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It started with a billing alert. Estimated charges had crossed $250. Not scary money, but enough to make me look. And what actually caught my attention wasn't the number, it was the alert itself. I'd clearly set this up at some point, and the threshold felt stale. So I went looking for where the alert lived. It came from a BillingAlerts CloudFormation stack I created back in 2014 and completely forgot about. So a thing I forgot about was warning me about all the other things I'd forgotten about. I opened my stack list and that's when I saw it wasn't alone. It was sitting in a lineup of stacks, and I didn't recognise half of them. Years of leftovers, just sitting there. The stuff you forget about doesn't break anything. It doesn't page you at 2am. It just sits there, quietly billing, until you glance at the invoice and can't remember what half of it is for. Forgotten, still-running resources are one of the biggest sources of wasted cloud spend, by some estimates around a quarter of the average cloud budget . I came to update one alert and I found a mess. To be clear, these stacks weren't really costing me much. Stopped instances, a few half-deleted leftovers. If they'd been the $252, the alert would've tripped every month. But that's the point. You can't tell what's actually costing you until the clutter is gone. So the alert could wait. I wanted these stale stacks gone first. Normally this is a chore. Open the console, find each stack, click delete, wait, refresh, check if it worked, OR write out CLI commands. It's not hard, just tedious. The kind of task I keep putting off. And that's exactly how this started. ClickOps through the console. I'd just finished deleting an old Directory Service directory over in the Mumbai region by hand, clicking through the screens and waiting out the spinner, when it hit me. Barely ten minutes of manual clicking, and I still had a pile of stacks to go. I didn't have to do any of this myself. Why was I still clicking? I opened Kiro ,

Angular apps can now run any agent, with the streaming, tool calls, and shared state already handled. Today we're releasing Angular support for CopilotKit , an open source client that brings any AG-UI agent into your Angular app. It's built with Angular's own patterns, standalone components, dependency injection and signals. You get the building blocks for agent-native apps in Angular: pre-built chat components or a fully headless setup, generative UI, shared state, human-in-the-loop, multimodal attachments, threads and more. Use the CLI to scaffold a full starter Angular app with a Google ADK agent. npx copilotkit@latest init --framework adk-angular Let's see how to set everything up, then go through each of the pieces and give your agent the context. Quickstart docs are on docs.copilotkit.ai/angular . Rainer Hahnekamp (Angular GDE, NgRx core) and Murat Sari helped build the integration and are now taking on its ongoing maintenance. How everything fits together Everything runs on Agent-User Interaction Protocol (AG-UI) , the open protocol that connects agents to user-facing apps. It streams an agent's entire lifecycle as events, the messages, the tool calls, the state changes, which is what keeps your Angular app and the agent in sync. That matters because the agent becomes a choice you can change. The runtime can point at a BuiltInAgent , LangGraph, Google ADK, Mastra, Pydantic AI, Claude Agents SDK or any framework that speaks AG-UI and your Angular code doesn't change. Here's the architecture. ┌──────────────────────────┐ ┌──────────────────────────┐ │ ANGULAR APP │ │ COPILOT RUNTIME (Node) │ │ │ │ │ │ provideCopilotKit() │ ─────► │ holds your model keys │ │ <copilot-chat /> │ AG-UI │ connects to your agent │ │ tools · context · state │ ◄───── │ streams events back │ └──────────────────────────┘ └──────────────┬───────────┘ │ ▼ ┌───────────────────────────┐ │ YOUR AGENT + MODEL │ │ LangGraph · ADK · Mastra │ │ OpenAI or a local model │ └─────────────────────────

When engineers talk about visual regression or website change monitoring, pixel-level diffing algorithms (like pixelmatch or Euclidean RGB distance) are usually the default solution. However, in real-world web environments, pixel-by-pixel comparisons fundamentally fail under normal user interactions and dynamic rendering conditions: Elastic Layout Shifts: A single 20px dynamic banner inserted at the top of a page pushes every subsequent DOM element down, causing 100% of the downstream pixels to fail a pixelmatch test, even if the content itself hasn't changed. Sub-Pixel Anti-Aliasing Jitter: Operating systems (macOS vs. Linux vs. Windows) render font glyphs with subtle sub-pixel anti-aliasing variations, creating thousands of false-positive pixel deltas. Semantic vs. Cosmetic Changes: Changing a single word in a paragraph should trigger a localized alert, but a minor color gradient shift in a hero image shouldn't trigger an emergency notification. At PageWatch.tech , we solved this by combining classical Structural Similarity (SSIM) , ORB Feature Alignment , and Siamese Neural Networks (SNN) for latent-space semantic comparison. In this article, I will dive into the mathematics, neural network architectures, and TypeScript implementation of our computer vision diff pipeline. 🧮 1. Beyond Pixel Comparison: Structural Similarity Index (SSIM) Unlike raw Mean Squared Error (MSE), SSIM measures visual change based on human perception across three dimensions: Luminance , Contrast , and Structure . Mathematically, the SSIM between two image windows $x$ and $y$ is defined as: $$\text{SSIM}(x, y) = \frac{(2\mu_x\mu_y + C_1)(2\sigma_{xy} + C_2)}{(\mu_x^2 + \mu_y^2 + C_1)(\sigma_x^2 + \sigma_y^2 + C_2)}$$ Where: $\mu_x, \mu_y$ are the local pixel mean intensities. $\sigma_x^2, \sigma_y^2$ are the local variances. $\sigma_{xy}$ is the covariance between $x$ and $y$. $C_1, C_2$ are stabilization constants. TypeScript Implementation of SSIM Window Sliding Below is a snippet of how

