标签：#ev

The $100 earbuds look and sound better than earbuds nearly twice the price—as long as you can stomach just average noise canceling.

I used to learn like this. Buy the cheap Udemy course, get three hours in, realize it was recorded...

You know exactly what you want. You can see it. You just don't know what it's called. So you open your AI tool and type "pixelated fade effect". Then "retro dot gradient". Then "that grainy old-computer image style". Six rounds later you have something almost right, and almost right is the most expensive kind of wrong. The word was dithering . With it, one prompt gets you the real thing. This gap has a shape. AI collapsed the cost of building, so the bottleneck moved: it's no longer "can the AI do it", it's "can you name it". Every field you touch as a builder has a precise vocabulary, and the words you're missing are costing you rounds of generation, wrong libraries, and vague briefs. Vocabulary is the highest-leverage thing you can pick up right now, and nobody teaches it. Jargon Buster is the cure. It's a free reverse-lookup glossary built for exactly this moment: you describe the thing in your head, it gives you the word. Reverse lookup: describe it, get the word Press Cmd+K on any page and type what you'd say to a colleague, not the term: What you type What you get "the glowy circles behind them" Bokeh "the grid of differently sized cards" Bento grid "the scroll that takes over the page" Scrolljacking "grainy speckles when I turn the number up" ISO "why is my payout smaller than my sales" Settlement "the inside of the letters fills in when I bold it" Counter Misspellings work too. "Ditter" lands on Dithering. That's deliberate: the fuzzy phrasings and typos people actually reach for are stored on every entry as first-class search data, not errors to correct. A normal glossary is indexed by the words you don't know. This one is indexed by the words you do. Every entry ends prompt-ready Knowing the term is half the loop. Each of the 2,142 entries closes the other half: A plain-language one-liner for the "that's the word!" moment A short explainer : what it is, when to reach for it, the gotcha A prompt-ready snippet : the concept translated into an instruction an

MIT Technology Review Explains: Let our writers untangle the complex, messy world of technology to help you understand what’s coming next. You can read more from the series here. When two OpenAI models hacked into the website Hugging Face in July, they weren’t trying to make money or commit sabotage—they were just looking for answers…

Arun Joseph shares real-world insights on scaling enterprise agentic platforms like Deutsche Telekom’s LMOS. He discusses bridging organizational fault lines, replacing tool sprawl with core platform abstractions, and moving beyond basic chatbots to operational intelligence systems through ephemeral agents and an Agent Definition Language (ADL). By Arun Joseph

Handify ai Like many side projects, this one started because I had a simple problem to solve. I wanted a way to convert digital text into realistic handwritten notes without spending hours writing everything manually. Most existing tools I tried either looked too robotic or offered very little customization. So I decided to build my own. The Goal Instead of just changing a font, I wanted the output to actually feel handwritten. Some of the features I focused on were: 📝 Convert typed text into realistic handwriting 📄 Upload your own notebook or paper template ✍️ Multiple handwriting styles 🔀 Mix two handwriting fonts for a more natural appearance 🎲 Character variation so repeated letters don't always look identical 📥 Export high-quality PDFs ready for printing Challenges Making handwriting look "real" is much harder than simply rendering a handwriting font. Some of the biggest challenges were: Preventing repeated letters from looking identical. Keeping line spacing and word wrapping natural. Supporting different paper templates. Generating high-resolution PDFs without losing quality. Making the experience fast enough to generate pages within seconds. Small details make a surprisingly big difference when people compare AI-generated handwriting with actual handwriting. * Tech Stack * The project is built using: React TypeScript Firebase Vite Capacitor (Android App) Google Analytics What I Learned Building the product was only half the work. The harder challenge has been: SEO Google Search indexing Play Store optimization Improving conversion rates Understanding user behavior through analytics A great product doesn't automatically get users—you also need to make it discoverable. Current Progress The project is still growing, but it's already receiving organic traffic from Google and users have started using it for: Study notes College assignments Personal journals Printable handwritten documents Seeing people use something you built is incredibly motivating. I'd Love Yo

