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Have you ever trained a model that performed beautifully on your training data but fell apart the moment it saw new data? Or perhaps you built something so simple it couldn't even learn the training data properly? These are the classic traps of overfitting and underfitting — and every machine learning practitioner runs into them. In this article, we'll cover what they are, how to detect them, how to fix them, and where the bias-variance tradeoff ties it all together — with real-world examples and code throughout. What is Model Fitting? Model fitting is the process of training a predictive model on a dataset to find the optimal parameters that best capture the underlying patterns in the data. The goal is simple: the model should generalize well to unseen data — not just memorize the training examples. There are three possible outcomes when fitting a model: Outcome Description Good fit Captures underlying patterns, generalizes well Underfitting Too simple, misses patterns even in training data Overfitting Too complex, memorizes noise, fails on new data What is Underfitting? Underfitting occurs when a model is too simple to capture the underlying patterns in the data. It performs poorly on both the training set and on new, unseen data. Think of it like this: imagine asking a child to predict house prices and they only use the rule "all houses cost $100,000." That model ignores all relevant features (size, location, age) and will be wrong almost every time. Why Does Underfitting Occur? Model is too simple : A linear model trying to fit a curved, nonlinear relationship Too few features : Important variables are left out Too much regularization : Penalizing complexity so heavily that the model can't learn anything meaningful Insufficient training : The model hasn't been trained long enough Real-World Example Suppose you're predicting whether an email is spam. If you only use the feature "email length" and ignore word content, sender, and links, your model will underfit —

Most AI memory features are a black box. The assistant remembers things about your users, but you...

If you’re building a generative AI chatbot, global e-commerce platform, or industrial IoT solution in 2026, picking the wrong database can sink performance, blow your budget, or delay your launch. For years, teams relied on one-size-fits-all relational databases for every workload, but modern applications demand specialized tools for specific use cases. AWS solves this challenge with 15+ purpose-built database engines across 8 distinct categories, optimized for performance, scalability, and cost efficiency for every imaginable workload. This guide breaks down every AWS database type, its core features, real-world use cases, and 2026 best practices to help you choose the right tool for your next project. Table of Contents Why Purpose-Built Databases Are the Standard in 2026 AWS Database Categories: A Deep Dive 2.1 Relational Databases 2.2 Key-Value Databases 2.3 In-Memory Databases 2.4 Document Databases 2.5 Graph Databases 2.6 Wide Column Databases 2.7 Time-Series Databases 2.8 Data Warehouse 2026 AWS Database Best Practices Common Mistakes to Avoid When Choosing AWS Databases Conclusion References Why Purpose-Built Databases Are the Standard in 2026 Modern workloads have vastly different requirements: a generative AI RAG system needs fast vector search, an IoT fleet needs high-throughput time-series data ingestion, and a global SaaS platform needs multi-region consistency with zero downtime. A single relational database cannot meet all these needs without tradeoffs. AWS purpose-built databases eliminate these tradeoffs by: Supporting open standard APIs to avoid vendor lock-in Offering serverless deployment options for all major engines Including built-in AI/ML and vector search capabilities Delivering up to 99.999% availability for mission-critical workloads Reducing TCO by 25-48% compared to self-managed or generic alternatives (per IDC) AWS Database Categories: A Deep Dive Relational Databases Relational databases store data in structured tables with fixed schema

