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Introducing InterceptX: The Ultimate Modern Alternative to ModHeader for HTTP Modifications As web developers, API engineers, and security auditors, we spend a significant portion of our time inspecting and tweaking HTTP traffic. For years, extensions like ModHeader have been the go-to utility for modifying request and response headers on the fly. However, as the browser extension landscape transitions fully to Manifest V3 —bringing stricter security, better performance, and tighter permission rules—many developers are looking for a modern, lightweight, and local-first alternative. Enter InterceptX . What is InterceptX? InterceptX is a high-performance, compact Chrome extension designed to give you complete control over browser network requests. Built from the ground up on Manifest V3 using the declarative declarativeNetRequest API, it is fast, secure, and preserves your battery life by running lightweight matching rules inside the browser engine itself. Whether you need to bypass CORS policies, simulate mobile user-agents, override security headers, or redirect API endpoints to your local development server, InterceptX does it all with a premium, glassmorphic UI. Key Features at a Glance If you are familiar with ModHeader, you will feel right at home with InterceptX—but with several modern upgrades: 1. Request & Response Header Modifications Inject, append, or strip headers on outgoing requests or incoming responses: Set : Override an existing header or add a new one (e.g., setting custom auth tokens or Origin ). Append : Append values to headers like Accept or Cookie . Remove : Completely strip headers (e.g., testing behaviors when header keys are omitted). 2. URL Redirections Need to test API endpoints or redirect files? InterceptX features a built-in regex redirect engine (using RE2 syntax). You can redirect matching patterns and even use capture groups (e.g., redirecting https://api.production.com/(.*) to http://localhost:3000/\1 ). 3. Granular URL & Domain Fil

The web has changed . Applications are no longer simple HTTP servers. Today we build real-time dashboards, AI-powered services, multiplayer systems, APIs, microservices, and applications that need to handle thousands of connections with minimal overhead. But our frameworks are still mostly designed for yesterday's problems. So we asked a simple question: What if a * Go * framework was built from the ground up for modern workloads? Meet Breeze . A high-performance Go framework designed around one idea: Performance should not come at the cost of developer experience. Why Breeze ? Go already gives us incredible performance. But the framework layer often becomes the bottleneck. Too much abstraction. Too many allocations. Too much hidden complexity. Breeze takes a different approach: ⚡ High-performance networking powered by gnet 🔥 Real-time WebSocket architecture built in 🧩 Modular middleware system 📚 Automatic Swagger/OpenAPI generation 🎨 Built-in SPA template engine 🚀 Optimized worker pool architecture 🗄️ BreezeORM for efficient database operations Everything you need to build production-grade applications — without assembling dozens of unrelated tools. The Future Is Real-Time Modern applications are moving toward instant experiences: Live collaboration Trading platforms AI assistants Gaming backends Monitoring systems Real-time analytics Breeze is designed for this world. Instead of adding real-time capabilities later, Breeze treats them as a first-class citizen. Less Glue Code. More Building. A common problem in backend development: You start with a simple API... Then suddenly you need: Authentication Documentation WebSockets Background workers Database optimization Frontend integration Your stack becomes a collection of disconnected pieces. Breeze tries to bring these pieces together into one coherent ecosystem. Built With Go Philosophy Go was created around simplicity, performance, and reliability. Breeze follows the same principles: Simple APIs. Predictable behavi

