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🔥 NomaDamas / k-skill - 한국인을 위한 스킬 모음집 - SRT, KTX, 카카오톡, 한글과컴퓨터, 날씨, 미세먼지, 법령, 주식정보,

GitHub热门项目 | 한국인을 위한 스킬 모음집 - SRT, KTX, 카카오톡, 한글과컴퓨터, 날씨, 미세먼지, 법령, 주식정보, 조선왕조실록, KBO, K-리그, LCK, 특허 검색, 토스 증권, 맞춤법 검사, 중고차 가격, 쿠팡, 네이버 블로그, 다이소, 올리브영, 택배 송장 조회 등등... | Stars: 5,411 | 19 stars today | 语言: JavaScript

2026-06-07 原文 →
AI 资讯

Building a Privacy-First Media Converter in the Browser: No Servers, No Cloud, 100% Client-Side (RAM-Friendly)

Most online file converters require uploading your documents, images, or videos to an unknown server. This is slow, inconvenient, and raises serious privacy concerns. I decided to build something different: a converter that works entirely inside your browser. Processing large files without a backend presents two main engineering challenges: How do you avoid consuming all available RAM? How do you prevent the user interface from freezing? This article explains how I solved these problems using OPFS, Web Workers, and a Backpressure mechanism. The result is a working tool you can try right now: PixelForge Free . The Architecture at a Glance Here is the simplified data flow of the entire pipeline: Drag & Drop → OPFS (Virtual Disk) → Worker Pool (Backpressure) → ZIP Stream → Download Problem 1: Out-of-Memory (OOM) Crashes The Challenge: Loading many files directly into the browser's memory is impossible. A user dropping a folder with 100+ high-resolution images would instantly crash the tab. The Solution: Origin Private File System (OPFS) OPFS provides a fast, isolated virtual disk inside the browser. Instead of loading files into RAM, my pipeline intercepts the drop event and streams the raw binary data directly to this virtual disk. Here is a simplified version of how it works: // Get a reference to the virtual disk const root = await navigator . storage . getDirectory (); const fileHandle = await root . getFileHandle ( `input_ ${ id } .raw` , { create : true }); // Create a writable stream to the disk const writable = await fileHandle . createWritable (); // Stream the file directly from the user's computer to the virtual disk await file . stream (). pipeTo ( writable ); This allows the application to accept a folder with 500+ items without consuming more than a few megabytes of actual RAM. The data stays on the user's SSD, not in memory. Problem 2: UI Freezing The Challenge: Image compression, PDF parsing, and video encoding are CPU-intensive operations. Running them

2026-06-07 原文 →
AI 资讯

async/await is a Generator in Disguise. Let's Build It From Scratch

You write await a dozen times before lunch. Fetch a row, await it. Call a service, await that. It works, you move on, and you never have to think about what the word is doing. Then one day someone asks you to explain it. Maybe it's an interviewer."But what does await actually do?" And you open your mouth and what comes out is "it, uh, waits for the promise." Which is true, and also explains nothing. We can build async/awit mechanism from scratch using generators as a learning exercise. It requires a pause button wired to a small loop that waits on a promise and then presses play again. You already know one half of that machinery if you read the last post in this series . The other half is a trick generators have that we glossed over. Put the two together and you can build a working version of async/await yourself, by hand, and watch it behave exactly like the real thing. Let's do that. The shape of the problem Strip await down to what it has to accomplish and you get two requirements: First, a function has to be able to stop in the middle. Right at the await, freeze everything, the local variables, the spot in the loop, all of it, and hand control back to whoever called it. Normal functions can't do this. They run start to finish and that's the deal. Second, something on the outside has to wait for the promise to settle and then nudge the frozen function back to life, handing it the resolved value as if the await expression had simply evaluated to it. That's the whole job. A function that pauses, and a driver that resumes it when a promise is ready. Hold that picture, because the rest of this is just filling in those two pieces with things JavaScript already gives you. The half you've seen: pausing A generator function, the function* kind, can pause itself with yield and resume later from the exact same spot. We leaned on that hard in the CSV piece to pull rows through a pipeline one at a time. A line came in, got yielded, and the generator sat frozen until someone

2026-06-07 原文 →
开发者

I built a free image converter that runs 100% in your browser — no upload, no signup

Hey DEV community! 👋 I built IMGVO — a free image tool that works entirely in your browser. What it does Convert JPG, PNG, WebP, AVIF, HEIC and more Compress images up to 90% without quality loss Crop, resize, rotate, watermark Works offline (PWA) Why I built it Most image tools upload your files to servers. I wanted something private and instant. Tech 100% vanilla JavaScript No backend, no server Works offline as PWA Privacy first No files uploaded to any server. Everything runs locally in your browser. 🆓 Free, no signup required. 👉 Try it: https://imgvo.com Would love your feedback! 🙏

