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The new Siri AI is conversational, omnipresent, and actually helpful.

Plus: Gay bars in San Francisco using face scanners, France quits Palantir, Apple plans to change its private email and more.

Researchers have demonstrated they can make coffee comparable to conventional espresso using ultrasonic waves. Because the process doesn’t need hot water, it consumes 75 percent less energy.

AWS recently introduced Amazon Cognito multi-region replication, which automatically replicates user identities and user pool configurations from a primary region to a secondary one. This enables applications to continue authenticating users from a replica region during outages, without requiring custom replication and failover mechanisms. By Renato Losio

I run Convertify , a free image converter built on Rust and libvips. Last week I decided to stop guessing about format performance and actually measure it. I took 50 real images (26 PNGs, 24 iPhone HEIC photos), ran 17 conversions through the production pipeline, and recorded every file size and encode time. Some results confirmed what everyone says. Others did not. The three results that surprised me 1. Converting HEIC to JPG makes files 14% bigger , not smaller. This one hurt. "Convert iPhone photos to JPG" is probably the most common advice on the internet. But HEIC wraps the HEVC codec, which compresses roughly 2x better than JPEG. Going from a better codec to a worse one means the file grows. Every time. If you actually want smaller iPhone photos: HEIC to WebP saves 43%, HEIC to AVIF saves 57%. 2. AVIF encodes 7x slower than WebP for 10% more compression. AVIF Q63: 55 KB, 1.30s per image. WebP Q80: 61 KB, 0.19s per image. That is a 10% size difference for a 7x speed penalty. For a single hero image, nobody cares. For a batch pipeline processing thousands of product photos, that is the difference between 3 minutes and 21 minutes. 3. PNG at 600 DPI is smaller than PNG at 300 DPI when rasterizing PDFs. This was the weirdest one. I was benchmarking PDF-to-image and noticed PNG output shrank from 2,221 KB at 300 DPI to 1,660 KB at 600 DPI. I spent an hour convinced I had a bug. Turns out it is a real property of PNG encoding. Higher DPI renders smoother gradients between adjacent pixels, and PNG's prediction filters (Paeth, sub, up) compress smooth gradients dramatically better than the sharp edges you get at lower resolutions. Not a bug. Just PNG being PNG. The quick reference table Conversion Size change Speed JPG to WebP Q80 -64% 0.19s JPG to AVIF Q63 -68% 1.30s PNG to WebP Q80 -92% 0.21s PNG to JPG Q85 -86% 0.07s HEIC to JPG Q85 +14% 1.90s HEIC to WebP Q80 -43% 5.64s HEIC to AVIF Q63 -57% 14.52s WebP to JPG Q85 +60% 0.09s AVIF to JPG Q85 +80% 0.15s What I actual

Over 60% of APAC finance leaders say AI-led automation is their top priority for 2026. For Indian businesses, that stat hides a quieter truth: most SMBs have no idea which automation to start with. They hear "AI for finance" and picture an enterprise suite with a six-figure licence fee. Wrong picture. I've built finance automations for CA firms, D2C brands, trading desks, family-run manufacturers, and a few fintech startups. The pattern is always the same. Five finance processes eat the most hours, hide the most errors, and respond best to a simple Python layer on top of whatever ledger you already use. This is the playbook. No enterprise suite. No subscriptions you don't need. Each automation is something I've shipped for real clients using Python, free APIs, and a ledger that's usually Tally or Zoho Books. 1. Bank Reconciliation — The Single Biggest Time Sink in Indian Finance Every finance team I meet has the same nightmare. Statements from three or four banks. Tally or Zoho on the other side. An Excel sheet in the middle. Eight hours a month — sometimes more — matching rows. A CA friend was losing two sleepless nights before every GST deadline on exactly this. We replaced it with a Python script that pulls statements from email attachments, categorizes transactions using keyword rules, cross-references entries with Tally, and flags only the mismatches in a clean Excel file. Eight hours dropped to fifteen minutes of review. "Tu 2 saal pehle kyu nahi mila?" (Why didn't I meet you two years ago?) If your team is still opening each bank statement manually, start here. It's the highest-ROI automation in Indian finance. I've written the full workflow in how a weekend Python script saved a CA firm 209 hours during ITR season . 2. Cash Application — Matching Payments to Invoices at Indian Speeds Globally, AI-driven cash application handles up to 90% of invoice matching without human touch. In India, it's harder — money arrives in more shapes than most tools expect: UPI,

