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Let them pick out their own hiking boots. Instead, try gifting a useful blade or a nature journal to delight your outdoorsy friend.

Published on Aug 21st, 2025 🚀 Made some progress Welcome back to my "Demolishing My Stack of Unfinished Projects" series! This week, I'm excited to share the story of how I transformed a simple chat idea into a fully-featured, production-ready real-time messaging application called CodeniChat (formerly Secret Chat Bolt). 🎯 The Project That Started It All Secret Chat began as one of those "wouldn't it be cool if..." moments. I had this idea for a secure, real-time chat application that could handle multiple rooms, user invitations, and password resets. You know the drill - you start with enthusiasm, then life happens, and suddenly it's been months with just a README file and some half-baked code. But this week, I decided enough was enough. It was time to demolish this unfinished project and turn it into something I could actually be proud of. I started off with a short prompt into the bolt.new platform 🛠️ The Tech Stack That Made It Possible Frontend Next.js 15 - The latest and greatest React framework React 19 - Cutting-edge React with the new compiler TypeScript - Because type safety is not optional Tailwind CSS - For rapid, beautiful UI development shadcn/ui - Pre-built, accessible components Backend & Database Neon Database - Serverless PostgreSQL that scales with you Drizzle ORM - Type-safe database operations NextAuth.js - Secure authentication system Bcryptjs - Password hashing and security Real-time Features Server-Sent Events - For real-time message updates WebSocket-like functionality - Without the complexity 🚀 The Week-Long Development Sprint Day 1: Foundation & Database I started by setting up the database schema with Drizzle ORM. The beauty of using a modern ORM is that you can define your relationships once and let TypeScript handle the rest. // Core tables for the chat system export const users = pgTable ( " users " , { id : text ( " id " ). primaryKey (), email : text ( " email " ). notNull (). unique (), name : text ( " name " ). notNull (), password

Lightweight, powerful, and generally inexpensive, the handheld vacuum is the perfect household helper.

A svelte smart ring can track important activity, sleep, and health metrics. Plus, unlike most smartwatches, it doesn’t need to be charged every day.

Our top-pick sleeping pads from Nemo, Therm-a-Rest, and Gossamer Gear use high-tech materials to engineer the best sleep you can get outdoors.

Motorola phones may seem old-school, but their reasonable prices, colorful designs, and simple software make them good, wallet-friendly Android smartphones.

You’re an action hero, and you need a camera to match. We guide you through all the models, plus accessory recommendations and hidden software tricks to try.

You don’t have to upload your video to the cloud or pay a monthly fee to secure your home. These security cameras record locally.

JFrog Security Research revealed "PixelSmash," a vulnerability in the FFmpeg media framework, allowing for Remote Code Execution and Denial of Service attacks. Present for sixteen years, it affects numerous applications using the MagicYUV decoder. Exploitation requires only a crafted media file. Users are advised to check for the vulnerability and apply patches or disable the decoder if necessary. By Olimpiu Pop

Four ways to orchestrate business logic in Ruby. One map to find yours. You're building something that involves multiple steps. Charge a card, send an email, update inventory. Simple. Then someone says "What if step 3 fails? What undoes steps 1 and 2?" and suddenly you're evaluating workflow libraries. There are four mainstream approaches in Ruby today — Ruby Reactor , dry-transaction , Trailblazer , and raw Sidekiq jobs. This guide helps you pick the right one — not by ranking them, but by mapping them to the problem you're actually solving. The 30-Second Decision Matrix If you only have 30 seconds, start here: You want... Pick... A simple pipeline — 3-5 steps, top-to-bottom, no parallelism dry-transaction Railway-oriented programming with success/failure tracks, already in the Trailblazer ecosystem Trailblazer A full saga orchestrator — DAG dependency resolution, Sidekiq async, auto-compensation, locks, dashboard Ruby Reactor One-off fire-and-forget jobs, no coordination needed Raw Sidekiq None of these are "better" than the others. They solve different problems. Let's walk through each one. Meet the Libraries dry-transaction (v0.16.0) dry-transaction is a thin, focused gem from the dry-rb ecosystem. It wraps a series of operations in a sequential pipeline with a clean step DSL: class CreateUser < Dry :: Transaction step :validate step :persist step :send_welcome_email def validate ( input ) = # ... def persist ( input ) = # ... def send_welcome_email ( input ) = # ... end Steps run top-to-bottom. If any step returns a Failure , the pipeline stops immediately — no further steps execute. It's a railway under the hood: each step can produce Success(value) or Failure(error) , and the pipeline routes accordingly. What it's great at: Simple, synchronous pipelines. It's the Ruby equivalent of an Either monad chained with bind — clean, predictable, and minimal. If you're already in the dry-rb ecosystem (dry-validation, dry-types, dry-monads), it fits naturally. What it d

Also: Forging the One Ring in Rings of Power S3 teaser; new Lanterns trailer; Spaceballs: The New One panel.

