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Ego’s newest, biggest, self-propelled mower handles larger yards in less time with impressive performance and battery life.

I requested a copy of my data from McDonald’s loyalty program and received an extensive, personalized report that algorithmically predicts my next purchase.

Marketplace sellers often discover image problems too late. A product photo may look fine in an editor, but after uploading it to a marketplace it can become: cropped in search thumbnails too small for zoom previews the wrong aspect ratio for a sales channel risky for Amazon-style main image requirements awkward when reused across Etsy, Amazon, TikTok Shop, Shopify, eBay, or Walmart I wanted a simple preflight step before publishing product images, so I built ListingPic : 👉 https://listingpic.com/ What it does ListingPic is a browser-based marketplace image checker and resizer. You upload a product photo, choose the marketplaces you care about, and get a readiness report covering things like: image dimensions aspect ratio file type file size thumbnail crop risk safe-area positioning marketplace-specific warnings The goal is not to replace manual review. It is to catch obvious image risks before sellers waste time uploading, previewing, deleting, resizing, and re-uploading. Why local-first? A lot of product photos are sensitive: unreleased SKUs private product photography branded assets client images images sellers do not want copied or stored elsewhere So ListingPic processes images locally in the browser. Your images are not uploaded to our server for analysis. That also makes the tool fast for quick checks: drop in an image, review the warnings, adjust before publishing. Current checkers The MVP includes marketplace-focused checks for: general marketplace readiness Etsy image checks Amazon product image checks TikTok Shop image checks There are also entry points for Shopify, eBay, and Walmart workflows. Example use case Imagine you have one product photo and want to reuse it across multiple channels. ListingPic can help answer: Is the image large enough? Will the product be cut off in thumbnails? Is the image close enough to square for a channel that prefers square previews? Is the product too close to the edge? Do I need a separate crop for Etsy or TikTok Shop? D

Retries are normal in distributed systems. A caller may time out after the server commits a decision, a queue may redeliver a message, or a webhook sender may repeat an event. A decision API that treats every request as new can double-charge, duplicate actions, or record conflicting outcomes. Give each business operation a stable key The idempotency key should identify the logical business request, not a network attempt. Store it with a normalized request fingerprint, processing state, outcome, rule version, and response. If the same key arrives with a different payload, reject it rather than returning an unrelated prior result. Handle concurrent duplicates atomically Two workers can receive the same key before either writes a result. Use a unique constraint, transaction, or compare-and-set operation so only one execution owns the request. Other attempts should wait, return an in-progress response, or read the completed outcome according to the API contract. Choose retention from business risk A short cache may stop immediate duplicates but fail when a delayed queue redelivers. A permanent record may create unnecessary storage or privacy burden. Document key expiry and what happens if a key is reused after that boundary. Put side effects behind the idempotent boundary If rule evaluation triggers a message or database write, use a transactional outbox or equivalent pattern so the decision and pending event are committed together. Consumers still need their own deduplication because downstream delivery is often at least once. Return decision provenance Include the decision ID, status, rule artifact version, timestamp, and whether the response was replayed. Do not regenerate a result under a newer rule version for a duplicate key unless the caller explicitly requests a new business operation. Test the failure modes, not only the happy path Cover concurrent duplicates, payload mismatch, worker crash after commit, delayed redelivery, key expiry, and downstream retry. Obs

Building a Laravel application involves much more than writing PHP code. A production application needs to solve a real business problem, handle users and data reliably, survive deployments, remain secure, and continue to be maintainable as requirements change. Laravel provides an excellent foundation for building modern web applications, but the framework is only one part of the development process. A successful Laravel project typically moves through several stages, from understanding the original business idea to deploying, monitoring, and improving the application in production. Here is what that process looks like. 1. Start With the Business Problem Before thinking about controllers, models, databases, or cloud infrastructure, the first step is understanding what the application actually needs to accomplish. A project might begin with a simple request: "We need a customer portal." That's a starting point, but it isn't a specification. What should customers be able to do? Create and manage accounts? Upload documents? Manage subscriptions? Make payments? View reports? Communicate with employees? Receive notifications? Manage multiple users within an organization? These questions start turning an idea into actual application requirements. One of the easiest ways for a software project to become unnecessarily expensive is to begin development before the problem has been clearly defined. Laravel can make development faster, but building the wrong application faster doesn't solve the underlying problem. 2. Define the MVP Once the requirements become clearer, the next step is determining what belongs in the first release. I generally separate features into two categories: What does the application need in order to provide value? and What can be added later? The first category becomes the Minimum Viable Product, or MVP. For example, a new SaaS application might initially require: User registration Authentication Account management Subscription billing The application's

