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.

Proton has launched a major upgrade for its Lumo chatbot, giving it image generation and editing capabilities.

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 "

Every developer I know has the same five tabs permanently open. A JSON formatter from some site that loads three cookie banners before the textarea appears. A Base64 encoder that's been running on a 2009-era PHP server. A unit converter that requires an email address for no obvious reason. A percentage calculator buried under a wall of AdSense. And a spreadsheet they opened last Tuesday and forgot to close. We've normalised this. We've accepted that reaching for a basic tool means either tolerating a terrible experience or building one ourselves. I got tired of both options. So I built The Calcu. Not a single tool. Not a focused niche product. Four hundred and thirty-four tools across calculators, converters, generators, formatters, and validators. Finance, tax, health, math, marketing, developer utilities, and everyday calculations, all in one place with no login, no paywall, and no data leaving your browser. The case for breadth over niche The conventional product advice is to pick one problem and go deep. I thought about that seriously. But when I mapped out how I actually use calculator-type tools in a day, I don't have one recurring need. I have twelve unrelated ones: compound interest this morning, a JSON diff this afternoon, a word count before I send a draft, a GST check before I raise an invoice. Building a single niche tool would have solved one of those and left the other eleven pushing me toward competitors. The constraint I set was that every tool had to load instantly, update in real time as you type, and never require an account. If I couldn't ship it to that standard, I didn't ship it. That discipline kept the breadth from becoming bloat. The architecture decision that made this possible All calculations run entirely in the browser. Nothing is sent to a server. This is partly a privacy decision, partly a performance decision, and partly the reason the whole thing stays free to run at scale. When there's no server-side compute to bill, the cost model

I'm Zabi, 16, from Pakistan. I got tired of 10-minute YouTube tutorials that skip the hard parts , so I spent 3 weeks writing a complete Node.js guide from scratch. It's 10,000+ words. No fluff. Just everything you need to go from console.log to production . I published it free on my site ZabiTech Community, but I'm sharing the key roadmap here for DEV. What this masterclass covers : Part 1: Fundamentals (most tutorials stop here) Event Loop explained like you're 5 Streams, Buffers, and why Node is fast CommonJS vs ES Modules in 2026 Part 2: Building Real Apps Express.js from zero REST API + GraphQL Authentication (JWT, sessions) File uploads, WebSockets Part 3: Production (what they never teach) Performance optimization techniques Clustering & Worker Threads Docker + deploying to free hosting Security checklist Part 4: Getting Hired 50+ Node.js interview questions with answers How to build a portfolio that gets callbacks Why I wrote this: I'm learning Node.js myself for backend development. Every tutorial I found was either too basic or assumed I knew DevOps. So I documented everything as I learned it – including the mistakes. The full guide has code examples, diagrams, and a complete project you can deploy. 👉 Read the full 10,000-word masterclass here: https://zabitechcommunity.netlify.app/posts/the-complete-nodejs-masterclass-2026-beginner-to-professional-guide.html?utm_source=dev.to&utm_medium=referral&utm_campaign=nodejs_june2026 Who is this for? Beginners who know basic JavaScript Anyone tired of fragmented tutorials Students in Pakistan/India looking for free resources I'm building in public at 16. If this helps you, follow me here – I'll be posting my next guide (Frontend Roadmap 2026) next week. What topic should I cover next? Drop it in comments. Originally published on ZabiTech Community

