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Apparently, the "return of Xbox" means a retreat from other platforms. At its Xbox Games Showcase today, Microsoft revealed that Gears of War: E-Day - which was previously rumored for a PS5 launch in addition to Xbox and PC - will not be coming to PlayStation. It'll be an Xbox console exclusive and is launching […]

Have you ever wondered what the most indispensable app is for your favorite musician or how the world’s tech CEOs stay focused? Well, that’s the sort of thing we aim to uncover in our Verge Weekend Questionnaire. Think of it as a spiritual successor to Five Minutes on the Verge. Every Saturday, a different guest […]

The console industry is in a weird place, and both Xbox and PlayStation have a chance to change the narrative a bit with their showcases at Summer Game Fest. Sony did that by focusing on the single-player titles it’s known for, and then it was Microsoft’s turn. The Xbox Games Showcase was focused mainly on […]

A living journal for your pet's life, with AI insights Discussion | Link

For enterprise AI/security teams, what would make AI SPM useful rather than just another category label? Is the important part AI asset inventory data access mapping, agent permissions, prompt/data monitoring, policy enforcement or something else? submitted by /u/Efficient_Western609 [link] [留言]

"Chat is dead" — at least, according to a senior OpenAI employee.

The revamped AI tool will roll out in the coming weeks, according to The Financial Times.

Ai for complex data Discussion | Link

Every online Fake Data Generator tool I used had the same quiet flaw: whatever you paste gets POSTed to someone's server. For a Fake Data Generator, that paste is often exactly the sensitive thing — a token, a config, an API response. So I built Fake Data Generator to not do that. Generate fake names, emails, UUIDs, addresses — custom columns, CSV/JSON export. 100% browser-side — and it runs entirely in your browser, so there's nothing to send and nothing to breach. You don't have to take my word for it: open DevTools → Network, use the tool, and watch the tab stay empty. One HTML file, View-Source-able. https://faker.platotools.com/ It's part of platotools.com — a set of single-purpose, client-side dev tools. Feedback and edge-case bug reports very welcome.

If you have an outdoor space that needs some extra lighting, either for fun or security reasons (or a mix of the two), the Govee outdoor lamp post light is a good pick that’s 23 percent off its regular price right now. Typically retailing for $260, the Govee outdoor lamp post light is available for […]

Somewhere along the way, computing became disposable. Devices became sealed. Systems became rented. Ownership became licensing. Repairability disappeared. Infrastructure moved away from the user and into distant cloud platforms. And I think we lost something important because of it. Lately, I’ve found myself becoming increasingly interested in hardware again. Not just software. Not just cloud systems. But actual computing devices. Servers. Home infrastructure. Repairable machines. Set-top systems. Local AI appliances. Sovereign computing. Because I believe the next era of computing will belong to people who own their infrastructure again. The Provider Box Realization One thing that kept sticking in my head was this: Almost every home in America already has a provider box. A Comcast box. An AT&T gateway. A router. A modem. A streaming box. People are already comfortable with the idea of a dedicated computing appliance sitting in their home quietly powering their digital life. That realization changed how I thought about computing infrastructure. What if those boxes worked for the user instead of the provider? What if they: hosted local AI, managed home storage, coordinated smart devices, powered media systems, handled automation, protected privacy, synchronized intelligently, and operated as sovereign infrastructure? That idea became part of the thinking behind KiwiHome. The Return Of Home Infrastructure For a long time, the industry moved toward centralization. Everything shifted toward: SaaS, subscriptions, streaming, cloud storage, cloud intelligence, and rented operational environments. Convenient? Absolutely. But also fragile. If: pricing changes, services disappear, companies shut down, APIs get revoked, or platforms change policies, entire workflows collapse overnight. I think people are starting to feel that tension. Especially creators. Especially businesses. Especially technical users. That’s why I believe we’re going to see a major resurgence in: home serv