On July 12, 2026 I asked a free API to guess the age of someone named Xzqwlptv. It answered in a few milliseconds: HTTP 200, valid JSON. # runnable, read-only: no key needed curl -s "https://api.agify.io?name=Xzqwlptv" {"count":0,"name":"Xzqwlptv","age":null} # HTTP 200 OK Status 200. The JSON parses. The age key is present, exactly where a schema says it belongs. Its value is null . Every guard I usually reach for passes this response: if resp.ok , if "age" in data , even a JSON Schema that requires an age property. The null walks straight past all of them and into the dataset. My earlier keyless-API posts kept circling one idea from different angles. HTTP 200 does not mean the read worked, because the body can be empty. HTTP 201 Created does not mean a write happened, because the read-back returns 404. This post moves the lie one level deeper than either of those. Here the status is 200, the body arrives, it parses, it matches your schema, and the field you came for is sitting right there. The value is just empty. The null that passes your schema check. A free fun or facts API here means a public endpoint that returns a joke, a fact, or a guess about a name, with no API key, no signup, and no credit card. A URL you can paste into a terminal right now. Ten of them clear that bar, and I re-verified every response below with a live curl on July 12, 2026: real HTTP code, real body, trimmed but never reworded. One scope note first, so the numbers stay honest. I curl-verified all ten APIs on July 12, 2026. I have not run any of them in production. My 2,190 production scraper runs (962 of them on a single Trustpilot scraper) are a different domain, and I cite them for one reason only: they are why I read a field's value and its confidence instead of its status line. That number is not a claim about these ten endpoints. # API What it returns Example call The empty success to watch 1 agify.io Age guess from a first name GET api.agify.io?name=Xzqwlptv 200 with age: null 2 g

In the previous article , we briefly discussed the relationship between Cookies and CORS . In this article, we'll take a closer look at how browsers decide whether a Cookie should be included in a Cross-Origin request. One of the most common misconceptions is that once CORS is configured correctly, Cookies are automatically sent with every request. In reality, that's not how browsers work. 📌 Default Browser Behavior When a Cross-Origin request is made using fetch() or XMLHttpRequest , browsers do not send Cookies, Authorization headers, or other credentials by default. For example: fetch ( " https://api.example.com/profile " ) Even if the user is already logged into api.example.com , the browser will not include any Cookies with this request. This default behavior helps prevent authentication data from being unintentionally leaked across different Origins. 📌 How Can We Send Cookies? If you want the browser to include Cookies in a Cross-Origin request, you must explicitly use the credentials option. For example: fetch ( " https://api.example.com/profile " , { credentials : " include " }) Using credentials: "include" does not guarantee that Cookies will be sent. Instead, it tells the browser: "If there are any Cookies that are eligible to be sent with this request, include them." 📌 What Makes a Cookie Eligible? Even with credentials: "include" , the browser still evaluates the Cookie before sending it. Some of the most important checks include: Domain Path SameSite For example: If the Cookie's Domain doesn't match the request destination, it won't be sent. If the request path doesn't satisfy the Cookie's Path attribute, it won't be sent. If the Cookie's SameSite policy blocks Cross-Site requests, it won't be sent. In other words, credentials is only the first requirement , not the final decision. 📌 Server Configuration Matters Too If your application expects JavaScript to access the response while using Cookies, the server must also be configured correctly. For exampl

In the previous article , we learned that an Origin consists of three components: Scheme (Protocol) Host Port Browsers use these three components to determine whether a request is Same-Origin or Cross-Origin . Whenever a web page attempts to access resources from a different Origin, a security mechanism called CORS (Cross-Origin Resource Sharing) comes into play. 📌 Why Does a CORS Error Occur? Suppose your web application is running at: https://app.example.com Now it tries to fetch data from: https://api.example.com Although both URLs belong to example.com , their Hosts are different. That means they have different Origins. As a result, the browser treats this as a Cross-Origin request. If the destination server does not explicitly allow this Origin, the browser prevents JavaScript from accessing the response, resulting in what we commonly call a CORS Error . 💡 Important: CORS is a browser security mechanism , not a server security mechanism. ⚠️ A Common Misconception About CORS Many developers believe that a CORS error means the request never reached the server. In most cases, that's simply not true. Typically: ✅ The browser sends the request. ✅ The server receives it. ✅ The server generates and returns a response. ❌ The browser blocks JavaScript from accessing that response. In other words, the request was successful—the browser simply refuses to expose the response to your application because the CORS policy was not satisfied. This is why sending the exact same request using tools like Postman or curl usually works without any problems. Those tools are not browsers, so they do not enforce browser security policies like CORS. 📦 How Does the Server Handle CORS? To allow JavaScript to access the response, the server must include the appropriate CORS headers. The most important one is: Access-Control-Allow-Origin: https://app.example.com This header tells the browser that JavaScript running on https://app.example.com is allowed to read the response. ✅ Examples Suppos