Bilingual post · Post bilíngue Jump to: English · Português English {#english} Sprint 7 Review: Generics.Collections Mintlify docs tour — after Sprint 4 exceptions , Sprint 7 brings the collections every Horse API and CRUD service needs. Generic containers are not syntactic sugar in Delphi — they are how you build caches, registries, and JSON maps. Sprint 7 ( v2.15.0 ) shipped System.Generics.Collections with working TDictionary<K,V> and TList<T> , including TryGetValue with proper var write-back. What the review documents From Sprint 7 Review : var parameters — Value::Reference , write-back in procedures/functions and TryGetValue . TDictionary::Add / TryGetValue — dispatch in simulate_function_execution ; direct routing for instantiated generics. Parser — generic types with Integer / String in expressions ( TDictionary<String,Integer> ). RTL — rtl/sys/System.Generics.Collections.pas ; semantic layer recognizes Generics.Collections . Tests — generics_collections.rs plus fixtures. Tag v2.15.0 approved. Dictionary pattern in production code program DictDemo ; uses System . SysUtils , System . Generics . Collections ; var D : TDictionary < string , Integer >; V : Integer ; begin D := TDictionary < string , Integer >. Create ; try D . Add ( 'apples' , 3 ); if D . TryGetValue ( 'apples' , V ) then WriteLn ( 'apples = ' , V ); finally D . Free ; end ; end . TryGetValue only writes V when the key exists — the Delphi idiom you expect in services and controllers. Why var was the sprint's hidden hero Before v2.15.0, incomplete var semantics blocked realistic APIs. TryGetValue requires the callee to mutate the caller's variable through a reference. CrabPascal introduced Value::Reference and write-back in procedure dispatch specifically for this surface. Three implementation lessons from the retrospective: D := TDictionary<...>.Create needs normalize_call_name in execute_assignment — not just in direct calls. Generic instance methods must hit intrinsics before empty VMT lookups

With rapid advances in robotics, autonomous navigation has become essential for mobile robots. The ROS Navigation Stack is the de facto open-source framework for building reliable, real-world navigation systems. It integrates perception, mapping, localization, path planning, and motion control into a unified pipeline. This article breaks down the core components, working principles, configuration best practices, and common pitfalls of the ROS Navigation Stack to help engineers build stable autonomous robots. Overview The ROS Navigation Stack is a collection of coordinated packages that enable a robot to: Localize itself on a map Plan global paths to a goal Avoid dynamic obstacles locally Control motion safely It relies on sensor inputs (LiDAR, depth cameras, wheel odometry, IMU) and outputs velocity commands to the robot base. Core Components move_base The central coordinator of the entire navigation system. Manages the navigation state machine Runs global and local planners Triggers recovery behaviors when the robot is stuck Exposes an Action interface for goal commands Key states: PLANNING, CONTROLLING, CLEARING, RECOVERY. AMCL (Adaptive Monte Carlo Localization) AMCL uses particle filter localization to estimate the robot’s pose on a pre-built map. Particle filter steps: Initialize particles over a pose distribution Predict motion using odometry Weight particles by sensor likelihood (LiDAR scan matching) Resample to keep high-confidence particles Output the weighted average pose AMCL is highly tunable: min_particles / max_particles laser_model_type odom_model_type update_min_d / update_min_a costmap_2d Costmaps represent the environment as a grid of “cost” values, indicating collision risk. Two costmaps: Global costmap: large-scale, slow-update, for path planning Local costmap: small-scale, fast-update, for obstacle avoidance Cost values: 0: free space 253: lethal obstacle 254: inscribed obstacle 255: circumscribed or unknown Inflation expands obstacles by the ro

Bilingual post · Post bilíngue Jump to: English · Português English {#english} Navigating the Reorganized CrabPascal Docs Phase 1 of this blog series covered fundamentals — CLI, lexer, sprints, and how to contribute. If you read Contributing to CrabPascal: Get Involved , you already know where the repo lives and how to open a PR. Phase 2 is different: we walk the Mintlify documentation site the way a new teammate would, mapping each article to the pages you actually need in daily work. The live docs live at crabpascal.mintlify.app . The source is under mintlify/ in the Bitbucket repo. Think of Mintlify as the canonical handbook; Dev.to posts are the guided tour. Why the docs were reorganized CrabPascal grew from a v1.5 experimental interpreter (October 2025) to v2.22.0 with sprint-driven releases, Horse E2E tests, and honest build-exe . Old folders like documentation/00-INICIO/ still exist in git history, but Mintlify groups content by job to be done : You want to… Start here Install and run Quickstart See current capabilities Project status Understand architecture Architecture Follow releases Changelog Historical reports from v1.5–v2.8 remain under Histórico with archive banners — useful for context, not for "what works today." The home page is your compass Open the Mintlify index . It shows v2.22.0 at a glance: real diagnostic spans, System.* RTL, Unicode strings, dual-mode run/build, and Horse HTTP parity. Four cards link to quickstart, changelog, sprint roadmap, and technical-debt backlog. Essential CLI commands are listed once: crab-pascal check programa.dpr # diagnostics with line/column crab-pascal run programa.dpr # interpreter / runtime crab-pascal build-exe programa.dpr # native binary via C toolchain Bookmark this page. When someone asks "is feature X done?", check status + changelog before diving into Rust sources. The documentation map: question-driven navigation The page essentials/documentation-map is the "I want X, go to Y" cheat sheet. It answers sc

