标签：#iOS

Leaked iPhone 18 Pro photos and parts lists appeared on the dark web following a data breach affecting one of Apple's key suppliers, according to a report from Reuters. The leaked images show a drop test of what a source tells Reuters is the iPhone 18 Pro, equipped with a three-camera layout and Apple logo. […]

Director Travis Knight is also the creative mind behind 2016's Oscar-nominated Kubo and the Two Strings .

The idea came from an extremely serious game of hide and seek with my cousins. We were adults, which made it ridiculous, but also strangely perfect. Someone was hiding behind a couch in plain sight, surviving only because the seeker did not look carefully enough. That made me wonder: what if looking carefully was not enough? What if the seeker could not freely look around the room? What if they could only see the world through their phone screen, while virtual obstacles blocked parts of their view? That became the core idea behind AR Hide and Seek: a local multiplayer hide and seek game where 2-5 players use the space they are already in. The hiders physically hide somewhere in the room, while the seeker views the environment through an iPhone. The phone fills the space with digital clutter, making familiar rooms harder to read. One phone. One seeker. Real hiding places. Virtual obstacles. Why LiDAR? LiDAR on iPhone Pro models gives the phone a real-time depth map of the environment, with centimeter-level understanding of the space around it. That means virtual objects can be placed in ways that respect real-world geometry: a crate can sit on the floor, a wall can align with an actual wall, and obstacles can feel like they belong in the room rather than floating on top of it. For a game where the virtual environment needs to feel like it genuinely fills the space, that difference matters immediately. Without reliable depth information, objects can drift, clip, or hover in ways that break the illusion. The tradeoff is device requirement. LiDAR is only available on iPhone Pro models, which narrows the audience. But for this game, the better AR experience was worth it. The seeker sees a version of the room cluttered with virtual obstacles. The hiders are still physically hiding behind real furniture; the phone does not make them disappear. It simply makes finding them harder. Designing the Core Loop The mechanic is simple on paper, but it took a surprising amount of tu

Currently available as a beta in Xcode 27, Swift 6.4 introduces a range of enhancements: better C interoperability, simplified OS availability check, fine-grained warning control, async support in defer, efficient iteration for non-noncopyable types, up to 4x faster URL parsing, and improved interoperability between Swift Testing and XCTest. By Sergio De Simone

If you've ever tried to send an iMessage programmatically , you've probably hit the same wall everyone does: Apple has no public iMessage API. There's no POST /imessage in the developer docs, no SDK, no OAuth scope. Yet "blue bubble" delivery has 3–4× the open rates of SMS, so the demand to send iMessages from code — for CRMs, bots, notifications, and outbound — keeps growing. This guide covers the realistic options, then walks through actually sending and receiving iMessages over a REST API with working Python , Node.js , and curl examples you can paste and run today. Why there's no official iMessage API iMessage is a closed, end-to-end-encrypted protocol tied to Apple IDs and Apple hardware. Apple has never shipped a public API to send iMessages, and "Messages for Business" is a support-inbox product gated behind an approval process — not a way to send outbound messages from a script. So historically, developers reached for hacks: Approach Works from a server? Reliability Receiving messages Notes AppleScript / osascript No — needs a logged-in Mac with Messages open Brittle Polling the local SQLite chat.db Mac-only, breaks on macOS updates Shortcuts automation No Brittle No Manual, not built for scale "Just use SMS" (Twilio etc.) Yes High Yes Green bubbles, no typing indicators/tapbacks/HD media Hosted iMessage REST API Yes High Yes (webhooks) What this guide uses The AppleScript route is fine for a one-off script on your own Mac. The moment you want to send from a server, send at scale, or receive replies reliably, you need a hosted API that manages the Apple side for you and exposes a normal HTTP interface. The setup For the examples below I'm using Blooio , an iMessage REST API. Any provider with a similar HTTP surface will follow the same patterns — the concepts (Bearer auth, a send endpoint, webhooks for inbound) are what matter. You'll need: An API key (Blooio gives you one in the dashboard — no credit card, no A2P/10DLC registration, no DUNS number) A phone