You've written setTimeout(fn, 0) expecting it to run "immediately." It didn't. A Promise.then() you scheduled a line later ran first, and somewhere a for loop of 50,000 iterations froze your UI for a full second despite every function being "async." None of this is a bug. It's the JavaScript event loop doing exactly what it always does — you just haven't seen the mechanism yet. What you'll learn By the end of this guide you'll be able to: Explain, precisely, why microtasks (Promises) always run before macrotasks ( setTimeout , setInterval ) — even at a zero delay Predict the exact console output order of any mix of synchronous code, setTimeout , and await Diagnose a frozen UI as a blocked call stack, not a "slow async function" Choose correctly between queueMicrotask , setTimeout(fn, 0) , and requestAnimationFrame for a given timing need Avoid the two most common event-loop bugs: microtask starvation and accidental serial await s in a loop Who this is for: you've written async / await and used setTimeout , but you want the model that makes their interaction predictable instead of memorized. Contents Why the JavaScript event loop exists The mental model Stage 1: the call stack and blocking code Stage 2: Web APIs and the macrotask queue Stage 3: Promises and the microtask queue Stage 4: async/await is sugar, not magic Stage 5: rendering, and Node's extra queues Edge cases and gotchas Best practices FAQ Cheat sheet Key takeaways Why the JavaScript event loop exists JavaScript runs on a single thread. One call stack, one thing executing at a time, no parallel function calls in the same realm. That's a deliberate design — it means you never need locks or mutexes to protect a shared variable — but it creates an obvious problem: how does a single-threaded language do anything concurrent, like waiting on a network response, without freezing the entire page while it waits? Here's the naive expectation, and why it would be a disaster if JavaScript worked this way: console . l

Imagine you're loading a dashboard. Four widgets, four APIs, fire them all at once: const [ users , revenue , alerts , activity ] = await Promise . all ([ fetchUsers (), fetchRevenue (), fetchAlerts (), fetchActivity (), ]); The alerts API is occasionally slow and sometimes returns a 500. When it does, your entire dashboard fails. Not one broken widget — four broken widgets. Promise.all rejects on the first failure and takes the other three successful results with it into the void. You reached for the right primitive for concurrency, but the wrong one for this use case. The four methods and what they actually do JavaScript gives you four ways to run promises concurrently. They differ in one thing: what happens when a promise fails or resolves first. Method Resolves when Rejects when Promise.all All succeed Any one fails Promise.allSettled All finish (success or failure) Never Promise.any Any one succeeds All fail Promise.race Any one finishes Any one fails first The instinct to reach for Promise.all is understandable — it returns all the values in a single array and feels like the obvious way to "do these things at the same time." But concurrency and failure handling are two separate questions. Promise.all answers both at once, and the answer to the second one is often wrong for the situation you're in. 🎮 Try it yourself ▶️ Open the interactive playground → Runs right in your browser — poke at it and watch the concept react live. Promise.allSettled — partial success Promise.allSettled waits for every promise to settle — resolve or reject — and returns a result array describing what happened to each one: const results = await Promise . allSettled ([ fetchUsers (), fetchRevenue (), fetchAlerts (), fetchActivity (), ]); for ( const result of results ) { if ( result . status === ' fulfilled ' ) { console . log ( ' got data: ' , result . value ); } else { console . error ( ' failed: ' , result . reason ); } } Each element has a status of 'fulfilled' (with a value ) or 'r