This is a submission for the GitHub Finish-Up-A-Thon Challenge What I Built I built TradeWeave — a B2B + B2C fashion marketplace that connects small scale weavers and manufacturers directly to customers and retailers, eliminating middlemen entirely. The project started as a notebook idea at midnight: "What if factory workers and artisans could sell directly, keeping the full margin instead of losing 40% to supply chain bloat?" Demo 🔗 Live Demo — TradeWeave — Try it right now, no installation needed How to use: Hover over any dress card — it flips to reveal sizes Pick a size, adjust quantity, click "Add to cart" Click "Sign up" to see the premium registration flow Click the TradeWeave logo 5 times to unlock the hidden admin dashboard Click "Wholesale" tab to see direct manufacturer listings 💻 Source Code: github.com/Deeraj25/tradeweave The Comeback Story Before: Idea scribbled in a notebook at midnight Zero code written Zero deployment Tucked away under "someday projects" Status: Abandoned After: Full-featured marketplace in production ~550 lines of polished HTML/CSS/JavaScript Deployed live on Netlify + GitHub Pages Anyone can use it instantly Real product with real features Core fixes: Built the entire marketplace from scratch (this was a notebook idea, not existing code) Implemented hover-to-flip cards with CSS 3D transforms (perspective + rotateY) Created responsive product grid with auto-fill layout Built localStorage persistence for cart + admin state What Changed, Fixed, and Added Features added: 5 product categories with 20+ items Hover-flip interaction (no page reload needed) AI Try-On modal with upload UX Wholesale B2B portal with manufacturer data Hidden admin dashboard (5-click unlock) Aurora-style signup with animated hero and steps Real-time analytics dashboard (revenue, top categories, order table) Mobile-responsive design Keyboard + mouse support Polish & optimization: Custom animations (staggered reveals, floats, shimmers) Cormorant Garamond typograp

I built something I desperately needed: daily AI audio lessons from real developer trends (plus a Dutch coach for inburgering B1). The hardest part wasn't the AI. It was figuring out how to score genuine rising skills vs. one-day noise. I ended up building a cross-day momentum signal that rewards skills accelerating over 3+ days and dampers spikes. But I'm stuck on the next problem: how do you personalize this without storing user data? (I'm privacy-first, so no subscriber DB — Telegram holds the member list.) If you've solved this, I'd love your take. And if you're learning Dutch + coding, I'd appreciate you trying it and telling me what's useless. What I'm curious about: Is the momentum signal actually working — am I surfacing real trends or just noise? Would the Dutch coach be useful for expat developers in NL, or is it too niche? Technical details (for those who care): • 7 sources: GitHub Trending, HN (Who-is-Hiring + front page), Stack Overflow tag deltas, dev.to, Reddit, Lobste.rs • Map-reduce skill extraction with deterministic attribution (corpus scan, not LLM tally) • Grounded briefs: reads actual source text via Jina + Exa, cited sources • Delivered via Telegram (audio + PDF), Spotify podcast, email • Privacy: PII redacted at ingestion, no subscriber data stored Live: https://yusuprozimemet.github.io/LearnX-Radar/ GitHub: https://github.com/Yusuprozimemet/LearnX-Radar (P.S. This is still beta — I'm looking for feedback, not users. If you try it, tell me what's useless, not what's good.)

If you’ve been following my previous posts, you know I’m a big advocate for Trunk-Based Development and shrinking your pull requests until they almost feel too small. In a perfect world, developers merge code directly into the main branch multiple times a day, everything flows smoothly, and production remains rock solid. But let’s be honest. When you actually try to pitch this to a backend team working on a core system, you almost always hit the exact same wall of resistance. Someone in the back of the room will inevitably raise their hand and ask: “That sounds great in theory, but I’m currently refactoring our legacy checkout service. It’s going to take me four days of deep architectural changes. Are you seriously telling me I should merge half-baked, broken code into the main trunk and push it straight to production where real customers are buying our products?” It’s a completely valid objection. If your only tool for hiding uncompleted work is holding onto a massive, long-lived feature branch, then trunk-based development breaks down immediately. You end up with the exact nightmare we talked about earlier: huge code reviews, painful merge conflicts, and code that rots before it ever sees a live environment. To make continuous delivery actually work without causing catastrophic production outages every single afternoon, you need to decouple two concepts that most engineering teams mistakenly treat as the exact same thing: Deployment and Release . Last article in this category is focused on Trunk-Based Development: https://codecraftdiary.com/2026/05/18/trunk-based-development-roadmap/ The Core Concept: Shifting Left by Decoupling In traditional development setups, deploying code and releasing a feature happen simultaneously. You merge your giant feature branch, the CI/CD pipeline runs, the code hits the live servers, and boom—your users immediately see the new functionality. This model is incredibly high-stakes. If something goes wrong, your only options are rollin