What is a Media Query? A media query is basically a condition in CSS. You tell the browser: IF the screen satisfies this condition, THEN apply these CSS rules. For example: @media (min-width: 768px) { .container { display: flex; } } Meaning: "If the viewport is at least 768px wide, make .container a flex container." So think: IF condition is TRUE ↓ apply these CSS rules 2. Why do we need Media Queries? Because users don't have one fixed screen. Your website could be opened on: 📱 Phone 375px wide 📱 Large phone 430px wide 📱 Tablet 768px wide 💻 Laptop 1366px wide 🖥️ Desktop 1920px wide You don't want to create five completely different websites. Instead: ONE HTML + BASE CSS + MEDIA QUERIES ↓ Responsive website 3. What does "Responsive" mean? Responsive means: The website adapts its layout and appearance according to the available screen/device size. `For example: Mobile [ Card ] [ Card ] [ Card ] [ Card ] but on desktop: [ Card ][ Card ][ Card ][ Card ]` Same HTML. CSS changes the layout. 4. The basic Media Query syntax The basic structure is: ``` @media media-type AND (condition) { /* CSS rules */ } For example: @media screen and (max-width: 600px) { body { background: lightblue; } } There are three important pieces: @media ↓ screen ↓ and ↓ (max-width: 600px) ↓ { CSS } Let's understand each one. 5. What is @media? @media tells CSS: "I'm about to write a media query." Example: @media (...) { } It's an at-rule in CSS. Similar CSS at-rules you'll eventually see: @media @import @font-face @keyframes For now, just remember: @media = start a media query 6. What are Media Types? You mentioned: screen / speech etc. YES. 👍 A media type tells CSS what kind of output/device the document is being presented on. Common media types include: screen For screens. Examples: 📱 Smartphone 📱 Tablet 💻 Laptop 🖥️ Desktop Example: @media screen and (max-width: 600px) { ... } print For printed documents / print preview. For example, your webpage looks like: Website [Header] [Navigation] [Button

1. O Problema que o Docker Resolve "Na minha máquina funciona." Poucas frases resumem tão bem um problema que atormentou (e ainda atormenta) times de desenvolvimento: um código que roda perfeitamente no notebook do desenvolvedor, mas quebra no servidor de produção — porque a versão do Python é outra, uma biblioteca do sistema está faltando, uma variável de ambiente não foi configurada, ou o sistema operacional simplesmente se comporta de forma diferente. O Docker resolve exatamente isso: ele empacota uma aplicação junto com tudo que ela precisa para rodar — código, dependências, bibliotecas do sistema, variáveis de ambiente, configuração — em uma unidade isolada e portátil chamada container . Essa unidade roda da mesma forma em qualquer lugar que tenha o Docker instalado: no notebook do desenvolvedor, no servidor de CI, ou em produção. Esta é a primeira parte de uma série que vai do zero ao avançado em Docker: hoje o foco é entender o problema que ele resolve, os conceitos fundamentais e como eles se encaixam. 2. Containers vs Máquinas Virtuais A comparação mais comum ao explicar Docker é com máquinas virtuais (VMs), porque ambos resolvem um problema parecido — isolar e empacotar aplicações — mas de formas muito diferentes. Uma máquina virtual virtualiza o hardware inteiro: cada VM roda seu próprio sistema operacional completo (kernel incluso), gerenciado por um hypervisor. Isso garante isolamento forte, mas tem um custo alto: cada VM consome centenas de MBs a alguns GBs de disco e memória só para o SO, e leva de dezenas de segundos a minutos para inicializar. Um container , por outro lado, virtualiza no nível do sistema operacional: todos os containers em uma máquina compartilham o mesmo kernel do host, mas cada um enxerga seu próprio sistema de arquivos, processos e rede isolados — usando recursos do kernel Linux como namespaces (isolamento de visão) e cgroups (limites de CPU/memória). O resultado é que containers são muito mais leves: alguns MBs a poucas centenas