I built 24 browser-based tools. Here is the complete technical guide for building your own and selling PRO licenses on Gumroad. Architecture: One HTML File + GitHub Pages + Gumroad No frameworks, no backend, no monthly costs. One HTML file on GitHub Pages. Gumroad handles payments. The PRO Flow User hits free limit → PRO modal Buy button → Gumroad popup ( window.open , NOT target=_blank ) Payment → Gumroad postMessage with license key message listener catches key Key verified via Gumroad API ( /v2/licenses/verify ) localStorage saves PRO (in try-catch!) Gotchas From 24 Tools postMessage security: Check d.success && d.purchase , never just d.license_key . I had this bug in 17 files. localStorage: Wrap in try-catch . Uncaught throws crash the whole page. CDN scripts: Always <script defer> . Without it, slow CDN blocks rendering. Gumroad publish: curl returns false every time. Use PowerShell. Buy button: Popup connects the page to Gumroad. Redirect breaks postMessage . Packed 5 templates with everything pre-configured: Browser Tools Starter Kit ($19)

When people say a database is "ACID-compliant" or "eventually consistent," they are making promises about what happens when things go wrong — concurrent writes, crashes, network failures. ACID and BASE are the two vocabularies for those promises, and knowing the difference tells you what you can and cannot rely on. What ACID guarantees ACID is the contract that traditional transactional databases — PostgreSQL, MySQL/InnoDB, Oracle, SQLite — make about a transaction (a group of operations treated as one unit). The four letters: Atomicity : the whole transaction succeeds or none of it does. If a bank transfer debits one account but the credit fails, the debit is rolled back. There is no half-done state. Consistency : a committed transaction moves the database from one valid state to another, never violating its declared rules (constraints, foreign keys, types). It will not let you, say, leave a foreign key pointing at a row that does not exist. Isolation : concurrent transactions do not step on each other. The result is as if they ran one at a time, even when they actually ran in parallel. (In practice databases offer tunable isolation levels — from "read committed" to "serializable" — trading strictness for speed.) Durability : once the database says "committed," that data survives a crash or power loss. It has been written somewhere persistent, not just held in memory. The payoff is that you can reason about your data simply: after a successful commit, the world is exactly what you asked for. The cost is coordination, which gets expensive when data is spread across many machines. What BASE trades away BASE is the model many distributed and NoSQL systems adopt — think Cassandra, DynamoDB, or Riak — when they need to scale across many nodes and stay up through failures. The acronym is deliberately a chemistry pun on ACID, and it stands for: Basically Available : the system answers requests even during partial failures, possibly with stale or incomplete data rather tha

Most people picture Git as a tool that records changes — a stack of diffs layered on top of each other. That mental model is wrong, and it makes Git feel mysterious. Git is really a small key-value database that stores snapshots, and once you see the four object types it uses, commands like reset , checkout , and rebase stop being magic. Git is a content-addressed object store Everything Git tracks lives in .git/objects as an object, and every object has an ID that is the hash of its own content. By default that hash is a 40-character SHA-1 digest (newer Git supports SHA-256). The same bytes always produce the same ID, so the ID is the content's address — change one byte and you get a completely different object. This is why Git data is effectively immutable: you never edit an object in place, you create a new one with a new name. You can look inside any object with git cat-file . The -t flag prints the type, -p pretty-prints the content: $ git cat-file -t 3b18e512 blob $ git cat-file -p 3b18e512 hello world There are exactly four object types: blob , tree , commit , and tag . A blob is just file contents — raw bytes, with no filename and no metadata. The blob for README.md knows nothing about being named README.md ; it only knows what's inside. A tree is a directory listing. It maps names to other objects: each entry has a mode (like a file vs. an executable vs. a subdirectory), a name, and the hash of either a blob (a file) or another tree (a subdirectory). Trees are how Git represents folder structure. Inspecting one shows exactly that: $ git cat-file -p HEAD^ { tree } 100644 blob a906cb... README.md 040000 tree fe8e3b... src A commit ties it together. A commit object points to exactly one top-level tree (the full state of your project at that moment), plus the hash of its parent commit (or parents, for a merge), the author and committer with timestamps, and the commit message. Running git cat-file -p HEAD shows these fields in plain text. Because each commit nam