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

Introduction As applications grow, traditional relational databases such as MySQL may struggle with analytical workloads involving millions of records and complex aggregations. While MySQL excels at Online Transaction Processing (OLTP), ClickHouse® is purpose-built for Online Analytical Processing (OLAP), enabling lightning-fast analytical queries on massive datasets. Migrating data from MySQL to ClickHouse® allows organizations to build high-performance reporting systems, dashboards, and real-time analytics without impacting transactional workloads. In this guide, you'll learn several approaches to migrate data from MySQL to ClickHouse®, along with their advantages, limitations, and ideal use cases. Why Migrate from MySQL to ClickHouse®? MySQL and ClickHouse® are designed for different workloads. Feature MySQL ClickHouse® Storage Model Row-based Columnar Best For Transactions (OLTP) Analytics (OLAP) Query Speed Fast for row lookups Extremely fast for large scans Aggregation Performance Moderate Extremely fast Scalability Primarily Vertical Optimized for analytical scaling Typical Use Cases Applications and transactional systems Reporting, dashboards, and analytics Migrating from MySQL to ClickHouse® makes sense when: Analytical queries are becoming slow in MySQL. You need real-time dashboards over large datasets. Reporting queries are impacting your production database. You regularly process millions or billions of rows. Migration Architecture MySQL │ ▼ Export / Synchronization │ ▼ Data Transformation │ ▼ ClickHouse® │ ▼ Dashboards / Analytics Migration Methods There are multiple ways to migrate data depending on your requirements. Method 1: CSV Export and Import (Recommended for Beginners) This is the simplest approach for performing a one-time migration of historical data. Step 1: Export Data from MySQL Run the following command inside MySQL: SELECT * INTO OUTFILE '/tmp/employees.csv' FIELDS TERMINATED BY ',' ENCLOSED BY '"' LINES TERMINATED BY ' \n ' FROM employ

A few months ago I noticed something annoying about how I worked: I was spending more time collecting information than actually thinking about it. The pattern was always the same. Open a search engine, open a dozen tabs, skim past the SEO filler and cookie banners, copy the paragraphs that actually mattered into a doc, paste the whole mess into an LLM and ask it to make sense of things. Then, a week later, do it again because whatever I was tracking had changed. At some point I stopped asking "how do I do this faster" and started asking why I was doing it by hand at all. Why the obvious answers didn't work ChatGPT and Perplexity are fine for a single question. They're worse at the part I actually needed help with, which was repetition: running the same research loop on a schedule, keeping a record of what changed, and getting a notification when it did. Neither tool is built to sit in the background and check on a topic for you. Plain scraping scripts have the opposite problem. They get you raw HTML, not understanding. You still have to strip out nav bars and footers by hand, and the moment you point one at a list-style page like Hacker News instead of a blog post, it falls apart. And bookmarking is just deferring the problem. A folder of forty saved links isn't research, it's homework you haven't done yet. I wanted something in between: automated enough to skip the tab-hoarding, but still producing something I could read and trust, not just a black-box answer. So I built Focal Harvest It's a modular CLI that runs the whole research loop, search, scrape, clean, synthesize, report, on its own, and stays lightweight enough to run on a laptop with no GPU and no database. A single run looks like this: you give it a topic and a focus area (what you specifically want answered), it searches the web, pulls and cleans the pages, synthesizes a report, and writes it to disk. There's also a loop mode, so the same query can re-run every few hours and ping you on Discord or Teleg

Most developers have experimented with ChatGPT or GitHub Copilot. But when it comes to building AI-powered applications, simply calling an LLM API isn't enough. Understanding what's happening behind the scenes helps you design systems that are scalable, reliable, and cost-effective. In this article, we'll explore four concepts every software engineer should know: tokens, embeddings, transformers, and Retrieval-Augmented Generation (RAG). 1. LLMs Think in Tokens, Not Words One of the biggest misconceptions about Large Language Models (LLMs) is that they understand words like humans do. In reality, they process tokens, which are smaller units of text. For example: Prompt: Explain dependency injection in Spring Boot. is first converted into a sequence of tokens before the model processes it. Why does this matter? API pricing is based on the number of input and output tokens. Longer prompts increase latency and cost. Every model has a maximum context window measured in tokens. When building AI applications, prompt design isn't just about getting better answers—it's also about optimizing performance and cost. 2. Transformers: The Breakthrough Behind Modern AI Before 2017, language models processed text one word at a time using architectures like RNNs and LSTMs. They struggled with long conversations because earlier context was gradually forgotten. The introduction of the Transformer architecture changed this with a mechanism called self-attention. Instead of reading text sequentially, transformers analyze the relationships between all tokens in a sentence simultaneously. Consider this sentence: "The server restarted because it ran out of memory." The model understands that "it" refers to "the server", not "memory", by assigning attention to the relevant words. This ability to capture context efficiently is what powers modern LLMs like GPT, Gemini, Claude, and Llama. 3. Embeddings Enable Semantic Search Suppose a customer searches: "How can I get my money back?" But your

