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Beyond Elon Musk’s interpretation of The Odyssey, Silicon Valley leaders have often misunderstood classic books like Foundation and The Hitchhiker’s Guide to the Galaxy. It’s evident in their tech.

Mini human brains are being grown in labs all over the world. Soon, they could outthink neural networks.

Originally published on Loop & Retry — field notes on building LLM agents that survive production. Here's the failure mode that surprises people who've only reasoned about agents statistically. You measure a per-step error rate — say 10% of steps produce something wrong — and you assume errors are independent, so a wrong step is a wrong step and the rest of the run is fine. Then you watch a real trajectory and see something else: step 4 gets a fact slightly wrong, step 5 reasons on top of that wrong fact and commits harder, step 6 takes an action premised on both, and by step 8 the agent is confidently executing a plan that was doomed at step 4. One mistake became five. The errors weren't independent — they were coupled through the context , and coupling is what turns a 10% step-error rate into a run that's wrong far more than 10% of the time. This is the cascade : a single fault amplifying down a single trajectory. It's distinct from the failure I wrote about in distributed retry patterns , where the problem is one bad condition hitting many workers at once — that's a blast radius, a horizontal spread. The cascade is vertical: it spreads through time within one run, because an agent's own past output is its future input. This post is about the vertical kind, why it's structural rather than bad luck, and where you can cut it. Why coupling is the default, not the exception A stateless function that fails just returns an error. An agent that fails does something worse: it writes the failure down where it can read it again. The mechanism is the same one that makes agents work at all — the transcript accumulates, and every step conditions on everything before it. That's a feature for carrying intent forward. It's also the exact channel a mistake travels down. Three ways a single fault propagates through the context: Poisoned premise. The agent derives or retrieves a wrong fact — a misparsed tool result, a hallucinated ID, a stale value — and it lands in the transcript a

Nuxt 4.5 launched last month and it's really neat. One of my most favorite features is the...

Most "AI gift finders" are a search box with a chatbot glued on. I wanted to build something different — a quiz-driven gift recommender that ranks real Amazon products by who the recipient actually is , not just keywords. I call it GiftHive . In this post I'll walk through the architecture, the conversion tricks I learned shipping it, and the bits I'm proudest of. The Problem Picking gifts is emotionally expensive. You scroll Amazon for an hour, second-guess every option, and end up buying a gift card. Existing tools don't help because they optimize for keyword match , not recipient fit . GiftHive flips the input: instead of "show me gifts under $50", you answer a 30-second quiz about the person (relationship, interests, occasion, budget) and get a ranked shortlist with explanations of why each gift fits. Stack Next.js (App Router) — SSR for fast first paint, RSC for product data Tailwind CSS — design system + dark mode via CSS variables Cloudflare Pages — edge-deployed, free tier covers the traffic Amazon Associates — affiliate revenue model The Funnel The whole site is a 3-step conversion funnel: Landing page — exit-intent modal + social proof toasts prime the visitor Quiz — 30-second, one-question-per-screen flow, no login Results — ranked products with countdown bar and "X people found gifts this week" social proof Every step has a single primary CTA. The exit-intent modal is route-aware — it only fires on / and stays silent on /quiz and /results so it never interrupts the funnel mid-flow. That bug cost me ~15% of quiz completions before I caught it. Personalization Logic Each quiz answer maps to a vector of attributes (interests, style, budget, relationship). Products in the catalog have matching tags. Ranking is a weighted score: score = tag_overlap * w1 + budget_match * w2 + occasion_match * w3 No ML model needed — a few hundred products and clean tagging is enough to feel personal. Amazon Affiliate Integration Every product link runs through getAmazonUrl() w