Every developer I know has the same reflex now. Hit an unfamiliar concept, paste it into ChatGPT, read the explanation, move on. I did this for months. It felt efficient. Then I noticed a pattern: I was reading a lot of clear explanations and retaining almost none of them. I could follow along perfectly in the moment and then draw a blank a week later when I actually needed the knowledge. The problem was not ChatGPT. The problem was using a general-purpose conversational tool for a job it was never designed to do. Here is what I switched to, and why it works better. The three failure modes of using a chatbot to learn Passive consumption feels like learning. Reading a good explanation triggers the feeling of understanding without the work that creates actual memory. You nod along, it makes sense, and nothing sticks. This is the biggest trap. There is no retrieval practice. The research on this is well established: you remember things by pulling them out of memory, not by putting them in repeatedly. A chatbot will explain the same concept ten different ways, but it will never make you answer a question you cannot immediately answer. That struggle is the mechanism. Confident hallucination is dangerous when you are the beginner. If you already know a topic, you can spot when an AI is subtly wrong. If you are learning it for the first time, you cannot, and you may internalize something incorrect with full confidence. For technical material, this is a real cost. What actually works better Tools that quiz you. Anything built around retrieval practice and spaced repetition beats passive reading by a wide margin. If a tool generates questions from your material and makes you answer them over spaced intervals, it is working with how memory actually forms rather than against it. Tools that read YOUR source material. This one is huge for technical learning. Instead of asking a model to answer from its general training data (which may be outdated or wrong for your specific libra

Jake Lazaroff discussed the AT Protocol as a framework for distributed applications beyond social networking. He emphasised a local-first architecture where users maintain data in PDSs while leveraging shared infrastructure for synchronisation and updates. The presentation included experiments showcasing collaborative tools and highlighted the benefits of reduced reliance on app-specific backends. By Olimpiu Pop

This is a submission for Weekend Challenge: Passion Edition What I Built I built Chaos Engine , an interactive F1 strategy simulator for people who can't stop arguing about pit calls. If you've ever watched a race with a die hard F1 fan, you know the argument happens every single weekend. "They should have pitted two laps earlier." "That undercut never had a chance." "Why didn't they just switch to the hards." Every fan thinks they'd have made the better call, and there's never really a way to settle it. That argument is where this whole project came from. You don't just watch F1, you live and die by strategy calls that happen in about four seconds on a pit wall. So I wanted to build something that actually lets fans test their gut calls against real race data instead of just yelling about it on Reddit or Twitter after the checkered flag. Chaos Engine takes real F1 races, automatically detects the moments in each one that were statistically the most dramatic (a pit stop that came way earlier or later than everyone else, a sudden pace spike, a big swing in track position), scores the whole race on a "Chaos Score," and then lets you pick one of those moments and rewrite it. Pick an alternate strategy, and the AI reasons over the real degradation curves, pit loss numbers, and traffic gaps from that race to tell you whether your call would have actually worked. Demo https://chaos-engine.ai.studio Code https://github.com/dhruvvvgg/Chaos-Engine How I Built It The whole thing runs on Google AI Studio's Build mode, using Gemini as the actual reasoning engine behind every "what if." The part I cared most about getting right was making sure the AI wasn't just generating a vibe-y paragraph. I wanted it to actually reason over real numbers, not make something up that sounded plausible. So instead of asking Gemini to freeform explain a scenario, I feed it a structured JSON block for each intervention, real pre-intervention pace data, the pit loss baseline for that race, degradat

This is a submission for Weekend Challenge: Passion Edition weekendchallenge. What I Built The project is an east-to-use application which helps user to identify deforestation in areas of interest. From the users selected area of interest, application downloads the satellite images, generates ndvi(Normalized Difference Vegetation Index), and identifies potentially deforested locations based on calculated vegetation indices. My goal was to evaluate the capabilities of AI agents in developing a complete application, production-ready with instructions provided by human. The project also explores the time required to build such an application with AI-assisted software. Demo https://huggingface.co/spaces/sgharti/crop-health Code https://huggingface.co/spaces/sgharti/crop-health/tree/main How I Built It I developed a plan for core software architecture and directed the entire application workflow including the following: Describing entire application lifecycle from user input to fastapi pipeline (GEE and Snowflake). Described the interface specification(leaflet map, design and user input) Described pipeline of how system connects to GEE, generates NDVI(Normalized Difference Vegetation Index) and stores in the Snowflake. Described the workflow of backend-frontend synchronization to read logs from snowflakes and display it on the frontend with visualization and text explanation. I decided to use Google AI (Antigravity with gemini) to build this application. Prize Categories I am applying for the Google AI (Gemini, Antigravity) and Snowflake tracks. Team Submissions: Shashi Gharti @shashigharti