If you build a full-screen mobile editor as a position: fixed overlay with a fixed toolbar on top and a nav bar on the bottom , iOS Safari will happily scroll your entire chrome off-screen the moment the soft keyboard opens — but only when the content is short . The fix isn't a JavaScript viewport dance. It's one line: .editor .ProseMirror { padding-bottom : 60vh ; } Give the inner scroll container something to scroll , and iOS keeps the scroll inside it instead of falling back to scrolling the document (which drags your "fixed" elements along). No html / body locking required. I hit this while building the mobile editor for PenPage , a local-first WYSIWYG markdown notes app (React + TipTap/ProseMirror). Everything below is verified on a real iOS device. The setup Picture a mobile note editor that takes over the whole screen: ┌──────────────────────────┐ │ Toolbar (absolute,top) │ ← stays put ├──────────────────────────┤ │ │ │ Editable content │ ← scrolls │ (overflow-y: auto) │ │ │ ├──────────────────────────┤ │ Nav bar (absolute,bottom)│ ← stays put └──────────────────────────┘ The outer container is position: fixed; inset: 0 . The toolbar and nav bar are position: absolute inside it. The middle is the only thing that scrolls. Standard app-shell layout. Works great on desktop and Android. The symptom Tap into the editor, the iOS keyboard slides up, and: Long document (taller than the viewport): perfect. The content scrolls under the keyboard, the toolbar and nav bar stay nailed in place. Short document (shorter than the viewport): broken. Trying to scroll drags the whole screen — toolbar and nav bar included — as if the entire fixed overlay were a normal scrolling page. That "only when short" detail is the whole story. The root cause This is the long tail of WebKit bug #191204 : when the soft keyboard appears, iOS Safari's layout viewport gets shorter than the visual viewport , and the document itself becomes scrollable by the keyboard's height. Worse, in that stat

When I first connected my GitLab CI/CD pipelines to AWS, I used the simplest solution: an IAM user with an Access Key and Secret Access Key stored as GitLab CI/CD variables. It worked. But there was one problem: those credentials were permanent. They had to be stored, protected and eventually rotated. If they were accidentally exposed in logs or compromised, they could remain valid until manually revoked. I wanted a cleaner solution. So I replaced permanent AWS credentials with OIDC federation between GitLab and AWS . The result is simple: GitLab pipelines can access AWS without storing any permanent AWS credentials. In this post, I'll explain how I implemented it, how the authentication flow works, and one important issue I faced when using it with EKS and Terraform. The architecture The authentication flow looks like this: ┌──────────────┐ │ GitLab CI │ └──────┬───────┘ │ │ OIDC token ▼ ┌──────────────┐ │ AWS STS │ └──────┬───────┘ │ │ Temporary credentials ▼ ┌──────────────┐ │ IAM Role │ └──────┬───────┘ │ ├──────────► Terraform │ ├──────────► ECR │ └──────────► EKS Instead of GitLab storing an AWS Access Key, it proves its identity to AWS using a short-lived OIDC token. AWS verifies the token and returns temporary credentials. How does OIDC authentication work? The process can be summarized in five steps: GitLab creates an OIDC token for the CI/CD job. The pipeline sends this token to AWS. AWS verifies that the token really comes from GitLab. AWS checks that the project is allowed to assume the requested IAM role. AWS STS returns temporary credentials. These credentials expire automatically. So there is nothing permanent to store or rotate inside GitLab. Step 1 — Register GitLab as an OIDC provider AWS first needs to trust GitLab as an identity provider. I configured the OIDC provider using Terraform: data "tls_certificate" "gitlab" { url = "${var.gitlab_url}/.well-known/openid-configuration" } resource "aws_iam_openid_connect_provider" "gitlab" { url = var . gi

