O Apple Creator Studio desbloqueia vários recursos bacanas de aplicativos já conhecidos pelos usuários de dispositivos da Maçã.
Dentre os exemplos, estão os apps Pages, Numbers e Keynote. Com eles, assinantes do Creator Studio podem gerar imagens e adicioná-las aos seus documentos, planilhas e apresentações de slides.
Depois de já termos publicado vários vídeos sobre as novidades do iOS 27, chegou a vez de começarmos a cobertura em vídeo do macOS Golden Gate 27 em nosso canal. E não tem jeito melhor de abrir essa série do que falando sobre a mudança mais aguardada do sistema: a Siri AI.
Neste primeiro vídeo, mostramos como funciona o novo app da Siri, os ajustes de voz disponíveis, as melhorias na busca do Spotlight, os recursos da Inteligência Visual a partir de screenshots e as ferramentas de escrita e revisão de texto integradas ao sistema.
Safari Notify Me gives Apple’s browser a useful new role in online shopping: watching a product page after the shopper has closed it. Instead of repeatedly returning to check whether an item is available or whether its price has changed, users can ask Safari to monitor the page and send an alert when the requested update appears.
The feature arrives as online shopping becomes increasingly fragmented. Prices can change several times during a promotion, popular items disappear within minutes and retailers often require shoppers to join separate mailing lists for availability alerts. Dedicated tracking apps can help, but each adds another account, extension or subscription.
Safari Notify Me places the monitoring tool inside the browser already being used to view the product. It is simple enough for occasional purchases while potentially useful for expensive electronics, limited releases, event registrations and products that move in and out of stock.
Safari Notify Me Watches the Page for You
Safari Notify Me is designed to monitor changes on a specific webpage. The user identifies what should be watched, such as a lower price, a product returning to stock or registration becoming available. Safari then checks the page and sends a notification after detecting the relevant change.
Apple has presented price changes and product restocks as central examples, though the feature is not restricted to shopping. It may also help with reservations, ticket availability, enrollment openings or other pages where one detail changes over time.
For shopping, the advantage is immediate. A customer can leave the product page without relying on memory, leaving dozens of tabs open or refreshing the site throughout the day.
The browser becomes temporarily responsible for the follow-up.
This differs from a retailer’s own email alert. Store notifications usually depend on the business offering the feature, and they may focus only on inventory. Safari can monitor the page according to the user’s request, potentially covering price and availability without requiring another marketing subscription.
Image Credit: Pond5
How Safari Price Alerts Help
Online prices are rarely as stable as the label suggests. Retailers adjust discounts, respond to competitors, launch short promotions and change prices according to stock levels.
A shopper interested in a Mac accessory, appliance or expensive display may not want to purchase immediately at the listed price. Checking the page every day is inefficient, while joining a store mailing list can produce far more promotional email than useful information.
Safari price alerts create a quieter alternative. The user can identify the product and wait for the browser to report a meaningful change.
The feature does not replace price history. Safari may detect that the amount has dropped, but that does not necessarily prove the new price is the lowest offered during the year. Dedicated price-tracking services may still provide charts, comparisons and retailer-specific history.
Safari Notify Me is better understood as a page-change assistant. It tells the shopper that something requested has happened. The decision to purchase remains with the user.
Restock Alerts May Be Even More Useful
Restock monitoring may become the feature’s strongest shopping use. Limited products can return without warning and sell out before a retailer’s email reaches every subscriber.
This often happens with game consoles, graphics hardware, collectibles, seasonal products, concert merchandise and popular configurations of newly released devices. Size and color variations can also return separately, making a general product alert less useful.
Safari restock alerts can focus on the page the user is already viewing. A notification provides a reason to return without requiring the tab to remain open.
The browser cannot reserve the item or guarantee that inventory will remain available. A notification may also arrive after other buyers have begun ordering. Safari removes the repeated checking, but it does not control the retailer’s inventory system.
That limitation is reasonable. Notify Me helps users notice the opportunity; it does not turn Safari into an automated purchasing bot.
Using Safari Notify Me
The feature is intended to require little setup. Users begin on the relevant product or registration page and tell Safari which change they want it to watch.
To create a Safari Notify Me alert:
Open the webpage in Safari > Select Notify Me > Describe the price, availability or page change to monitor > Allow Safari notifications when requested
The exact options shown may depend on the device, webpage and software version. Apple is introducing the feature with its new operating-system releases, and availability may vary by language or region.