If you ship a React Native / Expo app to the App Store, you know the ritual. Open the Apple Developer portal, create a bundle identifier, tick the capability checkboxes, generate a provisioning profile, pick the right certificate. Then hop over to the Expo dashboard, create the EAS app, wire up credentials, add your environment variables one screen at a time. It works, until you have to do it again for a second app, or a second environment, or a teammate needs to know why a capability is enabled. None of it is written down. It drifts. And the usual mobile tooling doesn't help much here: fastlane and the EAS CLI are great, but they're imperative — scripts that do things — not a declarative description of what your release setup should be . That's the gap these two providers fill: elevenode/appstore — App Store Connect: bundle identifiers, provisioning profiles, certificates. elevenode/expo — Expo Application Services (EAS): apps, credentials, environment variables, update channels. Both are open source (Apache 2.0) and published on the Terraform Registry. Let's use them together to describe a mobile app's release setup as code. What you'll need Terraform (or OpenTofu) An App Store Connect API key (Users and Access → Integrations → App Store Connect API): the key, its key ID, and your issuer ID An Expo access token (expo.dev → account settings → Access Tokens) and your Expo account name Export the credentials as environment variables so nothing sensitive lands in your config: export APPSTORE_KEY = " $( cat AuthKey_XXXX.p8 ) " export APPSTORE_KEY_ID = "XXXXXXXXXX" export APPSTORE_KEY_ISSUER_ID = "xxxxxxxx-xxxx-xxxx-xxxx-xxxxxxxxxxxx" export EXPO_TOKEN = "your-expo-access-token" export EXPO_ACCOUNT_NAME = "your-account-name" Wiring up both providers terraform { required_providers { appstore = { source = "elevenode/appstore" } expo = { source = "elevenode/expo" } } } # Reads APPSTORE_KEY / APPSTORE_KEY_ID / APPSTORE_KEY_ISSUER_ID from the env. provider "appstore" {} # Re

You have read five blog posts about monads. Each one started with category theory, mentioned burritos, and left you more confused than before. Let's skip all of that. Here is the short version: a monad is a container that supports three operations. You already use two monads every day in TypeScript — Promise and Array . Once you see the pattern, every monad in @oofp/core will feel familiar. The Three Operations Every monad has three things: of(a) — Put a value into the container. map(f) — Transform the value inside, keep the container shape. chain(f) — Transform the value into a new container, then flatten. That's it. If a type supports these three operations and follows a few simple laws, it's a monad. Let's see this with types you already know. Promise — a monad you use daily // of — wrap a value const p = Promise . resolve ( 42 ); // map — transform the inside (then with a plain return) const doubled = p . then (( x ) => x * 2 ); // Promise<number> // chain — transform into a new Promise (then with an async return) const fetched = p . then (( id ) => fetch ( `/api/users/ ${ id } ` )); // Promise<Response> Promise.resolve is of . .then acts as both map and chain — JavaScript conflates them. When your callback returns a plain value, it's map . When it returns a Promise , it's chain . The runtime flattens the nested Promise<Promise<T>> into Promise<T> automatically. Array — another monad hiding in plain sight // of — wrap a value const arr = [ 42 ]; // map — transform each element const doubled = arr . map (( x ) => x * 2 ); // [84] // chain — transform each element into an array, then flatten const expanded = arr . flatMap (( x ) => [ x , x * 10 ]); // [42, 420] Array.of (or just [value] ) is of . .map is map . .flatMap is chain . Same pattern, different container. The insight is: chain is map followed by flatten . That's why it's also called flatMap or bind . You apply a function that returns a new container, then flatten the nested result. Maybe: Eliminating null

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There’s no shortage of great shows and movies to watch, but discovering something new isn’t always easy. Amazon’s Fire TV Stick 4K Plus and Fire TV Stick 4K Max come with redesigned software that aims to make it easier to finding something you’ll like, and both 4K-ready streaming sticks are back to their lowest prices […]

It's impossible to talk about a new Xbox game without also talking about the state of Xbox. Microsoft's gaming division is in freefall: Recent headlines are dominated by extensive layoffs, decimated studios, and confusing strategies, most of which stem from years of bad decisions and expensive acquisitions. But it wasn't always that way. Through its […]

Gemini had over 750 million monthly users in February.

The film, from Weapons director Zach Cregger, hits theaters on September 18.

Amazon is hoping the addition of games can turn Prime Video into a one-stop entertainment destination, borrowing a strategy from Netflix, which has increasingly embraced party games over the past several years.

I have been quiet lately I know - but don't worry I haven't forgotten you all and how much you like...

MeBeMe delivers small tasks to help users become their best selves instead of endlessly scrolling.

Barrels of fun.

Expedia Group has introduced STAR, an internal AI-assisted observability platform that helps engineers investigate production incidents using service telemetry and LLMs. Built with FastAPI, Datadog, Celery, Redis, and Langfuse, STAR follows structured workflows to analyze telemetry, generate root cause assessments, and support incident response while keeping engineers in the loop. By Leela Kumili

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It's a sign of how hard we're braced for the RAMpocalypse that Samsung adding just $100 to its prices seems reasonable.

Luna will be first arrive in the Prime Video app on Fire TV devices in the US and UK.

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 […]

Hi all, So I have been working on document layout analysis for some time now. I have tried the models like Doclayout, Docling, Miner U, marker. I am working with Journals. Overall Docling performs well, but the problem is that it over performs. And mineru u misses some content like the corresponding author on the page-footer. And it is also missing the masthead mark, and the article-type label. In my opinion unlimited OCR performs well in all the tasks, but in general it is failing to recognise any style at all. And it is bad at recognising logos. So I am wondering are there any state of the art models (SOTA) that are good at PDF text extraction and layout extraction ? Thanks submitted by /u/Fickle-Aide9279 [link] [留言]