DASTAN — The Taste of Home 🍲 This is my submission for the Frontend Challenge – Comfort Food Edition, Perfect Landing. What I Built For this challenge, I wanted to create something that felt more like a story than a typical food website. That idea became DASTAN — The Taste of Home . “Dastan” means a story, and the concept behind the project is simple: food is rarely just food. A dish can remind us of a person, a place, a family gathering, or a moment we haven't thought about in years. DASTAN is a visual, editorial-style landing page that explores comfort food from different parts of the world through photography, cultural stories, ingredients, and the people behind the memories. The goal was to make the experience feel warm, premium, and personal from the moment someone opens the page. The Idea The line that shaped the whole design was: Every culture has a taste of home. I wanted the website to communicate that feeling without relying on a traditional recipe-blog layout. Instead, I treated each dish almost like a magazine story. You can discover dishes from different regions, read the story behind them, and explore how food connects people across cultures. The Design I went for an editorial-inspired visual direction rather than a conventional modern dashboard. The design uses: Warm cream backgrounds Deep charcoal sections Muted gold accents Large serif typography Editorial-style food photography Rounded cards Generous spacing Strong visual hierarchy Story-focused content Subtle borders and details I wanted the interface to feel like opening a beautifully designed food magazine. At the same time, I made sure the experience remains comfortable to use on smaller screens. What You'll Find 🌍 Global Comfort Food DASTAN brings together dishes and stories from different parts of the world. Examples include: Tonkotsu Ramen — Japan Hyderabadi Dum Biryani — India Kimchi Jjigae — Korea Lasagna alla Bolognese — Italy Lahori Chicken Karahi — Pakistan Each one is presented as more

TypeScript 6.0 --noPropertyAccessFromIndexSignature : The Flag That Forces Honest API Contracts This article was written with the assistance of AI, under human supervision and review. The Silent Type Hole in Your Codebase Most runtime property access errors stem from index signatures pretending to guarantee properties they don't. Teams define Record<string, T> or { [key: string]: T } for objects where specific properties might not exist, then access those properties with dot notation as if the type system proved their presence. The compiler stays silent. Production crashes follow when the property is undefined. The --noPropertyAccessFromIndexSignature flag eliminates this false confidence. When enabled, TypeScript prohibits dot notation for properties defined only through index signatures. The type system forces bracket notation instead, making the uncertainty explicit at every call site. This distinction is critical—it transforms implicit runtime failures into compile-time enforcement of honest contracts. When developers adopt this flag, the contract becomes explicit. Index signatures signal "this property might not exist" and the syntax enforces that uncertainty. Explicit properties signal "this property is guaranteed" and dot notation confirms the guarantee. The codebase gains honesty. Key Takeaways The --noPropertyAccessFromIndexSignature flag prevents dot notation on properties defined only through index signatures, forcing bracket notation that signals uncertainty. Index signatures ( [key: string]: T ) describe unknown property sets; explicit properties describe guaranteed contracts—the flag enforces this semantic difference. Enabling this flag exposes implicit runtime failures as compile errors, converting production crashes into immediate feedback during development. The migration path involves converting dot access to bracket notation for index-signature properties while keeping dot notation for explicit properties. Combining this flag with --noUncheckedInd

Search "acf_register_block_type to block.json mapping" and you get tutorials. Each one converts a single testimonial block and calls it done. None of them hand you the full key-by-key table: every legacy PHP argument, its block.json destination, and the handful of settings that have no destination at all. I migrated forty-plus blocks across two client themes this year. Some keys moved with zero friction. Others sat in gray areas I only resolved by testing in the block editor and watching what broke. This is the matrix I wish existed before I started. Why the gap exists ACF's own documentation covers acf_register_block_type() on one page and the block.json acf key on another. Both pages are accurate. Neither cross-references the other. A developer migrating a block has to hold both pages open and manually match render_template to renderTemplate , post_types to postTypes , and so on. Miss one, and a block silently loses a feature instead of throwing an error. Three categories of keys make this worse: Renamed keys. Same feature, different casing, different location. snake_case becomes camelCase , and the key moves from the flat settings array into a nested acf object. Relocated keys. Some legacy settings aren't ACF-specific at all. They belong to WordPress's core block registration and move to the top level of block.json, outside the acf object entirely. Orphaned keys. A few legacy settings have no documented block.json equivalent. You either drop the behavior, replicate it with WordPress's native supports API, or handle it in your render template instead. The full matrix Legacy acf_register_block_type() key block.json location Notes name Top-level name Must be namespaced, e.g. acf/testimonial instead of testimonial title Top-level title Direct match description Top-level description Direct match category Top-level category Direct match icon Top-level icon Direct match, including the array form for background/foreground colors keywords Top-level keywords Direct match p

