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"I'll just write a quick button style... and a text field with validation... and a card component..." Two weeks later you have a bespoke design system that only half the app uses, three slightly different buttons, and nothing shipped. If you're building with an AI coding agent, the drift problem is worse, not better. Ask Claude, Cursor, or Codex to "add a settings screen" three separate times and you'll get three different paddings, three different corner radii, and a button style that quietly forked itself somewhere around commit 40. Agents are great at writing plausible SwiftUI and bad at remembering what the rest of your app already decided. DesignFoundation is an open-source SwiftUI package that gives you a token-based theming engine, 25 components, and 6 feedback/overlay modifiers that all read from the same theme. You set it once at the app root, and everything underneath updates. That's the whole idea — and it ships with CLAUDE.md , AGENTS.md , and Cursor rules already written, so an agent working in your repo reaches for DFButton instead of inventing a new one. In this tutorial you'll go from zero to a themed, consistent SwiftUI UI — without writing a single custom button style. What We're Building By the end you'll have: A working app that uses DesignFoundation's theme system A themed form with inputs, validation states, and feedback components A custom theme built from tokens An understanding of how the style system composes Requirements: Xcode 16+, iOS 18+ / macOS 15+ target, Swift 6. Installation Via Xcode File → Add Package Dependencies Paste https://github.com/NerdSnipe-Inc/design-foundation Set the version rule to Up to Next Major from 1.0.0 Add DesignFoundation to your target Via Package.swift dependencies : [ . package ( url : "https://github.com/NerdSnipe-Inc/design-foundation" , from : "1.0.0" ) ], targets : [ . target ( name : "YourApp" , dependencies : [ "DesignFoundation" ]) ] Step 1: One Line Instead of a ThemeManager Singleton Every app that

Redux setup is a ceremony. You create a store, compose your reducers into a root tree, wrap your app in a Provider, register middleware, and configure enhancers — all before you write a single line of feature logic. SDuX Vault™ replaces that entire ceremony with two function calls and zero root configuration. Redux Store Ceremony A typical Redux application requires several files and configuration steps before state management is operational. Here is what a minimal Redux setup looks like for a single feature: // store.ts import { createStore , combineReducers , applyMiddleware } from ' redux ' ; import thunk from ' redux-thunk ' ; import { userReducer } from ' ./reducers/userReducer ' ; const rootReducer = combineReducers ({ users : userReducer , }); export const store = createStore ( rootReducer , applyMiddleware ( thunk ) ); // App.tsx — Provider wrapper required import { Provider } from ' react-redux ' ; import { store } from ' ./store ' ; function App () { return ( < Provider store = { store } > < UserList /> < /Provider > ); } That is 20+ lines of configuration across multiple files — and it only covers one feature. Add a second feature and you are back in the combineReducers file, composing another slice into the tree. Add middleware and you are threading enhancers through applyMiddleware . Add DevTools and you are composing composeWithDevTools on top. Every new feature touches the root configuration. Redux Requirement What It Does createStore() Creates the single global store instance combineReducers() Composes feature reducers into a root tree applyMiddleware() Registers middleware (thunk, saga, etc.) Provider Makes the store available to all components via context composeWithDevTools() Enables Redux DevTools integration ⚠️ Warning: Every entry in that table is root-level configuration. Adding a new feature means editing the root reducer composition, possibly the middleware stack, and potentially the Provider hierarchy. Root configuration is a shared depende

I came into this as a graphic designer, not a software engineer. I didn't have a computer science background, and a lot of what BrandStack needed — authentication, databases, payments, deployment — was new territory for me when I started. What made it possible wasn't some shortcut. It was breaking the problem down into pieces I could actually learn: how user accounts work, how a database should be structured so one person's data never leaks into another's, how to move from test payments to real ones without breaking checkout for actual customers. I made real mistakes along the way. Early on, every user shared the same underlying brand data because I hadn't scoped the database correctly to each account — a serious bug that I only caught by testing with two separate accounts myself. Finding and fixing that taught me more about proper application architecture than any tutorial could have. I don't think being a designer first is a disadvantage for building product. If anything, it means the interface and the experience get real attention, not just the backend logic. But it does mean being honest about what you don't know yet, and being willing to slow down and actually understand a problem instead of copying a fix you don't understand. BrandStack is still a work in progress. But it's a real, working product — built by someone who had to learn most of this from scratch, in public, one bug at a time.

