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On July 3, as a heat wave swept the region, New York State’s grid imported 52 gigawatt-hours of electricity from Canada—enough to meet about 9% of its total electricity demand that day. Some of that power shuttled in on a 339-mile power line stretching from Quebec to Queens called the Champlain Hudson Power Express (CHPE).…

Hear from six pediatricians in measles hot spot Utah, as the US faces infection levels not seen in over three decades and prepares to lose its status as a country that eliminated the dangerous disease.

The bottleneck in a mature SOC is rarely analyst triage; rather, it is the detection-engineering team's ability to keep the rule base aligned with a threat landscape that evolves faster than rules can be written. Learn how multi-agent system for production security operations has reduced mean times to detect and to respond by 40% and compressed the human work required by 12x. By Willem Berroubache

Increase your brand's AI visibility on autopilot Discussion | Link

Permission-first real-time senses for AI companions Discussion | Link

The upper stage of a Falcon 9 rocket has been drifting in space for over a year after it launched. It's expected to crash into the moon next month.

A human-in-loop support agent that tracks customer signals Discussion | Link

A private data-usage meter for your Mac menu bar Discussion | Link

Building Profitable Niche Lifestyle Stores: A Developer's Guide to E-Commerce Essentials Launching a niche lifestyle e-commerce store is more than just setting up a storefront—it's a technical challenge that requires thoughtful architecture, performance optimization, and strategic content planning. Whether you're building a store for outdoor gear, fashion accessories, or humor products like humor24.se , here's what developers need to know. Choose the Right Tech Stack Most profitable niche stores run on WordPress + WooCommerce + a performance-focused theme (like Blocksy). This combination offers: Flexibility : Extend functionality without rewrites SEO-friendly : Built-in structured data support Cost-effective : No complex deployment infrastructure needed Content integration : Blog + products on the same platform Use a headless CMS approach only if you have compelling reasons (high-traffic requirements, complex frontend needs). For 99% of niche stores, the overhead isn't worth it. Core Web Vitals = Revenue Google's Core Updates consistently penalize slow stores. Prioritize: LCP (Largest Contentful Paint) < 2.5s : Optimize images (WebP/AVIF format), lazy load below-fold content, defer non-critical CSS INP (Interaction to Next Paint) < 200ms : Minimize main thread blocking, defer JavaScript CLS (Cumulative Layout Shift) < 0.1 : Use fixed image dimensions, avoid late-loading ads/widgets # Generate WebP variants of product images convert image.jpg -quality 80 image.webp # Check your Core Web Vitals # Use PageSpeed Insights API or Lighthouse CI in your deployment pipeline Each 0.1s improvement in LCP can yield 3-5% conversion uplift. Performance is a feature. Structured Data Wins Traffic Rich snippets dramatically improve click-through rates. Implement: Product schema : name , price , availability , image , brand , AggregateRating BreadcrumbList : Navigation structure FAQ schema : If you have FAQs (47% higher CTR for FAQ snippets) { "@context" : "https://schema.org/" , "@t

TL;DR : most production AI tasks are not LLM tasks. To triage my email, I replaced a 7-billion-parameter model with a tiny classifier in Go. The rule fits in one sentence. Rules first, a small model next, the LLM only as a last resort. The result: no GPU, sub-millisecond inference, and a cloud call that became rare. Here is how, with the real numbers. This article is for developers who put an LLM in production and pay the bill. Not a demo. Most AI tasks are not LLM tasks In 2026, the default reflex is to wire a big model into everything. A question comes in, you call the LLM. But many tasks do not need it. Filing an email under "work" or "newsletter" is classification. A problem solved for twenty years, long before LLMs. To classify is to pick a label from a short, stable list. To generate text is something else. The first job needs a small model. The second earns a big one. The rule I defend fits in one sentence. Put the LLM last. The setup I built an agent that triages my inbox. It is a daemon. It reads new messages and files each one into a category: work, notification, newsletter, promo, and a few more. Nothing secret, just my real mailbox, with years of mail. The first version handed every email to a local LLM. A 7-billion-parameter model, Qwen 2.5 7B, served by Ollama on a GPU. Ollama is a tool that runs an LLM on your own machine. It worked. But the price was heavy. A GPU on all the time. One more container to watch. And an absurd slowness for the question asked. One day I asked myself: does deciding "is this a newsletter?" really need 7 billion parameters? No. The answer sits in two or three words from the sender and the subject. So I rethought the whole thing. Three layers, from cheapest to most expensive Every email goes through three layers, in order. It stops at the first one that can answer. Deterministic rules. Instant, exact, no cost. A small model. Sub-millisecond, on CPU. The LLM. Only if the small model is unsure. The routing code fits in a few lin

