标签：#Web

How a PHP SDK Can Save You Hundreds of Lines of API Integration Code Most APIs provide documentation, examples, and maybe even a Postman collection. That's usually enough to get started. But once your application grows, you'll quickly discover that working directly with HTTP requests introduces a surprising amount of repetitive code. You end up writing the same things over and over: Authentication headers Request serialization Response parsing Error handling Pagination logic DTO mapping This is exactly why SDKs exist. In this article, we'll look at how a PHP SDK can simplify API integrations and reduce maintenance costs over time. The Hidden Cost of Direct API Calls Let's imagine you're integrating a URL shortening API. A typical implementation might look like this: $client = new GuzzleHttp\Client (); $response = $client -> post ( 'https://example.com/api/links' , [ 'headers' => [ 'X-Api-Key' => $apiKey , 'Content-Type' => 'application/json' , ], 'json' => [ 'url' => 'https://example.com' ] ] ); $data = json_decode ( $response -> getBody () -> getContents (), true ); This doesn't seem bad. Now repeat it for: Create link Update link Delete link Get link List links Create group Update group Get profile Eventually your codebase becomes filled with API boilerplate. The business logic becomes harder to see because it's buried under HTTP implementation details. What a Good SDK Does A well-designed SDK abstracts repetitive tasks and exposes a clean programming interface. Instead of dealing with HTTP requests directly, developers work with resources and objects. For example: $link = $client -> links () -> create ([ 'url' => 'https://example.com' ]); This is easier to read and easier to maintain. The SDK becomes responsible for: Authentication Request building Validation Serialization Response mapping Exception handling Consistent Error Handling One common problem with raw API integrations is inconsistent error handling. Without an SDK, every request may need its own validat

Your API hands out 50 records at a time across 400 pages. You need all of them. You do not need them all at once. Here's a very familiar situation that shows up constantly on the backend. Some API returns data in pages, 50 or 100 records at a time, and you need to walk every page: sync them to your database, export them to a file, run a report. The endpoint gives you a cursor or a page number and you keep asking until there's nothing left. The way most of us write it the first time looks like this: async function getAllRecords () { const all = []; let cursor = 0 ; while ( cursor !== null ) { const { records , nextCursor } = await fetchPage ( cursor ); all . push (... records ); cursor = nextCursor ; } return all ; } const everything = await getAllRecords (); for ( const record of everything ) { process ( record ); } It works. At four hundred records it's fine. The trouble starts when the dataset grows, and it has three separate problems hiding in it. It holds the entire dataset in memory before you touch a single record. It's all or nothing: if page 380 fails, you've thrown away the 19,000 records you already fetched . And it's eager. You can't start processing record one until the very last page has landed , even if all you wanted was the first ten. There's a shape in JavaScript built for exactly this, and if you read the first two posts in this series you already have both halves of it. Two ideas you've already seen In the CSV post , we pulled rows out of a huge file one at a time with a generator, so the file never fully loaded into memory. Lazy. Pull-based. You ask for the next row, you get the next row, nothing more. In the async/await post , we saw that a generator can pause at a yield and resume later.A generator can hold its place across an asynchronous gap. Put those together. A generator that pulls data lazily, and can pause to await something between pulls. That's an async generator, and it's the natural tool for walking a paginated API. You pull records

How I Built My Indie AI Stack — A Practical Guide for 2026 A few months ago I hit a wall. I was bootstrapping a side project, burning through API credits way faster than my wallet could handle, and honestly questioning whether shipping a product as a solo dev in 2026 was even realistic anymore. The big-name providers were charging me an arm and a leg, and I kept reading about indie hackers who somehow made it work. So I went down a rabbit hole — tested dozens of models, tracked every dollar, and built what I now call my "indie AI stack." Let me show you exactly what I landed on, why it works, and how you can copy it. Why This Stack Exists (And Why I Almost Gave Up) Here's the thing nobody tells you when you're starting out: the default path — just throwing GPT-4o at everything — will quietly drain your runway. When you're an indie dev, every cent matters. I remember watching my first invoice roll in and doing actual math on whether I could sustain this for six months. The answer was no. So I started experimenting. I tested 184 different AI models (yes, really) through Global API, ran them against real workloads from my product, and started measuring not just quality but cost-per-useful-output. That's the metric that actually matters. The result? I landed on a stack that delivers 40-65% cost reduction versus just slamming GPT-4o on every request. Quality stayed comparable — sometimes better. Average latency sits at around 1.2 seconds with 320 tokens per second throughput. And the whole setup took me under 10 minutes. Let me walk you through it. The Models That Actually Made The Cut After weeks of testing, I narrowed my shortlist down to five models that form the backbone of my indie stack. Here's the pricing breakdown I'm working with today: DeepSeek V4 Flash — $0.27 input / $1.10 output, 128K context DeepSeek V4 Pro — $0.55 input / $2.20 output, 200K context Qwen3-32B — $0.30 input / $1.20 output, 32K context GLM-4 Plus — $0.20 input / $0.80 output, 128K context GPT

