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At WWDC 2026, Apple introduced Xcode 27, which makes it easy to kick off tasks with coding agents, iterate on new project ideas, and customize the workspace. It also introduces DeviceHub for unified simulator and device management, along with enhancements to Organizer and Instruments, among many other improvements. By Sergio De Simone

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Check this out: i run four monetization channels side by side. Sponsored posts, display ads, YouTube ad revenue, and affiliate links. After eighteen months of tracking every dollar in a spreadsheet I built myself, I can tell you with brutal honesty: affiliate income is the only one that scales without me having to constantly produce more content or chase the next brand deal. But the math only works if you pick the right program. Most affiliates I know are promoting garbage with terrible retention, and they have no idea they're burning their audience's trust for a $9 one-time payout. Let me walk you through how I evaluate affiliate programs, what I've learned from running real funnels, and why the AI API category has quietly become the most lucrative vertical for tech creators in 2026. My Monetization Stack After 18 Months of Testing Here's a snapshot of my monthly revenue from a tech newsletter with around 34,000 subscribers and a YouTube channel sitting at 88,000 subscribers: Sponsored posts: $2,100 per placement, but I can only land maybe 2-3 per month without annoying my list Display ads: $1,800 per month from Mediavine, but this number barely moves regardless of how hard I work YouTube ad revenue: $2,400 per month, capped by watch time and RPMs Affiliate income: $6,800 per month, and it grows every single month even when I publish nothing That last number is what got my attention. Affiliate income compounds. When I published a tutorial in February recommending a tool, that single piece of content still earned me $340 in May because users stayed subscribed. No other channel behaves like that. No other channel lets a piece of content from four months ago keep paying you. But here's the catch that took me a while to figure out: not all affiliate programs are built the same way. And the difference between a good program and a bad one can be 10x in lifetime earnings per referred user. # # How I Score an Affiliate Program (The Growth Hacker Scorecard) Before I promote

Have you ever felt that browsing the web has become "tiring"? We open a browser, search, close a page, then move to another... a dizzying cycle of distracted navigation between sites, while we are essentially looking for "knowledge," not "links." I asked myself: What if browsing was as fluid as scrolling through Facebook, but with the power and accuracy of search engines like Google? I finally decided to turn this idea into reality through my new project, Arabinum. What does Arabinum do? Turning websites into posts: The browser reformats the web so that content appears as fluid feeds, eliminating visual distraction. Smart categorization: No more getting lost; I have divided content into specialized sections like "Videos" and "Research Papers," so you can find what you need in one place. Browsing as a social activity: I added interactive features (Like, Comment, Repost) to make content consumption a collaborative experience rather than a rigid, individual process. I believe the web needs an interface that restores the user's focus, and this project is my attempt to merge the best of the worlds of "Search" and "Social Media." Notes: This is a beta version I launched just to see your thoughts on the idea. This version might not be compatible with small screens yet. This version includes Google Search, YouTube, and scientific papers from arXiv. I look forward to hearing your opinions. The site is free and ad-free, but I need your support to continue due to API and domain costs. I am sixteen years old and a high school student. Finally, I present to you my browser, Arabinum: https://arabinum.amrzlabs.com

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 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

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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I was working on a project and needed to convert some Markdown to HTML. Searched for it online, found a site, done. Next day I needed HTML back to Markdown. Searched again, different site. Then JSON to CSV. Then something else. Different site every time, half of them slow. At some point I just thought — why not build one site that handles all of this? So I did. That's QuickConvert . What It Is Just a collection of the conversions I kept searching for: JSON → CSV and back Markdown → HTML and back JSON → YAML XML → JSON CSV → JSON HTML → PDF Nothing fancy. No account needed. Everything runs directly in your browser — no data is sent anywhere, nothing is saved on a server. Why Astro I also wanted to try Astro for a while. I kept hearing it was great for content-heavy sites because of how little JavaScript it ships by default. A converter site felt like the perfect use case — mostly static pages with one interactive tool on each. Since Astro works with React components, it wasn't a big adjustment once I got the basics down. You write your page layout in .astro files and drop in React components where you need interactivity. Clicked pretty quickly. The result — 100 on Lighthouse across the board. The pages load instantly because there's barely anything to load. Hosting Deployed on Cloudflare Pages (now cloudflare workers). Free tier. The only thing this site costs me is the domain name. Try It quickconvert.dev Runs in your browser, no account, no data saved anywhere. I'm planning to keep adding more conversions — the everyday ones that developers reach for and end up Googling every single time. Maybe we can make something that becomes a tab that just stays open. Feedback welcome — especially if a conversion you need isn't there yet.