As a backend developer, I have build hundreds of endpoints, so the typical endpoint flow is deeply ingrained in how I think about web applications. But when I started building AI-powered endpoints, I noticed an interesting shift. At first, AI endpoints looked like simple proxy endpoints with some configuration for connecting to a model: API receives request ↓ send prompt to model ↓ receive response ↓ return it to the client And it worked well until I found out that passing a prompt directly from the client was not a good idea. The endpoint could be misused for a completely different purpose, allowing someone else to consume my AI usage credits. Then I realized that the input also needed limits. Sending a large context for a specific task costs more and may produce unexpected results. So when I started looking closer, especially when I needed reliable structured output and predictable application behavior, I quickly realized that it was not that simple. Validation was no longer only guarding execution, it had also become a post-processing step. AI models are probabilistic. Even with the same input, they may return different outputs, omit required information, misunderstand instructions or return something that is technically valid but logically wrong. And because every token has a price, I cannot simply retry the request and hope for a better result. That was when I started questioning whether AI endpoints should be designed in the same way as conventional Web API endpoints. Table of Contents Conventional Web API Endpoint Flow AI-powered Web API Endpoint Flow What This Difference Changes Unpredictable Latency Retry Logic Idempotency and Side Effects Testing AI Endpoints The Output Contract Observability and Cost Summary Conventional Web API Endpoint Flow A conventional Web API endpoint usually follows a similar flow: validate request ↓ execute business logic ↓ return representation The first phase is request validation. We validate the incoming data against property

I'll guide you through Python fundamentals by comparing concepts with JavaScript. Let's start! 1. Hello World & Basic Syntax JavaScript console . log ( " Hello World " ); let x = 5 ; Python print ( " Hello World " ) x = 5 # No semicolon, no let/const Key Differences: No semicolons in Python Indentation matters (replaces curly braces) Comments use # instead of // 2. Variables & Data Types JavaScript let name = " Alice " ; // string let age = 30 ; // number let isStudent = true ; // boolean let scores = [ 95 , 87 , 91 ]; // array let person = { // object name : " Bob " , age : 25 }; let nothing = null ; let notDefined = undefined ; Python name = " Alice " # str age = 30 # int (or float for decimals) is_student = True # bool (capital T/F) scores = [ 95 , 87 , 91 ] # list (mutable) person = { # dict (dictionary) " name " : " Bob " , " age " : 25 } nothing = None # Python's null/undefined Key Differences: Python uses snake_case (not camelCase) True / False capitalized None instead of null / undefined Lists ≈ Arrays, Dicts ≈ Objects 3. Control Flow JavaScript // If-else if ( age >= 18 ) { console . log ( " Adult " ); } else if ( age >= 13 ) { console . log ( " Teen " ); } else { console . log ( " Child " ); } // For loop for ( let i = 0 ; i < 5 ; i ++ ) { console . log ( i ); } // While loop let count = 0 ; while ( count < 5 ) { console . log ( count ); count ++ ; } Python # If-else (indentation instead of braces) if age >= 18 : print ( " Adult " ) elif age >= 13 : # NOT else if print ( " Teen " ) else : print ( " Child " ) # For loop (more like for...of in JS) for i in range ( 5 ): # range(5) = [0, 1, 2, 3, 4] print ( i ) # Iterate over list (like for...of) for score in scores : print ( score ) # While loop count = 0 while count < 5 : print ( count ) count += 1 # No ++ operator in Python 4. Functions JavaScript // Function declaration function add ( a , b ) { return a + b ; } // Arrow function const multiply = ( a , b ) => a * b ; // Default parameters function greet ( n