A useful request should be specific. Asking Safari to report when the item returns to stock is more actionable than asking for any page change. For price monitoring, the user may request a reduction or watch for a particular target when the page supports enough information.
Once the alert is active, there is no need to keep the product page open. Safari sends a notification after detecting the requested update, allowing the user to return directly to the page.
A Better Use for Apple Intelligence
Safari Notify Me is a practical example of Apple Intelligence operating as an assistant rather than a writing tool. It interprets what the user wants, watches for a future condition and reports back when that condition appears.
The feature does not need to generate a long answer or place a chatbot beside every shopping page. Its value comes from removing a repetitive task.
That restrained approach fits Safari. The browser already manages passwords, private browsing, tab organization, Reader and tracking protections. Monitoring a page extends that utility without turning Safari into a retailer or shopping marketplace.
It may also reduce the need for third-party extensions among casual users. Someone who occasionally watches one product does not necessarily need a dedicated deal platform with wish lists, affiliate links and account registration.
Specialized services will remain useful for serious bargain hunting. Safari covers the simpler request: tell me when this exact page changes in the way I care about.
Image Credit: Apple Inc.
Notifications Need Restraint
Any feature built around alerts can become noisy when used too freely. Monitoring every product considered during casual browsing could fill Notification Center with price changes and restocks that no longer feel relevant.
Safari Notify Me works better when reserved for purchases with genuine intent. A few focused alerts are easier to understand and act upon than dozens of loosely defined page watches.
Users should also remove alerts after buying the item or losing interest. A notification for a product purchased weeks earlier adds clutter without saving time.
Safari notifications can be managed through the device’s normal notification settings. Disabling all Safari alerts may also silence other useful browser notifications, so reviewing individual watches inside Safari is preferable when available.
Price Is Not the Only Shopping Decision
A lower price can encourage a quick purchase, but shoppers should still review the seller, return policy, warranty and delivery estimate. Notify Me reports a page change; it does not evaluate whether the store is reliable or whether the offer has hidden conditions.
A product may return through a third-party marketplace seller at a higher price. A discount may apply only after a membership, coupon or trade-in. Limited inventory can also create pressure to buy before reading the terms.
Safari’s privacy protections and fraud warnings remain relevant, but users should confirm that the page belongs to the intended retailer before entering payment information.
The alert is a starting signal, not an endorsement.
Safari Becomes More Useful After the Tab Closes
Browsers traditionally stop being helpful once users leave a page. Safari Notify Me changes that relationship by allowing selected pages to remain useful without remaining visible.
For shoppers, that means fewer repeated searches, fewer forgotten products and less dependence on promotional email. Price and restock alerts can operate quietly until there is a reason to return.
The feature will not replace advanced shopping trackers, and it cannot guarantee inventory. Its appeal is that most users do not need those larger systems for every purchase.
Sometimes the entire shopping problem is waiting for one price to fall or one button to change from unavailable to buy. Safari Notify Me allows the browser to handle that wait.
Apple Intelligence on-device processing remains Apple’s most convincing argument in artificial intelligence. Rivals may lead in public model rankings, chatbot adoption and the speed of releasing experimental features, but Apple is building AI inside devices people already use for their most personal work.
That distinction changes the definition of winning.
Apple does not need every iPhone owner to open a separate chatbot app. It can place intelligence inside Messages, Mail, Photos, Notes, Shortcuts, Siri and third-party apps. Local models can operate on private information without automatically transferring that information to an external server.
The strategy appeared slow while the AI market measured progress through chatbot launches and model size. It looks more competitive as the industry confronts cloud costs, privacy concerns, unreliable connectivity and the difficulty of turning general-purpose AI into useful daily actions.
In his video “Apple Lost the AI Race,” Marques Brownlee captures the contradiction in one concise line:
“Apple lost the AI race. Actually, Apple won the AI race.”
The apparent reversal describes Apple’s current position well. Apple may have lost the first race for public perception. It is increasingly prepared for the longer contest over where AI runs, how it reaches users and who controls the personal data required to make it useful.
Apple Intelligence On-Device Changes the AI Relationship
Most popular generative AI products began as cloud services. A user sends a request to a remote model, the provider processes it in a data center and the answer returns through an app or browser.
That structure supports enormous models but creates dependence on internet access, server capacity and continuing infrastructure expense. It also means personal prompts leave the device, even when the provider uses strong security and privacy policies.
Apple Intelligence on-device begins from the opposite direction. The iPhone, iPad or Mac first attempts to handle the request locally using Apple Foundation Models optimized for Apple silicon.