Eight years ago (a geological epoch or two ago in Internet terms) Nicholas Jitkoff released itty.bitty.site - a website which could render whole websites just based on what was in the link, something like: itty.bitty.site?SOMEBASE64ENCODEDVALUE== et voilà! Free web-hosting if you could make it fit ;) At the time, I was rather obsessed with qr-codes thanks to developing QRGoPass and was working with aster plots a lot, so I developed an app that could fit into a qr-code! Today itty.bitty.site no longer exists so I can't do that any more... But I did make it 80% smaller without "cheating" and using modern CSS instead of d3.js ;)

Every time you use an online PDF converter, your file travels to a server somewhere — processed by a company you don't know, stored temporarily on hardware you don't control. For a random meme? Fine. For a payslip, a contract, or a medical document? That's a real privacy problem. What I built ConvertiZen is a document converter that runs 100% in the browser using WebAssembly and modern JS APIs. Your file never leaves your device. Ever. Verify it yourself: open DevTools > Network tab > run a conversion. Zero outgoing requests containing your file data. What it supports 40+ format pairs: PDF ↔ Word, JPG, PNG, Text, HTML, Markdown Excel ↔ CSV, JSON, HTML Images: JPG ↔ PNG ↔ WebP, GIF/BMP → PDF JSON ↔ CSV, Markdown ↔ HTML, XML → JSON How it works PDF.js for PDF parsing docx library for Word generation SheetJS for Excel/CSV Canvas API for image conversions Everything client-side — no server required Pricing 3 free conversions/day, no account needed. Beyond that: €0.69/conversion, €4.99 for a pack of 10, or €4.99/month for unlimited Premium. Feedback welcome What formats are missing? What would make you trust a browser-based tool over a server-based one? 👉 https://convertizen.netlify.app

Converting Word documents to PDFs on the server is the classic approach: upload the file, run LibreOffice or a cloud API, send the result back. But that means your users’ resumes, contracts, and reports touch your infrastructure. I wanted something simpler for en.sotool.top : pick a .docx file in the browser, preview the parsed content, and download a PDF. No server involved. Here is how I built it with Vue 3, mammoth , and html2pdf.js . Why Client-Side? The main reason is privacy. Resumes, contracts, tax documents — users do not want them on a stranger’s server. Client-side conversion also means: No upload bandwidth limits No file size caps from your server No storage to clean up Works offline after the page loads The trade-off is that very complex documents are limited by the browser’s rendering capabilities. For typical office documents, that is fine. The Stack Vue 3 — UI, file handling, and reactive state mammoth — Parse .docx files into clean HTML html2pdf.js — Render the HTML into a PDF using html2canvas + jsPDF Native File API — File selection npm install mammoth html2pdf.js Loading the Word Document The first step is reading the uploaded .docx file into an ArrayBuffer , then converting it to HTML with mammoth . import mammoth from ' mammoth ' ; async function convertDocxToHtml ( file ) { const arrayBuffer = await file . arrayBuffer (); const result = await mammoth . convertToHtml ({ arrayBuffer }); return result . value ; } mammoth intentionally produces simple, clean HTML. It ignores complex formatting like text boxes and embedded fonts, which makes the output predictable. I keep the HTML in a reactive ref and render it in a preview panel: < template > <div ref= "previewRef" class= "word-preview" v-html= "htmlContent" ></div> </ template > < script setup > import { ref } from ' vue ' ; const htmlContent = ref ( '' ); const previewRef = ref ( null ); </ script > Generating the PDF Once the user is happy with the preview, html2pdf.js turns the preview element