🚀 Shipping a major update to my portfolio After weeks of designing, developing, and refining, I'm excited to share the latest version of my personal portfolio. Rather than building another static portfolio, I wanted to treat it like a real product—focusing on performance, interaction design, accessibility, analytics, and user experience. 🌐 Live: https://www.alanbabychan.online What I built 🟩 Matrix Theme A cyberpunk-inspired dark mode featuring animated binary effects, glowing UI elements, and an immersive developer experience. ⬜ Paper Theme A clean, modern light mode designed with readability, visual hierarchy, and clarity in mind. 🎵 Interactive Audio System •Background music •UI sound effects •Dedicated settings panel •Adjustable volume controls •Built using the Web Audio API 🖱️ Interactive Cursor A custom mouse-follow glow and subtle cursor interactions that enhance the browsing experience without becoming distracting. ✨ Micro-interactions Hover states, smooth page transitions, animated UI components, and responsive visual feedback to make every interaction feel intentional. 📖 UX & Accessibility Built around clear typography, intuitive navigation, responsive layouts, and accessibility-focused design to provide a consistent experience across devices. 📊 Performance & Analytics Built with Next.js and optimized for speed, SEO, and scalability. Implemented a complete Google Analytics 4 setup including: •SPA page tracking •Google Consent Mode v2 •Custom event tracking •User interaction analytics Tech Stack: Next.js • React • Tailwind CSS • Framer Motion • Web Audio API • Google Analytics 4 • Microsoft Clarity Coming Soon... 👀 I'm currently building a personal AI assistant that will allow visitors to interact with my portfolio, ask questions about my projects, experience, and skills, and explore everything conversationally. What I learned This project pushed me to dive deeper into: •Theme architecture •Frontend performance optimization •Animation systems •Custom UI inte

Lighthouse kept warning me about inefficient cache lifetimes, even though I had already added caching for my static files. The missing piece was Nuxt Image and its generated /_ipx URLs . In this post, I’ll share the simple caching setup I use for Nuxt build files, public assets, and optimized images without risking stale content after deployment. The basic rule is simple: Cache files aggressively when changing the file also changes its URL. Be more careful when the same URL can serve different content later. You have probably seen the same Lighthouse warning I have: Use efficient cache lifetimes. Browser caching for static files is usually straightforward. You add a Cache-Control header, choose a reasonable lifetime, and the browser avoids downloading the same files again on every visit. However, in a Nuxt application, not every static-looking file should use the same caching policy. Nuxt build files are automatically versioned. Files inside public/ usually are not. Nuxt Image also creates transformed image URLs under /_ipx , which need their own cache rule. In this post, I’ll go through the setup I use, including the Nuxt Image rule that was missing during my latest Lighthouse audit. The simple caching rule The most important question is not whether a file is an image, font, or JavaScript file. The important question is: Will the URL change when the file changes? When the answer is yes, you can safely cache the file for a very long time. When the answer is no, you should use a shorter cache lifetime. Otherwise, visitors may continue seeing an old version after you deploy an update. What the cache directives mean Here are the main directives used in this setup: public allows browsers and shared caches such as CDNs to store the response. max-age controls how long the browser considers the file fresh. s-maxage controls how long shared caches such as Cloudflare consider it fresh. immutable tells the browser that the file is not expected to change while that URL exists.

California's Prop 40 would impose a one-time 5% tax on the net worth of the state's billionaires.

A few months ago I got properly bitten by the Linux bug. Ubuntu became my daily driver, I started digging into terminal tools way past the point of “practical necessity,” and I got obsessed with an idea that wouldn’t leave me alone: old hardware doesn’t have to die just because it’s old. I work as an on-site IT coordinator, handling day-to-day IT operations for an industrial company. Between that and years of general sysadmin work, I’ve watched a lot of perfectly usable machines get pulled out of service and shipped off as e-waste — not because they were broken, but because someone decided they were “too old” for whatever OS they were running. A Core 2 Duo with a fresh SSD and a lightweight distro can still be a genuinely useful computer.That gap between “technically obsolete” and “actually still works great” is where a lot of my curiosity lives right now. The gap I kept running into The more I looked into the Norwegian Linux scene, the more I found — Skolelinux/Debian Edu has deep roots here, NUUG (Norwegian Unix User Group) has been active for decades, and there’s a project called PC-Aid that collects, wipes, and reinstalls Debian Edu on used PCs, then sends them to schoolchildren in Ukraine. It’s been running for a few years now, quietly doing real, tangible good. I wanted in. But when I looked for any of this activity near me — Sunnmøre, a district on Norway’s west coast (in Møre og Romsdal county, home to the town of Ålesund) — there was nothing. No local NUUG chapter, no meetup, no group. Just… a gap. (If you’re not from Norway, don’t worry, most Norwegians would need a map for this too.) So instead of waiting for someone else to fill it, I started SLUG — Sunnmøre Linux User Group. Reaching out, awkwardly, like you do Starting a group is the easy part. Getting it to mean anything is harder. So I did the obvious thing: I found people who’d actually been part of PC-Aid and reached out. First was someone who’d been active in the project early on. I sent a message