How to Monitor Website Changes Automatically I run a few websites and need to know immediately when something breaks. A CSS regression, a broken layout, a missing section. Manual checking doesn't scale, and text-based monitoring misses visual issues. The {{screenshot-diff}} on Apify takes two screenshots and produces a pixel-level comparison with an overlay showing exactly what changed. How It Works Take a baseline screenshot of the correct state. Then take a current screenshot of the live page. The actor compares pixel by pixel and returns a diff image with changed pixels highlighted, plus a percentage telling you how much changed. import requests , time API_TOKEN = " YOUR_APIFY_TOKEN " def capture_screenshot ( url ): resp = requests . post ( " https://api.apify.com/v2/acts/weeknds~website-screenshot-api/runs " , headers = { " Authorization " : f " Bearer { API_TOKEN } " }, json = { " url " : url , " fullPage " : True } ) run_id = resp . json ()[ " data " ][ " id " ] time . sleep ( 15 ) items = requests . get ( f " https://api.apify.com/v2/acts/weeknds~website-screenshot-api/runs/ { run_id } /dataset/items " , headers = { " Authorization " : f " Bearer { API_TOKEN } " } ). json () return items [ 0 ][ " screenshotUrl " ] def compare_screenshots ( baseline_url , current_url ): resp = requests . post ( " https://api.apify.com/v2/acts/weeknds~screenshot-comparison-tool/runs " , headers = { " Authorization " : f " Bearer { API_TOKEN } " }, json = { " baselineImageUrl " : baseline_url , " currentImageUrl " : current_url , " threshold " : 0.01 } ) run_id = resp . json ()[ " data " ][ " id " ] time . sleep ( 10 ) items = requests . get ( f " https://api.apify.com/v2/acts/weeknds~screenshot-comparison-tool/runs/ { run_id } /dataset/items " , headers = { " Authorization " : f " Bearer { API_TOKEN } " } ). json () return items [ 0 ] baseline = capture_screenshot ( " https://mysite.com " ) current = capture_screenshot ( " https://mysite.com " ) result = compare_screenshots ( b

Most backtests lie to you. Not intentionally. But they lie. You design a strategy, run it on historical data, and watch the returns look incredible. Then you run it live — and it underperforms a simple buy-and-hold from day one. The math wasn't wrong. The data was. If you're: testing momentum or mean-reversion strategies in Python, building quant tools for personal or professional use, or tired of backtests that collapse the moment real execution begins, This changes how you work. TL;DR What this covers: Backtesting trading strategies in Python using EODHD's historical OHLCV data API Stack: requests , pandas , numpy — no heavy frameworks (no backtrader, no vectorbt) Scripts included: Script 1 — Fetch adjusted historical price data from EODHD Script 2 — SMA crossover strategy (20/50-day) Script 3 — RSI mean-reversion strategy Script 4 — Performance metrics: Sharpe ratio, max drawdown, win rate EODHD pricing: Free tier available; full access from $19.99/month Best for: Developers and analysts who need reliable, split/dividend-adjusted data without scraping The Problem with Free Data Most developers start with Yahoo Finance or a scraped CSV. That works fine for a quick prototype. It stops working the moment your strategy includes anything that happened around a stock split, dividend payment, or ticker change. Non-adjusted price data creates ghost signals. A stock "drops 50%" when it actually split 2:1. Your moving average calculates a crossover that never happened in real life. Your strategy looks profitable because it's trading on a data artifact. The free path costs you accuracy. And in backtesting, accuracy is the whole point. The Fix Is Simpler Than You Think The real bottleneck isn't the strategy logic. It's the data source. Use split- and dividend-adjusted closing prices from a reliable provider, and half your backtest reliability problems disappear before you write a single signal. EODHD APIs provides exactly this. Their historical data endpoint returns adjusted