This is a submission for the GitHub Finish-Up-A-Thon Challenge What I Built SpendWise AI is a free tool that audits your AI tool spending (Cursor, Copilot, Claude, ChatGPT, Gemini, Windsurf) against verified vendor pricing and tells you exactly where you're overspending and what to do about it. I originally built this as a week-long assignment for a startup. The problem it solves is simple: founders and engineering managers pay for multiple AI tools but have no idea if they're getting ripped off. SpendWise gives them that answer in under a minute, no signup needed. The interesting part is that the core audit engine has zero AI in it. It runs 6 hardcoded rules against verified pricing data, so every recommendation is reproducible and verifiable. AI (Groq's Llama 3) only kicks in to write a friendly summary paragraph on top of the structured results. I made this choice because financial recommendations need to be deterministic. Same input, same output, every time. The stack is Next.js 16, TypeScript, Tailwind + shadcn/ui, Supabase for the database, Groq for AI summaries, Resend for emails, and Vitest for testing. Deployed on Vercel. Live app: spendwise-ai-test.vercel.app Source code: github.com/Karam-999/SpendWise-AI Demo The original audit tool: The comeback (re-audit on pricing change): You can try the Round 1 version live at spendwise-ai-test.vercel.app . Pick a tool like Cursor on Teams plan at $40/mo, run the audit, and see the full savings breakdown. The Round 2 features (pricing change detection, re-audit diff view) are on a separate branch and not merged to main yet, but the demo video above walks through the complete flow. The Comeback Story Where it was: The original version was basically a calculator. You fill in your AI tools, it shows you where you can save money, and that's it. If Cursor changed its pricing the next week, your audit was already stale and you'd never know about it. It worked fine as a one-time thing. It had the form, the audit engine, AI

Most side projects start with a simple frustration. Mine started with a banking app. One of my banks had a feature I really liked. It automatically categorized transactions and showed spending breakdowns in graphs and charts. For the first time, I could easily see how much I spent on restaurants, groceries, transport, subscriptions, and other categories. The problem was that only one of my banks offered this feature. Like many people, I use multiple bank accounts, credit cards, and savings accounts. Two of my other banks provided little more than a long list of transactions. If I wanted a complete picture of my finances, I had to switch between apps and manually piece everything together. As a software engineer, my first instinct was obvious: "Why don't I just build this myself?" That idea eventually became MyVault . The Original Goal The first version of the project was surprisingly simple. I wanted users to: Upload bank statements Extract transaction data Automatically categorize spending View useful charts and reports The goal wasn't budgeting. It wasn't investment tracking. It wasn't accounting. I simply wanted a single place where I could see spending across all of my bank accounts. Once I started building, however, I realized there was a much more interesting opportunity. If all transaction data was already extracted and structured, why not allow users to ask questions about their finances? Instead of searching through transactions manually, users could simply ask: How much did I spend on restaurants last year? What subscriptions am I paying for? Which categories increased the most this month? How much did I spend while traveling? That's when MyVault started evolving from a reporting tool into an AI-powered financial assistant. Building as a Solo Developer One of the biggest challenges wasn't technology. It was building everything alone. When you're working on a side project, you don't just write code. You become responsible for everything: Product decisions B

