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Launching a fast, secure, and scalable website no longer requires thousands in upfront server costs or dedicated DevOps teams. As of 2026, AWS powers 32% of the global public cloud market, offering flexible hosting options for every use case: from a 1-page personal portfolio to a high-traffic enterprise e-commerce platform. Whether you’re a beginner building your first site or a senior developer launching a production SaaS app, AWS lets you pay only for resources you use, with built-in tools for global performance, security, and automated deployments. This guide breaks down every AWS website hosting option, walks you through step-by-step setup for the most cost-effective popular stack, shares security best practices, and includes a transparent cost breakdown to help you avoid unexpected bills. Table of Contents How to Choose the Right AWS Website Hosting Option for Your Use Case Step-by-Step Guide: Launch a Static Website on AWS (S3 + CloudFront + Route 53) Deploy Modern Web Apps Faster with AWS Amplify Hosting Dynamic Website Hosting Options on AWS Critical Security Best Practices for AWS-Hosted Websites AWS Website Hosting Cost Breakdown (2026) Common Mistakes to Avoid When Launching a Website on AWS Conclusion References How to Choose the Right AWS Website Hosting Option for Your Use Case First, classify your website to pick the most cost-effective, low-overhead stack: Static vs Dynamic Websites Static websites : Made of pre-built HTML, CSS, JS, and media files with no server-side processing. Ideal for portfolios, landing pages, blogs, documentation, and marketing sites. Dynamic websites : Process user input, serve personalized content, or connect to databases. Ideal for WordPress, e-commerce, SaaS apps, social platforms, and membership sites. Quick Use Case Mapping Website Type Recommended AWS Stack Small static site / portfolio S3 + CloudFront + Route 53 Modern React/Next.js/Vue app with CI/CD AWS Amplify Small WordPress / LAMP stack site Amazon Lightsail Custo

Key Use Cases Power BI Visual Monitoring can be used for: power bi visual monitoring power bi report visual monitoring visual regression testing for Power BI power bi screenshot monitoring monitoring Power BI visuals visual monitoring for Power BI Report Server automated Power BI dashboard validation visual correctness control for BI reports Power BI Visual Monitoring: Automatically Detecting Broken Visuals in Power BI Reports In large Power BI environments, analytics teams often face the problem of silent regressions : even minor changes in data or models can break individual visuals without any obvious errors. Report owners frequently don’t notice that a visual has stopped rendering or is showing incorrect data — this can happen due to changes in data source structure, access rights, deleted fields, broken measures, or refresh failures. Manually checking hundreds of report pages across multiple dashboards in such conditions is extremely inefficient and nearly impossible. We, a team of BI developers and analysts, encountered this pain point during a large analytics implementation project and decided to create a solution for automated Power BI visual monitoring . Project Source Code: GitHub: https://github.com/svergio/Power-bi-report-visual-monitoring Documentation: https://svergio.github.io/Power-bi-report-visual-monitoring/ Wiki: https://github.com/svergio/Power-bi-report-visual-monitoring/wiki Why Standard Power BI Tools Don’t Solve the Problem Standard Power BI tools such as Usage Metrics and Performance Analyzer help analyze report usage and performance but do not detect visual issues. For example, built-in usage metrics show “how those dashboards and reports are being used” — number of views, popular reports, and who is viewing them. These metrics are important for assessing analytics adoption, but they say nothing about whether the visuals themselves are displaying correctly. Similarly, Performance Analyzer shows load times for each visual, helping identify s

In between beef tallow fries, raw milk, and vaccine denialism, Make America Healthy Again figureheads have set their sights on another slice of life: our clothing. "The MAHA movement doesn't stop with what we EAT - It's also about what we WEAR," Secretary of Agriculture Brooke Rollins said in a post on X in late […]

TL;DR Reconciliation is the part of a payments stack nobody architects for on day one and everyone pays for on day 200. The job: prove that every internal transaction matches the acquirer's settlement file, in the right currency, with the right fees, on the right value date — or surface the diff fast. The mechanics: normalize files → land into an events table → project to a read model → diff against the internal read model → buckets for ops to resolve. The boring details (file formats, fee parsing, FX rounding, value dates) are where 90% of the work lives. If you've ever opened a CSV from an acquirer at the end of the month, sorted by amount, and tried to "just match it in Excel" — yes, this post is for you. What "reconciled" actually means A transaction is reconciled when, for the same logical payment, three views agree: What you sent — your internal record of the charge/payout (your read model). What the acquirer says happened — their settlement file or API report. What the bank actually credited / debited — the bank statement. Disagreements are normal. Persistent disagreements are how you lose money slowly and never know. The shape of a settlement file Across the major acquirers, settlement files look broadly similar — and broadly different in the places that matter: Field Variants you'll see Transaction reference acquirer's transaction_id , sometimes plus a merchant_reference round-tripped from you Gross amount minor units / decimal; transaction currency vs settlement currency Fees inline per-row, or aggregated at the file footer, or in a separate fees file FX inline rate vs separate FX file; sometimes only the converted amount Value date when the bank actually moves money — often T+1/T+2 from event date Adjustments refunds, chargebacks, fee corrections, reserves — usually mixed in Encoding UTF-8 if you're lucky; CP1252 / fixed-width / SWIFT MT940 if you're not Granularity one row per transaction or daily aggregates per merchant or both There's no industry-clean

An “urban explorer” tells WIRED their mother checked in to make sure they weren’t one of the people seen scurrying out of a manhole with their friends.