At this time, AI is still a business that largely survives on valuation rather than profitability. The narrative surrounding artificial intelligence is driven as much — if not more — by financial speculation as by technological progress. This makes the twin narrative of an “AI infrastructure boom” essential. Ed Zitron has become well known for challenging this story. In reality, such an infrastructure boom is difficult to sustain when the underlying economics remain deeply unprofitable. Now, let us take a moment to reflect on the danger of relying — for our businesses, and worse, for our civilization — on a bubble that could burst at any moment, much like the dot-com bubble. Entire industries are restructuring themselves around assumptions that may ultimately prove irrational, even disastrous. The illusion and danger of replacing engineers There is a dangerous idea circulating in the world, born from the union of greed and ignorance: that software engineers have become obsolete. We no longer need them! Of course, someone who does not know how to write code cannot evaluate code quality. For such a person, any piece of code that works is just as good as any other piece of code that also works. They may see a functional demo and hastily conclude that AI can entirely replace software developers. An _experienced _engineer sees something different: brittle architecture, code with absurd or duplicated logic, security flaws, poor maintainability, and code that often collapses under real-world complexity. To the untrained eye, AI-generated code frequently looks convincing, while it may host invisible vectors of disaster. Hallucinations in software development are not harmless mistakes; they can become production bugs, security vulnerabilities, and eventually catastrophic business failures. The problem is not that AI writes code. The problem is that we seem to be heading toward an era in which we no longer fully understand the code powering our civilization. And because of th

If you have made it this far in 100 Days of Solana, you have been working in JavaScript and on the command line. You have been calling RPC methods, building instructions, signing transactions, and reading and writing account data in JavaScript, and most recently minting and sending tokens and NFTs from the CLI. Either way, you have been driving Solana with tools that let you assign a value and move on with your life. Soon the ground shifts. You are going to open a file called lib.rs , and it is going to be Rust, and for a day or two it is going to feel like you forgot how to program. That feeling is normal, it is temporary, and it is not a sign you are in the wrong place. Here is the thing nobody says out loud: you do not need to learn all of Rust to write Solana programs. Rust is a big language with a steep reputation, but the slice of it that shows up in an Anchor program is small and repetitive. You will see the same handful of patterns on almost every line. Learn those patterns and the wall turns back into a floor. This post is that handful. Not a Rust course, just the parts you need to read your first Anchor program and understand what every line is doing. Next week we start Arc 9, the Anchor introduction, where this all becomes real. This week is about making the language stop being scary before you get there. Why it feels like a wall JavaScript is dynamically typed and garbage collected. You write const x = 5 , you never tell anyone it is a number, and when you are done with it the runtime quietly cleans up. The language trusts you and sorts out the consequences at runtime, which is why a typo surfaces as undefined is not a function three minutes into a demo. Rust is the opposite philosophy. It is compiled and statically typed, so every value has a type the compiler knows about before the program ever runs, and it has no garbage collector, so it tracks who is responsible for every piece of memory through a system called ownership. The trade is blunt: Rust mak

I am a solo learner. I started coding last year with the help of AI and sometimes without any tutorials or courses. At first, I thought this journey would be easier. But soon I realized something important — no AI or tool can fully solve the real problems I was facing as a developer. I used AI a lot. It explained things with confidence and even provided code. But when I ran that code in my terminal, many times it didn’t work. That’s when I understood something important: AI can guide, but it cannot replace understanding. After facing these issues, I changed my way of learning. Instead of blindly trusting AI, I started: Finding real open-source projects Studying how they were built Listing important topics from those projects Reading documentation carefully Asking AI to explain specific lines of code This helped me understand real-world code better. From this learning journey, I realized something: I should also build my own open-source projects. At first, I believed that creating a powerful project could automatically bring attention and users. But I was wrong. I made a mistake — I was not active on any platform. I was just coding inside VS Code, without communication or sharing my work anywhere. Then I realized: Being a developer is not only about coding. Visibility and communication are also important. After that realization, I started being active on platforms like Dev.to, LinkedIn, and other developer communities. I started posting my work and sharing my progress. Even though I didn’t get many comments, I started getting reactions and engagement. That small feedback gave me motivation. From this journey, I learned something important: Open source is not only about code. It is about helping other developers, sharing knowledge, and being consistent and visible. A developer should not only code silently but also participate in the community. Now I understand that coding is only one part of being a developer. Community, communication, and consistency are equally imp