If you send SMS one-time codes, there's a decent chance you're paying scammers to phone-spam themselves on your dime. It even has a name: SMS pumping . And it's not a rounding error — Elon Musk claimed Twitter was losing ~$60M/year to fake 2FA traffic before they killed SMS 2FA for free accounts. Here's how the scam works, why SMS 2FA is structurally expensive, and why flipping the direction — mobile-originated (MO) 2FA , taken to its logical end over iMessage — fixes both the cost and the fraud at once. What is SMS pumping? SMS pumping (also called AIT — Artificially Inflated Traffic , or SMS toll fraud ) is a scheme where bad actors abuse a form that sends SMS one-time codes. They pump thousands of phone numbers — usually premium ranges they secretly control with a telecom — into your "send me a code" endpoint. You pay for every one of those messages. A cut of that termination fee flows back to the fraudsters via the carrier. The "users" never log in. They were never users. The entire point was to make your verification endpoint dial a meter that pays them. The structure that makes this possible is simple: you, the company, send (and pay for) the message. Every code is revenue for someone in the delivery chain — so there's a direct financial incentive to trigger as many as possible. Why SMS 2FA is expensive even without fraud Even with zero abuse, application-to-person ( A2P SMS ) is a bad cost curve: You pay per message. Volume spikes — a launch, a bot attack, an international audience — turn into surprise bills. International is brutal. Cross-border A2P carries steep carrier surcharges that vary wildly by destination. Carrier fees and registration overhead. In the US you're funneled through A2P 10DLC registration, brand vetting, and per-segment fees before you send a single legit code. So your 2FA line item is pay-per-event , unpredictable , and exploitable . Three bad properties for something that's supposed to be boring infrastructure. The Twitter/X case This

So far we've been working with Swift's built-in types — String, Int, Bool, Array. But what if you...

Your QA team built 50 automated tests in the first quarter. Leadership was thrilled. Coverage was...

Dai metadati in una lingua a 20 localizzazioni senza impazzire tra click e schermate: un flusso pratico per indie e piccoli team. Localizzare un’app non significa solo tradurre le stringhe dell’interfaccia. Una buona parte dell’acquisizione organica passa dai metadati su App Store Connect : titolo, sottotitolo, descrizione e keyword. Il problema è che, quando provi a farlo “a mano” dal pannello web, diventa subito un lavoro di pura resistenza: apri la scheda, cambi lingua, compili i campi, salvi, ripeti. Ora moltiplica per 10–20 lingue. Per molti indie (e in generale per chi ha poco tempo e zero voglia di click ripetitivi) il punto di svolta è usare ASC CLI per rendere questa attività automatizzabile, ripetibile e verificabile . Perché la localizzazione dei metadati è un caso d’uso perfetto per una CLI Dal punto di vista del flusso di lavoro, i metadati App Store hanno tre caratteristiche che li rendono ideali per l’automazione: Sono campi strutturati (title, subtitle, description, keywords): non stai “inventando” contenuti ogni volta, stai trasformando contenuti. Sono ripetitivi per lingua : la sequenza di operazioni è identica, cambia solo la locale. Sono tanti : più lingue aggiungi, più l’approccio manuale scala male (tempo, errori, incoerenze). Con una CLI, invece, il lavoro si sposta dal “fare cose” al definire un processo : prendi i metadati di partenza, generi le varianti linguistiche, applichi l’update in batch. Cosa conviene localizzare (e cosa no) In genere ha senso includere in un passaggio di localizzazione “massiva”: App name / title (attenzione ai limiti e ai trademark) Subtitle (spesso è la parte più ASO-oriented) Description (qui conta più la leggibilità che la traduzione letterale) Keywords (campo delicato: va adattato, non tradotto alla cieca) Al contrario, è meglio trattare con più cautela: Claim e frasi marketing molto creative : in alcune lingue risultano innaturali se tradotte letteralmente Keyword strategy : la ricerca utenti cambia per mercat

