Netflix and EA are both going to capitalize on the World Cup craze.

A few weeks ago I was on a call sharing my screen, walking a teammate through a prompt I'd been iterating on in ChatGPT. Mid-sentence I scrolled up — and there, three messages back, was a chunk of a customer's data I'd pasted in earlier to debug something. Real email, real account info, sitting right there on a shared screen. Nobody said anything. Maybe nobody noticed. But I noticed, and I spent the rest of the call only half-present, trying to remember everything else still in that thread. If you live in ChatGPT all day, you already know the problem. The thread is your scratchpad. You paste logs, keys, customer rows, half-finished internal docs — things you'd never put in a doc you planned to share. And then someone says "can you share your screen real quick" and suddenly your scratchpad is a presentation. Why the usual advice doesn't work The standard answers are all some version of "be careful": Open a clean tab before sharing. Scroll to the top. Use a separate "demo" account. These fail for the same reason all manual checklists fail under pressure: the moment you actually need them is the moment you're distracted, talking, and not thinking about hygiene. You remember after . The fix has to happen before the screen goes live, and it has to require zero discipline in the moment. What I wanted instead I wanted something that just sat there and blurred sensitive parts of a page automatically, so that even if I forgot, the leak couldn't happen. A few requirements: Local only. Whatever it does, it never sends page content anywhere. A privacy tool that phones home is a contradiction. Before, not after. It blurs while the page renders, not after I've already exposed it. Per-element, not whole-screen. A full black box is useless for a demo. I still need to show the working parts. The interesting technical bit The naive approach is to listen for some "I'm sharing now" signal and react. That's too late — there's a visible frame where the data is exposed before the blur kic

Kevin O'Leary agreed to halve the size of his planned 40,000-acre data center in Utah amid mounting pressure from residents and activists, as reported earlier by local affiliate ABC4. The Shark Tank star sent a letter to Utah Senate President J. Stuart Adams on Thursday, saying that he will remove 19,430 acres from the project, […]

Rust Ownership System Explained for JavaScript Developers Quick context (why you're writing this) I was trying to rewrite a small utility I’d written in JavaScript—a function that takes a string, splits it into words, and returns the longest one. In JS it’s trivial: you pass the string around, mutate arrays, and nothing blows up. When I attempted the same thing in Rust, the compiler kept yelling at me about “use of moved value” and “cannot borrow as mutable because it is also borrowed as immutable”. I spent a good chunk of an afternoon staring at those errors, thinking I’d missed some syntax detail, only to realize the real issue was a completely different way of thinking about data. If you’ve ever felt that Rust’s compiler is being overly pedantic, you’re not alone—but once you grasp what it’s protecting you from, the frustration turns into appreciation. The Insight Rust doesn’t treat variables like JavaScript’s loosely‑typed references. Instead, it enforces ownership at compile time. Three ideas tend to surprise developers coming from a garbage‑collected world: Move semantics – assigning a value to another variable moves it; the original is no longer usable unless you explicitly clone it. Borrowing rules – you can have either many immutable references or exactly one mutable reference to a piece of data, but never both at the same time. Lifetimes – the compiler tracks how long references are valid, preventing dangling pointers without a garbage collector. The first two are the ones that trip people up most often, and they directly address the class of bugs JavaScript developers know all too well: accidental shared‑state mutations and use‑after‑free‑like mistakes (though in JS they show up as weird undefined values rather than crashes). Let’s look at each with a concrete example, show the common mistake, and then see how to do it right. How (with code) Move semantics – the “you can’t use it after you give it away” surprise fn main () { let greeting = String :: from

