely cache the file for a very long time. When the answer is no, you should use a shorter cache lifetime. Otherwise, visitors may continue seeing an old version after you deploy an update. What the cache directives mean Here are the main directives used in this setup: public allows browsers and shared caches such as CDNs to store the response. max-age controls how long the browser considers the file fresh. s-maxage controls how long shared caches such as Cloudflare consider it fresh. immutable tells the browser that the file is not expected to change while that URL exists.

Hey everyone! I'm jinyuan, an indie developer. I recently launched DevTools Box — a free online toolbox with 75+ developer tools. What's in the box? DevTools Box includes tools like: JSON Formatter — beautify and validate JSON Regex Tester — test regular expressions with live matching Base64 Encoder/Decoder — quick encoding and decoding QR Code Generator — generate QR codes instantly Hash Calculator — MD5, SHA-1, SHA-256 and more Color Picker — pick colors and convert between formats ...and 69 more tools! Why I built it I was tired of jumping between different websites for simple dev tasks. Each tool runs entirely in your browser — no login, no ads, no data sent to any server. Tech stack Next.js 14 with App Router TypeScript Tailwind CSS Static export to Cloudflare Pages Try it out Check it out at tdboxs.com . All tools are 100% free. Would love to hear your feedback! What tools would you add?

Understanding Java's Virtual Threads: Lightweight Concurrency in Action Java 21 introduced virtual threads as a stable feature (JEP 444), fundamentally changing how we approach concurrency on the JVM. In this post, we'll explore what virtual threads are, why they matter, and how to use them effectively. The Problem with Platform Threads Traditional Java threads—now called platform threads —are thin wrappers around operating system threads. Each one consumes roughly 1MB of stack memory and involves the OS scheduler for context switching. This makes them expensive: java // Creating thousands of platform threads is costly for (int i = 0; i < 10_000; i++) { new Thread(() -> { // blocking I/O ties up an OS thread processRequest(); }).start(); } In high-throughput server applications, the classic "thread-per-request" model hits a ceiling because you simply cannot create enough OS threads. Enter Virtual Threads Virtual threads are managed by the JVM rather than the OS. Many virtual threads run on a small pool of carrier platform threads. When a virtual thread blocks (e.g., on I/O), the JVM detaches it from its carrier, freeing that carrier to run other virtual threads. java // Creating a million virtual threads is perfectly fine try (var executor = Executors.newVirtualThreadPerTaskExecutor()) { IntStream.range(0, 1_000_000).forEach(i -> { executor.submit(() -> { Thread.sleep(Duration.ofSeconds(1)); return i; }); }); } Key Benefits Cheap creation : Virtual threads start with a tiny stack that grows on demand. Familiar model : You write straightforward blocking code—no callbacks or reactive chains. Better scalability : Throughput is limited by resources, not thread count. Using Virtual Threads in Spring Boot As of Spring Boot 3.2, enabling virtual threads is a one-line configuration change: properties spring.threads.virtual.enabled=true This makes Tomcat handle each request on a virtual thread, allowing your application to serve many concurrent blocking requests without exha

A few years ago, I was just another developer trying to figure out how to build things that actually work, not just things that run once and fall apart the moment real users touch them. I tried a few paths. I read a lot. I broke a lot of things. And somewhere along that road, I found NestJS. At first, it looked like just another tool. Another framework to learn, another thing to add to my resume. But the more I used it, the more I realized something. NestJS wasn't just teaching me how to build backend systems. It was teaching me how to think like someone who builds things meant to last. I did not choose NestJS because it was trendy. I chose it because it made me feel organized in a way nothing else had. It gave structure to ideas that used to feel messy in my head. It made me feel like a professional, not just someone typing code and hoping it works. Here is the lesson I want you to take from this, even if you never write a single line of NestJS code. Anything you build, whether it is software, a business, or even your own life, lasts longer when it has structure. Not rules for the sake of rules, but structure that makes room for growth without everything falling apart. That is what NestJS taught me first, before it taught me anything technical. Organize your thinking, and the work becomes easier to carry. Today, when people ask me why I still use NestJS after all this time, my answer is simple. It is not just a tool I use. It is the reason I became confident in what I do. And once you experience that kind of confidence in your work, it is very hard to walk away from it. I write these thoughts as Peace Melodi, a backend software engineer who cares deeply about building things that hold up under real pressure, real users, and real growth. If any of this resonated with you, I would love to connect. LinkedIn: https://www.linkedin.com/in/melodi-peace-406494368 GitHub: https://github.com/PeaceMelodi

Enterprise Java developers have a new superpower—drive GitHub Copilot from idiomatic Java code with annotations, virtual threads, and more. The post Using the GitHub Copilot SDK for Java appeared first on The GitHub Blog .

This is a submission for DEV's Summer Bug Smash: Clear the Lineup powered by Sentry . There's a shape of bug I've learned to distrust: the one where the safety net is bolted to the thing it's supposed to catch. I was reading Element Web's reporting code looking for something worth fixing when I hit a function that builds the whole Sentry payload as a single object literal — with two await calls sitting inside it. One of them asks the crypto layer for diagnostics. Optional diagnostics. Nice-to-have detail on a report that is already complete without them. I stopped there, because I could already see how that sentence ends. If the optional thing rejects, the object never exists. If the object never exists, there is no capture call. And the same pattern was waiting one directory over, in the rageshake path. The subsystem being diagnosed could prevent the diagnostic report from leaving the browser. Somebody decides to tell you what broke, and the broken part gets a veto. One deliberate press of a button, both explicit channels gone: the rageshake bundle and the manual Sentry event. I measured it at the boundary that actually counts — a real Sentry Browser SDK with a local, network-free transport. Under the same synthetic failure: zero serialized events before the fix, exactly one after. Same synthetic crypto rejection Before After collectBugReport(): rejected report completed with available diagnostics Sentry envelopes: 0 Sentry events: 1 unrelated context families: retained auxiliary error message or stack: absent Project Overview Element Web is the web client behind Element, a Matrix-based communication app. Its bug-report dialog can send two independent things: a rageshake bundle — logs and diagnostics packed into multipart form data and posted to a configured endpoint — and, when Sentry is configured, a single manually captured Sentry event. Both are explicit. Nothing leaves the browser unless a person opens that dialog and submits it. That framing shaped every deci

JEP 401, integrated into JDK 28, introduces value objects. These new class instances feature final fields, altered behavior for equality checks, and stricter construction and synchronization rules. It aims to enhance efficiency and reduce memory allocation costs. However, the preview is disabled by default and requires specific configuration at compile and run time. By A N M Bazlur Rahman

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