The next attempt was the standard Maps URL: window . open ( `https://maps.google.com/maps?daddr= ${ lat } , ${ lng } ` , ' _blank ' ); This opens Maps, but in the browser — not the app. And it shows the route preview, not turn-by-turn navigation. What worked: Android Intent URLs Android supports a special URL scheme that tells Chrome to launch a native app directly: window . location . href = `intent://navigation/now?ll= ${ lat } , ${ lng } &title=Next+stop#Intent;scheme=google.navigation;package=com.google.android.apps.maps;end` ; Breaking it down: intent:// — tells Chrome this is an Android intent navigation/now?ll=${lat},${lng} — opens Maps in navigation mode, starting immediately #Intent;scheme=google.navigation — the URI scheme to use package=com.google.android.apps.maps — the target app package end — closes the intent syntax This opens the Google Maps app directly and starts turn-by-turn navigation automatically — no extra taps needed. It also works with Android Auto. The full function export function openNavigation ( destination : { lat : number ; lng : number }): void { window . location . href = `intent://navigation/now?ll= ${ destination . lat } , ${ destination . lng } &title=Next+stop#Intent;scheme=google.navigation;package=com.google.android.apps.maps;end` ; } Call it on any user gesture (tap, click) and it works without being blocked by the browser. The app The full PWA is open source if you want to see the context: 🔗 GitHub repo 🌐 Live app Built with React + TypeScript + Vite + Dexie.js + @vis .gl/react-google-maps. If you're building a PWA that needs to hand off to Google Maps navigation on Android, this intent URL is the cleanest solution I found. Hope it saves you the hour I spent figuring it out.

Anthropic’s Fable/Mythos shutdown is the first real model export-control shock The important AI story this week is not just that Anthropic launched bigger Claude models. It is that the US government then told Anthropic to switch two of them off for foreign nationals — and Anthropic says the practical answer was to disable them for customers while it works through compliance. That is a very different kind of platform risk than rate limits or pricing changes. If you are building on frontier models, model access can now move because of export-control decisions, safety claims, and geopolitical pressure. What happened Anthropic announced Claude Fable 5 and Claude Mythos 5 on June 9. Fable 5 was described as Anthropic’s most capable generally available model, with stronger performance across software engineering, knowledge work, vision, scientific research, and longer complex tasks. Mythos 5 was positioned above that: an upgrade to Claude Mythos Preview, with Anthropic calling out cyber-defence and life-sciences use cases. Three days later, Anthropic published a blunt update: the US government had issued an export-control directive requiring Anthropic to suspend all access to Fable 5 and Mythos 5 by any foreign national, whether inside or outside the United States — including foreign-national Anthropic employees. Anthropic said the order arrived at 5:21pm ET on June 12, did not include detailed specifics, and that its understanding was that the government believed it had become aware of a jailbreaking method for Fable 5. Anthropic said access to other models was not affected, but the “net effect” was that it had to abruptly disable Fable 5 and Mythos 5 for customers to ensure compliance. Al Jazeera’s follow-up on June 19 frames the downstream effect clearly: allied countries and companies are now being forced to think harder about dependence on US frontier-model access. It also reports that Anthropic had granted roughly 200 institutions across 15 countries access to Claud

For the last 30 years, stopping the flow of cybersecurity-related software has proven to be ineffective. It's unclear why it would work now with Anthropic’s cybersecurity model Mythos.

Go’s IPO — Japan’s biggest so far this year — has done more than provide a much-needed boost to the country’s languishing listing season. It has also supplied the taxi-hailing app with the capital required to address an existential issue: Japan’s shortage of drivers. Go, which went public Tuesday, plans to use the ¥88.6 billion […]

Ekehi platform has moved from hand-written HTML/CSS/JS pages to a typed, component-driven React 19 client and a module-based TypeScript Express/Node.js API. 0. Where we started and where we landed Before. A static client built from per-page folders ( landing/ , contributors/ , login/ , signup/ , admin/ ), each shipping its own index.html , a shared styles.css , and vanilla ES module scripts under client/shared/ . The server was an Express API written in plain JavaScript. After. Layer Before After Client Static HTML + CSS + vanilla JS React 19 + Vite 8 + TanStack Router + TypeScript 6 Styling One global styles.css Tailwind CSS 4 with @theme design tokens Data fetch scattered per page TanStack Query over a typed lib/api client Server Express in JavaScript Express + TypeScript, module-per-domain Repo Two loose folders pnpm workspace with shared git hooks Quality gate None ESLint 9, Prettier 3, Husky, commitlint, Vitest The migration ran in two phases on separate branches: **Phase 1 — client rewrite. **Phase 2 — server rewrite. 1. Why this framework? Choice: React 19 , rendered as a client-side SPA through Vite 8 , routed by TanStack Router . TanStack Router was chosen rather than React Router because it gives fully type-safe routes, first-class search-param typing, built-in code-splitting, and file-based route generation that pairs cleanly with Vite. 2. The folder and component structure The client uses a feature-sliced layout: code is grouped by domain, not by technical type. client/src/ ├── components/ │ ├── layout/ navbar.tsx, footer.tsx │ └── ui/ button, input, modal, select, dropdown, ... (design system) ├── config/ env.ts, env-schema.ts, endpoints.ts ├── features/ one folder per domain │ ├── auth/ auth.query.ts, auth.service.ts, auth.types.ts, components/, pages/ │ ├── opportunities/ pages/ │ ├── resources/ pages/ │ ├── submissions/ pages/ │ ├── admin/ pages/ │ └── site/ pages/ (landing, contributors) ├── lib/ │ ├── api/ request.ts, errors.ts, refresh.ts, types.t