Few lines of code look more innocent than this: retry ( 3 ) It feels responsible. Professional. Resilient. After all, networks fail. Servers become unavailable. Databases occasionally time out. Retrying seems like the obvious solution. And sometimes it is. But after enough years building production systems, I've become convinced of something: Retry is one of the most dangerous keywords in software. Not because retries are bad. Because retries amplify everything. Good systems become more reliable. Bad systems become disasters. The problem is that many developers treat retries as a reliability feature when they're actually a distributed systems feature. And distributed systems are where simple ideas go to become complicated. Why Retries Exist Imagine: await fetch ( " /api/users " ); The request fails. Maybe: Network hiccup Temporary database issue Load balancer restart Service deployment The operation might succeed if attempted again. So we write: retry ( 3 ) Seems reasonable. And in many cases: It Works Which is why retries become popular. The Dangerous Assumption Most developers unconsciously assume: Failure = Operation Did Not Execute Unfortunately that's not always true. A request can: Execute Successfully ↓ Response Never Arrives From the client's perspective: Failure From the server's perspective: Success Now a retry becomes dangerous. The Double Payment Problem Imagine a payment service. await chargeCard ( order ); The card processor successfully charges: $100 The response is lost due to a network issue. Client sees: Request Failed and retries. await chargeCard ( order ); again. Now: Charge #1 = Success Charge #2 = Success The customer paid twice. Nobody wrote bad logic. The retry created the bug. The Email Storm Problem Consider: await sendWelcomeEmail ( user ); Email provider accepts the message. Response times out. Application retries. await sendWelcomeEmail ( user ); again. Customer receives: Welcome! Welcome! Welcome! Welcome! Support ticket created. Marke

Everyone's debating whether coding is dead. I actually do this job.. with AI writing code beside me for most of my working hours. Here's what the headlines get wrong. Open your feed right now and you'll find the same headline in a dozen costumes: "Why AI will replace 80% of software engineers by 2026." "Is coding dead?" "Should you still learn to code?" It's the most-clicked anxiety in tech, and it's everywhere for a reason, it taps a real fear about real careers. But here's the thing about almost every one of those posts: they're written from the sidelines. Predictions about a job by people who don't do it. I'm writing this from the other side. I'm an engineer, and I drive AI coding agents every single day. They read code, write changes, run tests, and open reviews for most of my working hours. So when someone asks "should you still learn to code in 2026?" , I'm not guessing. Here's my honest answer: Yes. Absolutely. But the job you're learning for has quietly become a different job and almost nobody is telling you which one. The hype isn't entirely wrong Let me start by giving the doomers their due, because pretending the shift isn't real would make me exactly the kind of person I'm criticizing. The productivity jump is genuine, and it's not subtle. Industry surveys in 2026 put the share of new code that's AI-assisted somewhere north of 40%, and developers using these tools self-report double-digit speedups on routine work. That matches my experience. The agent now handles: Boilerplate and glue code —-> the stuff I used to type on autopilot, gone in seconds. First drafts —-> "scaffold something that does X" gets me 80% of the way instantly. Syntax recall —-> I stopped breaking focus to look up things I half-remember. Tedious refactors —-> rename-this-everywhere, migrate-this-pattern, done fast. and all the kludgy things that I dread to do. If your mental image of "coding" is typing syntax into an editor , then yes.. a big chunk of that is being automated. The vira

Google recently announced that WebMCP is entering origin trials in Chrome 149. The new WebMCP standard proposal lets sites expose tools (e.g., JavaScript functions and HTML forms) to in-browser AI agents, which can thus reliably simulate user actions instead of resorting to possibly expensive (e.g., on-screen reading) and often unreliable guesswork (e.g., DOM scraping). By Bruno Couriol