When building a small content site, the framework is usually not the hardest part. The harder part is deciding what each page should be responsible for. A lot of sites start as a simple article list. That works for a while, but it becomes messy when visitors arrive with different search intents. Some users want to learn what something means. Some want download or setup information. Others are trying to fix a specific issue. Those users should not all land on the same generic page. The structure I use For a small Next.js content hub, I like to separate routes by intent: Homepage: broad entry point Learn hub: basic explanations and guides Learn detail pages: specific guide topics Download page: download or install intent Fix hub: troubleshooting entry point Fix detail pages: specific issue pages English and Japanese routes: language-specific entry points This structure is simple, but it keeps the site easier to maintain. Page role comes first Before writing a page, I define its role. A learn page answers what something is, how it works, and what a beginner should understand first. A download page answers where a user should get something, what should be checked before installing, and which platform or device matters. A fix page answers what is not working, what should be checked first, and whether the problem is related to permissions, notifications, device settings, or installation. The page role decides the title, description, internal links, and body structure. Why this helps SEO This approach helps avoid pages competing with each other. For example, a download page should not try to rank for every tutorial query. A troubleshooting page should not read like a general homepage. Each page can link to related pages, but the primary intent stays clear. That makes the site cleaner for both users and search engines. Metadata and sitemap discipline In a Next.js App Router project, I also like to keep metadata and sitemap updates close to the route change. For example: If

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

You have explained your tech stack to your coding agent four times this month. You mentioned your preferred approach to a problem in January, and your agent has no idea it ever happened. You corrected a decision last week and the old version is still surfacing. You set up context at the start of every session because there is nowhere for it to go at the end. This is not a model problem, as GPT-4, Claude, and Gemini all have the same limitations. The model is stateless. They all have inbuilt memory, and still every session starts from zero unless you have the infrastructure to persist what matters and surface it at the right moment. That sophisticated memory infrastructure is what most developers do not have. VEKTOR Slipstream v1.6.3 is a local-first memory SDK for AI agents. This release adds the layer most memory systems skip: not just storing what you tell it, but managing what should still be there months later: curation. What you actually get Before the architecture: What changes for you as a developer embedding this SDK. Every AI memory system forces decisions you didn’t realise you were making. Where does your agent’s context actually lives, is it on your machine or on someone else’s server? Are you paying per token every time your agent understands a memory, or does that happen locally? When you connect your GitHub, your calendar, your files — where does all that data go, and who can see it? Most memory systems answer all four questions for you, quietly, in their terms of service. VEKTOR’s answer to all four is the same: your machine, your data, your rules. Memory lives in a single SQLite file you own. Embeddings run locally on CPU — no API calls, no per-token cost, no data leaving the process. MCP connectors spawn as local stdio processes; nothing is routed through an external service. There is no telemetry, no cloud sync, no account required. If you want to understand exactly what your agent knows about you, you open the database with any SQLite browser and

DIYer and TikTok user Annike Tan, who goes by @ubeboobey, can carry her cyberdeck around without anyone noticing because it doesn't look like a computer at all. Tan, who has been featured in The Cut and Wired, went viral earlier this year with a mermaid-themed cyberdeck she made inside an old purse. She has since […]

Google now lets big creators and publishers in the US claim dedicated profiles in Search to highlight things like videos, articles, and their other profiles online. But this feature won't be available to most people or organizations; Google is limiting it to people with at least 100,000 YouTube subscribers, 100,000 followers on Instagram or X, […]

Creators often have to parse through charts and dashboards to understand their performance, but with the new AI assistant, they can get quick answers to questions like "When should I post?" and "What are people saying in my comments?"

Apple's App Store generated $1.4 trillion in sales, up from $1.3 trillion last year, with $149 billion in sales for digital goods.

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

It's almost impossible to avoid seeing AI-generated content online, but it doesn't have to be this way. YouTube, Instagram, TikTok, and more have ramped up content authentication efforts over the last year, with many now automatically applying labels to distinguish AI-generated images, videos, and music from those made by real, human creators. That's all very […]

A homeschooling center in Manhattan is part of the company’s nationwide expansion. Internal documents reveal its strategy: “Opening date > safety.”

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)