标签：#t

Imagine you are the parent of an inquisitive seven-year-old who loves his family, playing baseball, and hanging out with his friends. It's the life you've always wanted. And then the worst thing that ever could happen happens: A freak accident takes your son away. But as it turns out, you don't have to live with […]

You've probably seen this: Kubernetes probes are green, /health returns 200, CPU isn't on fire - and users are timing out. Process is up. Just not doing anything useful. Classic event loop freeze. Something sync and expensive (or a dumb busy loop) grabs the thread, and suddenly timers, HTTP, DB callbacks - all of it waits. Including your health endpoint, because that also needs the loop. monitorEventLoopDelay() is fine for dashboards. It won't yell at you while the process is stuck, and it definitely won't write a log line from outside the frozen loop. That's the part that annoyed me enough to build this. I made @js-ak/watchdog - tiny N-API addon. The monitor runs in C++ on its own thread and writes JSON lines to stderr/file even while the JS event loop is stuck. Your freeze / recovered handlers only run after the loop unblocks. Optional RSS/CPU, optional stack sample. npm install @js-ak/watchdog const watchdog = require("@js-ak/watchdog"); watchdog.on("freeze", (e) => console.log(e.event, e.duration_ms)); watchdog.on("recovered", (e) => console.log("back after", e.duration_ms, "ms")); watchdog.start({ freezeThresholdMs: 1000, logTarget: "stderr", // or "file" | "both" source: "payments-api", }); Real freezes usually aren't while (true) . More like giant JSON.parse, a cursed regex, sync crypto/compress, or some dependency doing sync I/O on a hot path. Stuff you only notice after people complain. Prebuilds for the usual platforms (Node 22+). MIT. Repo: https://github.com/JS-AK/watchdog Curious how other people catch loop stalls in prod - and what you'd actually want in the freeze payload. Roast the API if it's wrong.

Letting a stranger contact a car owner without giving them the number There is a small, very common problem in Indian cities that turns out to be a surprisingly good systems design exercise. A car is parked badly. It is blocking a gate, a driveway, another car. Someone needs the owner to move it, right now. The traditional fix is a phone number written on a sticker on the windscreen. That works. It also means a stranger — any stranger, forever — has the owner's personal number. Why the sticker is worse than it looks Once a number is visible on a windscreen, a few things follow: It gets scraped. Numbers on vehicles end up in marketing lists. It cannot be revoked. Change your number and every sticker you own is now wrong. It has no context. A 2am call could be a genuine emergency or someone who saw the number three months ago. It is a safety issue for some owners in a way it is not for others. A number attached to a vehicle, at a known parking spot, at predictable times, is more information than most people realise they are publishing. So the requirement is oddly specific: a stranger must be able to reach the owner, immediately, without ever learning how to reach them again. That constraint is what makes this interesting. The naive version, and why it fails First instinct: put a QR code on the vehicle that opens a page with a "call owner" button, and put the number behind the button. This solves nothing. The number is still in the page source. Anyone who wants it can get it, and now you have added a scan step for the honest majority while stopping none of the dishonest minority. Second instinct: put a contact form behind the QR. The stranger types a message, the owner gets a notification. Better on privacy, useless in practice. The person needs the car moved in the next ninety seconds. They are not filling in a form and waiting for an email. If the fast path is not there, they go back to writing an angry note. The real requirement is synchronous contact with asynchron

