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

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

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.

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...

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.

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

AWS launched Lambda MicroVMs, a new serverless compute primitive that runs each user session or AI agent in its own Firecracker virtual machine with hardware-level isolation, snapshot-based rapid launch, and state preservation for up to eight hours. Reddit community analysis found the minimum setup costs $3.03/day, roughly 9x Fargate spot pricing. By Steef-Jan Wiggers

OKX is bringing together payments, identity and reputation into a marketplace for AI agents.

Event-driven architecture promises scalability, but in Java-based real-time systems the tradeoffs only surface in production. Drawing on a Java/Kafka contact center platform handling 80k BHCC across 10k agents, this article details where the design breaks down—state management, partition limits, deduplication, JVM tuning, cascading consumer failures—and the Redis-backed patterns that fixed each. By Sagar Deepak Joshi

Welcome to this week's Top 7, where the DEV editorial team handpicks their favorite posts from the...