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For a long time I found myself checking different European stores to see where products were cheapest. It was slow and annoying. I had to open lots of tabs, change countries, check delivery costs and compare prices myself. I used other comparison websites, but I wanted something simpler. So I decided to build Haggl.eu. Haggl lets you compare prices across Europe from one search, helping you find better deals and save money. This is my first public project and I am learning a lot while building it. The site is still a work in progress, but I have plenty of ideas for the future. I want to add more stores, more countries, price history tracking and better delivery comparisons. My goal is to make it as easy as possible to find the best deal without spending ages clicking through different pages. I would love to hear any feedback or suggestions. Thank you everyone :) https://haggl.eu

I Built a 4-Sided Plot Area Calculator with 2D & 3D Visualization Most online plot calculators only work for simple rectangular plots. However, many real-world properties have four sides with different measurements, making area estimation much more difficult. That's why I built a 4-Sided Plot Area Calculator that allows users to enter the North, South, East, and West dimensions and instantly calculate the approximate plot area. 🔗 https://www.premiumconverters.com/plot-area-calculator Features 📐 Supports irregular 4-sided plots 🏠 Calculates area in Marla, Kanal, Acres, and more 🖼️ Interactive 2D top-down visualization 🏗️ Isometric 3D plot rendering 📏 Feet & inches input support 📱 Mobile-friendly experience Why I Built It In many countries, especially in South Asia, property dimensions are often recorded as side measurements rather than perfect geometric shapes. Existing tools rarely address this use case properly. I wanted to create a simple solution that homeowners, buyers, real estate professionals, and developers could use without needing complex surveying software. The Result The calculator transforms four side lengths into a practical estimate while providing visual feedback that helps users better understand their property's shape. Building tools that solve real-world problems is one of the most rewarding parts of software engineering. Have you ever built a niche tool that unexpectedly helped thousands of users?

The test passed. The restore completed inside the window. The workload came online. The team signed off, closed the ticket, and filed the results. DR test: successful. And then, somewhere between the test environment and the next real incident, the recovery plan drifted out of alignment with the infrastructure it was written to protect. Not dramatically. Not all at once. Gradually — through a cloud migration, an IdP consolidation, a new SaaS dependency, a network redesign that didn't make it into the runbook. DR plan failure rarely happens where you tested. It happens at the assumptions the exercise never reached. The Test Has a Boundary. The Incident Doesn't. A DR exercise begins with a defined scope. A specific workload. A known starting state. A target environment that has been prepared in advance. The team is available, credentialed, and not managing anything else. The blast radius is controlled before the test starts. A real incident does none of that. Scope expands from the first alert. Authentication problems surface because the IdP that wasn't in exercise scope is now unreachable. Networking issues appear because the failover path assumes a routing table that was updated three months ago. A vendor the plan never named is unavailable, and the recovery sequence stalls waiting for a dependency that was never documented as a dependency. The plan was written for the conditions of the test. The incident arrives in conditions the plan never anticipated. That gap is where DR plan failure actually lives — not in the restore mechanism, but in everything the restore mechanism was assumed to be able to reach. Most DR Plans Depend on Things They Never Recover The recovery exercise validates a workload. What it rarely validates is the recovery infrastructure itself. Consider what a typical enterprise DR plan silently depends on: Assumed — Not Tested: Identity provider, backup management console, cloud account access, ticketing and incident management systems, third-party