There is a kind of loneliness that does not always announce itself. It can be quiet. Heavy. Hidden behind a smile. It can make someone feel as though no one truly understands what they are carrying. Dear Stranger was created for those moments. It is a place where someone can pause. Breathe. Read. Feel less alone. And maybe, just maybe, carry a little more hope than they came with. This is not just a project to me. It is a quiet promise. A small place I built with my heart. A space where words can travel gently across distance and still carry comfort. I built Dear Stranger because I believe that even the smallest page can hold something powerful: hope, clarity, warmth, and the feeling of being understood. What I Built Dear Stranger is a web experience designed to feel intimate, human, and deeply personal. It is a space where someone can open a page written by a stranger, read something honest, and feel, even for a moment, that they are not alone. The project is built like a book made of feelings. It invites the visitor to step into a calm, reflective experience where words matter more than noise. They can read pages that speak to comfort, strength, peace, and hope. They can save what touches them. They can leave behind their own words for someone else to find one day. It was never meant to be just another website. It was meant to feel like a page that was waiting for you. Demo The experience is best felt by opening it and letting it meet you where you are. It is meant to be soft, reflective, and quietly powerful. Dear Stranger - this is for you. Code The project is built with Next.js and designed as a personal, story-like interface where emotion is part of the experience. The structure allows users to move through a reading journey, interact with meaningful content, and leave behind something sincere. Konarksharma13 / Dear-Stranger Dear Stranger — A Page for You This website was never meant to be just another page on the internet. It is a quiet place made for the hea

This is a submission for Weekend Challenge: Passion Edition What I Built RivalRy is a passion tracker for football rivalries. Fans can log every match against their biggest rivals — win, loss, or draw — rate how intense it felt, and write down the moment as it happened. Over time, it builds a Timeline of every battle and a "Passion Card": a shareable stat card showing total battles, win/loss/draw tally, an overall Passion Rating out of 10, and your single most intense moment — narrated aloud by an AI hype announcer. Passion isn't just about the big clásicos — it's every derby, every gutting loss, every last-minute comeback that stays with you. RivalRy lets you bank that feeling instead of letting it fade, whether it's Real Madrid vs Barcelona, Argentina vs Brazil, or a World Cup 2026 group-stage upset. Demo Youtube demo link- https://youtu.be/6WiRpMUcmGk Live app:- https://rivalryweb.lovable.app/ Code https://github.com/arjunpratapdas/rivalry-pulse-logger How I Built It RivalRy is built with React, Vite, and Tailwind CSS, using browser localStorage to keep every rivalry entry persisted with zero backend setup — fast to build, fast to use. The standout feature is the "Narrate My Rivalry" button on the Passion Card, powered by the ElevenLabs Text to Speech API . When clicked, the app generates a short hype-announcer-style summary of the rivalry's stats and most intense moment, sends it to ElevenLabs, and plays back a natural AI voice narrating it — turning a stat card into something that actually feels like a stadium announcer hyping up your rivalry. One honest note: ElevenLabs free-tier credits are limited, and if they run out, the app gracefully falls back to the browser's built-in speech synthesis with a clear on-screen message, so the feature never breaks — it just degrades gracefully. Prize Categories Submitting for Best Use of ElevenLabs — the AI narration is central to the Passion Card experience, not a bolted-on extra.

Most people think building an AI voice agent means writing a clever prompt. I build these for a living, and I can tell you the prompt is maybe an hour of the work. The other week disappears into two places: wiring up everything the agent touches, and testing it against the twenty ways a real caller will break it. So I built a pipeline that points one AI coding tool at each of those problems. Claude Code generates and wires the agent from a spec. Comet, an AI browser automation tool, runs it through dozens of messy call scenarios before a human ever picks up the phone. This post is how that loop actually works, and where it still needs me. Why the build loop is slow (and it is not the prompt) When you picture building a voice agent, you picture the prompt. That is the easy part. The slow part is everything around it. A production agent for, say, a car garage is not one artifact. It is a conversation flow, a set of custom functions that hit your automation layer, calendar and CRM wiring, a telephony number with A2P registration, and a pile of edge-case handling that only shows up when someone calls in angry with a dog barking in the background. The reason it is slow is not typing. It is the round trips. You build a version, you call it, it fumbles when the caller interrupts or asks something off-script, you fix one thing, you call it again. Each loop is a few minutes of manual dialing and listening. Multiply that by the fifty scenarios a real agent needs to survive and you have burned a week. The pipeline exists to kill those round trips. Half one: Claude Code builds the agent from a spec The first insight is that most of what goes into a voice agent is structured and repetitive, which is exactly what an AI coding tool is good at. I do not hand-write every custom function and every n8n node from scratch for each new client. I write a spec, and I let Claude Code turn that spec into concrete artifacts. The spec is a plain description of the vertical and the business: wh

