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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

Lately I've noticed that "Who wants to be hired" posts are getting more comments than the "Who's hiring" ones. I pulled the data using HN API and confirmed this "reversal". "Who wants to be hired" posts are getting 2x more comments than "Who's hiring" posts (and seems to be accelerating). For reference, in 2022 it used to be 0.25. I'm old, I'm from the times where demand for software devs greatly outpaced supply, but maybe we're seeing a reversal of it? I don't want to draw any conclusions yet,

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 started leaning in on AI heavily this year, as I wanted to get more done autonomously, but then my token usage climbed dramatically to the point where my weekly quota would run out before the end of the week, sometimes a couple of days into the week. I realised I had to do something about it else I'd have to double my spend. So I decided to start tracking my cost per task type. This revealed that a lot of my spend went to searches/scans or simple things like scouting tasks. I then decided to t

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

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 […]

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

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

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 […]