We live in an era where our most intimate data—heart rates, sleep cycles, and step counts—is constantly uploaded to the cloud for "analysis." But what if you could have a world-class AI medical assistant living entirely on your device? Today, we are pushing the boundaries of Edge AI and Privacy-preserving machine learning by deploying a quantized Llama-3 model directly onto an iPhone using MLC-LLM . By leveraging Apple HealthKit and hardware acceleration via Metal , we can transform "Pixels and Pulses" into actionable insights without a single byte leaving the device. This tutorial dives deep into the architecture of on-device LLMs, specifically focusing on how to bridge the gap between high-performance C++ runtimes and a React Native UI. If you're interested in more advanced patterns for production-grade AI integration, be sure to explore the engineering deep-dives at the WellAlly Blog , which served as a massive inspiration for this architecture. 🚀 The Architecture: Why On-Device? The challenge with running Llama-3 on mobile isn't just memory—it's the data pipeline. We need to fetch sensitive data from HealthKit, format it into a prompt, and run inference using the phone's GPU. System Data Flow graph TD A[User Query: How was my sleep?] --> B[React Native UI] B --> C{Swift Bridge} C --> D[Apple HealthKit API] D --> E[Health Data Context] E --> F[MLC-LLM Engine] G[Quantized Llama-3 Weights] --> F F --> H[On-Device Inference via Metal] H --> I[AI Generated Health Report] I --> B 🛠 Prerequisites MLC-LLM : Our compiler stack for universal LLM deployment. TVM (Tensor Virtual Machine) : The backbone for hardware acceleration. React Native : For the cross-platform UI. Xcode & Swift : To interface with Apple's HealthKit. Llama-3-8B-Instruct (Quantized) : We'll use 4-bit quantization (q4f16_1) to fit within mobile RAM limits. Step 1: Quantizing Llama-3 for Mobile Standard Llama-3 is too heavy for a phone. We use the MLC-LLM CLI to compile the model into a format that the iP

Your payment tests passed in staging. Then PhonePe pushed an SDK update on Tuesday, the UPI intent...

An App Store rating looks like a verdict. It behaves more like a monument, built over years and slow to move. It says very little about how this month's users feel. I took the 12 most-rated Productivity apps on the US App Store, 32 million ratings between them, and split the headline star into the two numbers it hides: how far recent sentiment has fallen below the lifetime average, and whether the developer replies when users complain. How it is measured Population truth. Lifetime ratings and the star histogram come from Apple's full ratings data, every rating an app has ever received. Recent sentiment. A fixed window of the most recent reviews by date, so an app captured to a depth of thousands is not compared on a multi-year average against an app with a few hundred. Same window for everyone. Developer response. Reply share and median latency over that recent window. Complaints are bucketed with a rule-based taxonomy. It is a heuristic, not a trained classifier, and I treat it as one. What turned up The AI assistants now own this chart, and they reply to almost no one. App Lifetime Recent Reply share ChatGPT 4.8 4.18 0% Claude 4.7 3.06 0% Grok 4.9 3.77 0% Perplexity 4.8 3.60 0% Google Gemini 4.7 3.65 13% Dropbox 4.8 2.75 58% Gmail 4.7 2.40 26% Google Drive 4.8 3.90 23% Microsoft Authenticator 4.7 2.18 1% The older tools are the ones still in the trenches: Dropbox answers 58% of recent reviewers, Gmail 26%, Drive 23%. The steepest recent drops belong to Microsoft Authenticator (4.7 to 2.18), Gmail (4.7 to 2.40) and Dropbox (4.8 to 2.75). Plotted on two axes, backlash against response, every app falls into one of four archetypes: Firefighters, Ghost Ships, Complacent Giants and Resilient Leaders. Eight of the twelve are Ghost Ships, taking a recent hit in near silence. The honest limits Recent reviewers self-select toward the dissatisfied. A person who hits a bug is far more likely to leave a review than a contented one, so a low recent average blends genuine declin