It's that time of year: On Wednesday, Google is set to host its annual Made by Google hardware launch event for Pixel gadgets. Google itself has already teased new slab-style and foldable Pixel smartphones, but leaks also indicate that the company could announce updated watches, a new color for familiar earbuds, and perhaps a brand […]

The solar-powered limited edition may be here to mark the final Dutch Grand Prix taking place in Zandvoort, but it's the juicy iconic colorway WIRED's been waiting for.

`California's AB 723 has been in force since January 1, 2026. It amends Business & Professions Code § 10140.8 and it applies to any real estate listing image that has been digitally altered. Virtual staging is the obvious case, but the definition is wider than that. The rule has two parts: A statement that the image has been altered, "reasonably conspicuous" and placed on or adjacent to the image. A link to a publicly accessible URL, or a QR code, that includes and clearly identifies the original, unaltered image. Everyone builds the first part. It is a text label on a photo, an afternoon of work. The second part is a small piece of infrastructure: a permanent public URL, per image, that outlives the tab the agent had open when they exported. I build a virtual staging product, so I had to ship both. This is how the second part is put together, and the one thing I got wrong. What counts as altered Worth getting right before writing any code, because it decides which of your features need the label and which do not. Subsection (b)(2) carves out ordinary photo editing. Covered: Adding furniture, rugs, art or decor Removing furniture, clutter or personal items Changing paint, flooring or wall finishes Sky replacement and day to dusk Greening or reshaping lawns and landscaping Anything that changes the facade or the property itself Not covered: Exposure, lighting, white balance, color correction Sharpening Straightening, cropping, angle In the codebase that line is a set, and the two omissions are deliberate: ts export const TOOLS_ALTERING_LISTING_IMAGES = new Set([ "virtual-staging", "sky-replacement", "day-to-dusk", "grass-greener", "declutter", "object-remover", ]); image-enhancer is out because exposure and white balance are precisely what the statute excludes. A floor plan generator is out because a diagram is not an altered photograph. Attaching a legal claim to a feature the law does not cover is not a harmless extra: it is the fastest way to make the rest of your

Discovered Materials raised $9 million to fund the hunt for more novel materials to build more efficient chips.

Two-thirds of an octopus’s neurons are in its arms—each operating independently—including the one it uses to have sex.

MIT Technology Review’s What’s Next series looks across industries, trends, and technologies to give you a first look at the future. You can read the rest of them here. Way back in the summer of 2017, AI researchers at Google put out a paper called “Attention Is All You Need,” in which they described a new…

Originally published on tamiz.pro . Caching in modern web development is no longer just about serving static assets faster; it is the primary mechanism for balancing performance, cost, and data freshness. In the context of Next.js, the caching architecture has evolved significantly, shifting from a simple getStaticProps / getServerSideProps dichotomy to a sophisticated, multi-layered system that spans the Edge Runtime, the Server Components architecture, and the Node.js server environment. For software engineers and systems architects, understanding the default behaviors of Next.js caching is insufficient. To build production-grade applications that handle high concurrency without hammering your database, you must master the advanced patterns: granular revalidation, cache tagging, and external cache management. This article dives deep into these mechanisms, explaining how they work under the hood and how to orchestrate them for optimal performance. The Evolution of Next.js Caching To appreciate advanced patterns, we must first contextualize the current caching model. Next.js 13+ (App Router) introduced a new caching paradigm that is both simpler by default and more powerful when customized. The default behavior is now: App Router (RSC): Components are cached by default. Server Components are rendered once and cached on the server. The next request for the same data returns the cached result. Static Generation: Pages and layouts are built at build time and served statically. Server Components: Fetched data is cached in memory on the server, not in the browser. The critical shift here is that caching is opt-out, not opt-in . Previously, you had to explicitly mark things as static. Now, you must explicitly invalidate cache when data changes. This inversion of control places the responsibility of consistency squarely on the developer, requiring precise tools to manage invalidation. Granular Revalidation: The Tag-Based System The most significant advanced caching pattern