Books API Structure Create folders app/ app/controllers/ app/core/ app/models/ app/models/DAOs/ app/models/DTOs/ app/models/entities/ app/utils/ config/ public/ api/composer.json Configure Composer and the PSR-4 autoload so that classes with the namespace App\ are searched inside the app/ folder. Key content: { "name": "user/api", "autoload": { "psr-4": { "App\": "app/" } } } After creating it, run this command inside the api folder: composer dump-autoload api/config/config.php Defines the base URL of the project. The router removes it from REQUEST_URI to keep only routes such as /books/get. <?php define('BASE_URL', '/proyect/api/public'); api/config/dbconf.json MySQL DB connection: { "host": "localhost", "user": "root", "password": "", "db_name": "books_db" } api/public/.htaccess Makes Apache send all routes to index.php. RewriteEngine On RewriteCond %{REQUEST_FILENAME} !-f RewriteCond %{REQUEST_FILENAME} !-d RewriteRule ^ index.php [QSA,L] api/public/index.php This is the entry point. It loads Composer, loads the configuration and calls the router. <?php use App\Core\Router; require_once DIR . '/../vendor/autoload.php'; require_once DIR . '/../config/config.php'; (new Router())->dispatch($_SERVER['REQUEST_URI']); api/app/core/Router.php <?php namespace App\Core; class Router { protected array $routes = [ '/' => 'HomeController@index', '/books' => 'BookController@index', '/books/get' => 'BookController@getAll', '/books/getById' => 'BookController@getById', '/books/create' => 'BookController@create', '/books/update' => 'BookController@update', '/books/delete' => 'BookController@delete', ]; public function add($route, $params): void { $this->routes[$route] = $params; } public function dispatch($uri): void { $uri = parse_url(str_replace(BASE_URL, '', $uri), PHP_URL_PATH); if (!isset($this->routes[$uri])) { $this->sendNotFound(); return; } [$controller, $method] = explode('@', $this->routes[$uri]); $controller = 'App\\Controllers\\' . $controller; if (!class_exists($co

Here's a thing that happens on every team I've been on. Sprint ends. Someone schedules the retro....

Syndicated from the FavCRM blog . The old quote was two weeks. With an agent on the UI and a headless backend, it's an afternoon. A client needs a booking site. The old quote was two weeks: a calendar, a database, an availability engine, payments, a customer table. With an AI agent building the frontend and FavCRM as the headless backend , the real work is an afternoon. Here is the whole job, start to finish. The scenario A small clinic. Three services, one practitioner, online booking with deposit. You are the agency; you have an AI agent in your editor and a terminal. The plan: Register the clinic's FavCRM workspace Configure services and availability Wire one server route that talks to FavCRM Let the agent build the booking UI against that route Test a real booking end to end Step 1 — Register the workspace (~5 min) The favcrm CLI registers a workspace and issues an API key. No dashboard. favcrm signup request --email clinic@example.com \ --organisation-name "Bright Smile Clinic" favcrm signup verify --request-id < id > --code <6-digit-code> The verify step prints a fav_mcp_* key. Put it where your build can read it — never in the repo: export FAVCRM_API_KEY = fav_mcp_... Step 2 — Configure services and availability (~30 min) Hand the brief to your agent and let it call the tools. Inspect a schema first: favcrm tool describe create_service Then create each service: favcrm tool call create_service '{ "name": "New Patient Exam", "durationMinutes": 45, "price": "80.00" }' favcrm tool call create_service '{ "name": "Cleaning", "durationMinutes": 30, "price": "60.00" }' Set when the practitioner works, so availability is real: favcrm tool call set_staff_availability '{ "weekday": "mon", "start": "09:00", "end": "17:00" }' Repeat per weekday. At this point the backend is done — services, hours, an availability engine that knows about clashes. You wrote no schema. Step 3 — One server route (~30 min) The browser must never hold the API key. Put it in one server route tha