Friendly hobby machine or serious production tool? Here’s how to know which one is for you.

These are the AC units we’ve trusted to cool our homes for months, if not years.

From musical instruments and games to play pieces that encourage balance and dexterity, these toys will engage your child’s mind and body.

When you're building a multi-tenant SaaS, the first architectural question is usually: how do you keep tenant data isolated? The options range from separate databases per tenant (maximum isolation, maximum cost) to a shared database with row-level filtering (minimum cost, more careful coding required). But there's an equally important question that gets less attention: how does your API know which tenant context a request belongs to? This post covers a pattern we've used in production: a custom request header for tenant scoping, combined with JWT authentication. Simple to implement, easy to audit, and flexible enough to support multi-tenant access from a single user account. The Three Common Approaches 1. Subdomain-based ( tenant.yourdomain.com ) The tenant is encoded in the hostname. Each subdomain routes to the same backend, which extracts the tenant from the Host header. Good: Intuitive, visible in the URL. Bad: Requires wildcard TLS certs, more complex DNS setup, awkward in development, doesn't work for mobile API clients the same way. 2. URL path-based ( /api/tenants/{tenantId}/... ) The tenant identifier is part of every route path. Good: RESTful, self-documenting. Bad: Bloats all route definitions, requires every endpoint to include the tenant segment, makes API versioning messier. 3. Header-based ( x-tenant-id: <id> ) A custom header carries the tenant context. Routes stay clean. The tenant scope is resolved in middleware before the handler runs. Good: Routes stay simple, middleware handles scoping uniformly, works well with JWT auth, easy to test. Bad: Less visible (the tenant isn't in the URL), requires clients to always include the header. We use the header approach. The Implementation The API accepts two forms of auth: A JWT token in the Authorization header — identifies who is making the request A tenant ID in the x-tenant-id header — identifies on behalf of which tenant POST /api/v1/members Authorization: Bearer eyJhbGciOiJIUzI1NiIs... x-tenant-id: ten

I build products solo. Every single one of them sits on top of somebody else's API — Stripe for payments, OpenAI and Anthropic for AI features, Meta for ads, print-on-demand APIs, map APIs. My code is maybe half of what actually runs in production. The other half belongs to vendors, and it changes whenever they decide it changes. Twice this year the first notice I got about a breaking change was a production error. Not an email, not a warning. An error, and then me digging through the vendor's changelog trying to figure out what they changed and when. The information was public the whole time. It was sitting in a changelog page I never visit, because nobody visits changelog pages until something is on fire. So I'm building the thing I wanted to exist BreakWatch is simple: you tell it which APIs your product depends on, and it reads their public changelogs for you. It fetches each changelog page once a day Diffs it against yesterday's snapshot Classifies the real changes: breaking (endpoint removed, field deprecated, "migrate by September") vs. informational (new feature, docs clarification — stuff you can ignore) Alerts you only when something looks like it will break an existing integration Keeps everything in a searchable timeline, so six months later "what changed on their side right before this broke" takes ten seconds instead of an afternoon No SDK, no credentials, nothing installed in your codebase. It only reads public pages. What I tested this week I ran it against the real changelogs of the ten APIs I'm watching first: Stripe, Twilio, OpenAI, Anthropic, Shopify, GitHub, Slack, Cloudflare, Google Maps and Plaid. Some honest findings: 10/10 scrape cleanly now, but it took fixes. Stripe's changelog page alone is 3.3 MB. SendGrid's standalone changelog doesn't exist anymore (it merged into Twilio's). PayPal's developer site serves a JavaScript shell with an HTTP 404 to anything that isn't a full browser, so it's out until I add rendering. The thing I was most a

Also, the full trailer for Zach Cregger's Resident Evil features a likable everyman.

Amazon is hoping the addition of games can turn Prime Video into a one-stop entertainment destination, borrowing a strategy from Netflix, which has increasingly embraced party games over the past several years.

Deck out your room and make class a cinch with these WIRED-tested favorites.

Cordless, handheld, robot, and traditional—we tested them all to find the vacuum that’s fantastic for fur.