İlk yazıda Event Driven Architecture’ın temel kavramlarını, Kafka üzerinde topic/channel tasarımını, event-command ayrımını, schema contract’ları ve producer-consumer ilişkisini ele aldık. Bu yazıda odağı bir adım ileri taşıyıp event’in platform içindeki yaşam döngüsüne bakacağız. Çünkü EDA tasarımında asıl zorluk yalnızca event üretmek değildir. Asıl mesele, üretilen event’in güvenilir, izlenebilir, tekrar işlenebilir, zenginleştirilebilir ve farklı tüketiciler tarafından kullanılabilir hale gelmesidir. Bu yazıda şu sorulara odaklanacağız: Ham event platforma geldiğinde ne olur? Event nasıl doğrulanır, zenginleştirilir ve tüketilebilir hale gelir? Raw, validated, enriched ve curated topic’ler nasıl konumlandırılmalıdır? Bu yapı modern lakehouse mimarilerindeki Medallion yaklaşımıyla nasıl ilişkilendirilebilir? DLQ ve alert topic’leri ne zaman devreye girer? Replay, idempotency, monitoring, security ve governance nasıl düşünülmelidir? Event Pipeline Nedir? EDA mimarilerinde özellikle data platform projelerinde event’ler genellikle bir yaşam döngüsünden geçer. Bu yaşam döngüsü şöyle modellenebilir: raw -> validated -> enriched -> curated | | v v dlq alert Bu yapı, veri akışının aşama aşama olgunlaşmasını sağlar. Raw topic kaynaktan gelen ham event’i taşır. Validated topic schema ve temel kalite kontrollerinden geçmiş event’leri içerir. Enriched topic event’in referans veriler veya başka veri kaynaklarıyla zenginleştirilmiş halidir. Curated topic ise tüketiciler için güvenilir, normalize edilmiş ve iş anlamı netleşmiş event’leri temsil eder. Event Pipeline ve Medallion Architecture İlişkisi Bu yapı, modern lakehouse mimarilerinde sık kullanılan Medallion yaklaşımıyla doğal bir benzerlik taşır. Lakehouse tarafında Bronze katmanı ham veriyi, Silver katmanı temizlenmiş ve zenginleştirilmiş veriyi, Gold katmanı ise iş tüketimine hazır veri ürünlerini temsil eder. Kafka üzerindeki raw, validated, enriched ve curated topic’leri de benzer bir olgunlaşma mantığını akan veri ü

Sliding Your Way Out of Panic: The Mental Trick That Speeds Up Coding Under Fire Quick context (why you're writing this) I still remember the sweat on my palms during a technical interview a couple of years back. The interviewer tossed out the classic “longest substring without repeating characters” problem, gave me five minutes, and watched me stare at the whiteboard like I’d never seen a string before. I started with a brute‑force double loop, felt the clock ticking, and ended up writing a mess that was O(n²) and full of off‑by‑one errors. I walked out feeling like I’d choked, even though I knew the solution deep down. Later, after I’d spent way too many hours replaying that moment in my head, I realized the problem wasn’t my knowledge—it was the way I was framing the question while under pressure. I’d been trying to solve the whole thing at once instead of focusing on the tiny piece that actually mattered. When I finally isolated that piece, the answer clicked in seconds. That’s the mental framework I now teach anyone who’s about to face a ticking clock: identify the invariant you must keep true, and let everything else revolve around it . The Insight When the pressure’s on, your brain wants to grab the biggest chunk it can see and start hacking. That’s a recipe for wasted time and bugs. Top coders do the opposite: they strip away everything that isn’t a constant rule the solution must obey, then build the smallest possible state machine that enforces that rule. For the substring problem the invariant is simple: the current window must contain only unique characters . If you can guarantee that, the answer is just the biggest size that window ever reaches. All the fiddly details—where to move the left pointer, how to know when a duplicate appears—fall out of tracking the last index you saw each character. So the mental steps are: State the invariant (what must always be true). Find the minimal data you need to enforce it (usually a map or a set). Update that data

NCTA seeks waiver from foreign-router ban, citing memory and substrate shortages.