If your Magento store still depends on the old USPS Web Tools integration, you should assume your shipping rates are either already broken or one change away from breaking. That sounds dramatic, but it is the practical reality we have been seeing. USPS has moved away from the old Web Tools model and toward REST API v3 with OAuth 2.0 authentication. Magento's legacy USPS integration was built for a different era. For merchants, the symptom is simple: rates stop showing up, return inconsistently, or fail under conditions you did not use to worry about. For Magento developers, the reason is also simple: the built-in carrier module is not designed for the current USPS authentication and request model. This article explains what changed, why core Magento falls over here, how to migrate cleanly, and what to watch for whether you choose an extension or a custom build. What changed: USPS Web Tools is not the same platform anymore Historically, Magento's USPS integration talked to Web Tools-style USPS endpoints: structured shipping requests, legacy authentication, and XML responses. That is not the model USPS wants merchants using now. The modern USPS stack is based on: REST API v3 endpoints OAuth 2.0 for authentication Different request and response payloads Different onboarding and credential management patterns That shift matters because it is not just a URL update. It changes authentication, token handling, and request structure. In practical terms, a migration now means: Getting the right USPS developer credentials Exchanging those credentials for OAuth access tokens Updating the carrier request layer to use REST payloads Mapping the new response format back into Magento shipping methods If you skip any of that and try to "patch" the old module with endpoint changes, you are going to waste time. Why Magento 2's built-in USPS module no longer works Magento's built-in USPS module was not architected around OAuth-backed REST API calls. It expects a legacy carrier contract

A couple of years ago, I built a custom flashcard app. I had a huge list of words and sentences in Japanese that I collected in an Excel file. I wanted an app that could easily take them and display them on flashcards. The flashcard app was useful, but the main issue was that I could only use it on my laptop. This meant that when I wasn't home, I had no access to it. I made some updates so that I could deploy it to Azure and now I can use it on the train or at the park. I wanted to share the app and lessons learned during development. What It Does The app is a straightforward spaced repetition flashcard tool. You create collections, fill them with cards (front/back/optional notes), and review them. After each card you rate your recall: Button Meaning Easy Remembered without effort Good Remembered correctly Hard Remembered with difficulty Again Forgot (resets to day 1) Ratings feed the SM-2 algorithm, which is the same algorithm as other popular spaced repetition apps like Anki. Cards that are easy get pushed further and further into the future. Cards that are difficult will come back sooner. After a while, you're just reviewing what you actually need to review. There's also a 45-second timer per card. If it expires before you complete the card, it automatically counts as Again (Resets to day 1). Before the timer, I found it easy to lose focus or open another tab and forget about the current card. This has helped me stay focused for longer and stay on this task. The CSS is specifically designed to be mobile friendly. The Tech Stack Frontend: Blazor WebAssembly (.NET 10) Backend: ASP.NET Core minimal API (.NET 10) Database: Azure SQL (Basic DTU tier) Hosting: Azure Static Web Apps (frontend) + Azure App Service F1 free tier (backend) I mostly use C# at work, so Blazor WASM was a natural fit. The whole app shares models and flows together without jumping between languages. Importing Cards from Excel This Excel import function is one of the main reasons I made this app.

We've been a Magento agency in Chicago since 2008. When Hyvä Themes hit the ecosystem, we were skeptical—another theme promise. Then we measured Core Web Vitals on client stores and the case became obvious: Hyvä is the most practical path to a fast Magento storefront without a full replatform. This is the migration framework we use at Towering Media for US and Canadian merchants moving off Luma (or aged custom frontends) onto Hyvä. Why Hyvä now (not next year) Google's CWV thresholds affect ad quality and organic visibility. Luma checkout and catalog pages often ship 1.5–2+ MB of JavaScript before you add analytics, chat, and personalization. Hyvä replaces Knockout/RequireJS on the storefront with Alpine.js and Tailwind. Typical results on our projects: 50–70% less frontend JS on category and product pages LCP improvements of 1–3 seconds on mobile field data (highly variable by hosting and images) Lower maintenance — fewer JS conflicts between theme and extensions Delaying migration means paying for performance twice: once in emergency fixes, again in the eventual theme project. Phase 1: Discovery (1–2 weeks) Extension audit List every module that touches the frontend: bin/magento module:status | grep -v "Module is disabled" Flag anything with view/frontend , RequireJS , or Knockout in: Layered navigation and search Checkout and cart Page Builder widgets Blog and CMS enhancements Hyvä maintains a compatibility module ecosystem; unsupported extensions need replacements or custom Hyvä templates. Towering Media includes extension compatibility mapping in every Hyvä migration engagement. CWV baseline Capture before metrics from: Google PageSpeed Insights (origin-level) Chrome UX Report for key templates: home, category, product, cart Real-user monitoring if the client has it (GA4, SpeedCurve, etc.) Store screenshots. Stakeholders forget how slow the old site felt. Business constraints Document: Peak seasons (do not launch in November without war room