The worst test suite I ever inherited had 400 tests, and I trusted about six of them. The rest were either testing implementation details nobody cared about, duplicating each other, or so tightly coupled to internal function names that a harmless refactor broke thirty tests for no real reason. Reading that codebase taught me more about what not to do than any greenfield project ever has. So when you're starting from zero, the goal isn't "write a lot of tests fast." It's building a suite you'll still trust a year from now. If you're new to this, getting the software testing basics right early matters more than covering everything - learning how to build a first test suite from scratch teaches you what to prioritize in a way that inheriting someone else's bloated suite never will. Here's roughly how I'd approach it. Start with what would actually hurt if it broke Before writing a single test, list the handful of things that would be genuinely bad if they silently broke - checkout completing, auth working, the core thing your product does actually happening. Not every function, not every branch. Just the stuff where a silent failure costs you money, users, or trust. This list is usually shorter than people expect. Five to ten flows for most early-stage products. That's your actual test suite's job in the first few months, not "100% coverage." Unit tests for logic, not for plumbing Unit tests are for things with actual decision-making in them - pricing calculations, validation rules, state transitions, anything where "given this input, is the output correct" is a real question with a wrong answer possible. They're fast, they're cheap, and they should make up the bulk of your suite. Skip unit-testing pure plumbing: a function that just calls another function and returns its result doesn't need its own test. That's the kind of test that pads a coverage number without catching anything real, and it's exactly the kind of test that made that 400-test suite so hard to trust.

Roblox exposes game and group stats through public, unauthenticated endpoints — no login, no scraping tricks: GET https://games.roblox.com/v1/games?universeIds=<id>,<id>,... GET https://games.roblox.com/v2/groups/<groupId>/games?limit=50 I used them to pull the full public games list for a few independent creator groups that each ship multiple games in the same cheap-to-build "obby" template genre (think: dozens of studios building the same core traversal loop with a different skin). The question was simple: within one studio's own catalog, how concentrated is traffic in the single best title versus everything else they've shipped? The answer is the same shape every time: a small number of throwaway builds with near-zero traffic, and one outlier that accounts for nearly all of the studio's lifetime visits. Not "most games do okay and one does great" — more like one game is the studio, traffic-wise, and the rest are lottery tickets that didn't hit. As a sanity check against numbers that are already public knowledge (no anonymity concern), I ran the same script against Uplift Games' group (id 295182): $ python3 fetch_group_stats.py 295182 Group 295182: 371 published experiment(s) Total lifetime visits across all games: 44,412,921,224 Top title alone: 44,377,094,324 visits (99.9% of the group's total traffic) Visit-count distribution: 0-10K: 355 game(s) 10K-500K: 12 game(s) 500K-5M: 2 game(s) 5M-50M: 1 game(s) > 50M: 1 game ( s ) One title (Adopt Me) is 99.9% of that group's entire lifetime traffic across 371 shipped experiments. Same power-law concentration as the smaller, anonymized groups in the full writeup — just at a much larger scale. Why this is more than a curiosity : if you're building in a genre like this, the template itself is clearly not the moat — everyone in it ships near-identical mechanics. The variance between a 45-visit build and a 700M-visit build using the same template looks like it's mostly about timing and whatever the discovery algorithm rewar

Every dev course teaches you to build the app. Almost none teach you how to actually ship it. I found that out the hard way on my first job — no signing, no publish, no exceptions. So I wrote the full setup, both platforms, both ways: → Android: keystore via CLI and Android Studio's GUI → iOS: manual signing (certs, App IDs, provisioning profiles) AND automatic signing → The exact steps before every archive/build Full walkthrough → https://medium.com/@smitp7502/from-keystore-to-app-store-understanding-code-signing-for-android-ios-30671b5fd2a2 flutter #android #ios #mobiledev

This is a submission for Frontend Challenge - Comfort Food Edition, Perfect Landing Most food...

