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Watching Elon Musk fulminate at Bill Savitt during Musk v. Altman - the case in which Musk sued Sam Altman and OpenAI instead of seeing a therapist about his AI failures - was a bit like watching a toddler have a temper tantrum at his nursery school teacher. Savitt's questions were "designed to trick me," […]

Artificial intelligence is transforming what is possible in agriculture, but industry leaders should be wary of investing in AI without first laying the groundwork. The use cases are promising, especially for an industry navigating volatile fertilizer costs, unpredictable weather, and margins that leave little room for error. Research shows AI-enabled predictive models can improve crop…

The original Ninja Slushi has been replaced! The new best slushie machines chill faster and make soft serve.

TL;DR Anthropic recently published When AI Builds Itself, an essay explaining how AI is...

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Bitdefender VPN has an excellent starting price, even if it lacks the advanced features that privacy nerds may want.

I had a lot of work to get through, and for once I didn't want to crawl through it one ticket at a...

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Imagine an e-bike motor that lets you select your preferred pedaling cadence and then automatically adjusts the gears to keep your legs spinning at that exact speed, no matter how steep the hill gets - all without a fragile derailleur or heavy multi-speed cassette to maintain. Prefer manual control? No problem, you can have as […]

For decades, the senator has argued that concentrated wealth threatened American democracy. Now he’s betting that frustration with Big Tech, billionaires, and unchecked AI is reaching a tipping point.

Apple. Anthropic. Disney Research. Google. Meta. Microsoft. NVIDIA. OpenAI. Few places outside Silicon Valley can claim R&D hubs from all of these companies. Fewer still are concentrated in a city of just over 400,000 people—roughly half the size of San Francisco. Over the past two decades, however, many of the world’s most influential technology companies…

This article covers the MCP setup and configuration for using Google Nano Banana 2 Lite and...

WIRED spent months talking to America’s favorite failson as he plotted his return to public life. Now he’s feeding the trolls—and everyone else.

Introduction I keep hearing the term loop engineering. It's all over my feed, every AI...

Hello Dev Community! 👋 It is officially Day 89 of my 100-day full-stack engineering run! 🎯 Yesterday, I kicked off my competitive solving streak on HackerRank. Today, I advanced from standard linear filters into the powerful world of textual pattern recognition by mastering: SQL Regular Expressions (REGEXP) and String Anchors! 🔍🛡️ When processing real-world data pipelines—like validating structured phone inputs, email domains, or parsing specific text queries—standard LIKE operators can make your code messy and repetitive. Today, I solved these constraints elegantly. 🧠 Shifting from Bulky LIKE Statements to Sleek REGEXP As tracked inside my workspace files across "Screenshot (193).png" and "Screenshot (195).png" , I solved two distinct core challenges from the HackerRank series: 1. Match from the Start: Weather Observation Station 6 The Goal: Query the list of CITY names from STATION that start with vowels ( a , e , i , o , u ), ensuring no duplicates are returned. The Evolution: Instead of chaining multiple LIKE queries or cutting sub-strings with LEFT() , I utilized the caret anchor ( ^ ) inside a regular expression array to verify the string's starting boundary instantly: sql SELECT DISTINCT CITY FROM STATION WHERE CITY REGEXP "^(A|E|I|O|U)";

Samsung is expected to unveil its next generation of foldables at a Galaxy Unpacked event next month, but now we know what they might look like, courtesy of some leaked images published by Android Headlines. Images shared by the publication include case designs for two new Galaxy Z Fold 8 models and the Galaxy Z […]

If you've ever worked with e-commerce data, you know that "a pair of jeans" is never just one product. A single style might come in 5 washes, 8 sizes, and 3 inseam lengths. That's 120 potential SKUs. Handling this correctly in an API can be tricky, so let me share a pattern I've used for structuring product variants. The core problem is balancing flexibility with performance. You want customers to filter by size, color, and fit without making dozens of API calls. Here's a simple but effective approach using a normalized database schema with a flat query layer: -- Products table (the "parent") CREATE TABLE products ( id UUID PRIMARY KEY , name TEXT NOT NULL , description TEXT , base_price DECIMAL ( 10 , 2 ), category TEXT ); -- Variants table (the actual sellable items) CREATE TABLE variants ( id UUID PRIMARY KEY , product_id UUID REFERENCES products ( id ), sku TEXT UNIQUE NOT NULL , size TEXT , color TEXT , wash TEXT , inseam TEXT , price DECIMAL ( 10 , 2 ), -- can override base price stock_quantity INT , image_url TEXT ); The key insight? Keep the product metadata (description, care instructions, brand story) in the products table, but put all the sellable attributes in variants. This lets you run queries like: -- Find all size 28 jeans in "mid wash" under $80 SELECT p . name , v . color , v . wash , v . price , v . stock_quantity FROM products p JOIN variants v ON p . id = v . product_id WHERE p . category = &# 039 ; women - jeans &# 039 ; AND v . size = &# 039 ; 28 &# 039 ; AND v . wash LIKE &# 039 ; % mid %&# 039 ; AND v . price & lt ; 80 AND v . stock_quantity & gt ; 0 ORDER BY v . price ; For the frontend, I usually return a flattened structure: { "product": { "id": "abc -123 " , "name": "Classic Straight Leg Jean" , "description": "High-rise fit in stretch denim..." , "availableSizes": [ " 24 " , " 25 " , " 26 " , " 27 "