Track Codex and Claude Code and respond from your Notch Discussion | Link

Every day, I see discussions about how AI assistants and Copilot are changing software development. And honestly? I agree. AI is helping us save time, automate repetitive work, and learn faster than ever before. But recently, I've noticed something that concerns me. Some interviewers, managers, and even developers are starting to treat AI-generated answers as the "correct" answers. That's where I think we're making a mistake. 🤔 Does Copilot Know Your Responsibilities? We've all seen responses like: "With 10 years of experience, you should know this." But who decides that? Does Copilot know: The projects you've worked on? The systems you've built? The challenges you've solved? The responsibilities you've carried for the last 10 years? The answer is simple: No. Two developers can have 10 years of experience and possess completely different skill sets. One may be an expert in distributed systems. Another may specialize in frontend architecture. A third may have spent years building enterprise applications. Meanwhile, a developer with only 5 years of experience may know a modern technology that none of them have ever needed. Does that make anyone less capable? Absolutely not. It simply means their journeys were different. 🚨 Experience Is Not a Checklist Let's take a different example. Suppose someone has spent 10 years mastering Figma and has become an exceptional designer. Does that automatically mean they should be an expert in Photoshop, Illustrator, CorelDRAW, Sketch, and every other design tool? Of course not. Their expertise reflects the work they've done and the problems they've solved. The same applies to software engineering. Experience is about depth, not knowing everything. ⚠️ The Risk of Over-Relying on AI Don't get me wrong. I use AI. Most developers I know use AI. And it saves hours of effort. But there's a difference between: ✅ Taking help from AI and ❌ Letting AI think for you When every answer, every opinion, and every decision comes from AI, something

Next.js 16 shipped Cache Components - the feature that finally lets a single route mix static HTML, cached data, and per-request dynamic content without splitting it into separate pages. It is Partial Prerendering (PPR) made stable, plus a new use cache directive that replaces the old unstable_cache and the awkward route-segment config flags. This guide covers what changed, the three content types you now think in, and the runtime-data rule that trips up almost everyone on day one. What actually changed If you were using the experimental PPR flag, it is gone. Cache Components is a single config switch, and it turns on the whole model - static shell, cached segments, and streamed dynamic content in one route. // next.config.ts import type { NextConfig } from ' next ' const nextConfig : NextConfig = { cacheComponents : true , // replaces experimental.ppr } export default nextConfig Once it is on, every piece of your route falls into one of three buckets. The whole mental model is learning which bucket each component belongs in. The three content types Static - synchronous code, imports, and pure markup. Prerendered at build time and served instantly from the CDN. Your header, nav, and layout shell. Cached - async data that does not need to be fresh on every request. Marked with use cache . Think product lists, blog posts, dashboard stats. Dynamic - runtime data that must be fresh (cookies, headers, per-user state). Wrapped in Suspense so it streams in after the shell paints. import { Suspense } from ' react ' import { cookies } from ' next/headers ' import { cacheLife } from ' next/cache ' export default function DashboardPage () { return ( <> { /* Static - instant from the CDN */ } < header >< h1 > Dashboard </ h1 ></ header > { /* Cached - fast, revalidates hourly */ } < Stats /> { /* Dynamic - streams in with fresh data */ } < Suspense fallback = { < NotificationsSkeleton /> } > < Notifications /> </ Suspense > </> ) } async function Stats () { ' use cache ' cacheL