That local model does not need to know everything available on the internet. It needs to understand the user’s request, work with relevant personal context and complete common tasks efficiently.
The difference is similar to owning a capable computer rather than renting every calculation from a distant server. Cloud systems remain useful for difficult workloads, but they no longer need to participate in every interaction.
For Apple, local processing is also a product advantage. The company controls the chip, operating system, model architecture, memory system and apps. Each layer can be designed around the others rather than assembled from unrelated services.
Image Credit: Apple Inc.
Privacy Becomes a Technical Feature
Technology companies frequently describe privacy as a policy. Apple is trying to make it part of the AI architecture.
When a request runs entirely on an iPhone, the associated data does not need to leave the device. A model can work with messages, appointments, photos or app content without uploading that personal context to a conventional cloud account.
That structure is especially relevant to Siri AI. A truly personal assistant needs access to information that users may never place willingly inside an ordinary chatbot: private conversations, travel plans, health-related details, work documents and relationships between people.
An assistant becomes more useful as it gains context. It also becomes more invasive when the same context is stored, analyzed or used for model training outside the device.
Apple Intelligence on-device allows Apple to expand personal context while maintaining a stronger boundary around it. The assistant can understand information locally, perform an action and discard temporary processing data without building a permanent advertising or training profile.
This is difficult for competitors whose businesses depend on cloud services, large-scale data collection or engagement inside separate AI products. Apple’s hardware business gives it different incentives.
Private Cloud Compute Extends the Model
Not every AI task can fit within the power and memory limits of an iPhone. Apple uses Private Cloud Compute when a request requires a larger model.
Private Cloud Compute is designed to extend the privacy architecture of Apple devices into the cloud. User information is processed only for the request, is not stored and is not made available to Apple for unrelated purposes.
Independent researchers can inspect software images used by the system, creating an unusual level of outside visibility into the cloud environment. Apple has also expanded the architecture while maintaining control over the software and privacy protections used for processing.
This creates a layered system rather than a simple choice between local and cloud AI.
Apple Intelligence first determines whether the device can handle the request. More complex work can move to Private Cloud Compute. External models may be offered separately when the user chooses to involve them.
The user does not need to understand the technical path behind every answer. The system chooses the smallest and most private environment capable of completing the task.
Apple Silicon Was an AI Investment
Apple’s control of its processors has become one of its largest advantages in the AI era.
The Neural Engine first appeared years before generative AI became a consumer obsession. It supported Face ID, photography, speech recognition and other machine-learning tasks. Apple continued expanding that hardware across the iPhone, iPad and Mac.
Those investments now provide a large installed base of devices designed to run machine-learning workloads locally. Recent Apple chips combine CPU, GPU, Neural Engine and unified memory in one architecture that can move information efficiently between different types of processing.
The same design helps foundation models run without requiring the power consumption or separate memory systems associated with conventional data-center hardware.
Apple also controls the operating systems that schedule those workloads. A model can be optimized for the exact processors, memory limits and energy behavior of supported devices.
Competitors can build excellent models, but they often need to support hardware from several manufacturers with inconsistent capabilities. Apple can decide that an AI feature requires a specific generation of silicon and design the complete experience around it.
Developers Gain a Local Model
The Foundation Models framework extends Apple Intelligence on-device beyond Apple’s own apps. Developers can use the system model through native Swift tools rather than shipping a separate large model or paying for every cloud request.
An app can generate structured content, interpret natural language or use tool calling while keeping supported processing on the device. Apple handles model delivery and system integration.
This can reduce operating costs for developers. A cloud-based AI feature creates an ongoing expense each time a user submits a request. Local inference uses hardware the customer already owns.
It can also improve reliability. Features may remain available without an internet connection and respond without waiting for a remote service.
The framework encourages a quieter form of AI adoption. Apps do not need to add a prominent chatbot tab or redesign themselves around a conversational interface. Intelligence can appear inside existing workflows as a useful capability.
That is where Apple’s strategy may reach scale. Millions of apps can incorporate small AI functions that users experience as normal software behavior rather than as visits to an AI destination.
Image Credit: Apple Inc.
The Installed Base Is Apple’s Distribution Advantage
The AI race is often discussed as a contest between models. Consumer adoption also depends on distribution.
Apple can deliver new intelligence features through operating-system updates across the iPhone, iPad, Mac and Vision Pro. Users do not need to create another account, choose a provider or transfer their personal information into a new service.
