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A GUI for Apple's native container system Discussion | Link

submitted by /u/hiIAmJan [link] [留言]

Our early testing has already shown that Siri AI knows when to shut up, and that's very much by design. In an interview with Mostly Human, Apple's Craig Federighi said new Siri won't act all sycophantic like chatbots made by OpenAI, Google, and others. "As you may know, if you use many of the existing […]

TL;DR: You'll learn how to replace Postman collections with plain-text Hurl files that live in Git, run in CI, and test your API’s geo behavior from any country. Debugging API issues always boils down to taking the tests you already have, running them from a different network or region, and comparing what your API receives. But I bet most of us can’t actually do that right away because our test infrastructure itself is fragmented. Your CI could be using a different config entirely from the “correct” one on a local dev machine, and half the collections may still point to a staging URL that changed months ago. Postman doesn’t produce diff-friendly artifacts, so even figuring out what changed is a full-on investigation. If any of that sounds familiar, I’d recommend finally taking the big step and replacing your Postman collection with Hurl — a command-line HTTP test runner where tests are plain **.hurl** text files that live in Git. For this tutorial, I’ll also walk you through multi-region testing — how to run those tests using a proxy to get a German egress IP, and see what the API actually returns from there. Everything below is self-contained, you’ll only need a new Git repo, then create the tests (three plaintext files.) The Test Suite at a Glance Create a folder (name it whatever you like — hurl-api-tests works) and these are the files we're going to be creating at its root . That folder is your Git repo root; paths in commands and CI are relative to it. Note how we’re using multiple .env files. If you're new to API testing, you'll find out why in a bit. hurl-api-tests/ ├── tests/ │ ├── health.hurl # smoke test -- Makes sure Hurl works, basic asserts. Skip if you want │ ├── auth-flow.hurl # Tests chaining + jsonpath capture + Bearer header │ └── geo-detail.hurl # Tests two egress profiles, real country/ASN diff (direct or proxied) ├── .env.local.example ├── .env.ci.example ├── .env.proxy-de.example ├── run-tests.mjs # cross-platform runner └── .github/workflows/a

Your complete medical history privately on your device Discussion | Link

Full-screen 3D clock scenes for your iPhone or Mac Discussion | Link

Buenas, esto es algo que he tenido que sacar esta semana, algo que no esta resuelto en ningún módulo ni contribuido ni del core, no he hecho un módulo, únicamente un simple form_alter para resolverlo. Contexto: se ha creado un tipo de contenido (nodo) llamado incidencia para que los usuarios anónimos puedan dar de alta incidencias, se ha creado un nodo y no un webform porque esas incidencias tendrán estados, comentarios de las personas que tienen que resolverlas y supervisarlas, etc. Uno de los campos de este contenido es un cambo booleano tipo "Acepto condiciones y política de privacidad" y se quería añadir un enlace en la palabra "política de privacidad" para que llevase al nodo de política de privacidad. aquí el código: /** * Privacy policy link. */ #[Hook('form_alter')] public function privacy_policy_link ( array & $form , FormStateInterface $form_state , $form_id ): void { if ( $form_id !== 'node_issue_form' ) { return ; } // NOTE: Para una segunda iteración hacer un formulario para elegir cual es el nodo de politica de privacidad y desde el hook_form_alter llamarlo para no tener a fuego la ruta. $url = Url :: fromUserInput ( '/politica-privacidad' ); $form [ 'field_issue_privacy_policy' ][ 'widget' ][ 'value' ][ '#title_display' ] = 'after' ; $form [ 'field_issue_privacy_policy' ][ 'widget' ][ 'value' ][ '#title' ] = t ( 'Acepto la <a href=":url" target="_blank">política de privacidad</a>' , [ ':url' => $url -> toString ()] ); }

Floating-point addition isn't associative. For a corporate inventory with tens of thousands of rows, naive summation drifts — and the number you disclose depends on row order. Here's why, and the fix. Here's a result that should bother anyone building carbon software. Take a corporate emissions inventory — tens of thousands of line items, each a number in tonnes CO₂e. Sum it. Now sort the same rows differently and sum again. The totals don't match. Not by much — maybe the third or fourth decimal place — but they don't match, and nothing in your code changed except the order. If you've never seen this, open a console: 0.1 + 0.2 === 0.3 // false That's the same bug, scaled up to a reporting deliverable. Why order changes the answer IEEE 754 doubles have 52 bits of mantissa. That's about 15–16 significant decimal digits of precision — generous, until you add numbers of very different magnitudes. When you add a small number to a large running total, the small number gets shifted right to line up the exponents before the addition happens. Bits that fall off the end of the mantissa are gone. Add a 0.0001 tCO₂e line to a running total of 80000.0 and there simply aren't enough mantissa bits to hold both the 80,000 and the 0.0001 — the small value is partially or completely swallowed. Float addition, as a result, isn't associative. (a + b) + c is not guaranteed to equal a + (b + c) . Sum your rows largest-first and the small values vanish early against a big accumulator. Sum smallest-first and they accumulate into something large enough to survive. Same data, different total. Here's the effect, deliberately constructed to be visible: const big = 80000 ; const smalls = Array ( 50000 ). fill ( 0.0001 ); // small values first, then the big one let a = 0 ; for ( const x of [... smalls , big ]) a += x ; // big value first, then the smalls let b = 0 ; for ( const x of [ big , ... smalls ]) b += x ; console . log ( a ); // 80004.99999999... console . log ( b ); // 80004.99999999...