Bittensor pays roughly eight dollars in TAO token emissions for every dollar of real AI revenue that flows through the network. The exact ratio fluctuates by quarter, but the shape is durable. Q1 2026: about $328 million in annual emissions against $43 million in real AI revenue. That is 7.6 to 1. It is what the crypto-skeptical press has called "extractive by default." It is also what the crypto-friendly analysts call "the subsidy treadmill." The Bittensor engineering team is sophisticated. The subnet validators run real ML evaluation. The miners serve real inference. The revenue is real. The emissions are also real. The cause is the token model itself. One asset is asked to do two jobs that do not belong together. I want to be specific about this part, because every other decentralized AI compute network I have looked at has the same problem, and the fix is well-known. What the token does A token in a decentralized AI compute network does two structurally distinct things. The first job is utility settlement . Contributors run inference, and someone has to pay them for the compute work they did. The payment medium has to scale with usage, has to be denominated in something the contributor can spend on the network or convert to fiat, and has to remain stable enough that contributors can plan around it. This is a billing system. The second job is value capture . Early supporters, investors, and contributors take risk to bootstrap a network that does not yet exist. They have to be paid back for that risk in a way that scales with the eventual success of the network. The payment medium has to be a speculative asset that appreciates as the network grows. This is an equity instrument. A billing system and an equity instrument want opposite things. A billing system that is also a speculative asset means that contributors who get paid in it cannot help but hold a speculative position. An equity instrument that is also a billing system means that token-price volatility show

How I combined spaced repetition, adaptive algorithms, and clean UX to create a multi-purpose learning tool. I’ve always believed that memorisation doesn’t have to feel like a chore. After years of using (and sometimes getting frustrated with) existing tools, I decided to build my own. That’s how MemOrLearn was born in early 2026. MemOrLearn is a web-based adaptive learning platform that brings together flashcards, typing practice, math drills, and Bible memory tools — all powered by intelligent spaced repetition and performance-based adaptation. The Core Idea: Most flashcard apps follow a rigid spaced repetition schedule. I wanted something smarter — a system that actually adapts to the user in real time. If a learner is struggling with a concept, the algorithm increases review frequency and offers slight variations. If they’re crushing it, reviews are intelligently spaced out. This dynamic approach is what makes the experience feel responsive and human. Key Features: Adaptive Flashcards: The heart of the platform. Users can create decks or browse public ones. The system tracks performance per card and automatically adjusts difficulty and frequency. Clean, fast, and minimal interface — exactly how I like my tools as a developer. Typing Tutor: Built to help users improve speed and accuracy through gamified, adaptive drills. It adjusts to your current level so you’re always progressing. Math Drills: Focused practice on math facts with real-time adaptation. The system identifies weak areas quickly and targets them without wasting time on mastered content. Bible Memory Mode: A specialized tool many users love. It applies the same adaptive principles to Scripture memorization, making it effective for individuals, families, and small groups. Teacher / Parent Dashboard: A clean admin view that lets educators assign work, monitor progress, and adjust settings per student. Built with simplicity in mind. Technical Approach (For Fellow Builders): I focused on keeping the back

Google now lets big creators and publishers in the US claim dedicated profiles in Search to highlight things like videos, articles, and their other profiles online. But this feature won't be available to most people or organizations; Google is limiting it to people with at least 100,000 YouTube subscribers, 100,000 followers on Instagram or X, […]