How I Built 7 Apify Actors and Started Earning Passive Income from Web Scraping A few weeks ago I had zero Apify actors. Now I have seven, all published on the Apify Store, monetized with pay-per-event pricing, and slowly building a passive income stream. Here's exactly how I did it — the strategy, the tech stack, the mistakes, and what I'd do differently. The Strategy: Zero Competition Most new Apify developers go after hot categories. LinkedIn scrapers, Amazon product extractors, Twitter data. Makes sense — those have demand. But they also have dozens of established actors with hundreds of reviews. I took the opposite approach. Find niches with zero existing actors. This means lower total addressable market, but 100% of whatever traffic exists goes to you. No competing on price, no fighting for reviews, no SEO war against actors with years of history. How I found the niches: Browsed Apify Store categories sorted by actor count Searched for common developer pain points with no existing Apify solution Checked search volume for "[keyword] API" and "[keyword] scraper" Verified zero results on Apify Store for each candidate The winners: domain intelligence, screenshot comparison, Swedish company registry, IP geolocation, QR code generation, and link metadata extraction. The Tech Stack Every actor uses the same foundation. Apify Python SDK v3.4 handles input/output, storage, proxy, and deployment. Playwright for JavaScript-heavy sites and screenshots. aiohttp for lightweight API scraping (way faster than a full browser). Pillow for image processing. Deployment is one command: apify push The Actors {{domain-intel}} WHOIS, DNS, SSL, and tech stack in one API call. Uses socket + ssl + python-whois for data collection, no external API dependency. $0.005 per run. {{screenshot-api}} Full-page screenshots via Playwright. Handles lazy-loading, infinite scroll, and viewport sizing. $0.003 per run. {{metadata-extractor}} Open Graph, Twitter Cards, JSON-LD, and meta tags from any

I hesitated to write this. Not because I don’t have an opinion about AI in software engineering, but...

This is today’s edition of The Download, our weekday newsletter that provides a daily dose of what’s going on in the world of technology. Your family’s $300 stake in OpenAI Sam Altman’s proposal that Americans should share in the wealth created by AI is back in the spotlight, with reports that he is discussing giving…

The company just raised $7 million in seed funding, and is launching its app for iPhone and Android on Tuesday.

Amazon Web Services (AWS) has announced a major expansion of its AWS DevOps Agent, introducing new release management capabilities designed to assess code changes and autonomously test software before it reaches production. By Craig Risi

It's another bad week for the video game industry. Microsoft outlined a series of layoffs on Monday that Xbox CEO Asha Sharma described as "the most significant restructure in Xbox history." But buried in Sharma's memo was a curiously optimistic statement: "I want Xbox to be one of the few companies that entertains more than […]

Marshall announced new versions of its Acton and Stanmore Bluetooth speakers today with upgraded tweeters, bass ports, and internal designs that improve their ability to fill a room with sound. Both the Acton IV and Stanmore IV replace their four-year-old predecessors with a new focus on repairability. Parts including knobs, feet, and the speakers' front […]

Protect your expensive iPhone 17, iPhone Air, iPhone Pro, or iPhone 17e with our favorite cases and screen protectors.

Amazon says it has enough satellites in orbit to begin initial broadband service at mid-latitudes later this year.

With the rapid progress of AI capabilities and the move to agentic systems, organizations are expanding their use cases as the technology continues to grow. That constant evolution also introduces risk, leaving IT leaders to wonder which investments will prove valuable even six months into the future. Returning to the foundational elements of AI architecture—the…

The brand’s UR9 competes with similar offerings from higher-end brands like Samsung and LG.

Top robotics researchers and founders explain how robot autonomy is evolving.

Top robotics researchers and founders explain how robot autonomy is evolving.

In the summer of 2024, former Army National Guard member Andrew Scott Hastings spent a sweaty afternoon carefully packing boxes with parts he made using his 3D printer. These weren't novelty figurines or replacement Ikea pieces. The boxes were instead filled with a handful of homemade firearm lower receivers and more than 100 "switches," small […]

Not sure this will have any effect, but I support the effort: According to Google’s legal filing, Outsider Enterprise operates through Telegram. The group offers phishing-as-a-service to individuals who may not be technically savvy enough to set up fraudulent websites and text campaigns on their own. In its Telegram channels, Outsider Enterprise reportedly provided instructions on how to use Google’s Gemini AI to create websites that imitate those of Google, YouTube, and government agencies such as New York’s E-ZPass. The group offered nearly 300 scam templates...