Every few months, a post goes viral: "Please stop using [perfectly good tool]." This time it's lucide-react. And honestly? The take is lazy What's the actual problem? Nothing. The library is actively maintained, tree-shakable, TypeScript-friendly, and has 1000+ consistent icons. A new icon library dropped? Cool. That doesn't make this one broken. Tools don't expire — context does Before switching anything in production, ask: Is it maintained? ✅ Does it solve my problem? ✅ Is my team comfortable with it? ✅ Then keep using it. Real usage — still clean in 2026 import { Search , Bell , User } from ' lucide-react ' ; export default function Navbar () { return ( < nav > < Search size = { 20 } /> < Bell size = { 20 } strokeWidth = { 1.5 } /> < User size = { 20 } color = "#6366f1" /> </ nav > ); } Tree-shaking works perfectly — only Search, Bell, and User are bundled. Not the entire library. When you should switch Unpatched security vulnerability Repo abandoned for 2+ years Bundle size issue you've actually measured If none of these apply, you're switching for hype, not logic. The real issue "Please stop using X" posts get engagement. Developers see them, second-guess stable choices, and waste hours migrating things that weren't broken. Don't let LinkedIn trends drive your architecture. Build products. Not migrations.

There's a quiet lie rotting at the heart of modern software development, and almost nobody wants to say it out loud: the industry is drowning in people who can ace a LeetCode interview, argue endlessly about architectural patterns, and hold strong opinions about tabs versus spaces — but who have never actually shipped a product that real people use. We've built an entire culture around the performance of engineering rather than the practice of it. Walk into any dev team today and you'll find brilliant people who can recite the SOLID principles from memory, who have strong feelings about hexagonal architecture, and who will spend three weeks bikeshedding a folder structure — but who have never felt the particular anxiety of watching your own code process a real user's real money. Never felt that accountability. Never shipped something and then lived with the consequences. That's a problem! The industry has confused credentials and vocabulary with competence. A computer science degree, a GitHub profile full of tutorial clones, and the ability to reverse a linked list in a whiteboard setting are now routinely mistaken for the ability to build software that matters. They're not the same thing. The people who actually make software work — who ship under pressure, who fix production bugs at 11pm, who make pragmatic calls with incomplete information — often look terrible on paper. They'll choose a boring, proven technology over the shiny new thing. They'll skip the elegant abstraction in favour of something they can debug in a hurry. They understand that a running system that's 80% right beats a perfect system that's still in design. AI has made this worse. Now you can generate plausible-looking code, confidently-written documentation, and technically-accurate architecture diagrams without ever having built a system that stayed up under real load, without ever having migrated a production database at 2am, without ever having owned something from idea to invoice. Knowing ho

Ein KI-Agent ist nur so zuverlässig wie die APIs, die er aufruft. Das Modell wählt ein Tool aus, füllt Argumente ein und sendet eine Anfrage. Wenn diese Anfrage fehlschlägt, die falsche Form zurückgibt oder hängen bleibt, trifft Ihr Agent eine selbstbewusste Entscheidung auf Basis schlechter Daten. Produktions-Agenten stehen und fallen deshalb mit einer getesteten Tool- und API-Schicht. Apidog noch heute ausprobieren Diese Anleitung zeigt, wie Sie einen Agenten erstellen, der reale Tools aufruft, und wie Sie Apidog als API-Schicht und Testumgebung verwenden. Sie definieren Tool-Endpunkte, mocken sie für die Offline-Entwicklung und schreiben Assertions, die fehlerhafte Tool-Aufrufe abfangen, bevor sie Benutzer erreichen. Was ein Agent auf der API-Ebene tatsächlich tut Reduziert auf die technische Schleife passiert Folgendes: Das Modell erhält ein Benutzerziel und eine Liste verfügbarer Tools. Es gibt einen Tool-Aufruf zurück: Tool-Name plus JSON-Argumente. Ihr Code führt den Aufruf aus, meist als HTTP-Request. Das API-Ergebnis geht zurück an das Modell. Das Modell ruft ein weiteres Tool auf oder antwortet dem Benutzer. Die kritischen Fehler entstehen fast immer in Schritt 3 und 4: Das Modell halluziniert ein Argument. Die API gibt 400 , 422 , 429 oder 500 zurück. Das Antwortschema hat sich geändert. Der Request läuft in ein Timeout. Eine Ratenbegrenzung greift mitten in der Agenten-Schleife. Wenn Sie KI-Agenten als neue API-Konsumenten betrachten, wird klar: Ihr Agent ist ein API-Client. Er braucht dieselbe Teststrenge wie jeder andere produktive Client. Die Arbeit besteht aus zwei Teilen: Tools als reale, testbare API-Operationen definieren. Prüfen, ob der Agent diese Tools unter guten und schlechten Bedingungen korrekt aufruft. Schritt 1: Tools als reale API-Operationen entwerfen Definieren Sie jedes Tool zuerst als API-Endpunkt in Apidog. Behandeln Sie Tool-Schema und API-Schema als denselben Vertrag. Beispiel: Tool: get_weather API-Operation: GET /weather Paramet