Enlace a post en Español Click If you are developing games in Godot and using AI to help you code, you are probably tired of constantly switching tabs between your editor and the browser. Copying code, pasting it, explaining your scene context over and over again... it is a massive workflow killer. To solve this, I built Golem-AI (named after the Godot Engine logo because let's face it, it looks like a tiny, friendly mechanical golem). It is a "Cursor-style" AI assistant extension integrated directly into a dock right inside your Godot 4.2+ editor. Today, I am opening the repository to the community as a completely open-source project. It is currently in Beta and has some bugs, but it is fully functional, and I want to share it so we can improve it together. / ____/___ / /__ ____ ___ / | / _/ / / __/ __ \/ / _ \/ __ `__ \______/ /| | / / / /_/ / /_/ / / __/ / / / / /_____/ ___ |_/ / \____/\____/_/\___/_/ /_/ /_/ /_/ |_/___/ 🎮 How it Looks Inside the Editor Here is a glimpse of the integrated dock interface, its session history, and the context autocomplete system in action: 🔥 Key Features 🦙 Local & Cloud Providers: Connect it to Ollama or LM Studio for a 100% free, offline local workflow, or hook it up to OpenAI, Anthropic, Gemini, or Cursor proxies. 🧠 Cursor-Style UX & Context (@ Mentions): Type @ in the chat composer to automatically attach open scenes, specific project files, or custom skills directly into the prompt. 🛠️ Editor Tool Calling: It features an optional multi-step verification loop. The AI can actually interact with native Godot editor tools to help you iterate and fix things faster. 📚 Markdown Skills System: Feed the assistant specific workflows, style guides, or documentation using standard markdown files (/skill or @skill :id). 💬 Advanced Chat UI: Built-in "thinking blocks", agent step progress tracking, searchable history sessions, and a native bilingual UI (English / Spanish). 🛠️ The Current State: "It works, but..." (Looking for Beta Testers!) L

If you've ever worked on an e-commerce project in Chile, sooner or later you bump into Transbank. There's no avoiding it. I built a reference template that use NestJS on the backend and Next.js 16 on the frontend, and in this post I want to walk you through the whole thing: what Webpay Plus actually is, why the integration looks the way it does, and how each piece fits together. Github Repository Reference: Link A bit of context Webpay Plus is one of the most important online payment methods in Chile. The user experience is straightforward: your customer enters an amount on your site, gets redirected to Transbank's branded payment page, fills in their card details there, and comes back to your site with a result. From a UX perspective it's not as slick as Stripe Elements or a fully embedded checkout — the user always sees Transbank's domain in the address bar during the payment — but from a developer's perspective that's actually a feature. You never touch card numbers. PCI scope stays minimal. Transbank handles 3D Secure, fraud rules, and bank routing. The trade-off is that the integration model is what you might politely call classical . It's built around full-page redirects and form POSTs, not modern APIs with JSON responses and webhooks. That's important to understand up front, because it shapes every decision you'll make in the code. The integration flow, step by step Before looking at any code, it helps to have a clear mental model of what's happening . There are three distinct moments where your system talks to Transbank's system, and each one has a specific shape. First Step: When the customer clicks "Pay", the backend asks Transbank to create a transaction (amount, order ID, return URL) . Transbank returns a transaction token and a redirect URL where you must send the user. Second Step: Transbank requires the redirect to be an HTTP POST with the token in a token_ws form field, so you must generate an HTML form with a hidden token_ws input and submit it prog