If you've ever tried building a sports betting application, odds tracker, arbitrage scanner, value betting tool, or sports analytics dashboard, you've probably experienced the same thing: You start with the exciting part. The idea. The algorithm. The UI. The business logic. And then reality hits. The Hidden Problem Nobody Talks About Most developers assume the hardest part of a betting-related project is the prediction model or arbitrage logic. In practice, the real challenge is data infrastructure. Before your project can calculate anything, you need: Live events Accurate odds Multiple bookmakers Consistent market structures Historical updates Reliable refresh rates And suddenly your "weekend project" turns into a full-time data engineering job. The Scraping Trap Most developers begin by scraping bookmaker websites. At first it seems simple: Open DevTools Find the API request Parse the response Save the data Done, right? Not quite. Within a few weeks you'll likely encounter: Changed endpoints Rate limits Cloudflare protection Different JSON formats Missing markets Broken parsers Increased maintenance costs Instead of improving your product, you're fixing scrapers. Again. And again. And again. Every Bookmaker Speaks a Different Language Let's say you want to compare odds from five sportsbooks. You quickly discover that every provider structures data differently. One bookmaker might return: { "home" : "Liverpool" , "away" : "Arsenal" } Another might return: { "team1" : "Liverpool" , "team2" : "Arsenal" } A third one could use: { "participants" : [ "Liverpool" , "Arsenal" ] } Now multiply that problem across: dozens of bookmakers hundreds of leagues thousands of events You end up spending more time normalizing data than building features. Real-Time Data Changes Everything Many projects work perfectly during testing. Then live data arrives. Odds can move multiple times within a minute. If your system refreshes too slowly: arbitrage opportunities disappear alerts become

If you write Kotlin Multiplatform code that involves integer division, you may have already hit this: the exact same expression behaves completely differently depending on which platform compiles it. 🐛 The problem Take this innocuous expression: val quotient = 12 / 0 val remainder = 12 % 0 On JVM and Native , both lines throw an ArithmeticException . That is the behavior most Kotlin developers expect and design around. On JavaScript , both lines execute without any exception and silently return 0 . Here is a concrete illustration drawn directly from the Kotlin test suites for each platform: // Kotlin/JS check ( 12 / 0 == 0 ) // passes — no exception check ( 12 % 0 == 0 ) // passes — no exception // Kotlin/JVM and Kotlin/Native val quotient : Result < Int > = runCatching { 12 / 0 } val remainder : Result < Int > = runCatching { 12 % 0 } check ( quotient . exceptionOrNull () is ArithmeticException ) // passes check ( remainder . exceptionOrNull () is ArithmeticException ) // passes Summary table: Expression JVM / Native JavaScript 12 / 0 ArithmeticException 0 12 % 0 ArithmeticException 0 🤔 Why it happens On Kotlin/JS, Int values are represented as JavaScript numbers, and 12 / 0 evaluates to Infinity while 12 % 0 evaluates to NaN . Kotlin/JS truncates Int arithmetic to 32 bits using JavaScript's | 0 operator, and per the ECMAScript ToInt32 conversion, both Infinity | 0 and NaN | 0 evaluate to 0 — so the division-by-zero result silently becomes 0 , with no exception thrown. JVM and Native follow Java's long-standing contract: integer division by zero is always an ArithmeticException . The practical consequence is that any guard you write and test on JVM — a try/catch(ArithmeticException) or a pre-condition check that relies on an exception — is silently bypassed when the same code runs on JS. No compile error, no warning, just a wrong result. ✅ The fix: Integer from Kotools Types 5.1.1 The Integer type in Kotools Types explicitly checks for a zero divisor before delegat

Did chatbot abandon mental health guardrails when a vulnerable user pushed back?

Gaming handhelds are amazing. They make it so much easier to fit all kinds of games into my day. Sadly, they’re less affordable than they’ve ever been — due to an unprecedented, AI-fueled shortage of memory chips, an unforced oil crisis, rampant inflation, fallout from tariffs, and more. But that’s not going to stop you. […]

These nifty tools combine the ease of jotting notes by hand with the power of saving them digitally.

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The idea of running a local LLM (Large Language Model) has always appealed to me, especially concerning data privacy and cost control. However, when I first delved into this, I realized through my own experiences how misleading market claims like "a few GB of RAM is enough" can be. In real-world scenarios, running a 70B parameter model with 8GB of VRAM is only possible with significant optimizations, which come with certain trade-offs. In this post, I will share my experiences, the problems I encountered, and the solutions I found, from hardware selection to optimization techniques for local LLMs. My goal is to offer a concrete, practical, and "good enough" perspective to anyone interested in this field. As we begin, we must remember that VRAM is the most critical part of this equation. VRAM: The Heart of Local LLMs and Capacity Limits At the core of running an LLM locally is keeping the model's weights in the GPU's VRAM. As the model size grows, the amount of VRAM it needs naturally increases. For example, a 7 billion parameter (7B) model in 16-bit float (FP16) format requires about 14GB of VRAM, while a 70B parameter model can demand up to 140GB. These values are far beyond the hardware owned by an average user. While working on AI-powered operations for my side product and a production planning model for a client project, I had the opportunity to experiment with models of different sizes. I clearly saw that there can sometimes be differences between theoretical VRAM requirements on paper and practical usage, especially as the context window grows. A 7B model, with a common quantization like Q4_K_M, can generally run with around 5-6GB of VRAM. However, for a 13B model, this value jumps to 8-10GB, and for a 70B model, it can soar to 40-50GB. This also varies depending on parameters like context window and batch size. 💡 VRAM Monitoring Tips You can monitor the real-time status of your GPU and VRAM with the nvidia-smi command. Using watch -n 1 nvidia-smi to update VR

Lyft details a new pickup experience to improve reliability in gated communities, where 25–30% of rides face routing and access challenges. The system uses mapping signals, boundary detection, and routing improvements to reduce cancellations and coordination overhead between riders and drivers, highlighting how real-world constraints drive evolution in geospatial systems. By Leela Kumili

I played the ultimate game of strategy on a variety of smart chess boards to find the best for online and in-person matches.