When I started learning Excel as part of my Data Science & Analytics course, I assumed it was just a tool for creating tables and performing basic calculations. After spending a week exploring its features, I quickly realized that Excel is much more powerful than I thought. Almost every organization generates data. Businesses track sales, schools monitor student performance, hospitals manage patient records, and marketers analyze campaign results. Before data can be analyzed, it needs to be organized, cleaned, and summarized—and that's where Excel comes in. In this article, I'll share some of the Excel concepts I've learned so far and how they're used in real-world data analysis. Understanding the Excel Workspace Before working with data, it's important to understand the basic structure of Excel. When you open Excel, you're working inside a workbook . A workbook can contain multiple worksheets (often called sheets), which help organize different sets of data. At the top of the screen is the Ribbon , which contains tabs such as Home, Insert, Page Layout, Formulas, Data, and View. The Ribbon acts like a control center where you can access Excel's tools and features. Rows run horizontally and are identified by numbers, while columns run vertically and are identified by letters. The intersection of a row and column is called a cell , where data is entered. At first, all these parts seemed overwhelming, but after using Excel regularly, navigating through them has become much easier. The Different Types of Data in Excel One of the first things I learned is that not all data is the same. Excel commonly works with: Text data (names, product categories, locations) Numeric data (sales figures, quantities, prices) Date and time data (order dates, deadlines) Logical data (TRUE or FALSE values) Understanding data types is important because Excel treats each type differently when performing calculations and analysis. Number Formats Matter More Than I Expected Another concept that

The Problem If you've ever audited your Windows network traffic during a boot-up sequence, you know the truth: there's a "blind spot." Between the moment your network drivers initialize and your VPN/WireGuard tunnel actually establishes, your traffic is leaking. Many third-party solutions exist, but they are often bloated, use proprietary binaries, or act as black boxes. I wanted something transparent, native, and bulletproof. The Solution: WG-KillSwitch I developed a pure PowerShell-based kill switch architecture. It doesn't rely on third-party libraries—it uses native Windows system components to enforce security. Key Architectural Features: Zero-Trust Firewall Matrix: Hardens the system by blocking all outbound traffic by default, allowing only authenticated tunnel traffic. WMI Persistent Watchdog: Unlike standard scripts that can be killed via Task Manager, this project uses WMI Event Subscriptions. If the watchdog process is terminated, Windows itself immediately respawns it. Resilience: Survives hard reboots, modem resets, and Windows service cycling. Resilience & Leak Testing I've put this through a gauntlet of tests: Forced Reboots: Zero leaks detected during driver load. Process Termination: The WMI engine restores the protection in milliseconds. Dynamic Network Resets: The firewall matrix remains active regardless of adapter status. Let's Collaborate This is open source, transparent, and built for the community. I'm looking for security audits and feedback. Check out the source code, open an issue, or submit a PR: https://github.com/ryderlacin-pixel/Windows-WireGuard-KillSwitch

Last year, I examined RUM indexes within this series on multi-key indexing, demonstrating that they cannot substitute MongoDB's compound indexes for sorted queries. A year later, Microsoft has fixed this in the DocumentDB extension for PostgreSQL with an Extended RUM index that preserves the ordering of the keys, allowing an ordered scan rather than a bitmap scan. Let's revisit our pagination query to see how it performs now. I start a container with the latest DocumentDB (version v0.112-0 from May 26, 2026): docker run -d --name documentdb-local -p 10260:10260 -p 9712:9712 ghcr.io/documentdb/documentdb/documentdb-local:latest --username franck --password franck --start-pg I can connect to PostgreSQL on port 9712, where many extensions are installed, including the extended RUM index: docker exec -it documentdb-local psql -p 9712 postgres psql ( 17.10 ( Debian 17.10-1.pgdg13+1 )) Type "help" for help. postgres = # \dx List of installed extensions Name | Version | Schema | Description ------------------------- +---------+------------+------------------------------------------------------------ documentdb | 0.112-0 | public | API surface for DocumentDB for PostgreSQL documentdb_core | 0.112-0 | public | Core API surface for DocumentDB on PostgreSQL documentdb_extended_rum | 0.112-0 | public | DocumentDB Extended RUM index access method pg_cron | 1.6 | pg_catalog | Job scheduler for PostgreSQL plpgsql | 1.0 | pg_catalog | PL/pgSQL procedural language postgis | 3.6.3 | public | PostGIS geometry and geography spatial types and functions tsm_system_rows | 1.0 | public | TABLESAMPLE method which accepts number of rows as a limit vector | 0.8.2 | public | vector data type and ivfflat and hnsw access methods ( 8 rows ) postgres = # I can also connect to the MongoDB-compatible API: docker exec -it documentdb-local mongosh -u franck -p franck 'mongodb://localhost:10260/?tls=true&tlsAllowInvalidCertificates=true' Current Mongosh Log ID: 6a0b3b537d2a1c3471d1a7ba Connecting to: mo