Siri already occupies the system interface. Apple Intelligence can work across first-party apps. Developers can connect their own features through Apple frameworks.
The scale is difficult to reproduce. A model provider may have stronger benchmark scores but still need users to download an app and consciously open it. Apple can introduce AI during actions people already perform: replying to a message, locating a photo, organizing a note or completing a shortcut.
This does not guarantee success. Apple must make Siri AI reliable, keep feature availability consistent and prove that local models can deliver enough capability to satisfy users accustomed to powerful cloud assistants.
The distribution advantage only becomes meaningful when the experience works.
Winning Without Building the Loudest Chatbot
Apple has made mistakes during its AI transition. Siri improvements arrived later than promised, regional regulations delayed features and competitors established powerful consumer brands before Apple completed its new system.
Those setbacks explain why the claim that Apple lost the AI race became persuasive.
They do not settle the outcome.
The first stage rewarded companies that released chatbots quickly and demonstrated increasingly large models. The next stage may reward systems that can integrate AI into ordinary life without demanding constant cloud access, high subscription costs or broad collection of personal information.
Apple Intelligence on-device is built for that stage. It uses hardware already in the user’s hand, protects more personal context locally and gives developers access to intelligence without requiring every interaction to become a server transaction.
Apple does not need to win every benchmark or release the most talkative assistant. It needs AI to become a reliable part of the devices people already trust.
Brownlee’s title works because both statements can be true. Apple lost the visible sprint that defined the early generative AI market. By concentrating on local processing, silicon, privacy and operating-system integration, it may already be positioning itself to win the race that follows.
EU tech regulation, Digital Markets Act, Apple Europe sales, Apple regulation, European Union, Siri AI, iPhone features, device interoperability, App Store changes, alternative marketplaces, iPhone Mirroring, AirPods translation, Apple privacy, iPhone security, app sideloading, platform competition, DMA compliance, Apple Intelligence, European technology, government control
EU tech regulation is no longer limited to App Store payment rules or competition disputes hidden from ordinary consumers. It is beginning to determine which features reach European devices, how quickly they arrive and how deeply third-party companies can interact with the iPhone, iPad and Mac.
The Digital Markets Act was designed to reduce the control of large technology platforms over software distribution, payments, browsers, digital assistants and connected hardware. For Apple, compliance has already produced alternative app marketplaces, web distribution, third-party payment options, additional default-app controls and expanded access to iPhone technologies.
The next phase reaches much deeper into the operating system. European regulators want competing accessories, cloud services and AI assistants to receive access to capabilities traditionally reserved for Apple’s own products.
Apple argues that some of those demands could weaken privacy and security. The European Commission says the company is responsible for creating compliant systems that preserve both competition and user protection.
Between those positions sits a large and commercially significant group of customers. Europe generated $28.06 billion in Apple sales during the quarter ending March 28, 2026, up from $24.45 billion one year earlier. Six-month European sales reached $66.20 billion, compared with $58.32 billion during the same period of fiscal 2025.
Europe therefore represented more than one-quarter of Apple’s latest quarterly revenue. It is too valuable to leave behind, but increasingly difficult to serve with the same software experience available elsewhere.
EU Tech Regulation Is Splitting Feature Availability
Regional differences once centered mainly on language support, licensing agreements or local services. EU tech regulation is creating a more structural divide.
Apple has blamed Digital Markets Act requirements for delaying Siri AI across the European Union. The company says regulators expect competing AI assistants to receive extensive access to personal information, apps and device actions before Apple can reserve those capabilities for its own assistant.
The disagreement concerns more than answering questions. Siri AI is designed to read personal context, understand onscreen information and complete actions across apps. That may include finding messages, accessing files, making purchases or sending information on the user’s behalf.
Opening those capabilities to outside AI systems requires new permission structures, technical interfaces and safeguards. Apple says it cannot release its assistant first and add competing access later without risking regulatory action. The Commission says Apple had sufficient time to design an interoperable solution.
Other delayed European features have included iPhone Mirroring on Mac, Live Translation through AirPods and certain location-based Maps capabilities. Each feature touches a different part of the ecosystem, but all depend on privileged communication between Apple devices, software and services.
The consequence is becoming visible at purchase. Two customers can buy the same iPhone model at similar prices and receive different capabilities because one lives inside the European Union.
Image Credit: Apple Inc.
Feature Adoption Becomes Slower and Less Predictable
Delayed availability can weaken the adoption of new hardware features even when the devices themselves continue selling well.