I've been going through Jim Kurose's networking lectures lately, and I kept finding myself pausing to re-read the same sections. Not because they were confusing - because things I'd been using for years were finally clicking into place. This post is me writing down what I learned, in the order it started making sense. Before HTTP, there's a webpage A webpage isn't one file. When you open a URL, your browser fetches a base HTML file - and that file references other objects. Images. Scripts. Stylesheets. Each one lives at its own URL. Each one has to be fetched separately. So loading a single "page" might mean firing off 20+ individual requests. This detail matters because the entire evolution of HTTP - from 1.0 to 3 - is basically the story of making those 20 fetches faster. HTTP runs on TCP. That has consequences. HTTP doesn't manage its own connections. It hands that job to TCP. When your browser wants something, it first opens a TCP connection to the server (port 80 for HTTP, 443 for HTTPS), and then asks for the object. Opening a TCP connection isn't free. It takes a round-trip - your machine says "hello," the server says "hello back," and then you can actually talk. That's one RTT(Round Trip Time) just to shake hands, before a single byte of your webpage arrives. So every HTTP request carries at least 2 RTTs of overhead: 1 to open the TCP connection, 1 for the actual request/response. Do that 20 times and you've spent 40 RTTs before the page renders. HTTP/1.0 vs HTTP/1.1: one change that mattered a lot HTTP/1.0 (non-persistent): open a TCP connection, fetch one object, close the connection. Repeat for every object. HTTP/1.1 (persistent): open a TCP connection, fetch as many objects as you need, then close. The server leaves the connection open after each response. That one change cuts subsequent fetches from 2 RTTs to 1 RTT each. For a page with 20 objects, that's real time saved - not microseconds, but hundreds of milliseconds that users actually feel. What an

I was trying to tell someone something real in her first language — not "I missed you" from a dropdown, but the version that sounds like a person said it. Google Translate gave me one answer. No indication whether it was what you'd text at midnight or what you'd write in a letter to someone's grandmother. That's the failure mode of literal translators: one output, no register, no sense of what you're actually choosing between. konid returns 3 options per query, ordered casual to formal, with the register explained and a cultural note on the difference between them. For Mandarin or Japanese, audio plays through your speakers via node-edge-tts — no API key, no browser tab — because reading a pinyin romanization and actually hearing the tone contour are two different things. The vowel length in Korean, the pitch drop in Japanese, the stress pattern in Arabic: you don't internalize those from text. You internalize them from hearing them repeated back while you're still in the context of trying to say something. The setup for Claude Code is one line: claude mcp add konid-ai -- npx -y konid-ai It runs as an MCP server, so it works in Cursor, VS Code Copilot, Windsurf, Zed, JetBrains, and Claude Cowork. Also installs as a ChatGPT app via Developer mode using the endpoint https://konid.fly.dev/mcp . Supports 13+ languages: Mandarin, Japanese, Korean, Spanish, French, German, Portuguese, Italian, Russian, Arabic, Hindi, and more. The name is Farsi — konid (کنید) means "do." MIT licensed. https://github.com/robertnowell/konid-language-learning

Weekly Challenge 377 Each week Mohammad S. Anwar sends out The Weekly Challenge , a chance for all of us to come up with solutions to two weekly tasks. My solutions are written in Python first, and then converted to Perl. Unless otherwise stated, Copilot (and other AI tools) have NOT been used to generate the solution. It's a great way for us all to practice some coding. Challenge , My solutions Task 1: Reverse Existence Task You are given a string. Write a script to find whether any substring of length 2 is also present in the reverse of the given string. My solution This is relatively straight forward. I create a variable reversed_string which is the string reversed. I then have a loop called start_pos that goes from 0 to 2 less than the length of the string. At each position, I check if the two letters at that position are in the original string. def reverse_existence ( input_string : str ) -> bool : reversed_string = input_string [:: - 1 ] for start_pos in range ( len ( input_string ) - 1 ): if reversed_string [ start_pos : start_pos + 2 ] in input_string : return True return False The Perl solution follows the same logic. sub main ($input_string) { my $reversed_string = reverse ( $input_string ); foreach my $start_pos ( 0 .. length ( $input_string ) - 2 ) { if ( index ( $input_string , substr ( $reversed_string , $start_pos , 2 ) ) != - 1 ) { say " true "; return ; } } say " false "; } Examples $ ./ch-1.py abcba true $ ./ch-1.py racecar true $ ./ch-1.py abcd false $ ./ch-1.py banana true $ ./ch-1.py hello true Task 2: Prefix Suffix Task You are given an array of strings. Write a script to find if the two strings ( str1 , str2 ) in the given array such that str1 is prefix and suffix of str2. Return the total count of such pairs. My solution In Python, this is a one line solution. I use the combinations function from the itertools module to produce all combinations of pairs of str1 and str2 . For each pair, I use the startwith and endswith function on the strings