The main idea of a Carousel isn't just about moving a bunch of elements from left to right because there must be a smoothly infinite movement, this can be done by duplicating the element, but wouldn't it be a waste of time and resources to do so. So the best solution would be rather than moving the whole element, we just move each element on a time based manner, all elements will have the animation but each element will have a unique (incremented) index, by which we will delay its start, and if we made this delay negative, we will have a smooth movement without any lagging adding a will-change will make a separate compositing layer to make the animation run on gpu rather than cpu below is a demo by which, you can understand the effect You can reach me (if you had any problems with the effect): X / twitter "where I post a lot!" LinkedIn

Dolphins, sharks, turtles, and human workers are all victims of unregulated squid fishing fleets. Another news article . As usual, you can also use this squid post to talk about the security stories in the news that I haven’t covered. Blog moderation policy.

I was debugging an internal project management application built with SvelteKit and a Rust API. Locally, navigation felt almost instant. On the VPS, opening the Tickets, Timeline, and OpenSpec docs pages felt noticeably slower. Clicking a ticket also took too long before the preview panel became useful. My first assumption was infrastructure: Maybe the VPS was underpowered. Maybe PostgreSQL queries were slow. Maybe the reverse proxy added latency. Maybe SvelteKit SSR was taking too long. The measurements pointed somewhere else. The Baseline I started with the feature list endpoint used by both Tickets and Timeline. For a project with 52 tickets: Metric Result API response time ~4 ms Response size 353,956 bytes Number of tickets 52 The API was not slow. But it was returning around 354 KB for a list of only 52 items. The SvelteKit route payload showed the same pattern: Route Data payload Tickets 349,857 bytes Timeline 354,731 bytes This explained why local testing was misleading. On localhost, transferring and parsing a few hundred kilobytes is easy to miss. Once the app runs behind a VPS, reverse proxy, TLS, and a real network connection, the payload becomes much more visible. What Was Inside the Payload? I broke down the feature response by field. The descriptions alone accounted for: 296,177 bytes That was more than 80% of the complete response. The list endpoint was returning something similar to this for every ticket: interface FeatureListItem { id : string ; title : string ; status : string ; priority : string ; storyPoints : number | null ; dueDate : string | null ; description : string | null ; checkoutCommand : string | null ; openSpecCommand : string | null ; } The problem was not that these fields were useless. They were useful on the ticket detail panel. They were not useful when rendering the initial list. Timeline was even more wasteful. It used ticket status, dates, dependencies, and assignees, but still downloaded every full Markdown description. The D

Imagine a user trying to upload a 100MB video or a high-resolution photo to your app. If you use the standard Rails file upload, that file travels from the user's browser to your Rails server, and then your server sends it to S3 or Google Cloud. This is a terrible way to do it. While that 100MB file is transferring, your Rails worker (Puma) is frozen. It can't handle other users. If three people upload large files at once, your whole app will stop responding. In 2026, the professional way to handle this is Direct Uploads . With Direct Uploads, the file goes directly from the user's browser to your cloud storage (S3, R2, etc.). Your Rails server only handles a tiny bit of metadata. It is faster for the user and much safer for your server. Here is how to set it up in Rails 8. STEP 1: Configure Your Storage First, make sure you aren't using the local disk for production. You need a cloud provider like AWS S3 or Cloudflare R2. In your config/storage.yml : amazon : service : S3 access_key_id : <%= ENV['AWS_ACCESS_KEY_ID'] %> secret_access_key : <%= ENV['AWS_SECRET_ACCESS_KEY'] %> region : us-east-1 bucket : my-app-uploads # Crucial for Direct Uploads! public : true Note: You must configure CORS in your S3/R2 dashboard to allow requests from your domain. If you don't do this, the browser will block the upload. STEP 2: The Rails Form Rails makes the backend part incredibly easy. You just add one attribute to your file field: direct_upload: true . <!-- app/views/users/_form.html.erb --> <%= form_with ( model: user ) do | f | %> <div class= "field" > <%= f . label :avatar %> <%= f . file_field :avatar , direct_upload: true %> </div> <%= f . submit "Save Profile" %> <% end %> When you add direct_upload: true , Rails automatically includes a JavaScript library that handles the "handshake" with S3. STEP 3: Adding a Progress Bar (The UX Win) Direct uploads can take a few seconds. If nothing happens on the screen, the user will think your app is broken. We can use the built-in Ac