Watermarking a PDF — adding semi-transparent text over pages — sounds like something only desktop software handles. But with pdf-lib and a bit of canvas math, you can build a fully browser-based watermark tool. This post walks through the implementation details, including text rendering, rotation, and multi-page support. Why client-side? Traditional watermark tools upload your file, process it on a server, and send the result back. For documents that might be confidential or contain sensitive information, this introduces an unnecessary privacy risk. A browser-based approach: Processes everything locally Keeps files on the user's device Works offline after loading Avoids server-side bandwidth costs The stack Vue 3 + Composition API pdf-lib for PDF manipulation and watermarks PDF.js ( pdfjs-dist ) for preview rendering Vite for bundling Adding a text watermark pdf-lib provides a built-in PDFDocument.embedFont() method for custom fonts and page.drawText() for placing text. Here's how to add a rotated, semi-transparent watermark across all pages: < script setup lang= "ts" > import { ref } from ' vue ' import { PDFDocument , rgb , StandardFonts } from ' pdf-lib ' const file = ref < File | null > ( null ) const watermarkText = ref ( ' DRAFT ' ) const opacity = ref ( 0.3 ) const fontSize = ref ( 72 ) const rotationDeg = ref ( - 45 ) const applying = ref ( false ) async function handleFileUpload ( selected : File ) { file . value = selected } async function applyWatermark () { if ( ! file . value ) return applying . value = true try { const arrayBuffer = await file . value . arrayBuffer () const pdfDoc = await PDFDocument . load ( arrayBuffer ) // Embed the Helvetica font — required for correct rendering const helveticaFont = await pdfDoc . embedFont ( StandardFonts . HelveticaBold ) const pages = pdfDoc . getPages () pages . forEach (( page ) => { const { width , height } = page . getSize () page . drawText ( watermarkText . value , { x : ( width - helveticaFont . widthOfT

Hi! I built EasyInvoicePDF because I was tired of paying for bloated invoicing tools when all I needed was a simple way to generate and send professional invoices. https://easyinvoicepdf.com https://github.com/VladSez/easy-invoice-pdf Features: No sign-up required & no ads Live PDF preview & instant download Save Seller & Buyer Profiles Flexible tax support (VAT, Sales Tax, etc.) Customizable invoice templates 120+ currencies & multi-language support and more… All feedback, questions, bug reports, and feature ideas are welcome! Demo:

If you've been developing with React Native or Expo for a while, chances are you've had a project that suddenly refuses to build for what seems like no reason. Maybe you installed a new package. Maybe you upgraded Expo. Maybe you only changed a single dependency. You confidently run your Android build, expecting it to compile like always... Instead, Gradle greets you with something like this: Execution failed for task ':app:checkDebugDuplicateClasses'. Duplicate class androidx.lifecycle.ViewModelLazy found in modules lifecycle-viewmodel-2.8.2.aar and lifecycle-viewmodel-ktx-2.6.1.aar Duplicate class com.google.android.gms.internal.measurement.zzab found in modules play-services-measurement-base-22.0.0.aar and play-services-measurement-impl-21.6.2.aar Duplicate class ... At first glance, the error looks straightforward: there are duplicate classes . Easy enough, right? Not exactly. In reality, this is one of those Android build errors that can send you down a rabbit hole of Gradle files, dependency trees and Stack Overflow threads, only to realize the real cause was something completely different. Let's break down what's actually happening and, more importantly, how to fix it. Why this error happens Every Android library included in your project contains compiled Java or Kotlin classes. When Gradle builds your application, it combines all of those libraries into a single APK or AAB. If two different dependencies contain the exact same class, Gradle doesn't know which version should be packaged. Instead of guessing, it stops the build with a Duplicate class error. The difficult part is that the dependency causing the conflict usually isn't the one shown in the error. Very often, it's another package pulling in an older or incompatible version behind the scenes. The most common causes After seeing this error many times, these are usually the culprits. 1. Two libraries depend on different versions of the same package This is by far the most common scenario. For example:

You give the AI two examples of a new task. It understands. It completes the third example correctly. It has not changed its weights. It has not been fine-tuned. It has learned from the context of the prompt alone. This is in-context learning. It is one of the most remarkable properties of large language models. But it is not learning in the human sense. It is pattern matching. It is using the examples as a template. It is not generalizing. It is adapting. This is the distinction that matters: in-context learning is not true generalization. It is a form of rapid pattern completion. The model does not update its internal knowledge. It simply uses the examples to adjust its predictions. What Is In-Context Learning? In-context learning is the ability of a model to learn from examples provided in the prompt. The Process: The prompt contains a few examples. The model uses these examples to infer the task. It applies the inferred task to a new input. The Mechanism: The model does not update its weights. It uses the examples as a template. It generates the most likely completion. A Contrarian Take: In-Context Learning Is Not Learning. It Is Pattern Completion. We call it "learning." But it is not learning in the human sense. It is pattern completion. The model is not generalizing. It is matching patterns. How Does It Work? The mechanism of in-context learning is still debated. But there are leading theories. The Pattern Completion Theory: The model has seen similar tasks during training. The examples activate the relevant patterns. The model completes the pattern. The Induction Head Theory: The model has "induction heads" that detect repeated patterns. These heads identify the relationship between examples. They apply the relationship to the new input. A Contrarian Take: The Mechanism Is Not Important. The Outcome Is. We debate the mechanism. But the outcome is what matters. The model can learn from examples. The mechanism is a technical detail. The outcome is a practical

Hi, my name is Dan, I'm a frontend engineer and open-source maintainer. I've spent the last couple of years building Nano Kit — a lightweight, modular state management ecosystem for modern web apps: signals-based stores , a router , data fetching , i18n , and SSR support , all built on the same tiny reactive core. It recently hit 1.0 , and in this post I want to give you four honest reasons to try it. 1. It's fast At the heart of Nano Kit is a push-pull reactivity system based on the algorithm from alien-signals — one of the fastest signal implementations in the JavaScript ecosystem. I didn't use alien-signals directly, though. Nano Kit needed things it doesn't provide, so I built a dedicated fork called Agera : Signal lifecycles — you can listen to signal activation and deactivation, which powers Nano Kit's mountable stores (run setup logic on first listener, clean up on last). Real tree-shaking — Agera is designed so that only the code you use ends up in your bundle; alien-signals is not well tree-shakable. The result keeps almost all of alien-signals' raw speed. Here is how @nano_kit/store compares to other popular state management libraries in a reactivity benchmark : Library Latency avg (ns) Throughput avg (ops/s) alien-signals 294.00 ± 2.24% 3,559,763 @nano_kit/store 303.55 ± 0.75% 3,365,816 svelte/store 428.58 ± 0.61% 2,479,118 rxjs 454.74 ± 0.07% 2,250,397 nanostores 1,373.2 ± 5.96% 952,399 mobx 3,474.7 ± 1.86% 306,094 valtio 5,041.3 ± 11.46% 254,109 jotai 9,454.6 ± 16.45% 157,853 effector 24,885 ± 11.78% 62,744 @reatom/core 59,430 ± 15.61% 22,741 Benchmark was run on AMD Ryzen 5 PRO 3400G with Node.js v24.14.1 That's ~3.5× faster than nanostores and an order of magnitude faster than most atomic state managers — while shipping lifecycles and mountable stores on top. 2. It's small Nano Kit exists largely because of Nano Stores . I love its philosophy: atomic stores, mountable resources, logic moved out of components, and an obsessive focus on bundle size. Nan