This is a submission for Weekend Challenge: Passion Edition What I Built Rivalry Engine — pick two national football teams, and Snowflake reads 150 years of their matches, scores how heated the rivalry is, calls a one-word verdict on its shape, predicts the next result, and lets Cortex narrate the one story inside it. All of it — the data, the analytics, the AI, and the app — runs inside Snowflake. Nothing ever leaves the warehouse. A scoreboard tells you who won. It never tells you what the rivalry means . Argentina and Brazil have met over a hundred times across a century — a razor-thin ledger that has never let either side feel safe. Germany and England meet rarely, but every meeting carries a tournament's weight. Most national teams have never played each other at all. My goal was a product that could feel the difference between those three shapes — not another stats table, because a table is a report and I wanted an argument. The passion is football. But the real engineering question underneath it was the one I actually cared about: can an AI tell the story of a rivalry without lying about the facts? So I gave myself one rule before writing a line of code: The AI interprets the shape of a rivalry. It never invents the facts. Every count, date, score and streak on screen is computed in SQL from real matches. Cortex is handed only those computed facts, and told explicitly to never produce a number. And its honest corollary: two nations that never met get a "first chapter unwritten" card — and Cortex is never called. A product that can't return nothing will invent something. This one returns nothing. Demo It runs entirely in Streamlit in Snowflake (Snowsight) — no public host, no API keys — so here's a walkthrough: The 90-second tour: Argentina vs Brazil → the heat gauge pins to 🔥 Blood rivalry , recent-form chips light up, and the SQL detector's verdict reads Blood feud . ✨ Generate the story → Cortex writes a narrative that cites the real biggest thrashing and f

Try the demo: K-Saju Crew For fun only. K-Saju is an entertainment project. The K-Pop roles below are a playful interpretation of saju-inspired signals, not personality assessment or advice. A two-person compatibility page can stay stateless with almost no effort. Put both birth dates in a URL, render the result on the server, and the link is the record. No account, no database, no cleanup job. That was already a product rule in K-Saju. We do not retain personal inputs. A result is reproducible from its GET parameters. Then we built /crew : “What if your friend group debuted as a K-Pop group?” A creator makes a link, sends it to a group chat, and each friend enters their own birth date. At three to seven members, the app assigns distinct positions, shows pairwise chemistry, and creates a shareable poster. The fun part is the casting. The engineering problem is that the social flow needs a temporary shared state. A link cannot accumulate submissions by itself. This post is about the decisions behind that feature: where we allowed state, how we made the result durable without retaining a lobby forever, and how we kept the casting explainable instead of treating it as a black-box score. The conflict: a self-service group flow needs somewhere to collect data There were two clean but incomplete options. The first was to keep everything stateless. The creator would enter all members' dates at once, then receive a result URL. It matched our existing architecture, but it defeated the point of sharing a link. The person who starts the group often does not know everyone else's date, and asking them to collect it in a chat creates friction before the feature has started. The second was a conventional persistent group object. It would make joining easy, but it would turn a deliberately stateless service into one that keeps user-provided dates indefinitely unless we built retention and deletion policies around it. We chose a hybrid instead: The lobby is temporary state. It store

Hello, I'm Maneshwar. I'm building git-lrc, a Micro AI code reviewer that runs on every commit. It is...

