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Introduction With the World Cup 2026 group stage reaching its climax, football fans worldwide are speculating about who will make it to the finals. To make this experience interactive, I built a fully dynamic World Cup 2026 Bracket Simulator. Instead of just letting users click and choose winners, this app dynamically calculates ELO win probabilities and probabilistically generates realistic match scores (including extra time and penalties) based on team ratings. It also syncs with live match data in real-time. Live URL: https://worldcup-predict2026.github.io/champion/ Tech Stack: Vanilla JS, CSS3 (3D parallax), GitHub Actions, Python, football-data.org API Core Features & Technical Implementation ELO-Based Win Probability & Score Simulation Each team in the database is assigned an ELO-based strength rating. When a user runs the AI auto-prediction, the script calculates win probability and generates a realistic scoreline. Here is the goal roll algorithm (Poisson-like simulation) implemented in Vanilla JS: javascript function generateMatchScore(team1, team2, winner) { if (team1 === "TBD" || team2 === "TBD" || !winner) return null; const s1 = teamStrengths[team1] || 70; const s2 = teamStrengths[team2] || 70; const winnerIsTeam1 = (winner === team1); const strengthDiff = Math.abs(s1 - s2); const baseGoalExpected = 1.1; const bonusGoal = Math.min(1.8, strengthDiff / 12.0); // Goal weight based on ELO difference const rollGoals = (lambda) => { let L = Math.exp(-lambda); let k = 0; let p = 1.0; do { k++; p *= Math.random(); } while (p > L && k < 10); return k - 1; }; let gWin = 0; let gLose = 0; const r = Math.random(); if (r < 0.75) { // Regular time win (90 mins) gLose = rollGoals(baseGoalExpected); gWin = gLose + 1 + rollGoals(0.7 + bonusGoal); return winnerIsTeam1 ? ${gWin} - ${gLose} : ${gLose} - ${gWin} ; } else if (r < 0.92) { // Extra time win (AET) const normalGoals = rollGoals(baseGoalExpected); gLose = normalGoals; gWin = normalGoals + 1; return winnerIsTeam1 ?

Indie movie fans are upset about Google DeepMind’s $75 million investment in the studio, which comes as AI companies are deepening their influence in Hollywood.

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"Operation Endgame" simultaneously disrupts two widely used crime tools.

Your product might rank on page one of Google and still be invisible to AI. When someone asks ChatGPT "what's the best project management tool for small teams," does your product show up? For most SaaS companies under 50 employees, the answer is no. At Inithouse, we built Be Recommended to answer that question with a number: a single AI visibility score from 0 to 100 that tells you exactly where you stand across four major AI engines. Here is how the scoring works under the hood. What the score measures The Be Recommended score captures how often, how prominently, and how positively AI engines mention your product when users ask category-relevant questions. A score of 0 means no AI engine mentions you at all. A score of 100 means every tested prompt across all four engines names your product as a top recommendation. The four engines we test against: ChatGPT (OpenAI), Perplexity , Claude (Anthropic), and Gemini (Google). Step 1: Prompt generation We start by building a bank of 50+ real prompts that a potential customer would actually type into an AI assistant. These are not keyword-stuffed test queries. They mirror how real people ask for recommendations. For a CRM product, that looks like: "What CRM should a 10-person startup use?" "Best alternatives to Salesforce for small businesses" "Compare CRM tools with good API integration" "Which CRM has the best free tier in 2026?" We group prompts into three categories: direct (user names the product category), comparative (user asks for alternatives or comparisons), and situational (user describes a problem without naming a category). Each category tests a different signal: brand recognition, competitive positioning, and contextual relevance. Step 2: Multi-engine querying Each prompt gets sent to all four AI engines through their APIs. We capture the full response text, not just a yes/no for whether your product appeared. The raw responses go into a structured analysis pipeline. We run queries from neutral accounts with n

TL;DR - A finished app that only runs on one laptop is a private demo. Getting it online means connecting three things: a place to store the code (version control), a place to run it (a host), and an address people can type (a domain). The same AI tool that helped build the app can walk a beginner through all three, often without ever opening a terminal. An important step you don’t want to skip is the security check before going live, because the fastest way to ruin a launch is to ship with the database wide open. So you’ve done it. You built your first tool. And it works. The button does the thing. Now’s the moment. It’s time to get your tool online, but how? A project running on a laptop is real, but it lives in exactly one place, the machine it was built on. Nobody else can open it. Getting that project online is its own small skill, separate from building, and it trips up more beginners than the building did. A new coder can finish a working photo booth app in an afternoon and still have no idea how to hand it to a friend short of pulling up the GitHub link while sitting together over coffee. The good news is that the part that used to eat a whole weekend now takes a conversation. Three Things Every App Needs to Go Live Almost every deployment, whatever the tool, comes down to three things working together. Version control: This is a place to store the code and track every change made to it. For most people that means GitHub, which we’ve talked about before. The same way Google Docs keeps a version history, GitHub keeps one for a project. This piece does not re-explain it; the GitHub walkthrough covers the whole thing. A host: A host is really just a computer that stays powered on and connected to the internet with a public address of its own. When a visitor types in the app's address, their browser sends a request across the internet to that machine, the machine runs the code, and it sends the finished page back. A laptop was quietly doing both jobs during the

A critique published in Nature Wednesday calls the basic technology behind Microsoft's "breakthrough" quantum computing chip the Majorana 1 into question. Microsoft unveiled the chip in February 2025 and said it featured a brand-new technology known as a topological qubit. Topological qubits, they said, would be the "building blocks" for their future quantum computer. Microsoft […]

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