We are excited to announce the first alpha release of Arc, a lightweight, C-based programming language and interpreter designed for simplicity, performance, and educational clarity. Version Overview Version: v0.0.1-alpha Status: Alpha (Experimental) License: GPL-3.0 This initial release establishes the foundational pipeline of the Arc language, from lexical analysis to AST-based interpretation, featuring a robust set of core language constructs and a custom memory management system. Key Features Language Core Variable System: Declaration and updates using the VAR keyword. Functions: Support for custom functions (FN) with parameters and RETURN values. Control Flow: Conditional branching with IF, THEN, ELIF, and ELSE. Iterative loops with WHILE, FOR, and THEN. Loop control with BREAK and CONTINUE. Exception Handling: Graceful error recovery using TRY...CATCH blocks. Data Types: Integrated support for Numbers (Integers/Floats), Strings, Booleans, and Lists. Import System: Modularize projects by importing other .arc files using IMPORT. Syntax Highlights Case Sensitivity: Keywords (e.g., VAR, WHILE, IF) are case-insensitive. Identifiers (variable and function names) are case-sensitive. Operators: Comprehensive set of arithmetic (+, -, *, /, ^), comparison (==, !=, <, >, <=, >=), and logical (AND, OR, NOT) operators. Comments: Single-line comments starting with #. Built-in Standard Library I/O Operations: print, get_input, open_file, read_file, write_file, close_file. Data Manipulation: len_of, typeof, to_int, split_string, append_list, range. Math Library: A comprehensive math.arc providing constants (PI, E) and functions (sin, cos, tan, sqrt, log, etc.). Tooling & CLI Arc comes with a powerful CLI and an interactive REPL: Interactive REPL: Run code line-by-line with syntax highlighting. CLI Options --debug (-d): View tokens and AST tree during execution. --code (-c): Execute a string of code directly. --float-precision (-p): Control decimal output. --mempool-size (-m):

The indie artist has played a string of surprise, small-venue shows with no phones allowed, prompting fans to piece together clues about a potential upcoming album.

Excited to share SkillMap AI, a platform designed to help organizations make faster and more accurate staffing decisions. The idea is simple: project requirements and candidate profiles often live in separate documents, making team allocation slow and inconsistent. SkillMap AI bridges that gap by converting project requirements into structured skill demand and matching them against candidate capabilities. ✨ Key Features • Requirement Intelligence – Transform project briefs into normalized skill requirements • Candidate Matching – Compare resumes against actual project needs, not just keywords • Skill Gap Analysis – Identify missing capabilities before project execution • Staffing Decision Support – Recommend validation, interviews, and upskilling paths 📊 Outcomes ✓ Faster staffing shortlists ✓ Reduced manual resume screening ✓ Better project-team alignment ✓ Evidence-based skill gap identification ✓ Improved workforce planning 🌐 Live Demo: https://skill-map-ai-delta.vercel.app Would love to hear your thoughts and feedback!

When using the Postman extension in vsCode I was wondering where all the Collections credentials and tokens are stored. Are they secure and what is best practice for its use? Should I switch to curl? And can I use environment variables like curl? The postman documentation just talks about using the postman vault but doesn't say where they are stored and how securely if you aren't using it. submitted by /u/ElectricYFronts [link] [留言]

TLDR: I’ve built a single player browser game which is static assets and 0 cost, I plan to release multiplayer which will cost me - would you split into 2 different URLs eg “multi.myurl.gg” vs “myurl.gg” etc (placeholder URLs) Hey reddit, I’ve built a browser game in my spare time and have a question about potentially hosting on 2 different URLs. My specific issue is - right now the game is single player only + bots, and cost wise most of the game is free for me to host at any scale due to the way I’ve architected single player. This includes sharing features and things like watching replays, I could effectively have a billion users and still pay the same as when I have 1 user. This is great! But, once I introduce multiplayer, I have to pay for Cloudflare workers, storage, egress fees potentially etc for those same sharing and replay features. I’ve estimated these costs to be quite low even at scale, but I’m extremely frugal and want to always keep the single player experience alive. The multiplayer experience is more dependant on how well it performs with the public. So I was thinking of hosting single player at something like “myurl.gg” and multiplayer at something like “multi.myurl.gg” for a clean separation of concerns. Am I over-engineering here?? @mods this is not self promotion, I’ve used placeholder URLs etc submitted by /u/ComfortablePeace8859 [link] [留言]

Hello. I am a 9th grade student from India. AI was used only for grammar correction. These are my own questions. I am confused about two concepts: 1. Second Law of Thermodynamics: It states that the entropy of an isolated system increases. Decreasing entropy requires work. Does this imply that while time dilation from General Relativity can cause time to pass slower, time can never run backwards, as that would require a spontaneous decrease in entropy? 2. General Relativity: Time passes slower i