The Setup I run a Proxmox 9 homelab (pve-manager/9.0.5, kernel 6.14.8-2-pve) and I needed to run Docker inside an LXC container — not a VM — to test a customer-style "bring-your-own-VPS" deployment path for a PaaS I'm building. The container had to act like a standard Ubuntu cloud VM: Docker, systemd, the works. LXC over a full VM gets me near-bare-metal performance, a fraction of the RAM overhead, and instant boots. The catch: the "Docker on LXC" recipes you'll find on most blog posts and Proxmox forum threads are out of date . They assume kernel 5.x and runc 1.1.x. On a modern Proxmox (kernel 6.14 + runc 1.2+ shipped with Docker 29) those recipes fail in two new and confusing ways before you even reach the workarounds we used to know about. This article walks through exactly what fails, why it fails, and the config that actually works in 2026 — plus an honest look at the security tradeoffs, because spoiler: the working config is privileged , and that matters. The Goal A Proxmox LXC container that can: Run docker run hello-world cleanly Pull and build complex images (multi-stage builds, overlay2 storage driver) Run nested containers with their own systemd Use systemd --user for per-service lingering processes Attempt 1: Unprivileged LXC (the path you "should" take) Conventional wisdom says: use unprivileged LXC. The container's root is mapped to an unprivileged UID on the host (typically 100000 ), so even a full container compromise can't escape to host root. Modern Proxmox makes this the default and recommended mode. I started with an unprivileged Ubuntu 25.04 container, added the now-standard features: pct set <vmid> -features nesting = 1,keyctl = 1,fuse = 1 Then inside the container, installed Docker from the official download.docker.com repo and ran: docker run --rm hello-world Here's what happened: docker: Error response from daemon: failed to create task for container: failed to create shim task: OCI runtime create failed: runc create failed: unable to start

PageSpeed Insights had been staring at me for weeks. Desktop was holding at 91. Mobile was stuck at 63. I'd already fixed the obvious stuff — non-blocking fonts, preconnects, fetchpriority on the hero image. But there it was, every single run: Improve image delivery — Est savings of 985 KiB Nearly a megabyte of wasted transfer, just from six project screenshots. And that was just the images visible above the fold. The full list across all projects was worse. The culprit: every image I'd ever uploaded through the Django admin was a PNG. Some of them were over 1 MB. WebP would have cut most of them by 80%. I knew this. I just hadn't done anything about it. So I wrote a management command to fix the backlog, and then made the model auto-convert on every future upload so I'd never have to think about it again. The Problem With PNGs in a Portfolio When you're building a portfolio, you screenshot your work and drag it into the admin. That screenshot is usually a PNG — lossless, full-size, straight from your display. Nobody optimises it because the admin accepts it and it shows up fine in the browser. But "shows up fine" isn't the same as "loads fast." A 1.4 MB PNG of a law firm homepage does not need to be 1.4 MB. Served as WebP at quality 85, it's 175 KB. Same visual result. Eight times smaller. Multiply that across 28 projects and you're looking at tens of megabytes that mobile users on slow 4G are downloading just to scroll past thumbnails. The One-Time Backlog Fix: A Management Command First, I needed a way to convert everything that was already in S3. A management command was the right tool — it runs in the production container with full access to the Django ORM and the configured storage backend, so it can read and rewrite files without needing to know whether they're on S3, local disk, or anywhere else. # backend/projects/management/commands/convert_images_to_webp.py from io import BytesIO from django.core.files.base import ContentFile from django.core.management.b

Crypto APIs are no longer just tools for fetching Bitcoin prices. In 2026, they are becoming the...

