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Every time you run a JavaScript program, a lot happens behind the scenes. Variables are allocated memory, execution contexts are created, functions are pushed onto the call stack, and the engine starts executing your code. But before we dive into all of that, let's first understand what JavaScript actually is and why it was created. An Introduction to JavaScript What is JavaScript? JavaScript is a programming language that was originally created in 1995 by Brendan Eich in just 10 days while he was working at Netscape. JavaScript is a high-level programming language primarily used to make web pages interactive. Today, it is also used to build servers, mobile applications, desktop software, and much more. Why was JavaScript created? JavaScript was created to make web pages alive . But what does "alive" mean? it means adding interactivity (e.g., animations, clickable buttons, popup menus, etc.) to the static web pages. Today, JavaScript isn't limited to browsers. With runtimes like Node.js, it can also be used to build backend applications and APIs, which allow you to add more functionality to a website. Did you know? When JavaScript was created, it initially had another name: “LiveScript”. Where can JavaScript be used? In your browser — every interactive website uses it (Facebook, YouTube, Gmail). On servers — through Node.js, you can build backend APIs. In mobile apps — using frameworks like React Native. In desktop apps — VS Code itself is built using JavaScript (Electron). In smart devices, games, robots, and much more. Now that we know what JavaScript is, another question comes to mind: How does JavaScript execute my code? Before answering that, let's first understand Who executes my code? . The answer is: The JavaScript Engine The JavaScript Engine We already know what JavaScript is, but what exactly is this engine ? The "Engine" A JavaScript engine is a piece of software responsible for executing JavaScript code. Every environment that runs JavaScript, whether i

Why Your WeChat Mini-Program Work Is Actually Relevant US data engineering interviews prioritize hands-on experience with data pipelines, SQL, and scalable storage. WeChat mini-programs, though often seen as front-end tools, generate and process substantial backend data. If you designed the logic behind user actions, session management, or real-time updates, you already have transferable skills. The key is to stop describing the mini-program as a "feature" and start describing it as a "data system." Recruiters don't care about WeChat's UI components; they care about how you moved, transformed, and stored data. The Core Translation: From Mini-Program to Data Engineering When rewriting your resume, map each mini-program task to a standard data engineering function: User interaction logic → Event-driven data pipeline (e.g., Kafka, AWS Kinesis, or custom queues) WeChat cloud functions → Serverless compute (e.g., AWS Lambda, Azure Functions) Database reads/writes via WeChat API → NoSQL or relational database operations (e.g., MongoDB, MySQL, DynamoDB) Session tracking or analytics → ETL pipeline extracting user behavior data into a warehouse Do not use WeChat-specific terms like "wx.request" or "Mini Program SDK" without explaining the data they handled. Replace them with equivalent English terms. A Quick Side-by-Side Translation Table WeChat Term US Data Engineering Equivalent WeChat Cloud Database Managed NoSQL database (like MongoDB Atlas) Mini Program Backend with Cloud Functions Serverless data processing (AWS Lambda) User login & session management Authentication pipeline with token storage Real-time message push Event-driven notification system Reshaping Your Bullet Points: Before and After Generic bullet points kill your chances. Here is a concrete rewrite that turns a typical WeChat mini-program description into a data engineering achievement. Before (Chinese-English hybrid, vague): "Developed a WeChat mini-program for e-commerce with 50k users. Used wx.request

This is a submission for DEV's Summer Bug Smash: Smash Stories powered by Sentry . 🎭 The Recurring Villain If you've built WordPress sites long enough, you know this bug. It doesn't show up as an error in your console. It doesn't throw a fatal exception. It just quietly sits there, page after page, client after client — and it's called video bloat . Nine years into web development, I lost count of how many times a client sent me a site with a message like "it's just... slow." And nine times out of ten, when I opened DevTools and checked the waterfall, the story was the same: three, four, sometimes ten embedded YouTube videos, each one dragging in its own iframe, its own set of scripts, its own chunk of megabytes — all loading the second the page rendered, whether the visitor scrolled past them or not. One video embed alone can pull in 500KB–1MB+ before a user even presses play. Multiply that by a landing page stacked with testimonials, product demos, and a hero video, and you've got a page that fights your Core Web Vitals before it's even finished loading. 🔁 The Manual Fix (Every. Single. Time.) For years, my solution was the same tedious ritual: Find every video embed on the page. Replace the iframe with a static thumbnail image. Write a bit of custom JavaScript to swap the thumbnail for the real embed on click or scroll. Repeat it for YouTube. Repeat it again for Vimeo, because Vimeo's oEmbed API works differently. Repeat it again for self-hosted <video> tags, because now you're dealing with <source> tags instead of iframes. Test it across whatever page builder the client was using — Elementor, Divi, WPBakery, or straight-up Gutenberg blocks — because every one of them nests the video markup slightly differently. It worked. But it was never reusable. Every project meant writing the lazy-load script again, half from memory, half copy-pasted from my own old projects, tweaked just enough to fit the new theme's markup. It was the same bug, wearing a different site's c

Everything Claude Code: How One AI Assistant Becomes a Full Engineering Team Most developers use AI coding assistants in the same way. They open a repository and type: Build this feature. The assistant reads a few files, generates code, and reports that the task is finished. Sometimes the result is impressive. Sometimes it solves the wrong problem with perfectly formatted code. The issue is not always the intelligence of the model. The issue is the workflow around it. A single prompt often expects one AI assistant to behave like: a product planner a software architect a frontend developer a backend developer a security engineer a test engineer a debugger a code reviewer a release manager Real engineering teams do not work that way. They divide responsibility. One person plans. Another implements. Someone reviews security. Someone tests edge cases. Someone challenges the architecture. The final result improves because different people examine the same work from different angles. That is the idea behind Everything Claude Code , often shortened to ECC . ECC is an open-source collection of specialized AI agents, reusable skills, commands, security tooling, hooks, memory systems, and development workflows designed to turn Claude Code into something closer to an AI software engineering team. Instead of asking one general-purpose assistant to do everything in one pass, ECC separates software development into focused roles. That architectural idea is more important than any individual command. This article explains how ECC works, why developers are excited about it, how to install only what you need, and what security boundaries you should establish before giving any AI agent access to a real codebase. What Is Everything Claude Code? Everything Claude Code is not a new language model. It does not replace Claude. It is a workflow and configuration layer built around Claude Code. Think of Claude Code as one highly capable engineer. ECC attempts to turn that engineer into a co

Two critical security flaws in WordPress’ software have given hackers the chance to remotely take over tens of millions of websites, according to an estimate by a cybersecurity researcher.

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Online retailer AliExpress says it's shocked by largest DSA fine yet.

Hi HN - I'm Venkat, founder of Stayflexi (YC), CMU CS grad and Ex-Oracle Query Engine team (patents in core databases) DeepSQL started as an internal tool to stop our own databases from becoming the bottleneck they were becoming (13,000+ hotels in production). It worked well enough that we're releasing it. DeepSQL is an AI agent that operates a database the way a senior DBA and Data Engineer would 1. Fixes slow queries (we cutdown DB spend by 4x) 2. Fixes DB bloat (blocks unnecessary schema chan

YouTube has updated its monetization policies to more clearly define the kinds of AI-generated and low-quality videos that can’t earn ad revenue.

AI infrastructure company Infinity announced Monday a $15 million raise at a $100 million valuation from investors including Touring Capital, Principal VC, and researchers from companies such as OpenAI and Anthropic.