Remember when Artificial Intelligence (AI) felt like something from a science fiction movie? Well, it's not just for movies anymore! AI is here, and it's rapidly changing how businesses of all sizes operate. From making customers happier to solving tricky problems faster, AI is becoming a vital tool for success. But how exactly is AI making such a big difference? Many business owners wonder about the real-world uses of AI. That's why we've put together this comprehensive guide. We're going to explore 50 specific ways AI development is transforming modern businesses, helping them work smarter, grow faster, and serve their customers better. Get ready to see how AI isn't just a buzzword, but a powerful engine driving real change in the business world! Boosting Customer Service & Experience (CX) (1-10) AI is making customer interactions smoother, faster, and more personal. Instant Customer Support (Chatbots): AI-powered chatbots answer common questions 24/7, so customers get help right away. Personalized Recommendations: AI suggests products or services customers might like, based on their past choices, making shopping feel more personal. Faster Problem Solving: AI helps support agents quickly find solutions by sifting through information. Predicting Customer Needs: AI can guess what a customer might want or need before they even ask, allowing businesses to be proactive. Voice Assistants for Support: AI voice assistants can handle basic customer calls, freeing up human agents for more complex issues. Sentiment Analysis: AI understands how customers feel about a product or service by analyzing their feedback (reviews, social media posts). Automated Email Responses: AI can draft quick, helpful replies to common customer email inquiries. Targeted Customer Outreach: AI helps businesses send the right message to the right customer at the right time. Improved Loyalty Programs: AI personalizes rewards and offers, making customers feel more valued and increasing their loyalty.

Stop Writing the Same Laravel Boilerplate: Generate a Complete Module with One Artisan Command Every Laravel developer has experienced this. You start implementing a new feature and immediately create the same files you've created dozens of times before: Model Migration Repository Service Form Request API Resource Policy Filter Status Enum Feature Tests Unit Tests Swagger/OpenAPI annotations The process is repetitive, time-consuming, and easy to get wrong. The Problem While Laravel provides excellent generators, building a production-ready API module still requires running many Artisan commands and wiring everything together manually. For large projects following Repository and Service Layer architectures, this becomes even more repetitive. The Solution I built Laravel Base , an open-source package that generates an entire production-ready module from a single command. php artisan make:module Product The generated module includes: ✅ Model ✅ Migration ✅ Repository Pattern ✅ Service Layer ✅ Form Requests ✅ API Resources ✅ Filters & Pagination ✅ Policies ✅ Status Enums ✅ Swagger/OpenAPI annotations ✅ Feature Tests ✅ Unit Tests Modern Development Experience The package is actively maintained and includes: Laravel 10–13 support PHP 8.1–8.4 compatibility GitHub Actions CI PHPStan static analysis Laravel Pint code style Automated releases Repository automation Why I Built It After working on multiple Laravel projects, I noticed I was spending too much time generating the same project structure instead of focusing on business logic. I wanted a tool that lets developers start implementing features immediately rather than setting up folders and classes. Feedback Welcome Laravel Base is open source, and I'd love to hear your thoughts. GitHub Repository: https://github.com/MuhammedMSalama/LaravelBase Packagist: https://packagist.org/packages/muhammedsalama/laravel-base The package was recently featured by Laravel News, and I'm continuing to improve it based on community feedbac

For twenty years, "ranking" meant one thing: get indexed, get crawled, get a position on a results page. Every Shopify store's SEO checklist was built around that single goal. Sitemap submitted, meta tags filled in, Core Web Vitals green, done. That checklist still matters. It's also no longer sufficient, and most stores haven't noticed yet. Two different systems, two different jobs Google's index and an LLM's answer engine are not the same kind of system, even though they both "read" your store. A search index is a retrieval system. It crawls a page, tokenizes the content, stores it, and matches it against a query at request time. Ranking is a function of relevance signals backlinks, click-through behavior, freshness, page experience. The unit of output is a list of links. The user does the synthesis. An LLM-based answer engine is a generation system. When someone asks ChatGPT, Perplexity, or Claude "what's a good Shopify store for sustainable activewear," the model isn't returning a ranked list of crawled pages. It's generating a single answer, and it decides which brands to name in that answer based on which entities it has high confidence are real, relevant, and well-attested across multiple sources. The unit of output is a sentence. The model does the synthesis, and your store either gets a mention in that sentence or it doesn't. This is the gap. A store can be fully indexed sitemap clean, every product page crawlable, ranking on page one for its category and still never get named in an AI-generated answer. Indexing is a necessary condition for citation. It is not a sufficient one. What "citable" actually requires Citation in an LLM context isn't about keyword matching. It's closer to reputation modeling. Three things tend to separate stores that get cited from stores that don't: Entity consistency across the web. The model needs to resolve "your brand" as a single, stable entity across multiple independent sources your own site, marketplaces, press mentions, r