We are all using AI to write code now. Whether it's Claude Code, Aider, or Copilot, the speed is incredible. But there is a glaring bottleneck we don't talk about enough: AI code is often just slightly broken. 🤖💥 It forgets an import, misses a type definition, or fails a test. Usually, you only find out after you push to GitHub. Your CI/CD pipeline turns red 🔴, and you end up polluting your Git history with a dozen commits titled fix: linting or fix: missing test variable . I recently found a repository called no-mistakes that solves this brilliantly. It acts as a local proxy between your terminal and GitHub, forcing AI to test and fix its own code before anyone else sees it. Here is why it's worth a look. 👇 😩 The Problem With Traditional Workflows Right now, developers handle broken code in two ways: The CI/CD Walk of Shame 🚶: You push code, wait 5 minutes for GitHub Actions to fail, pull the error locally, fix it, and push again. Pre-commit Hooks (Husky) 🐶: You set up local hooks. When you try to commit, it yells at you about formatting and blocks the commit until you manually fix it. Both methods are passive . They tell you something is broken, but they leave the cleanup to you. When you're using an AI coding agent to generate the code in the first place, manually babysitting its output defeats the purpose. 🚀 Enter no-mistakes no-mistakes is a CLI tool that intercepts your git push . Instead of sending your code straight to origin, it routes it through a localized validation pipeline. ⚙️ How it actually works: 🧱 The Hidden Sandbox: When you trigger it, it creates a temporary git worktree in the background. Your active editor stays completely undisturbed. ✅ Validation: It runs your tests, linter, and build steps inside that isolated sandbox. 🔁 The AI Feedback Loop: If a test fails, it captures the error log and hands it back to an AI agent, essentially saying: "You broke this test. Fix it." 🟢 The Clean Push: Once the AI patches the code and all tests pass, it push

Point at any UI to send exact context to your AI Discussion | Link

Two related, Oracle-backed projects published opposing policies on open-source contributions created with generative AI: The OpenJDK Governing Board approved an interim policy prohibiting such contributions, while the Coding Assistants policy from GraalVM permits them. Both projects require contributors to sign the same Oracle Contributor Agreement (OCA) for intellectual property. By Karsten Silz

TL;DR Welcome back to Dev Opportunity Radar. This is a weekly series where I share opportunities,...

Discover how to save on T-Mobile Business Internet and phone plans, from sign-up perks and switching rewards to bundled offers and free lines.

submitted by /u/ernesernesto [link] [留言]

Avataar AI's distilled video model is priced at $0.005 for every second of generation

Equal AI said that its AI-powered call assistant now has over a million monthly active users.

Imagine you write a letter in a secret code that only your old house key can read. Then you move. You photocopy the coded letter, carry it to the new house… and realise the new key can't decode any of it. The letter is valid, just useless. That's effectively what happens when you back up encrypted values from a Laravel database and restore them onto a different server. I hit exactly this while working on laravel-config-backup today, so here's the problem and the fix. The real cause: Crypt is bound to APP_KEY When you store sensitive settings (think API tokens or OAuth secrets) in the database, you typically encrypt them with Crypt::encryptString() . Lovely — until you remember Crypt uses your app's APP_KEY as the key. A naive backup copies that ciphertext straight across: // Naive approach — move the ciphertext as-is $value = DB :: table ( 'settings' ) -> where ( 'key' , 'some.secret' ) -> value ( 'value' ); // this value is encrypted with the OLD server's APP_KEY The new server has a different APP_KEY . Try to decrypt → DecryptException: The payload is invalid . Your backup is technically complete but practically dead. The fix: decrypt on the way out, re-encrypt on the way in The decision is easy to state, hard to stay disciplined about: never carry ciphertext across a server boundary. Instead — On create : decrypt the values with the source server's APP_KEY , store plaintext inside the archive. Protect that archive with AES-256 and a password (a human-held secret, not the APP_KEY). On restore : re-encrypt the values with the destination server's APP_KEY before writing to the DB. Back to the analogy: you decode the letter, carry the plain letter in a locked briefcase (the password-protected archive), and re-encode it with the new house's lock on arrival. The briefcase handles security in transit — not the old code that's no longer relevant. I made that intent explicit right where the behaviour lives, in ConfigBackupService : /** * Config Backup & Restore. * * Bundl