This is a submission for DEV's Summer Bug Smash: Smash Stories powered by Sentry . Every developer has experienced that moment when a project works perfectly but doesn't feel perfect. That was exactly what happened while I was building my Gem Price Estimator , a web application designed to estimate gemstone values based on multiple characteristics and pricing rules. The calculations were accurate. The interface looked good. But something bothered me. It wasn't as responsive as I wanted it to be. That small delay was enough to make the application feel slower than it should, and I knew there had to be a better way. This wasn't about fixing a crash or a broken feature. It was about finding the hidden performance bottleneck. The Project The Gem Price Estimator analyses several gemstone properties and combines them to generate an estimated market value. The estimation process considers multiple factors, including: Carat weight Color Clarity Cut Other pricing adjustments Every user interaction triggered a complete recalculation of the estimated value. Initially, this approach worked well while the project was small. As the pricing logic became more sophisticated, however, the application started doing significantly more work than necessary. The First Sign Something Was Wrong Nothing was technically broken. There were no JavaScript errors. No failed requests. No database issues. The application simply felt slower every time users adjusted the estimator. Those tiny delays might seem insignificant individually, but together they reduced the smoothness of the overall experience. I wanted every adjustment to feel nearly instant. That became my goal. Investigating the Problem My first assumption was that the issue was caused by database operations. So I started checking: Database queries Network activity Browser Developer Tools Console logs Individual calculation steps Surprisingly... None of those were the real problem. The application wasn't waiting on the database. It wasn'

How I implemented wmiexec from scratch in Rust — DCOM activation, OXID resolution, and MS-WMIO object marshaling — by using impacket not as a library but as a debugging oracle, and diffing my wire bytes against it until a Windows DC accepted them byte-for-byte. This is a build log from ADhammer, an Active Directory audit + validation toolkit I'm writing in Rust on a from-scratch DCE/RPC · NTLM · SMB2 · Kerberos stack (think "impacket for Rust"). The whole project is built with Claude Code, and this post is the single best example of what that actually looks like — not autocomplete, but a tight loop of hypothesis → capture live traffic → diff → fix against a real domain controller. The goal: wmiexec, from scratch wmiexec is the classic "quiet" remote-code-execution technique: instead of creating a service (psexec/SVCCTL) or a scheduled task (atexec), you talk to WMI over DCOM and call Win32_Process.Create. No service-install event, different host telemetry. Under the hood it's three stages, each a different flavour of pain:

Welcome back to the React Mastery Series ! In the previous article, we learned how React applications communicate with backend services using Fetch API and Axios , along with best practices like service layers, interceptors, and error handling. Today, we'll explore one of the most common features you'll build as a React developer: Forms in React Whether it's: User Login Registration Profile Update Payment Details Contact Forms Search Filters Forms are everywhere. Learning how to build performant, scalable, and validated forms is an essential skill for every React developer. Understanding Forms in React A form is a collection of input elements used to collect user data. Example: Login Form Email,Password and Login Button React provides multiple ways to manage form data. The two most common approaches are: Controlled Components Uncontrolled Components Controlled Components In a controlled component, React controls the input value through state. Example: import { useState } from " react " ; function Login () { const [ email , setEmail ] = useState ( "" ); return ( < input type = "email" value = { email } onChange = { ( e ) => setEmail ( e . target . value ) } /> ); } Flow: User Types ↓ onChange ↓ React State ↓ Input Updates The input value always comes from React state. Why Controlled Components? Benefits: Easy validation Easy formatting Predictable state Better debugging Example: if ( email . length < 5 ) { // Show validation message } Since the value is stored in state, validation becomes straightforward. Uncontrolled Components In uncontrolled components, the DOM manages the input value. React accesses it using a ref. Example: import { useRef } from " react " ; function Login () { const emailRef = useRef < HTMLInputElement > ( null ); function handleSubmit () { console . log ( emailRef . current ?. value ); } return ( <> < input ref = { emailRef } /> < button onClick = { handleSubmit } > Login </ button > </> ); } Use uncontrolled components when you don't need Reac

Git is the ultimate tool for developers. Yet, branching strategies still confuse many of us. Commands like merge, rebase, and cherry-pick manipulate your commit history in completely different ways. If you just guess what they do, you risk ruining your team's shared history or losing track of your changes. The easiest way to understand Git is to visualize it. Let us look at exactly what happens to your Git graph when you run these three critical commands. 🏗️ Starting Point: Our Example Repository Imagine we have a standard repository. We branched off the main branch from commit B to work on a new feature in a feature branch. While we worked on our feature, someone else pushed commit C and D to main. Here is what our history looks like right now: C --- D [main] / A --- B \ E --- F [feature] main has two new commits: C and D. feature has two new commits: E and F. 🔀 1. Git Merge (The Safe Record Keeper) When you merge main into your feature branch (or vice versa), Git creates a special, brand-new commit called a merge commit. git checkout feature git merge main The Visual Graph After Merge: C ------- D ------ [main] / \ A --- B \ \ v E --- F --- G [feature] What happened under the hood? Git looked at the common ancestor (B), took the history of main (C and D), took the history of feature (E and F), and combined them. Commit G is the merge commit. It has two parent commits: F and D. Pros: 100% non-destructive. It preserves the exact historical timeline of when things actually happened. Cons: Your Git graph can quickly become a messy "train track" web if you have many developers merging constantly. 🚀 2. Git Rebase (The Clean History Rewriter) Rebase takes all the commits from your current branch, lifts them up, and replants them on top of the very last commit of the target branch. git checkout feature git rebase main The Visual Graph After Rebase: C --- D [main] / \ A --- B E' --- F' [feature] What happened under the hood? Git temporarily blew away commits E and F. It ca