This is The Stepback, a weekly newsletter breaking down one essential story from the tech world. For more on e-bikes, power stations, and how to work anywhere, follow Thomas Ricker. The Stepback arrives in our subscribers' inboxes at 8AM ET. Opt in for The Stepback here. How it started Lithium-ion batteries are everywhere as we […]

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[Excerpted from THE QUANTUM COLLAPSE CHRONICLES — not science fiction, but a grounded forecast of what may come when quantum computation dismantles the cryptographic foundations of our digital civilization. These articles explore the collapse of computational trust and the brutal reconstruction of the world that follows.] The history of human civilization is often defined by sudden, violent shifts in the nature of power. We speak of the fall of empires, the industrial revolutions, and the splitting of the atom. But in the mid-2030s, the world experienced a collapse that was not made of steel or stone, but of mathematics. It was a quiet, clinical, and utterly devastating unraveling of the digital fabric that held modern society together. To understand The Quantum Collapse , one must look past the headlines of the era and into the humming, sub-Kelvin depths of the dilution refrigerators that changed everything. This is the story of how the transition from probabilistic experimentation to deterministic computation rendered the world's secrets transparent and its economies obsolete. The Death of Noise: The Rise of Dr. Aris Thorne For the first three decades of the 21st century, quantum computing was a game of chance. Scientists operated in the era of Noisy Intermediate-Scale Quantum (NISQ) devices—machines so temperamental and prone to error that every calculation was a desperate struggle against environmental noise. In those days, a single stray photon or a microscopic fluctuation in temperature could collapse a delicate superposition, turning a groundbreaking calculation into useless digital static. The turning point arrived in 2036 at the Institute for Advanced Quantum Engineering (IAQE) in the High Sierras. The air in the facility didn't vibrate with the erratic drone of the late 2020s; instead, it carried a heavy, rhythmic thrum—the sonic signature of the Lattice-Array-9 (LA-9). At the center of this revolution was Dr. Aris Thorne, the lead architect of the LA-9 pr

La trahison du chiffre Vendredi 15 mai, 16 h 13. L'alerte Sentry remonte sur le téléphone. La première réinscrite Phase 1 attend devant l'écran de paiement, son nom est en haut de mon onglet. Je pose la canette, je rouvre l'écran. La tasse à tête de Françoise, sur le poste d'à côté, capte un reflet jaune que je remarque sans le regarder. La stack trace tient en plein écran. Le stack trace s'ouvre, neuf champs sur dix à null , et un chiffre que je n'ai pas vu venir. type = "StripeConnectionError" message = "An error occurred with our connection to Stripe." code = null statusCode = null requestId = null duration = 9 ms Neuf millisecondes. Sur une route Vercel en région Paris, un DNS résout en quarante millisecondes, un handshake TLS coûte cent à deux cents. Neuf millisecondes, ce n'est pas un appel réseau qui a échoué. C'est un appel réseau qui n'a jamais eu lieu. Le SDK n'est pas arrivé jusqu'à la fibre. L'instinct propose immédiatement trois patchs. Timeout serverless Vercel — j'ajoute maxDuration , je redéploie. Clé révoquée — je vais la rouler. Compte Stripe restreint après le passage en mode live — j'ouvre un ticket support. Ces trois hypothèses sont plausibles. Aucune des trois n'est falsifiable par le symptôme seul, et c'est précisément ce qui les rend dangereuses : chacune ouvre un cycle de quinze à trente minutes avec rollback à la fin si elle se trompe. Multiplié par trois, on tient une demi-journée perdue avec la cliente toujours en train de cliquer. Je n'ai pas le temps. Une réinscrite attend. Quatre tests, dans l'ordre Je connais la classe d'incident — « preview marche, prod casse » , ou son symétrique. La règle, pour cette classe, c'est qu'on ne corrige rien tant qu'on n'a pas discriminé les couches. Quatre tests, exécutés dans l'ordre. Chacun élimine une famille d'hypothèses, pas une hypothèse isolée. Et chacun est conçu pour réfuter ce qu'il vient interroger — parce qu'un test qui cherche à confirmer trouve toujours, par sélection, ce qu'il cherche. Te