What makes a widget worth building Apple frames good widgets around three qualities, and they're worth keeping in your head as design constraints, not just slogans: Glanceable — someone should understand it in a fraction of a second. Think Weather showing you just enough of today's forecast. Relevant — content should match the moment, the place, and the person's patterns. Calendar surfacing your next event is the canonical example. Personalizable — it should be configurable with the content that matters to that specific user. These three map directly onto the technical decisions you'll make: glanceable drives your view design, relevant drives your timeline strategy, and personalizable drives whether you reach for a configurable (App Intent) widget. The mental model: how a widget actually runs This is the part most newcomers get wrong, so it's worth being precise. Your widgets are delivered to the system from a widget extension , which is a separate process from your app. That separation has a real consequence: your app can't just hand data to the extension in memory. You share data through an app group container — a shared database, or UserDefaults backed by the group. Wire this up early; it's the thing people forget. Whether your app is UIKit or SwiftUI, the widgets themselves are always built in SwiftUI. The data flow is: WidgetKit asks your extension for content. That content is a timeline — a series of timeline entries . Each entry carries the data needed to render your view at a specific point in time. The rendered views are archived, and the system displays each one at its relevant time. The key insight hiding in step 4: your code is not running while the widget is on screen. The system renders archived views. This explains a lot of WidgetKit's API design, including why interactive elements use App Intents rather than closures. Building your first widget When you add a widget extension target, Xcode scaffolds most of what you need. The body returns a WidgetCon

Doomscrolling on social media is a very common problem and can feel like an endless loop of constantly losing track of time. Instead of tackling this issue with strict limits, a new mindful screen time app called Mivo Scrolling is trying to shake things up. Launched last month, Mivo doesn’t look super different from other […]

The moment a customer taps "Place Order," the most anxiety-driven part of the delivery experience...

Apple's HomeKit Secure Video service is getting in on the Apple Intelligence party to bring more descriptive alerts from your connected cameras and let you search footage using natural language. The Apple Home app is also getting better notifications powered by AI and is finally adding support for energy reporting. These improvements were announced at […]

Every food delivery app has the same promise: you tap a button, food shows up at your door. Testing...

Swift Testing shipped with Xcode 16 back in 2024. Swift Testing was built from the ground up for Swift. That means Swift concurrency is a first-class citizen, test cases run in parallel by default, and the API surface is dramatically smaller than XCTest's forty-plus assertion functions. One macro, #expect , replaces most of them. If you are still on XCTest, you have probably felt the friction: class inheritance for every test suite, function names that must start with test , assertion messages that tell you what the values were but not where the expression came from. Swift Testing fixes all of this. That said, you do not need to migrate everything at once, and WWDC 2026 is emphatic about this. The Migration Strategy: Small Chunks, No Big Bang The session opens with something refreshing: permission to be slow about this. The recommended approach is to leave your existing XCTests where they are and start using Swift Testing only for new tests. Both frameworks can coexist in the same target and even the same file. You do not need a separate test target, and you do not need a migration sprint. The one rule: Swift Testing tests cannot live inside XCTestCase subclasses. Everything else is fair game. Raw Identifiers for Readable Test Names One small quality-of-life improvement worth knowing about from the start: Swift supports raw identifiers using backticks, and Swift Testing takes full advantage of this. import Testing @testable import DemoApp @Test func ` Default climate : tropical ` () async throws { let fruit = Fruit ( name : "Coconut" ) #expect(fruit.climate == .tropical) } No more testDefaultClimateTropical or dealing with camelCase names in test output. The test name is the test name. Interoperability: The Key to Reusing Your Helper Code This is the main new story in WWDC 2026 and the feature that makes incremental migration actually work. The problem: you have test helper functions that wrap XCTFail . You want to call them from new Swift Testing tests. Previously,

At WWDC 2026, Apple introduced Xcode 27, which makes it easy to kick off tasks with coding agents, iterate on new project ideas, and customize the workspace. It also introduces DeviceHub for unified simulator and device management, along with enhancements to Organizer and Instruments, among many other improvements. By Sergio De Simone

Even the aging iPhone 11 will feel a little more responsive soon, thanks to improvements in an unsung iOS feature.