Hoi hoi! I'm @nyaomaru, a frontend engineer who is trying to lose weight. 🐖🙀 I maintain a type...

Part 1 was about waiting well. Part 2 was about not waiting at all. This one is about a value I...

I have four different load_env() functions in my MCP server project ( my-git-manager ) — one in server.py , one in publish_devto.py , one in reply_comments.py , one in scripts/list_all_published_titles.py . All four exist for the same dumb reason: this repo has no dependency on python-dotenv , so each script that needs GITHUB_TOKEN or DEV_TO_API reads .env by hand. I went digging for a fresh bug in this repo this week — I write a lot about it, and the well is getting shallow — and decided to actually diff all four load_env() implementations against each other instead of reading them one at a time like I usually do. They'd never been compared side by side before. That's how I found this one. The line that started it Every one of them does roughly this: for line in f : line = line . strip () if " = " in line and not line . startswith ( " # " ): k , v = line . split ( " = " , 1 ) os . environ . setdefault ( k , v . strip (). strip ( '"' ). strip ( "'" )) Look closely at what gets .strip() ed there. v — the value — gets stripped of whitespace and surrounding quotes. k — the key, the actual name of the environment variable — gets nothing. That's fine if your .env file looks like this: DEV_TO_API = abc123 It's not fine if it looks like this: DEV_TO_API = abc123 Spaces around = are a completely normal thing to type. Plenty of .env examples online use them. Plenty of people reach for that style out of habit from other config formats. And line.split("=", 1) doesn't care — it splits on the first = no matter what's next to it, so k comes out as "DEV_TO_API " , trailing space included. What that trailing space actually does os.environ.setdefault("DEV_TO_API ", "abc123") sets an environment variable. It's just not the one anything is looking for. Every caller in this repo does os.environ.get("DEV_TO_API") — no trailing space, because that's the name everyone actually types. That lookup returns None , or whatever was already sitting in the environment before .env ever got read. I

Every mobile developer knows the frustration of "Design QA Day." You finish building a screen and send it to your UI/UX team or QA engineer. Then you receive a list of minor padding mismatch tickets. "This card padding should be 16dp, not 12dp." "The title baseline is slightly off on smaller device densities." Usually, fixing these issues means taking screenshots on devices, placing them onto a Figma canvas, lowering the opacity, and measuring pixels. You can streamline this entire process directly on your physical Android test devices using Designer Tools. Overlay Figma Comps Directly Over Live Apps Instead of comparing your app build side-by-side with a Figma preview on a monitor, you can overlay the target design file onto your screen using SYSTEM_ALERT_WINDOW permissions. Export your frame from Figma as a PNG. Load it into Designer Tools under Image Mockup Overlay. Set the opacity to 50%. When you open your app build underneath, any layout misalignment, incorrect text scaling, or constraint issue will become quickly visible as a drop-shadow ghost. Verify Density-Independent (dp) Grids Testing layouts across different device densities (mdpi, hdpi, xxhdpi) often results in unexpected spacing bugs. With the Custom Grid System in Designer Tools, you can create a grid native to dp: Set standard 4dp/8dp vertical and horizontal spacing grids. Set origin points (Top-Left, Center, or Safe Area bounds). You can verify component placement on target hardware instantly without manual measurements. Infinite Alignment Guides For measuring dynamic list items, headers, or bottom sheets, you can place interactive vertical and horizontal guidelines directly onto the active screen. This removes uncertainty about distances between different UI elements. Try It Out Designer Tools is lightweight and privacy-focused, as all image assets stay strictly on-device. It is built for Android engineers, QA teams, and UI designers. Download on Google Play: ( Pro Design: Designer Tools ) How doe