All tests run on an 8-year-old MacBook Air. All results from shipping 7 Mac apps as a solo developer. No sponsored opinion. After 7 Tauri apps, I type the same commands constantly. Here's the reference I wish existed when I started. Project setup # New project npm create tauri-app@latest # Add to existing project npm install --save-dev @tauri-apps/cli npx tauri init Development # Dev mode (hot reload) npm run tauri dev # Dev with specific log level RUST_LOG = debug npm run tauri dev # Dev with backend logs visible npm run tauri dev 2>&1 | grep -v "^$" Building # Standard build npm run tauri build # Universal binary (Intel + Apple Silicon) npm run tauri build -- --target universal-apple-darwin # Debug build (faster, no optimization) npm run tauri build -- --debug Plugins npm run tauri add global-shortcut npm run tauri add fs npm run tauri add shell npm run tauri add notification This updates both Cargo.toml and the plugin registration. Faster than doing it manually. Permissions (tauri.conf.json) { "app" : { "security" : { "capabilities" : [ { "identifier" : "main-capability" , "description" : "Main window capabilities" , "windows" : [ "main" ], "permissions" : [ "fs:read-all" , "fs:write-all" , "shell:execute" , "global-shortcut:allow-register" ] } ] } } } Tauri v2 requires explicit permission declarations. If a command silently does nothing, check permissions first. Common Rust patterns // Get app data directory let data_dir = app .path () .app_data_dir () .unwrap (); // Emit event to frontend app_handle .emit ( "event-name" , payload ) .ok (); // Get window let window = app .get_webview_window ( "main" ) .unwrap (); // App state app .manage ( MyState :: new ()); let state = app .state :: < MyState > (); Notarization (macOS) # Submit for notarization xcrun notarytool submit app.dmg \ --apple-id YOUR_APPLE_ID \ --team-id YOUR_TEAM_ID \ --password YOUR_APP_PASSWORD \ --wait # Staple after notarization xcrun stapler staple app.dmg Debugging # Check what's in the bundle

I have been frustrated with YouTube for a while. Not the content, but the everything around it. The homepage full of bait, the auto-play into things I didn't ask for, the Shorts that hijack your scroll, the recommendations that somehow know exactly what will keep you there longest. So I built NoSuggest. What it is A YouTube feed reader that shows you only the channels you follow, nothing else. No algorithm, no recommendations, no Shorts, no homepage, no auto-play, no endless side cards of videos. You add a channel, it fetches their latest videos, done. It lives at nosuggest.com and installs as a PWA on any device — iPhone, Android, desktop — straight from the browser. No app store. The interesting technical constraint: one HTML file The entire app is a single index.html. No account setup, no sign-in, no data collection. Everything that needs to persist — your channel list, saved videos, settings — lives in localStorage. No search history. No watch history. No "you might also like." No trending section. No notification badges designed to create anxiety. No dark patterns anywhere. Every time I was tempted to add something convenient, I asked: does this serve the user's intention, or does it serve engagement? If it was the latter, it didn't make the cut. Try it nosuggest.com — Source Available here , free forever. Curious what others think about this as useful. Thank you.

Originally written for bulldo.gs — republished here with the canonical link pointing home. I'm running a script that processes a large spreadsheet and it keeps dying with "Exceeded maximum execution time" before it finishes. // Checkpoint-resume pattern for long-running sheet jobs function processInBatches () { var props = PropertiesService . getScriptProperties (); var startRow = parseInt ( props . getProperty ( ' lastRow ' ) || ' 2 ' , 10 ); var sheet = SpreadsheetApp . getActiveSpreadsheet (). getActiveSheet (); var lastDataRow = sheet . getLastRow (); var BATCH = 200 ; var SAFE_MS = 5 * 60 * 1000 ; var started = Date . now (); var endRow = Math . min ( startRow + BATCH - 1 , lastDataRow ); var data = sheet . getRange ( startRow , 1 , endRow - startRow + 1 , 5 ). getValues (); for ( var i = 0 ; i < data . length ; i ++ ) { if ( Date . now () - started > SAFE_MS ) { props . setProperty ( ' lastRow ' , String ( startRow + i )); return ; } // process data[i] here } if ( endRow >= lastDataRow ) { props . deleteProperty ( ' lastRow ' ); deleteTrigger_ (); } else { props . setProperty ( ' lastRow ' , String ( endRow + 1 )); } } The 6-minute wall is per-execution, not per-task Apps Script enforces a hard 6-minute execution time limit per run, regardless of whether you're on a free account or a Workspace account (which bumps the limit to 30 minutes, but the same cliff exists). The error doesn't mean your logic is wrong; it means one continuous call to your function took too long. The fix is to stop thinking of your job as a single execution and start thinking of it as a pipeline of short runs. The first time I hit this, I wasted an afternoon trying to speed up the loop. Marginal gains didn't move the needle because the data volume was the real problem — 4,000 rows at one Sheets API call per row will always breach 6 minutes. The correct frame is: how do I save where I stopped and pick up there next run? Saving and restoring a cursor with PropertiesService PropertiesServic