Divergence escalates the wrong population: unanimous misses auto-pass Agent Determinism Illusions (Part 7) Where this fits: This part does not continue Part 13's probe-vs-prose thread. It returns to Part 6 's L2→L3 escalation rule — Dipankar's move of treating vote disagreement as the human-review signal. Alexey Spinov's follow-up comment says that signal points at the wrong population. Two experiments check whether he is right, and what to put in the tripwire instead. Part 6 drew this control flow: L2 multi-perspective votes │ unanimous ──────────► AUTO-PASS / AUTO-REJECT │ divergence (e.g. 2–1) ► L3 human The caveat was already in the text: divergence measures ambiguity; it does not fix unanimous systematic bias. Alexey's point is sharper — and it is about routing , not about another caveat paragraph. 1. Alexey's population mismatch On the Part 6 thread, Alexey Spinov wrote (paraphrased tightly): The dangerous failures are high-confidence and directional — systematic. Systematic bias is shared across prompts, not idiosyncratic (your own P3: majority voting doesn't fix it). So the three perspectives will tend to agree on exactly those cases. Divergence-to-human then routes you the safely-ambiguous ones and auto-passes the confidently-wrong ones. The escalation signal is pointing at the wrong population. He proposed two cheap replacements: T1 — deterministic tripwire on known-reversal classes (escalate regardless of agreement). T2 — treat unanimous + high-confidence on a historically reversal-prone class as escalate — the inverse of “high confidence, auto-pass.” That is the claim under test. Not “divergence is useless,” but “divergence alone is the wrong primary tripwire for the failure mode you already measured.” 2. Experiment A — offline proxy on DF v2 (no new API) Part 6's Mike Update already showed: of 96 DF v2 MISS runs, 95.8% sat at self-reported confidence ≥ 0.9 (avg 0.969). That mass is concentrated — Part 6 also reported ~80% of MISS runs from qwen3:0.5b —

One of the biggest mistakes beginners make when learning Terraform is treating their local machine as the deployment server. A typical workflow looks like this: terraform init terraform plan terraform apply While this approach is perfectly fine for learning, it quickly becomes problematic when working on real-world projects with multiple engineers. Consider these questions: Who deployed the infrastructure? Was the infrastructure reviewed before deployment? Can someone else reproduce the deployment? What happens if the engineer's laptop is lost or misconfigured? How do we know exactly what changed? These are some of the reasons Infrastructure as Code (IaC) is almost always integrated with Continuous Integration and Continuous Deployment (CI/CD) pipelines in professional environments. In this article, we'll build a simple Terraform CI/CD pipeline using GitHub Actions. Instead of focusing only on the YAML syntax, we'll first understand why each stage exists and how they work together to produce safe, repeatable infrastructure deployments. What is Terraform CI/CD? Terraform CI/CD is the process of automating the validation, planning, and deployment of infrastructure whenever changes are made to Terraform code. Instead of running Terraform commands manually from a developer's laptop, a CI/CD platform executes those commands automatically in a controlled environment. The workflow typically looks like this: Developer │ ▼ Git Push │ ▼ GitHub Repository │ ▼ GitHub Actions │ ▼ Terraform Init │ ▼ Terraform Validate │ ▼ Terraform Plan │ ▼ Manual Approval │ ▼ Terraform Apply │ ▼ AWS Infrastructure This approach provides consistency, visibility, and security while reducing the chances of human error. Why Not Run Terraform Manually? Running Terraform from your laptop works well for personal projects, but it introduces several risks in a team environment. Manual Deployment CI/CD Deployment Requires someone to remember every command Runs automatically Easy to skip validation Validat

In 2023, Bitly's free plan gave you 100 links per month with full analytics. In 2025, they slashed it to 5 links per month . Five. And they started showing full-screen interstitial ads before redirecting — even on links you created years ago. In 2026, their cheapest paid plan is $10/month — just to shorten links without ads. I was paying $120/year to turn long URLs into short ones. A problem that takes 3 lines of code to solve. So I solved it myself. For free. Forever. Introducing ZipLink ZipLink is a fast, self-hostable URL shortener built with Next.js and Firebase. Deploy it once, use it forever. No monthly fees. No link limits. No ads. No "upgrade to unlock analytics." Your links. Your data. Your domain. Your rules. https://yourdomain.com/abc123 → https://some-really-long-url.com/path/to/thing?utm=whatever That's it. That's the product. Except you own it completely. The State of URL Shorteners in 2026 (It's Embarrassing) Let me show you what the "industry leaders" are charging for what is essentially a database lookup: The Comparison Table Feature Bitly Rebrandly Short.io Dub.co TinyURL Pro ZipLink Monthly cost $10-$300/mo $13-$349/mo $19-$149/mo $24-$299/mo $9.99/mo $0 Annual cost $96-$2,388/yr $156-$4,188/yr $228-$1,788/yr $288-$3,588/yr $119.88/yr $0 Free tier links 5/month 10/month 1,000 total 25/month Unlimited (no analytics) Unlimited Custom domain Paid only Free (1 domain) Required Paid only Paid only Yes (yours) Click analytics Paid only Paid only Free (basic) Paid only Paid only Full, free Link expiration Paid only Paid only Yes Yes No Yes Password protection Enterprise only Paid only Paid Paid No Yes API access Paid only Paid only Yes Yes Paid Full, free QR codes 2 free, then paid Paid Yes Paid Paid Unlimited Self-hostable No No No Yes (complex) No Yes (simple) Data ownership Their servers Their servers Their servers Their servers Their servers Your Firebase Ads/interstitials Yes (free tier) No No No Yes (free tier) Never Links disappear if you stop pay