2026-06-07 原文 →
AI 资讯

JavaScript Data Types Explained: Primitive vs Non-Primitive Data Types

JavaScript Data Types: Primitive and Non-Primitive Data Types Data types are an important concept in JavaScript because they define the kind of value a variable can store. Understanding data types helps developers write reliable and efficient code. What is a Data Type? A data type defines what kind of value a variable can hold. Example let name = " John " ; // String let age = 25 ; // Number let isActive = true ; // Boolean In the above example, each variable stores a different type of value. Types of Data Types in JavaScript JavaScript data types are broadly classified into two categories: Primitive Data Types Non-Primitive Data Types Primitive Data Types Primitive data types store a single and simple value. Characteristics Store a single value. Immutable (cannot be changed directly). Compared by value. Stored directly in memory. Types of Primitive Data Types 1. String Used to store textual data. let name = " John " ; 2. Number Used to store numeric values. let age = 25 ; let price = 99.99 ; 3. Boolean Represents either true or false . let isLoggedIn = true ; 4. Undefined A variable that has been declared but not assigned a value. let city ; console . log ( city ); // undefined 5. Null Represents the intentional absence of a value. let user = null ; 6. Symbol Used to create unique identifiers. let id = Symbol ( " id " ); 7. BigInt Used to store very large integers beyond the safe Number limit. let largeNumber = 123456789012345678901234567890 n ; Non-Primitive Data Types Non-primitive data types store multiple values or complex data structures. Characteristics Can store collections of data. Mutable (their contents can be modified). Compared by reference. Stored as references in memory. Types of Non-Primitive Data Types 1. Array Used to store multiple values in a single variable. let colors = [ " red " , " green " , " blue " ]; 2. Object Used to store data as key-value pairs. let person = { name : " John " , age : 25 }; 3. Function Functions are reusable blocks of co

2026-06-06 原文 →
AI 资讯

Cypress Testing: Complete Beginner's Guide

Section 1: Getting Started with Cypress 1. Installing and Setting Up Cypress Prerequisites Before installing Cypress, ensure you have Node.js installed on your machine. Cypress requires Node.js 18.x or 20.x and above. You should also have an existing React project or create a new one. Check your Node.js version by running this command in your terminal: node --version # Should output v18.x.x or higher Creating a React Project (Optional) If you don't have an existing React project, create one using Vite which is the recommended approach for new React projects: npm create vite@latest my-react-app -- --template react cd my-react-app npm install Installing Cypress Navigate to your React project directory and install Cypress as a development dependency. Cypress is a fairly large package, so the installation might take a minute or two: npm install cypress --save-dev # Or using yarn yarn add cypress --dev Opening Cypress for the First Time After installation, open Cypress for the first time. This will create the initial folder structure and configuration files: npx cypress open When Cypress opens for the first time, you'll see a welcome screen where you can choose between E2E Testing and Component Testing. Select E2E Testing to get started with end-to-end tests. Cypress will then prompt you to choose a browser. You can select Chrome, Firefox, Edge, or Electron. Choose your preferred browser and click Start E2E Testing in [Browser] . Adding NPM Scripts Add convenient scripts to your package.json for running Cypress tests: { "scripts" : { "dev" : "vite" , "build" : "vite build" , "cy:open" : "cypress open" , "cy:run" : "cypress run" , "test:e2e" : "start-server-and-test dev http://localhost:5173 cy:run" } } Tip: Use npm run cy:open for interactive development with the Cypress Test Runner. Use npm run cy:run for headless execution in CI/CD pipelines. 2. Understanding Cypress Project Structure Project Directory Overview After initializing Cypress, you'll notice several new fold

2026-06-06 原文 →
AI 资讯

I Built a Free Open-Source EU AI Act / NIST AI RMF / ISO 42001 Crosswalk Tool - Here Is What I Found