Hey tech family! 👋 If you’ve noticed your favorite Chrome extensions acting a bit differently lately or if you're a developer currently sweating over a massive codebase rewrite you are experiencing the era of Manifest V3 (MV3) . 🤖 Google has officially pushed the web ecosystem forward by deprecating Manifest V2, making MV3 the absolute standard for how browser extensions behave. But why is this happening, what actually changed, and why is the internet so divided over it? Let’s break it all down in plain English! 👇 🧐 What Exactly is Manifest V3? Think of a "Manifest" as the blueprint file ( manifest.json ) that tells the browser exactly what an extension is, what files it uses, and what permissions it needs to run. Manifest V3 is Google's major architectural overhaul of this system. Its core mission sounds great on paper: improve user privacy, beef up security, and boost browser performance . However, achieving those goals meant rewriting the core rules of how extensions interact with your browser. 🛠️ The Biggest Changes & New Features MV3 isn't just a small patch; it fundamentally alters the underlying extension engine. Here are the headline shifts: Goodbye Background Pages, Hello Service Workers! 💤 In MV2, extensions used hidden, persistent background pages that ran 24/7, hogging your computer's RAM even when you weren't using them. MV3 replaces these with Service Workers. They are event-driven meaning they wake up, execute a task (like clicking an extension icon), and go right back to sleep. Hello, free RAM! 🐏 The Ad-Blocker Shakeup: webRequest vs. declarativeNetRequest 🛑 This is the most controversial change. In MV2, powerful extensions like uBlock Origin used the webRequest API to intercept, read, and block network requests in real-time using complex code. MV3 replaces the blocking version of this with declarativeNetRequest . Instead of letting the extension intercept the data, the extension must now hand Chrome a pre-defined list of rules, and Chrome does the b

The first message ever sent across the network that became the internet was not "Hello, world." It was not a grand declaration. It was two letters, transmitted by accident, before the system fell over: LO . That two-letter packet is the ancestor of every connected device, every IoT sensor, and every web request running today. The story of how it happened is also a surprisingly useful lesson for anyone building embedded systems and connected hardware right now. What actually happened on October 29, 1969 On the evening of October 29, 1969, a programmer named Charley Kline sat at a terminal in Leonard Kleinrock's lab at UCLA. His job was simple on paper: log in to a remote computer at the Stanford Research Institute (SRI), roughly 350 miles away, over a brand-new experimental network called ARPANET. The plan was to type the command LOGIN . The remote machine at SRI was set up to auto-complete the rest once it saw the first few characters, so Kline only needed to start typing. He had a colleague on the phone at the Stanford end to confirm each letter arrived. He typed L . Stanford confirmed: "Got the L." He typed O . Stanford confirmed: "Got the O." He typed G - and the SRI system crashed. So the first message ever transmitted over ARPANET was "LO." As Kleinrock later liked to point out, it was an accidental but fitting first word: "LO" as in "lo and behold." About an hour later they fixed the bug and completed the full login, but the historic first packet had already gone out, two letters at a time. Why a crash is the perfect origin story It is tempting to read this as a cute footnote. It is more than that. The very first thing the internet ever did was fail partway through a transaction - and the system was built well enough that the humans on both ends knew exactly how far it had gotten before it died. That is the entire discipline of networked systems in miniature. Connections drop. Remote machines crash mid-request. Packets arrive out of order, or not at all. The n