I just joined DEV to explore the community and get into the habit of writing about what I'm learning. I also set up a blog on Hashnode — figuring out how the two fit together. Here's a quick code block to test formatting: ​ function greet ( name ) { console . log ( `Hello, ${ name } !` ); } greet ( " DEV " ); ​ ``` More to come as I find my way around 👋

Co to jest WCAG 2.1? WCAG 2.1 ( Web Content Accessibility Guidelines ) to międzynarodowy standard techniczny określający, jak tworzyć strony www i aplikacje mobilne, aby były dostępne dla osób z niepełnosprawnościami (wzroku, słuchu, ruchu, poznawczymi). Wersja 2.1 rozszerza wcześniejsze zasady o wytyczne dla urządzeń mobilnych oraz osób słabowidzących. Struktura WCAG 2.1 i poziomy zgodności Standard opiera się na 4 głównych zasadach. Dzielą się one na wytyczne, do których przypisane są konkretne kryteria sukcesu wdrażane na trzech poziomach: Poziom A: Absolutne minimum. Bez niego strona jest całkowicie niefunkcjonalna dla wielu użytkowników. Poziom AA: Standard rynkowy i prawny. Wymagany przez polskie i europejskie przepisy dla sektora publicznego i biznesu. Poziom AAA: Najwyższy stopień dostępności, trudny do wdrożenia w całym serwisie. Zasady POUR – Fundamenty WCAG 2.1 Wszystkie wytyczne WCAG 2.1 opierają się na czterech głównych zasadach tworzących akronim POUR : P erceivable ( Postrzegalność ) - Treść musi być dostarczana w sposób czytelny dla zmysłów użytkownika (wzroku, słuchu). O perable ( Funkcjonalność ) - Interfejs i nawigacja muszą być możliwe do obsługi za pomocą różnych urządzeń (np. samej klawiatury). U nderstandable ( Zrozumiałość ) - Informacje oraz obsługa strony muszą być jasne, logiczne i przewidywalne. R obust ( Solidność ) - Kod strony musi być poprawny i kompatybilny z obecnymi oraz przyszłymi technologiami (przeglądarki, czytniki ekranu). Kto musi spełniać standardy WCAG? Dostępność cyfrowa to już od dawna nie tylko "dobra praktyka", ale twardy wymóg prawny, który stale się rozszerza: Sektor publiczny (Obecnie): W Polsce urzędy państwowe i samorządowe, szkoły, uczelnie, szpitale oraz spółki skarbu państwa mają bezwzględny obowiązek spełniania standardu WCAG 2.1 na poziomie AA. Wynika to wprost z Ustawy z dnia 4 kwietnia 2019 r. o dostępności cyfrowej . Za brak zgodności grożą kary finansowe. Sektor prywatny i biznes: Na mocy Europejskiego Akt

I Built My First Token on Solana — Here's What Actually Surprised Me This week I went from zero tokens to minting, transferring, charging fees, and locking tokens so they can never move. Here's what stuck with me. Tokens don't live in your wallet Coming from Web2, I assumed tokens would just... show up in your account. Nope. On Solana, every wallet needs a separate token account for each token it holds. One mint, one folder. It felt weird at first. Now it makes sense. You can charge fees without writing a single backend The Token Extensions Program has a built-in transfer fee. One flag at mint creation time: spl-token create-token \ --program-id TokenzQdBNbLqP5VEhdkAS6EPFLC1PHnBqCXEpPxuEb \ --transfer-fee-basis-points 100 That's a 1% fee on every transfer, enforced by the blockchain. No middleware. No payment processor. No way to bypass it. You can make a token that literally cannot be transferred spl-token create-token \ --program-id TokenzQdBNbLqP5VEhdkAS6EPFLC1PHnBqCXEpPxuEb \ --enable-non-transferable I minted 10, tried to send 5, and watched the transaction get rejected. Not by my code — by the program itself. Perfect for credentials, badges, or certificates that should belong to one person forever. The biggest shift from Web2: these rules are set at creation and are permanent. You can't add a transfer fee to an existing token. You can't make a transferable token non-transferable later. It forces you to think about token design upfront, which is honestly a good constraint. solana #blockchain #webdev #beginners #100DaysOfSolana