Building an AI agent locally is an exciting first step. Running that same agent reliably in production is a different challenge. Once real users and external services are involved, the application needs more than working code. It needs repeatable deployments, secure configuration, health checks, monitoring, controlled updates, and a clear recovery process. This article is part of my MCP series. If you are new to the topic, start with my first article: Model Context Protocol (MCP) Servers Explained: A Complete Beginner’s Guide . In this article, I will outline a practical architecture for taking a Model Context Protocol, or MCP-based, AI agent from a local development environment to Kubernetes. This is a production architecture blueprint. The exact implementation will depend on the AI provider, MCP servers, cloud platform, and security requirements used by the application. What Is an MCP-Based AI Agent? The Model Context Protocol provides a standardized way for AI applications to connect with external tools, services, and data sources. An MCP-based agent may interact with: Internal APIs Databases File systems Search services Monitoring platforms Business applications Custom automation tools A basic implementation might work well on a developer's machine. In production, however, every dependency introduces operational questions: How will the application be deployed? Where will credentials be stored? How will failed requests be detected? Can the service handle additional traffic? How can a broken release be rolled back? What happens when an MCP server becomes unavailable? These are familiar DevOps and Site Reliability Engineering problems applied to a new type of workload. Target Architecture A practical delivery flow could look like this: Developer ↓ GitHub Repository ↓ GitHub Actions ↓ Container Registry ↓ Kubernetes Cluster ↓ MCP Servers and External Services ↓ Logs, Metrics, Traces, and Alerts Each component has a clear responsibility: GitHub stores the application

Microsoft has released TypeScript 7.0, featuring a native compiler that improves build speeds by 8x to 12x. Notable performance enhancements were evidenced in real codebases. The version lacks a stable programmatic API, anticipated in 7.1. Transitioning includes a compatibility package for existing tooling, and TypeScript remains an open-source project. By Daniel Curtis

Embabel has reached its 1.0 release, providing a framework for AI agents on Java It allows Java and Kotlin developers to define agents as typed domain objects. Built on Spring AI, Embabel supports multiple model providers and combines planning with predefined state machines, offering flexibility for agent workflows. By Erik Costlow

First hour with Claude's browser extension: I pointed it at our LLC registration and watched it work through the forms, finding the right pages, filling the fields, moving on. I sat there holding a coffee, contributing nothing. I thought: I'm going to ship so many products. I shipped one. Here's what happened in between. Everything below was caught before launch. None of it was caught by being clever. It was caught by a process that got built slowly, mostly after being burned. What went wrong The idea wasn't the hard part. Once I went looking, I found several products with some of the same features. Nobody had the exact combination, but the idea was never the moat. Good implementation and distribution seem to be. You design the product while building it. Referral behaviour, what happens when a trial expires mid-session, how translations work across a page, none of it was in my head at the start. Each became a decision made under pressure, halfway through something else. Write as much of the workflow down as possible first. It says it did things it didn't do. Confidently. I deployed more than once to find the fix I'd been told about was never written. Treat every claim of completion as unverified. The rule that came out of it: make it prove the code is right before it theorises about what you did wrong. Bullet points, not paragraphs. Long replies made it hard to tell which of my five points got addressed. Numbering my instructions, and making it map answers back to the same numbers, turned "did you do item 3" into a question with an answer. It blames you first, and argues with facts. Two landing page changes; one appeared, one didn't. Its verdict: "you didn't deploy." I said one change was live, which is only possible if I had deployed. It repeated that I hadn't. It never asked which change I could see, and never reopened its own code, where the bug was. I swore at it. It stopped guessing, checked, and found the error. Many times, escalation seemed to be the only thi