The number that lied Friday May 15, 4:13 PM. The Sentry alert pings on my phone. The first Phase 1 re-enrolling student waits in front of the payment screen, her name at the top of my tab. I put down the can, I reopen the screen. The mug with Françoise's face on it, on the desk next door, catches a yellow reflection I notice without looking at. The stack trace fills the screen. The stack trace opens, nine fields out of ten at null , and a number I didn't see coming. type = "StripeConnectionError" message = "An error occurred with our connection to Stripe." code = null statusCode = null requestId = null duration = 9 ms Nine milliseconds. On a Vercel route in Paris region, DNS resolves in forty ms, a TLS handshake costs one to two hundred. Nine milliseconds isn't a network call that failed. It's a network call that never happened. The SDK didn't reach the wire. Instinct immediately offers three patches. Vercel serverless timeout — I add maxDuration , redeploy. Revoked key — I'll rotate it. Stripe account restricted after the live switch — I open a support ticket. These three hypotheses are plausible. None of the three is falsifiable from the symptom alone, and that's precisely what makes them dangerous: each opens a fifteen-to-thirty-minute cycle with rollback at the end if it's wrong. Multiplied by three, half a day lost with the customer still clicking. I don't have time. A student is waiting. Four tests, in order I know the incident class — "preview works, prod breaks" , or its mirror. The rule for this class is that you fix nothing until you've discriminated the layers. Four tests, executed in order. Each eliminates a family of hypotheses, not an isolated hypothesis. And each is designed to refute what it interrogates — because a test that seeks to confirm always finds, by selection, what it's looking for. Test 1 — reproduce in the witness environment. I rerun the same funnel in preview, with the sk_test_ key. Checkout opens in three hundred fourteen milliseconds,

Hello Dev Community! 👋 It is officially Day 26 of my journey to master the MERN stack! Today, I continued with Lecture 9 of Apna College's JavaScript playlist with Shradha Didi, transitioning from raw prototype object manipulation into modern ES6 structural design: Classes and Inheritance . Yesterday we saw how single objects share methods; today I learned how to create scalable blueprints to manufacture objects efficiently. 🧠 Key Learnings From JS Lecture 9 (Classes & OOP) I explored the professional layout of Object-Oriented Programming (OOP) in modern JavaScript: 1. What is a Class and a Constructor? A class is a standardized blueprint for creating objects. Inside every class, we can define a special method called a constructor() . The constructor triggers automatically the exact moment a new object is instantiated using the new keyword. It is the standard place to initialize instance properties dynamically. javascript class Car { constructor(brand, hp) { this.brandName = brand; this.horsepower = hp; } } let myCar = new Car("Toyota", 180); // Instantiates a fresh object instantly

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The obsession with centralizing heavy compute on backend servers is a massive bottleneck for both cost and latency. In 2026, as more applications move to the edge, developers are realizing that the user's browser is an incredibly powerful, untapped compute engine. Recently, I challenged myself to build a free live chess game analyzer for my developer utility suite, CipherKit. The traditional architecture for this requires passing FEN strings to a dedicated backend cluster running the Stockfish engine, which introduces network latency and scales operational costs linearly. I wanted to achieve a 100% client-side, zero-latency experience. Here is how I offloaded the heavy lifting entirely to the browser edge. The Architecture: WASM + Web Workers Running a heavy calculation engine directly in JavaScript instantly blocks the main UI thread. To achieve a flawless 60fps UI, I completely decoupled the state from the computation. The UI Thread: Handles strict DOM rendering, board states, and piece animations. The Worker Thread: Instantiates the Stockfish engine via WebAssembly within the browser's memory. When a live game update occurs, the main thread fires a simple FEN payload via worker.postMessage() . The Worker processes the deep-line evaluations (Depth 20+) asynchronously in the background. It then streams the evaluation lines back to the main thread without causing a single micro-freeze. The Result By treating the browser as the edge compute layer, the tool achieves: Zero Server Latency: Bypassing API rate limits and network bottlenecks. $0 Infrastructure Cost: Heavy compute is crowd-sourced to the user's local device. Absolute Privacy: Sensitive payloads never leave the browser. If you want to see this local asynchronous thread management in action, you can test the live analyzer (and inspect the network tab) here: 👉 CipherKit Live Chess Analyzer Are you offloading heavy computations to the client side in your current projects, or are you still relying on traditional