Before I hand this over, I want to tell you where I'm actually standing right now. I'm looking at the biggest mountain yet: building our hospital's own ERP and EMR — the systems that run the business side and the medical records side of the whole place. That's usually the job of specialized companies, and it's tangled up with outside regulators too. Nobody knows how long it'll take. I'm assuming at least three years. Honestly, it feels overwhelming. The size of the fear hasn't changed. My grip on it has. Here's the strange part. That overwhelmed feeling isn't unfamiliar at all. A year ago, staring at a blank screen and not knowing what code even was, I felt exactly this lost. What's different isn't the size of the fear. It's that I now know what to do with it. Get curious, look it up. Look it up, a direction shows up. See a direction, find a method, and just start. Fail, and you've learned something. Succeed, and you move to the next thing. That loop is the only thing that ever carried me anywhere , and it's the same loop I'm running at the foot of this much bigger mountain. Right now the wall isn't technical The thing I don't know isn't code. It's other departments. I moved from the treatment room to the planning office not long ago, and I still don't really know how the other departments do their work. You can't automate what you don't understand. So before anything else, I'm doing the one thing that actually comes first: listening. Hearing what people in the field actually need, what's grinding on them, and figuring out the shape of a fix together, one conversation at a time. I've noticed something before, more than once: problems that look completely different from the outside — counting a stack of forms, tracking down misplaced supplies, sorting a pile of ID cards — usually reduce to the exact same shape underneath. Make the pile of things findable. I suspect the same pattern is waiting inside every other department too. I just haven't looked yet. That's how I

This is today’s edition of The Download, our weekday newsletter that provides a daily dose of what’s going on in the world of technology. The power line that could reshape New York’s grid is hitting snags During a heat wave on July 3, New York State’s grid imported enough electricity from Canada to meet about…

Deck out your room and make class a cinch with these WIRED-tested favorites.

On a gray, humid Saturday morning in central Florida, a little under a dozen people gathered outside the Spring Hill Branch Library to protest the construction of a hyperscale data center in their community. There was no immediate threat - the Hernando County commission had unanimously approved a one-year moratorium on such developments in June […]

Designing and developing a new medicine is an expensive, failure-prone scientific challenge. A new drug can take many years to develop, at the cost of a significant investment. And even then, most possible candidates never reach the patient. For biologic medicines, therapies made from engineered proteins rather than synthetic chemistry (which are often used to…

When a new platform team set out on implementing their roadmap through forced workflows with poor documentation, developer experience declined. Success came from simplifying governance, prioritizing what matters, and rolling out compliance incrementally through prevention, detection, and communication. Empathy, focus, and shared purpose drove successful adoption. By Ben Linders

Cordless, handheld, robot, and traditional—we tested them all to find the vacuum that’s fantastic for fur.

Executive Leadership Focused on Growth and Innovation Discussion | Link

The resulting report is a near-random mixture of real problems and grievances.