This is a submission for Weekend Challenge: Passion Edition ( https://dev.to/challenges/weekend-2026-07-09 ) What I Built Rivalry Radar — a live "Heat Index" for World Cup rivalries. Fans drop 280-character Terrace Takes on any matchup (Brazil vs Argentina, England vs France, whatever's got you shouting at the TV), rate how much the moment hurt or thrilled them from 1–10, and the app does the rest: Google AI (Gemini) scores every take's sentiment the instant it lands — positive, negative, mixed, or neutral — and separately writes a short "Hype Verdict" in the voice of a stadium announcer, based on the latest takes for a matchup. That sentiment score feeds a Heat Index, computed and ranked in Snowflake with RANK() OVER (ORDER BY heat_index DESC), combining take volume, sentiment intensity, and self-rated passion into one live number per rivalry. Two leaderboards: which rivalry is hottest right now, and which fanbase is bringing the most passion overall. Demo frontend/index.html is fully self-contained: opening it in a browser lets anyone submit takes, watch the Heat Index flip digit-by-digit like an airport departure board, and see the leaderboards re-rank in real time. It ships with seed takes from eight classic rivalries so it's not empty on first load. Code NandhuTee / Rivalry-Radar-World-Cup-passion-engine-with-Snowflake-Google-AI 🔥 Rivalry Radar — World Cup Passion Engine Fans drop 280-character Terrace Takes on any World Cup matchup. Google AI (Gemini) scores the emotion behind every word and writes a stadium-announcer Hype Verdict ; Snowflake stores every take and computes a live Heat Index that ranks exactly which rivalry is boiling hottest right now. Built for the DEV Weekend Challenge: Passion Edition 🏆 Best Use of Google AI and Best Use of Snowflake Why this exists Passion is easy to feel and hard to measure. Every World Cup rivalry generates an ocean of unstructured text — chants, rants, one-line hot takes — that traditionally just... disappears into grou

Hello Dev Community! 👋 It is officially Day 136 of my software engineering marathon! Today, I engineered the absolute heart of my MERN Stack capstone application, Sprintix : The complete Product Collection Grid & Faceted Filter Sidebar View ( /collection ) ! ⚛️🛍️🗂️ To prepare the application for seamless full-stack state management integration later, I built this layout using dynamic state arrays and object schemas. This ensures that switching from demo arrays to live API streams will happen effortlessly. 🛠️ Deconstructing the Day 136 Catalog Architecture As displayed across my browser rendering workspace in "Screenshot (311).jpg" and "Screenshot (312).jpg" , phase one of the product engine splits into structural layout segments: 1. Faceted Category Filter Sidebar Organized dedicated verification check-boxes mapping out specific consumer collections: Categories: Segmented target groups (Men, Women, Kids). Type Filters: Segmented style formats (Top Wear, Bottom Wear, Winter Wear). Styled within minimal box borders to give users an uncluttered desktop searching experience. 2. Header Control Grid & Sort Registries Installed a top-level workspace header showing "All Collection" alongside an interactive drop-down management node ( Sort by: relevant / low-to-high / high-to-low ). Ready to hold local state flags that rearrange the data arrays instantly before looping. 3. Deep Route Parameter Mapping Preparation Look at the hover elements in "Screenshot (311).jpg" ! Every single rendering card passes localized hex-token structures mapping toward dynamic pathways like: text /product/:id (e.g., /product/6a436b5c921b7aa010d29318)

Hey DEV community! 👋 Like a lot of you, I had a mental list of "quick tool" bookmarks scattered everywhere — a word counter here, a slug generator there, a Lorem Ipsum generator somewhere else. I got tired of it, so I built Yanapex: a single site with free, no-signup tools for text, images, and everyday dev tasks. A few things I focused on: Everything runs client-side. No text or files get uploaded to a server, so it's safe to paste sensitive drafts or code. No accounts, no paywalls. Open a tool and use it immediately. Fast and lightweight, built for quick one-off tasks instead of full blown apps. One of the first tools is a Word Counter ( https://yanapex.com/en/tools/text-tools/word-counter/ ) with real-time word/character/sentence counts and reading time estimates. There are 26 tools so far across text, image, and developer utilities. Would love feedback from this community: what's a small tool you constantly have to search for online that you wish just existed in one place?