Siri AI is intended to become a central part of newer iPhones, iPads and Macs. Buyers who cannot access it lose part of the justification for newer chips, additional memory and on-device AI processing. Developers also face a smaller addressable audience for apps built around the new assistant.
AirPods Live Translation presents a similar problem. A feature designed to demonstrate the combination of iPhone processing, microphones and wearable audio becomes unavailable in a region built around dozens of languages and frequent cross-border travel.
iPhone Mirroring affects the relationship between iPhone and Mac. Users outside the EU can interact with the phone from macOS, while European users may receive notifications without gaining the complete experience.
These differences do not necessarily stop hardware sales. Apple’s newest results show strong European demand despite years of regulatory conflict. They can, however, reduce the speed at which customers use the ecosystem’s newest capabilities.
The effect may grow as software becomes a larger part of hardware differentiation. A faster processor has limited emotional value when the feature designed to demonstrate it is unavailable.
Apple Sales Show Why Europe Cannot Be Ignored
Apple does not report European Union revenue separately. Its Europe segment also includes countries outside the bloc, along with India, the Middle East and Africa. The official segment remains the best regularly published measure of the region’s commercial weight.
European sales rose approximately 15% year over year in Apple’s latest reported quarter. During the first six months of fiscal 2026, the segment expanded by roughly 14%.
Those figures weaken the argument that regulation is already driving customers away from Apple hardware. Demand remains strong, supported by the iPhone 17 family, Mac sales, services and the company’s installed base.
The financial risk is more gradual. Regional software fragmentation can raise engineering costs, delay launches and make European devices less competitive against products able to offer complete feature sets.
Apple must maintain separate distribution rules, payment systems, browser options and eligibility checks. Developers must decide whether alternative stores, external payments or EU-specific business terms justify additional work. Support teams must explain why features differ between countries using the same operating-system version.
A market responsible for more than $28 billion in one quarter can absorb considerable compliance expense. It can also influence Apple’s global product decisions when regulators demand interfaces that cannot be restricted to Europe easily.
A Slow Transfer of Ecosystem Control
The escalating regulatory environment also creates a more fundamental concern: control over complex technology ecosystems can gradually move away from the companies that designed them and toward government committees, regulatory bodies and administrative processes.
That transfer rarely happens through one dramatic order. It develops through repeated obligations covering app distribution, payment systems, browser engines, digital assistants, hardware access, data portability and interoperability. Each requirement may appear narrow when considered alone. Together, they can redefine who determines how the ecosystem operates.
Apple built its platform around tight integration between hardware, software, services and security. Regulators increasingly treat parts of that integration as market power that must be opened to competitors. The danger is that an ecosystem designed as one coordinated environment becomes subject to decisions made by committees that do not carry direct responsibility for product quality, reliability or long-term maintenance.
This can become a slow trap. Once a platform is required to expose one layer, regulators may use that access as the basis for demanding another. A payment exception can be followed by alternative app stores. Alternative stores can lead to broader installation rights. Device interoperability can expand into access for AI assistants, personal data and system actions.
The boundaries of the platform are then set less by engineering judgment and more by regulatory negotiation.
Government agencies have legitimate authority to address competition and consumer harm. The concern appears when regulation shifts from preventing abusive conduct to directing product architecture. At that point, public authorities begin influencing feature design, release timing, software permissions and the relationship between first-party and third-party services.
That creates consequences beyond Apple. Any company considering a tightly integrated product may decide that deep integration invites intervention. Businesses could avoid building ambitious ecosystems if successful coordination later becomes grounds for forced access.
Innovation then becomes shaped by what regulators are likely to permit rather than by what engineers believe will produce the safest or most coherent experience.
Image Credit: Ink Drop / Alamy Stock Photo
Competition Gains Come With Real Consumer Choices
EU policy has produced benefits that Apple rarely emphasizes. Users can install apps through alternative marketplaces or directly from authorized developers. Developers can offer external payment methods, promote different prices and avoid relying entirely on Apple’s distribution system.
Alternative browser engines can compete with WebKit, while users have more control over default apps. Contactless payment access has expanded beyond Apple Pay, allowing competing wallet providers to use the iPhone’s NFC technology under approved conditions.
Interoperability requirements could also improve third-party accessories. Smartwatches, headphones and other devices may gain better notification access, background connectivity and proximity pairing with the iPhone.
These changes can increase competition and reduce the advantage Apple gives its own services and accessories. A third-party smartwatch should not become artificially less useful simply because it was not made in Cupertino.