Every week I see the same question in AI governance communities: "We already have NIST AI RMF implemented. Does that cover our EU AI Act obligations?" The honest answer is: sometimes yes, sometimes partially, and sometimes not at all. The problem is that nobody had built a clean, free, interactive tool that showed exactly which controls map to which, how strong those mappings actually are, and where the genuine gaps are. So I built one. Live tool: suhanasayyad.github.io GitHub: SuhanaSayyad / eu-ai-act-crosswalk-tool Interactive crosswalk mapping EU AI Act obligations to NIST AI RMF and ISO 42001 controls, with mapping strength indicators, gap analysis, and source links. 30 controls mapped. Free and open source. EU AI Act × NIST AI RMF × ISO 42001 - Interactive Compliance Crosswalk Tool An open-source tool that maps EU AI Act obligations to their equivalents in NIST AI RMF and ISO 42001, with mapping strength indicators, gap analysis, and source document links. Built for compliance teams, AI governance practitioners, and anyone trying to understand how these three frameworks relate to each other. Live demo: https://suhanasayyad.github.io/eu-ai-act-crosswalk-tool Built by: Suhana Sayyad | MSc Cybersecurity, TUS Athlone Why I built this Every organisation dealing with the EU AI Act is being asked the same questions: "We already have NIST AI RMF controls in place. Does that cover our EU AI Act obligations?" "We're pursuing ISO 42001 certification. Does that satisfy the regulation?" The honest answer is: sometimes yes, sometimes partially, and sometimes not at all. The problem is that nobody had built a clean, free, interactive tool that showed exactly which… View on GitHub What the tool does The EU AI Act / NIST AI RMF / ISO 42001 Interactive Crosswalk Tool maps 30 EU AI Act obligations to their nearest equivalents in NIST AI RMF and ISO 42001. For each mapping it shows a strength rating - Strong, Partial, Indirect, or No Equivalent - so compliance teams know which map

2026-06-06 原文 →
AI 资讯

From an Abandoned To-Do App to a Smart Productivity Engine: Upgrading Taskr into Solomon's Taskr

What I Built : ( https://github.com/Sai-Emani25/Solomon-s-Taskr ) I transformed my initial, bare-bones task management application, Taskr, into Solomon's Taskr—a significantly smarter, more robust productivity platform. The original project started as a standard way to log to-dos, but it lacked the intelligence to actually help manage time or prioritize effectively. With Solomon's Taskr, I wanted to build a system that doesn't just store data, but actively assists the user. Building this project means a lot to me because it represents a leap from writing basic applications to architecting intelligent, dynamic systems that solve real-world workflow bottlenecks. Demo Link to Final Repository: https://github.com/Sai-Emani25/Solomon-s-Taskr Link to Original Repository: https://github.com/Sai-Emani25/Taskr (Here is a quick walkthrough of Solomon's Taskr in action!) The Comeback Story The original Taskr project had been sitting in my repositories, unfinished and gathering dust. It was a classic case of starting a project with good intentions but abandoning it once the basic structure was complete. It could create, read, update, and delete tasks, but that was it. For the Finish-Up-A-Thon, I decided to completely resurrect and overhaul it. Here are the key changes and implementations that turned it into Solomon's Taskr: Complete Codebase Refactoring: I stripped down the old, inefficient logic and rebuilt the architecture to be highly scalable and maintainable. Intelligent Prioritization ("The Solomon Touch"): I integrated smart features to help organize and prioritize tasks rather than just listing them chronologically. (Note: If you integrated Gemini API or LLMs here for smart tagging, explicitly mention it!) Enhanced UI/UX: I moved away from the clunky, basic interface of the original Taskr and implemented a clean, responsive dashboard that provides a real-time overview of pending and completed tasks. Optimized Data Handling: I refined how the application processes and st

2026-06-06 原文 →
AI 资讯

Why I stopped reading "Old vs New" posts

Why I stopped reading "❌ Old vs ✅ New" posts I used to scroll past them. Then I started ignoring them. Now? I don't read them at all. Not because they're "wrong". But because they're incomplete . The problem with "❌ Old vs ✅ New" These posts make everything look easy: One error One fix One clean "New Way" Three bullet points Save the post Done. Right? No. What these posts don't show you 🔹 The 200 failed deployments before that one working fix 🔹 The 300+ errors you solve along the way — not just one 🔹 The Vercel pipelines that break for no documented reason 🔹 The "New Way" that also fails in production 🔹 The gap between documentation and reality What happens in production That clean "New Way" code snippet? It might work on your local machine. But in production, with real traffic, real data, real edge cases? It can fail. Hard. And no three-line post prepares you for that. Why I stopped reading Because these posts teach me solutions to problems I don't have yet . But they don't teach me how to think when nothing works. They don't teach me: How to read error logs properly How to trace a pipeline failure across services How to stay consistent after multiple failed deploys How to know when the "New Way" is actually worse What actually helped me Not templates. Not shortcuts. Real experience: 200+ failed deployments 300+ errors solved (one by one) Broken pipelines fixed by understanding, not copy-paste Production live — not a "demo" or a "tutorial" This is not a "❌ vs ✅" post I'm not giving you a "Here's the fix". Because the real fix isn't three lines of code. It's patience. It's persistence. It's failing and getting back up. And no post can save that to your bookmarks. 👇 Have you ever followed a "New Way" post and had it fail in production?

2026-06-06 原文 →
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