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I have just finished writing API Artisan: A Guide to Building APIs with Laravel , and I am giving it away for free. Before you commit to 300-odd pages, let me give you the short version: the tips, patterns, and small decisions that separate an API that technically works from one that developers are genuinely happy to depend on. None of this needs more hardware, a different framework, or a bigger team. It needs you to point your attention at the right things. These are the ones I keep coming back to. Start by measuring the right thing Ask most teams how they know their API is good and you get a single question back: does it work? Can I hit this endpoint and get a response? That question is necessary, and it is nowhere near enough. The question I want you to ask instead is whether your API is liveable with. Can a developer read your docs, understand your auth model, make a successful request, and handle an error without contacting support, trawling a forum, or guessing what a status code is trying to tell them? The gap between "works" and "liveable with" never shows up in a sprint retro, but it shows up everywhere else: in support volume, in integration timelines that overrun, and in the quiet moment a developer decides to build around your API rather than with it. Everything else in the book hangs off one mindset shift: an API is a product. It has users. Treat it as an implementation detail and it will behave like one. It will change without warning when your internals change, and it will be inconsistent because different people wrote different parts on different days with different conventions. Write the contract before the code The natural way to build an endpoint is to write the handler, return some data, and document it afterwards if there is time. It feels efficient, and in the short term it is. The problem is what it produces: a contract that was never designed, only discovered. Let me show you the trap, because I have watched it catch good developers. You have

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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

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