The difficulty is separating legitimate platform access from unlimited access. A notification interface is one thing. An autonomous AI assistant capable of reading messages, accessing files and acting across apps introduces a different level of sensitivity.
Privacy and Security Risks Are Not Theoretical
Alternative app distribution increases user choice, but it also moves some responsibility away from the App Store. Apple continues to notarize apps distributed through approved alternative channels, checking for known malware and severe fraud. That process is narrower than a complete App Store review.
Purchases made outside Apple’s payment system also fall outside its normal refund and billing support. Users may need to resolve disputes directly with the developer or payment provider.
A malicious marketplace, deceptive payment page or compromised developer account can create opportunities for fraud. Enterprise users face additional concerns when employees install software from sources their organization has not approved.
The AI question reaches further. A third-party assistant may need permission to access messages, photos, calendars, documents and app controls. Even a reputable provider can collect more information than the user expects or introduce vulnerabilities through poorly designed integrations.
Apple can build permission prompts, app isolation and revocation controls, but technical access always creates some exposure. Security depends on how narrowly permissions are defined, how visibly they are used and whether the operating system can stop unauthorized actions.
The Commission maintains that interoperability and privacy can coexist. Apple maintains that regulators are demanding access before adequate protections are ready. Both positions contain a legitimate concern: dominant platforms should not block competition, and competition should not require users to surrender control of personal data.
When regulators require a company to alter its architecture, responsibility can become blurred.
Apple remains the company customers blame when an iPhone feature fails, a third-party marketplace distributes a harmful app or an outside assistant misuses personal data. Regulators may have required the access, but they do not operate the device, provide technical support or repair the damage to the platform’s reputation.
This creates an accountability gap. Government committees can mandate openness without assuming the commercial and security consequences of that openness. Apple must comply, maintain the system and explain the outcome to users.
The same problem applies to delayed features. Regulators may argue that Apple chose not to release a service, while Apple may argue that the required conditions made the launch unsafe or technically impractical. Customers are left with less functionality and no simple way to determine who caused the delay.
Over time, the ecosystem can become a negotiated compromise rather than a product with one coherent direction. That may satisfy formal compliance while producing a weaker experience for everyone involved.
The risk is not that governments will literally operate Apple’s software. It is that committees increasingly define the conditions under which Apple can design, connect and release its own features. Control shifts through constraints.
The Industry Will Follow Europe’s Experiment
The consequences extend beyond one company. Google, Meta, Microsoft, Amazon and other platform operators face related European obligations. Hardware makers must consider whether ecosystem advantages can survive when core services are required to support rivals.
Smaller developers may gain new distribution and payment choices, but they may also inherit more compliance, customer-service and security responsibilities. Accessory companies could compete more effectively while becoming accountable for handling sensitive device data.
Other governments are watching. Rules introduced in Europe can influence legislation elsewhere, particularly when companies decide that maintaining one global technical framework is easier than operating several regional versions.
Apple is unlikely to abandon Europe or stop adapting its products. Its sales provide too much evidence of the region’s importance. The company will continue negotiating, appealing decisions and building compliance systems while withholding features it considers unsafe under current demands.
European customers may receive more app stores, payment choices and accessory compatibility. They may also wait longer for headline features included with the same hardware elsewhere.
The deeper consequence may be a technology industry that becomes more cautious about building tightly integrated products. Companies may design around anticipated regulatory demands from the beginning, limiting exclusive features, reducing private-system integration or avoiding innovations that depend on controlled access.
EU tech regulation began as an effort to constrain dominant platforms. Its long-term effect may be to transfer part of the power to shape those platforms from companies and users to regulatory institutions.
That shift deserves scrutiny equal to the market power it is intended to correct. Competition rules can restrain abuse. They can also become a slow mechanism for placing product design, security choices and ecosystem evolution under government supervision.
Shady Control and Bureaucracy
The danger grows when regulatory discretion becomes buried beneath permits, approvals, negotiations and technical committees that operate beyond meaningful public scrutiny.
The more power officials hold over which features, contracts or technologies may proceed—and when—the greater the opportunity for lobbying, favoritism and selective enforcement to influence outcomes. What begins as oversight can gradually become a system in which delay itself is leverage, while responsibility disappears inside layers of administration.
For the technology industry, that risks replacing transparent product decisions with political access, negotiated exceptions and regulatory influence exercised far from the consumers expected to live with the consequences.
