AI 资讯

The Financial Times has a good article on how AI is changing the capabilities of video surveillance, with information from both Israel/Iran and Russia. I wrote about this sort of thing a few years ago, how AI enables mass spying in the way that computers and networks enabled mass surveillance. The interesting development in the article is that AI allows people to ask natural language questions about video footage to AIs—and AIs can answer them. In contrast with older tools restricted to a few dozen preset searches, these new tools allow an almost unlimited range of enquiries by enabling language-based searches on video...

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…

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," […]

Microsoft has announced the limited public preview of Copilot Autofix for GitHub Advanced Security for Azure DevOps, extending AI-powered vulnerability remediation to teams using Azure Repos. By Craig Risi

Teams can generate UI faster than ever, but they still have to guarantee that what they ship is usable, secure, and maintainable. Accessibility as an operational capability rather than a compliance checklist or end-of-project audit, and what that looks like in practice.

I am building alternative ways explore scientifc literature. The goal was to make the large number of papers published daily easier to keep up with by visualising the macro scopic trend. It is free to use at The Global Research Space for any one interested in giving it a try! How I built it I sourced the latest 11M papers from OpenAlex and Arxiv and ecoded them using SPECTER 2 on titles and abstracts then projecting it down to 2d using UMAP and creating labels within voronoi bounds around high density peaks at increasingly deep depths. There is also support for both keyword and semantic queries, and there's an analytics layer for ranking institutions, authors, and topics etc. I have also more recently added to ability to slide back and forth in time and a daily auto ingestion script to ensure the map is up to date. Feedback or suggestions is very welcome! submitted by /u/icannotchangethename [link] [留言]

The product that makes AI recommend your brand Discussion | Link

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

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

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…

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 "

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.