Keyboard accessibility is one of the most important — and most neglected — aspects of web accessibility. An estimated 2.5 million Americans have motor disabilities that prevent mouse use. If your site can't be operated entirely by keyboard, you're excluding them completely. The Four Core Principles WCAG 2.2 Principle 2 (Operable) contains the keyboard requirements: 2.1.1 Keyboard (AA): All functionality must be operable via keyboard 2.1.2 No Keyboard Trap (AA): If focus moves into a component, it must be possible to move it out 2.4.3 Focus Order (AA): If page can be navigated sequentially, order must be logical and predictable 2.4.7 Focus Visible (AA): Any keyboard-operable UI must have a visible focus indicator 2.4.11 Focus Appearance (AA, new in 2.2): Focus indicator must meet size and contrast requirements Testing Without Automated Tools Start with the basic keyboard test: Unplug (or ignore) your mouse Press Tab to move forward through interactive elements Press Shift+Tab to move backward Use Enter/Space to activate buttons, links, checkboxes Use arrow keys for radio groups, menus, sliders Use Escape to close dialogs and menus Any element you can't reach or activate? That's a WCAG 2.1.1 failure. The Most Common Keyboard Failures Custom dropdowns and menus // ❌ Keyboard inaccessible function Dropdown ({ items }) { return ( < div onClick = { toggle } className = "dropdown" > { items . map ( item => ( < div onClick = { () => select ( item ) } > { item . label } </ div > )) } </ div > ); } // ✅ Fully keyboard accessible function Dropdown ({ items }) { return ( < div role = "combobox" aria-haspopup = "listbox" aria-expanded = { isOpen } tabIndex = { 0 } onKeyDown = { handleKeyDown } // handles Enter, Space, Arrows, Escape className = "dropdown" > < ul role = "listbox" > { items . map (( item , i ) => ( < li key = { item . id } role = "option" tabIndex = { - 1 } aria-selected = { i === activeIndex } onKeyDown = { e => e . key === ' Enter ' && select ( item ) } > { item

Cómo solucionar el error "Text content does not match server-rendered HTML" en Next.js Este error ocurre cuando el HTML generado en el servidor (SSR) no coincide con el árbol de React que se construye durante la hidratación inicial en el navegador. Es un problema crítico que rompe la experiencia de usuario y puede causar comportamientos impredecibles. Causa raíz En tu caso, el error está relacionado con contenido dinámico que varía entre renderizado del servidor y renderizado del cliente , probablemente por: Uso de Date() o new Date() en el renderizado (ej. fechas de eventos como JUN 9 , JUN 11 , etc.) Uso de typeof window !== 'undefined' o APIs del navegador directamente en el render Metaetiquetas o scripts que modifican el DOM antes de la hidratación (como iOS detectando fechas como enlaces) Configuración incorrecta de librerías CSS-in-JS o Edge/CDN que modifiquen el HTML Solución definitiva (pasos) ✅ Paso 1: Aisla el contenido dinámico con suppressHydrationWarning Si el contenido que varía es intencional (como fechas de eventos), envuelve solo el elemento problemático con suppressHydrationWarning={true} : // app/page.tsx o app/events/page.tsx export default function EventsPage () { const events = [ { name : ' NEXT.JS NIGHTS ' , date : new Date ( ' 2024-06-09 ' ) }, { name : ' AMS ' , date : new Date ( ' 2024-06-11 ' ) }, { name : ' LDN ' , date : new Date ( ' 2024-06-18 ' ) }, ]; return ( < div > < h2 > VIEW EVENTS </ h2 > < ul > { events . map (( event , i ) => ( < li key = { i } > < strong > { event . name } </ strong > { /* ✅ Solo este elemento usa suppressHydrationWarning */ } < time dateTime = { event . date . toISOString () } suppressHydrationWarning > { event . date . toLocaleDateString ( ' en-US ' , { month : ' short ' , day : ' numeric ' }) } </ time > </ li > )) } </ ul > </ div > ); } ⚠️ Importante : suppressHydrationWarning solo funciona en el elemento inmediato, no en hijos. Usa span , time , div , etc., no en contenedores grandes. ✅ Paso 2: Evita Da