A conversation between Uncle (a backend architect) and Nephew (a curious developer) about events, publishers, subscribers, Redis Pub/Sub, RabbitMQ, and Kafka The Beginning: What is an Event? 👦 Nephew: Uncle, I see words like "Redis Pub/Sub", "RabbitMQ", "Kafka" everywhere in job descriptions. What do they all do? Are they the same thing? 👨‍🦳 Uncle: (smiles) No, they're different. But before I confuse you with names, let me ask you something. Have you ever watched the news on TV? 👦 Nephew: Yes uncle, every morning! 👨‍🦳 Uncle: Perfect! So when the news channel says "Breaking News: India won the cricket match" - what happened? 👦 Nephew: Something important occurred... and they announced it! 👨‍🦳 Uncle: Exactly! That "something important occurred" is called an Event . In software, when something happens - like a user placing an order, a payment succeeding, or a file uploading - that's an event. 👦 Nephew: So event = something that happened? 👨‍🦳 Uncle: Yes! Think of it as news. When you place an order at Zomato, that's an event. When you get a payment notification from Google Pay, that's an event. When someone follows you on Instagram, that's an event. 👦 Nephew: Okay, I get it. But uncle, why is this "event" concept important? I can just write code directly, right? 👨‍🦳 Uncle: Ah! That's where the real story begins... The Problem: Spaghetti Code 👨‍🦳 Uncle: Imagine you own a food delivery company. A customer places an order. Now, what all needs to happen? 👦 Nephew: Umm... save the order, send email confirmation, alert the restaurant? 👨‍🦳 Uncle: Good! Let me write the code without events: function placeOrder ( orderId ) { saveOrder ( orderId ); sendEmailConfirmation ( orderId ); sendSMSNotification ( orderId ); notifyRestaurant ( orderId ); updateAnalyticsDashboard ( orderId ); addLoyaltyPoints ( orderId ); updateInventory ( orderId ); } Now, one day your boss says "Also send WhatsApp notification". What do you do? 👦 Nephew: Add another function call in the same code? 👨‍🦳 Unc

AI Agents Are the Best Thing to Happen to Network Administration Since SDN A single API key, an AI agent, and a router behind a double-NAT in Southeast Asia. What happened next changed how I think about network management. I manage UniFi routers spread throughout the ASEAN region — some for friends, some for relatives, one for a charity. They're in different cities, different ISPs, different levels of network hostility. Most sit behind carrier-grade NAT. A few are in places where the government firewall blocks VPN protocols at the transport layer. UniFi's own management interface has always been good. The web dashboard, accessible through Ubiquiti's cloud, gives me visibility into every site: device health, client lists, traffic stats, WiFi experience scores. It's one of the reasons I chose UniFi in the first place — the centralized GUI just works. But the GUI is still a GUI. It's clicks and menus and dropdowns. It's fast for one site, manageable for three, and tedious at ten. For anything beyond what Ubiquiti built into the interface, you'd need to write your own tooling. I never bothered, because I'm not a developer, and the built-in dashboard was good enough. Then AI agents arrived, and suddenly the calculation changed. The Discovery I knew UniFi had an API. I'd heard about it in passing — some REST endpoints for the controller, vaguely documented, probably read-only. I never looked into it seriously because what was I going to do with it? Write a Python script to poll client counts? Build a custom dashboard? Without a team of developers, an API is just a locked door. But when I started working with an AI agent, I gave it my UniFi cloud API key on a whim. I figured it could pull basic stats — the stuff from the Site Manager API at api.ui.com/v1 . Read-only. Dashboard-level. Useful as context for answering questions. Then the agent discovered something I'd completely missed: the Cloud Connector API . I owe this discovery in large part to the Art of WiFi PHP client