The maps and images show the extent of destruction and give rescue operations a tool to find any remaining survivors.

A device that resets in the field is not always the hardest problem. The harder problem is a device that resets, comes back online, and leaves no evidence about what happened before the reboot. That is where a firmware black box becomes useful. This is the DEV.to edition of a Silicon LogiX technical article. The canonical English source is linked at the end. What a firmware black box is A firmware black box is a small diagnostic subsystem inside the firmware. Its job is to preserve enough information to support post-mortem analysis after a reset, watchdog event, HardFault, panic or unexpected reboot. It does not need to record everything. It needs to record the data that helps answer the first diagnostic questions: why did the device reset? how long had it been running? which firmware build was installed? what state was the application in? which task was active? did the watchdog fire? did memory, stack or heap margins collapse? did the network, modem, BLE, Wi-Fi or OTA flow fail just before the reboot? Without that data, every field reset deletes most of the evidence. Why sporadic resets are expensive Rare embedded bugs are often more expensive than obvious failures. A crash that happens every time in the same function can usually be analyzed with a debugger, logs and a repeatable test. A reset that appears once every ten days on a customer device is different. The cause may depend on a combination of: temperature unstable power brown-out cable length enclosure heating network drops modem state memory fragmentation stack exhaustion long uptime race conditions a peripheral that stops responding an OTA edge case In the lab, the product may look clean. In the field, the environment changes. The customer report often becomes: "it rebooted", "it stopped communicating", or "we had to power-cycle it". That is not enough for firmware diagnosis. What to capture A good first version does not need to be large. Start with a compact structure that survives the next boot: reset r

React useIntersectionObserver Hook: Lazy Load & Detect Visibility (2026) You want to load an image only when it scrolls near the viewport. Or fire an analytics event the first time a card is actually seen . Or trigger "load more" when the user reaches the bottom of a list. Every one of these is the same question — is this element on screen yet? — and for years the answer was a scroll listener that fired hundreds of times a second, re-read getBoundingClientRect() on each tick, and still managed to miss the edge cases. IntersectionObserver is the browser API that answers that question correctly, asynchronously, and off the main thread. useIntersectionObserver is the hook that wires it into React without the useEffect / useRef /cleanup boilerplate — and without the leak-on-unmount and stale-closure bugs the hand-rolled version always ships. This post covers the real @reactuses/core API, the three patterns you'll actually reach for, and how to tune threshold , rootMargin , and root . SSR-safe and typed. Why Not Just Use a Scroll Listener? The old way to know whether an element was visible looked like this: listen to scroll , and on every event measure the element against the viewport. useEffect (() => { function onScroll () { const rect = el . getBoundingClientRect (); if ( rect . top < window . innerHeight ) { setVisible ( true ); } } window . addEventListener ( ' scroll ' , onScroll ); return () => window . removeEventListener ( ' scroll ' , onScroll ); }, []); This has two problems baked in. First, scroll fires on the main thread, dozens of times per second, and getBoundingClientRect() forces a synchronous layout each time — that's exactly the recipe for janky scrolling. Second, it only catches elements crossing the viewport ; the moment your scroll happens inside a container, you're re-deriving geometry by hand. IntersectionObserver flips the model. You hand the browser a target and a threshold, and it tells you — asynchronously, batched, off the scroll path — when