When I was building my backend API, I realized a big problem: anyone could spam my endpoints. If a user repeatedly reloads a page or hits an endpoint calling an external AI service, it can crash the server or run up high API costs. To fix this, I added Rate Limiting . Here is why I used Redis for it and how I set it up. The Problem with Simple In-Memory Limiters At first, I thought about saving request counts in a simple JavaScript object: // ❌ Simple in-memory check (Not good for production) const requestCounts = {}; app . use (( req , res , next ) => { const ip = req . ip ; requestCounts [ ip ] = ( requestCounts [ ip ] || 0 ) + 1 ; if ( requestCounts [ ip ] > 100 ) { return res . status ( 429 ). json ({ error : " Too many requests " }); } next (); }); This works locally, but has two big flaws: 1)Memory Leaks: The requestCounts object keeps growing in memory forever. 2)Breaks when Scaling: If you deploy multiple instances of your app behind a load balancer, each server keeps its own count. A user can easily bypass the limit by hitting different servers. The Solution: Centralized Redis Store Redis stores data in RAM outside our Node.js app. Because it is centralized, all server instances share the exact same count. [ Incoming Client Requests ] │ ▼ [ Cloud Load Balancer ] │ ┌───────────────┼───────────────┐ ▼ ▼ ▼ [ Express Node 1 ] [ Express Node 2 ] [ Express Node 3 ] │ │ │ └───────────────┼───────────────┘ ▼ [ Central Redis Store ] (Checks Request Limits) How I Configured It in My Project In my app, I use two levels of protection: Global Limit: 100 requests per 15 minutes for normal routes. Strict Limit: 5 requests per 10 minutes for heavy routes (like AI generation or OTP emails). 1 . Redis Connection ( config / redis . js ) import { createClient } from ' redis ' ; const redisClient = createClient ({ url : process . env . REDIS_URL || ' redis://localhost:6379 ' }); redisClient . on ( ' error ' , ( err ) => console . error ( ' Redis Error: ' , err )); redisClient .

Welcome back to TechCrunch Mobility, your hub for the future of transportation and now, more than ever, the role AI is playing in it.

More gaming companies are moving toward stopping production of game discs and cartridges. Is it a bad thing?

There is a quiet moment of panic every developer knows. You hit deploy, open the live site on your phone, and wait. One second. Two seconds. Four seconds. Still a blank white screen. A while back, I was working on a Next JS application that looked fast on high speed office Wi Fi. But when tested on a spotty mobile connection, it felt painfully slow. The initial page load was clocking in at nearly 8 seconds, and our main JavaScript bundle was a bloated 1.8 megabytes. Here is how we diagnosed the bloat, cut our load times by 47 percent, and the simple performance rules every developer should know. The Investigation: Where Was the Weight Coming From? When a website is slow, our first instinct is often to blame slow backend APIs or heavy database queries. But when I ran a performance audit, the backend was not the problem at all. The front door was just jammed with too much stuff. We were making three classic mistakes: First, we were packing for a long trip on a short walk. We were loading heavy charting libraries, complex admin tables, and pop up modals the second a user landed on the home page, even if that user only came to read a single line of text. Second, giant images were being served to tiny mobile screens, hogging precious bandwidth before any interactive buttons could even load. Third, a single state update at the top of our app was causing dozens of unseen child components to recalculate and re render unnecessarily behind the scenes. The Strategy: Trimming the Fat Instead of rewriting the entire codebase from scratch, we focused on three targeted fixes. 1. Don't Load It Until They Ask For It Why force a user to download a complex analytics chart if they have not even clicked on the dashboard tab yet? We split the app into smaller, independent code chunks. Now, the user downloads only the absolute bare minimum needed to view the immediate screen. The heavy features stay on the server until the exact moment the user interacts with them. 2. Smart Asset Delivery