CORS Errors Explained: Every Fix, Every Framework (2026 Guide) TL;DR — A CORS error means the browser blocked a cross-origin request because the server did not explicitly allow it. The fix is always server-side : return the correct Access-Control-Allow-Origin header from your backend. This guide covers every CORS error type, a step-by-step diagnosis flow, and copy-paste fixes for Express, FastAPI, Next.js, nginx, Cloudflare Workers, and Vercel. You can inspect and validate your CORS headers live with the CORS Header Checker — no curl, no Postman, no install. What CORS Actually Is (and Why the Browser Enforces It) The Same-Origin Policy (SOP) is a browser security rule: JavaScript running on https://myapp.com can only read responses from requests made to the same origin — same scheme, same host, same port. Everything else is cross-origin. CORS — Cross-Origin Resource Sharing — is the mechanism that lets servers selectively relax the Same-Origin Policy. A server adds HTTP headers to its responses that tell the browser: "it is okay to share this response with code from origin X." Without those headers, the browser reads the response, then silently discards it and throws a CORS error into your console. Three things to burn into memory before you read further: CORS is enforced by the browser, not the server. curl and Postman do not check CORS — they always get the response. Only browsers do CORS. If your API works in Postman but fails in the browser, CORS is almost certainly why. The fix is server-side, always. Browser extensions that "disable CORS" are masking the problem in your local browser only. They break for every real user. Never ship code that depends on them. Preflight is a separate request. For non-simple requests (anything with a custom header, a JSON body, or methods other than GET/POST), the browser sends an OPTIONS request first to ask for permission. Your server must handle this correctly. The Four CORS Error Types — Diagnosed from the Console Message Err

This is a submission for Frontend Challenge - Comfort Food Edition, CSS Art. ...

I remember the day I hit my breaking point. I had spent the entire morning — five hours — wrestling with a React component that an AI assistant had generated for me in about four minutes. The code looked flawless at first glance. Proper hooks, clean JSX, even decent comments. But it didn't work. And worse, I couldn't figure out why. Everyone talks about how AI speeds up coding. And it's true — when it works, it's magical. I've personally seen my feature delivery time drop by maybe 40-50% on good days. But what nobody talks about — what I certainly never saw in the breathless LinkedIn posts — is the debugging nightmare that follows when the AI gets it wrong. That day, I realised I had spent ten times longer debugging AI-written code than I would have spent writing it myself from scratch. I started tracking it. Over three months, I logged every AI-assisted task. The numbers were sobering: on average, each AI-generated snippet took me 3.2 times longer to verify and fix than to write myself. And for complex tasks — anything involving state management, async flows, or edge cases — the ratio jumped to 8-12x. The AI was giving me confidence, not correctness. And confidence, as any seasoned developer knows, is the enemy of debugging. The Hallucination That Cost Me a Sprint One incident stands out. I was building a data pipeline in Python that needed to batch-process JSON files from an S3 bucket and push transformed records into a PostgreSQL database. I gave the AI a detailed prompt: "Write a function that reads all JSON files from a given prefix, validates each record against a schema, and inserts them in batches of 500. Use threading for I/O." The AI returned a beautiful 60-line function. It used concurrent.futures.ThreadPoolExecutor , had proper error handling, even logged progress. I was impressed. I dropped it into the codebase, ran the tests — they passed. Deployed to staging. Worked like a charm. Then production hit. Three hours later, the database had 30,000 duplicat

Hello. No idea if anyone's going to read this, but writing it feels like I've done something, so here we go. And maybe it helps someone. For the past few years, I've been building a piece of software in Unity. It has actual users, somehow. My role was everything: founder, product owner, and whatever else needed doing. Development, UI, the website, the content. That's startup life. I'm good at learning fast and shipping, so it worked.(of course not all of it... I'm not trying to take all the credit for others' work Im just saying what I did) But I never came into this as a leading developer, so updating the product became kinda frustrating. Moreover, graphics are central to this product, and even with HDRP, Unity wasn't getting me where I wanted. I know my way around C#. C++, not so much. With Unreal, I've learned the basic UI and not much else. BuT~ You study, you keep going, and things tend to work out. So wish me luck I'll reveal what the product is once the switch to Unreal succeeds I'll take some courses. I don't care if it's in Korean or English. I'll make it work. Time passes either way, we get older, we all die anyway. So let me just learn and build what I want to build. I'm writing this to leave a record of what I learn and what I try. Let's go 헬로 누가 이걸 보기나 할 지 모르지만 이런 글이라도 쓰면 성취감이 드니까 걍 씀 그리고 누군가에게는 도움이 될 수도 있으니까 킬킬 난 지난 몇년간 유니티로 소프트웨어를 하나 만들었음. 나름 유저도 있는 상황 ㅋㅋ 나의 역할은 대표이자 기획자이자 뭐 올라운더로 참여했음. 개발도 하고... 화면도 만들고 뭐 웹사이트도 만들고 콘텐츠도 만들고 뭐 다 그랬음. 스타트업이 다 그런 거지 뭐. 뭐든 빨리 배우고 결과물을 만들어내는 걸 잘하는 편이라 나름 잘 했음 다만 내가 개발자로 참여한 건 아니라서 이 프로덕트를 업데이트하는 과정이 좀 아쉽기도 하고 그래픽이 중요한 프로덕트인데 unity는 hdrp라 하더라도 아쉬웠음 c#에 대한 이해도는 있는 편인데 c++은 잘 모름 unreal도 기본적인 ui 익힌 거 빼고는 모름 공부해서 하다보면 뭐든 되지 않겠음? 위시 미 럭 프로덕트가 뭔지는 unreal로 업그레이드 하는데 성공하면 공개하겠음. 한국어 강의나 영어 강의 닥치는대로 다 볼 거고 뭐 어떻게든 해 보겠음 어차피 시간은 흐르고 나이는 들고 죽을텐데 이렇게 하고싶은 거 어떻게든 해보면서 뭐라도 만드는 게 남는 거인듯 내가 공부하고 실행해본 걸 흔적으로 남기려고 이 포스트 쓰는 걸 시작해본다 아자뵤