ARK augmented reality describes a form of spatial interaction in which artificial intelligence does more than place digital objects in the physical world. It uses knowledge from foundation models to understand unfamiliar scenes, generate useful information and adapt its response to the person, task and environment.
The concept comes from a 2023 research paper titled “ArK: Augmented Reality with Knowledge Interactive Emergent Ability.” Its authors proposed a system that transfers knowledge from large foundation models into new physical and virtual environments. Instead of training an AI agent separately for every room, object or activity, ArK uses existing knowledge memory to interpret what it sees and generate appropriate 2D or 3D content.
Traditional augmented reality places information over reality. ARK augmented reality tries to understand reality before deciding what should appear.
That distinction creates a compelling direction for Apple. Vision Pro already blends digital content with physical space. Apple Intelligence adds language, image understanding and personal context. Visual Intelligence lets users look at objects, text and scenes before asking for information. Siri AI adds a conversational layer. ARKit, RealityKit and visionOS provide the spatial foundation.
Apple now has many of the components required to build a knowledge layer over the physical world.
ARK Augmented Reality Goes Beyond Digital Overlays
Most familiar augmented reality experiences are relatively fixed. A furniture app places a digital sofa in a room. A navigation app displays arrows over a street. A museum app identifies an artwork. A repair app shows where a component belongs.
These experiences can be useful, but they usually depend on predefined content and rules. The system recognizes a known object or location, then presents prepared information.
ARK augmented reality proposes something more adaptive. A knowledge-enabled system could enter an unfamiliar environment, interpret what is present and generate assistance based on a broader understanding of objects, relationships and possible actions.
A user looking at a complicated electrical panel would not only see labels. The system could identify the panel type, explain the purpose of each section, compare the arrangement with standard configurations and warn the user not to touch dangerous components.
A student examining a machine could ask how it works and receive a spatial explanation attached to its real parts. A surgeon could review anatomical information positioned around a medical model. A technician could receive repair guidance even when the exact equipment had not been included in a previous training dataset.
The display becomes less like a floating screen and more like an informed interpretation of the environment.
Image Credit: Apple Inc.
Why Apple Is Positioned to Build It
Apple’s advantage would not come from inventing every part of ARK augmented reality. It would come from combining hardware, software, artificial intelligence and personal context into one controlled ecosystem.
Vision Pro already understands rooms, surfaces, hands, eyes and objects well enough to place digital content convincingly in physical space. Its cameras and sensors continuously map the environment while visionOS manages windows, volumes and immersive experiences.
Apple Intelligence provides another layer. Foundation models can interpret language, summarize information, generate content and connect requests with app actions. Visual Intelligence can identify text, images and real-world objects. Siri AI can act as the conversational interface.
The Apple ecosystem also provides personal context that a generic AR platform may not have. Calendar knows the user’s schedule. Maps understands location. Photos contains visual history. Notes and Mail hold project information. Health contains personal trends. Home understands rooms, accessories and automations.
An Apple-built knowledge layer could combine the environment in front of the user with the information already available across trusted devices and apps.
A person preparing for a meeting could look at a conference room and see the agenda, participant names and relevant documents arranged around the table. A traveler could look across a train station and receive directions connected to the correct platform, departure time and digital ticket. A homeowner could examine a room and see live information from temperature, air-quality and energy sensors.
The physical environment becomes the interface.
Vision Pro Is the Laboratory
Vision Pro is the obvious development platform for this idea because it already offers the cameras, displays, sensors and processing required for advanced spatial understanding. Its size and price limit broad adoption, but those same characteristics allow Apple and developers to test experiences that would be harder to build first on lightweight glasses.
ARK augmented reality on Vision Pro could become especially useful in enterprise environments. Apple already promotes the headset for employee training, product design, field service, collaboration and specialized visualization.
A worker could enter an unfamiliar facility and receive contextual instructions attached to real equipment. An engineer could inspect a physical prototype while design changes, performance data and maintenance history appear around it. A new employee could learn a workflow through spatial guidance generated from company manuals and operational knowledge.
The system would not need to display every available fact. It would need to understand which information fits the current task.
That is where AI knowledge becomes more valuable than a traditional AR overlay. Too much information can be as disruptive as too little. A strong system should filter, prioritize and explain rather than cover the room with floating labels.
The iPhone Could Bring ARK to More People
Vision Pro may be the development environment, but iPhone could provide the scale. The iPhone already combines cameras, LiDAR on Pro models, location, motion sensing, Visual Intelligence and Apple Intelligence.
An iPhone-based ARK experience would be less continuous because users would need to hold up the device. It could still deliver useful knowledge interaction without requiring a headset.