Just as last week was ending, the US government forced Anthropic to pull its two newest models, Fable 5 and Mythos 5, citing national security concerns after Amazon researchers allegedly found a way to bypass Fable 5’s guardrails. Cybersecurity researchers have since signed an open letter calling the move dangerous, and Anthropic itself noted the same jailbreaks exist in other models. So is […]

Durante mucho tiempo asocié el desarrollo de software con programar funcionalidades: crear entidades, armar controladores, conectar una base de datos, validar formularios y hacer que una aplicación responda correctamente. Sin embargo, durante el Trabajo Final de la asignatura Desarrollo de Aplicaciones Web , entendí que programar es solo una parte del problema. El verdadero desafío aparece antes de escribir código: decidir qué arquitectura conviene, por qué conviene, cuánto cuesta, qué riesgos resuelve y qué complejidad agrega. El trabajo consistió en diseñar un sistema de gestión clínica que comenzaba como un MVP para una única clínica y evolucionaba progresivamente hacia una plataforma SaaS multi-tenant . Aunque fue un proyecto académico, el ejercicio nos obligó a pensar como si estuviéramos tomando decisiones técnicas en un contexto real: con restricciones de negocio, costos, equipo, seguridad, datos sensibles y crecimiento futuro. La principal enseñanza fue: la mejor arquitectura es la que responde mejor al momento del producto . El primer desafío: no sobrediseñar desde el inicio Cuando empezamos a pensar el sistema, la tentación era ir directamente a una arquitectura compleja: microservicios, eventos, colas, Kubernetes, múltiples bases de datos y despliegues independientes. Pero al analizar el escenario inicial, esa decisión no tenía sentido. El sistema comenzaba para una sola clínica, con un presupuesto reducido y con requisitos todavía en etapa de validación. En ese contexto, arrancar con microservicios hubiera agregado más problemas que beneficios: comunicación entre servicios, contratos, versionado, observabilidad distribuida, debugging más difícil y mayor costo de infraestructura. Por eso, una de las decisiones más importantes fue comenzar con una arquitectura en capas , desplegada como un único proceso. Esta elección permitió separar responsabilidades sin asumir desde el principio la complejidad de un sistema distribuido. La capa de presentación se encarg

(or: How I Turned My Laptop Into a Lightsaber for the Market) The Quest Begins (The “Why”) Ever stared at a blinking cursor at 2 a.m., wishing you could make your laptop do the heavy lifting while you chased dreams (or just caught up on sleep)? I was there, scrolling through Reddit’s r/investing, watching folks brag about “algo‑trading gains” while I was still manually refreshing Yahoo Finance like a peasant in a medieval market. One night, after yet another failed attempt to predict a stock’s move with gut feeling (spoiler: my gut is terrible at math), I remembered a line from The Matrix : “There is no spoon.” Turns out, there is no magic either—just code, data, and a healthy dose of stubbornness. I decided to slay the dragon of emotion‑driven trading and build a bot that could execute a simple strategy while I binge‑watched Stranger Things . Spoiler alert: the first version was a hot mess, but the journey taught me more about Python, APIs, and risk management than any textbook ever could. Let’s walk through that adventure together—code, pitfalls, and all the triumphant “I‑did‑it!” moments. The Revelation (The Insight) The big “aha!” moment came when I realized a trading bot isn’t some omniscient AI that predicts the future; it’s just a disciplined executor of rules you define. Think of it as Indiana Jones whip‑cracking through a booby‑trapped temple: you set the traps (your strategy), the bot avoids them (risk checks), and grabs the idol (profit) when the conditions are right. For my first bot I chose a mean‑reversion idea: if a stock’s price deviates too far from its 20‑day moving average, I bet it’ll snap back. It’s not flashy, but it’s easy to understand, back‑test, and implement. The magic happens in three simple steps: Fetch data – pull recent price bars from a free API (I used Alpha Vantage; you can swap for Polygon, IEX Cloud, etc.). Calculate the signal – compare the latest close to the moving average and compute a z‑score. Execute – if the z‑score crosses

A French launch startup is scrapping the name of its rocket, apparently due to a trademark issue.

Let's poke at the differences between scroll- driven and scroll- triggered animations. A First Look at Scroll-Triggered Animations originally handwritten and published with love on CSS-Tricks . You should really get the newsletter as well.