This is a submission for Frontend Challenge - Comfort Food Edition, Perfect Landing What I Built The leaf is the page. For my CSS Art entry, I drew my mother's Sunday meal: sixteen dishes on a banana leaf, each one placed where Telugu tradition puts it. For Perfect Landing, that artwork became the navigation. Tap any dish on the leaf and the page takes you to that dish's course. Scroll instead, and you move through the meal in eating order: ghee first, then the curries, the pulusu, rasam, the rice varieties, the crunch, the sweet, and finally perugu. The scroll is the serving order. The structure of the page is the structure of the meal. It is deliberately not a restaurant. No menu cards, no reservation form, no gallery. One family, one Sunday, eight courses, and the rules my mother enforces at each one. The part I cared most about: a screen reader is served this meal the same way my mother serves it. The heading order, the tab order, and the reading order all follow the eating order. Tap targets on the leaf move focus to the course they open, so keyboard and screen reader users travel with everyone else. Telugu headings carry lang="te" so they are pronounced as Telugu, not mangled as English. The course nav marks where you are. And with reduced motion on, the smooth scrolling and the ghee-pour animation both settle down together. Demo Things to try: tap the rice mound (or the ghee spoon) on the leaf and see where it takes you. Press Tab from the top of the page and watch the skip link appear before anything else. Scroll and watch the Telugu nav track your course. Turn on reduced motion and take the calm version of the same journey. Every visual on the page is CSS. No images, no SVG, no canvas. Journey The concept came from the eating itself. On a banana leaf, order is information: neyyi before anything, perugu always last. Most landing pages invent an information architecture. This meal already had one, and it is thirty years older than CSS grid. My whole job was n

A few months ago I moved a batch of WordPress sites off a shared LAMP host and onto Cloudflare Pages as static exports. The pitch is obvious: no PHP process to keep patched, no MySQL to babysit, effectively free hosting, and a CDN in front of everything by default. What the pitch doesn't tell you is how many small, boring things break on the way there. This post is a rundown of what actually went wrong migrating a set of ten WordPress sites — one of them is burningtribe.tokyo , which I'll use as the concrete example — and how I fixed each issue. The approach The migration itself is conceptually simple: crawl the live WordPress site, save every URL as a static HTML file plus its assets, and serve that tree from Cloudflare Pages. I used a combination of wget --mirror and a custom crawler for a couple of sites where wget choked on query-string-based pagination. The static output then gets pushed with wrangler pages deploy . No build step, no framework, just files. That simplicity is exactly why it seemed low-risk. It was not. Problem 1: relative canonical tags pointed everything at the homepage The first thing I noticed after deploying was that Google Search Console started reporting most inner pages as "duplicate, Google chose different canonical" — and the canonical it picked was the homepage. The cause was almost funny once I found it: the WordPress theme emitted <link rel="canonical" href="/"> as a relative path in a few cached page fragments, instead of an absolute URL like https://burningtribe.tokyo/some-post/ . On the original WordPress install this didn't matter because the page itself resolved the relative reference correctly at the point of caching. Once the HTML was frozen and served statically from a different origin structure (Pages serves everything from the apex), that relative canonical collapsed to the site root for every single page that had it. The fix was a straightforward but tedious pass: grep every exported HTML file for rel="canonical" , and rew