A user could point the camera at a plant and receive care guidance based on its species, visible condition, local weather and placement in the room. Someone assembling furniture could receive generated guidance positioned over the actual parts. A shopper could compare products by looking at them rather than manually searching for each model.
The difference from ordinary visual search would be interaction. The user could ask follow-up questions, change the goal and let the system update the visual guidance.
Future glasses could eventually make the experience persistent. The iPhone would help Apple develop the intelligence, privacy controls and app ecosystem before lightweight eyewear becomes ready.
A World That Explains Itself
The most ambitious version of ARK augmented reality is a world in which information is attached to objects, places and situations only when requested or needed.
A city could become understandable without searching through maps, review sites and separate transit apps. Buildings could reveal history. Streets could explain temporary closures. Restaurants could surface reservations, dietary options and accessibility details. Public transport could show the correct entrance and platform.
Inside a home, Apple’s knowledge layer could explain energy use, identify maintenance problems and guide repairs. In a classroom, physical models could become interactive lessons. In healthcare, patients could receive visual instructions for approved home procedures or medication devices.
The experience would depend on context rather than menus. The user looks, asks and receives information positioned where it belongs.
This is the transition from augmented reality as decoration to augmented reality as understanding.
Privacy Will Decide Whether It Works
A knowledge layer capable of understanding the physical world also creates serious privacy questions. The system may see homes, workplaces, documents, faces, screens and private conversations. The more context it receives, the more useful it becomes. The same access can also make it invasive.
Apple’s privacy architecture could become one of its strongest advantages. On-device processing would allow some scene understanding without sending continuous camera feeds to external servers. Private Cloud Compute could handle more complex requests while preserving stricter controls than an ordinary cloud AI service.
Apple would also need visible controls over what is analyzed, remembered and shared. A user should know when the system is interpreting the environment and whether information is temporary or retained. Workplaces and public spaces would need rules for recording, identification and sensitive data.
ARK augmented reality cannot become socially acceptable if people feel that nearby devices are constantly cataloging them.
The successful version would behave less like surveillance and more like intentional assistance. The user asks for understanding. The system processes only what is needed. The knowledge disappears when the task ends unless the user chooses to save it.
Image Credit: Apple Inc.
Developers Could Build Knowledge Worlds
Apple would not need to create every knowledge experience itself. visionOS, ARKit, RealityKit and App Intents could let developers connect specialized knowledge with physical environments.
A medical company could build guidance for approved equipment. A manufacturer could create spatial maintenance systems. A university could attach interactive explanations to laboratories and museums. A retailer could create product comparisons based on inventory and customer preferences.
Apple could provide the shared intelligence layer: scene understanding, object recognition, language interaction, privacy permissions and spatial presentation. Developers would provide the trusted domain knowledge.
This division would be essential. General AI can explain many things, but professional environments require verified information. A generated answer about a historical building has different consequences from an answer about industrial machinery or medical treatment.
Apple’s platform could distinguish between general knowledge and certified domain sources, allowing users to see where information comes from before acting.
From Spatial Computing to Contextual Computing
Apple describes Vision Pro as a spatial computer because digital content can occupy physical space. ARK augmented reality suggests the next progression: contextual computing.
A spatial computer knows where digital content is placed. A contextual computer understands why the content is useful in that location, what the user is trying to accomplish and which knowledge should appear.
That would connect several Apple strategies that currently look separate. Vision Pro supplies spatial awareness. Apple Intelligence supplies reasoning and generation. Siri AI supplies conversation. Visual Intelligence supplies real-world understanding. The ecosystem supplies personal context.
Together, they could create a system that does not wait for users to open the correct app and search through the correct menu. The environment itself becomes the starting point.
Apple’s opportunity is to make that interaction restrained rather than overwhelming. The company does not need to cover reality with digital noise. It can use design, privacy and contextual intelligence to make information appear only when it improves the moment.
ARK augmented reality offers a useful description of that future. Reality remains visible. Knowledge becomes interactive. The computer begins to understand the world in front of the user instead of asking the user to translate everything into a search box.
PrismML AI compression could give Apple a way to place far more capable artificial intelligence directly inside the iPhone. The Caltech spinout says it reduced a 27-billion-parameter model from approximately 54GB to less than 4GB, small enough to run locally on an iPhone 17 Pro.
That represents a reduction of about 93%, but the more useful number may be memory efficiency. PrismML says its technology can use up to 15 times less memory than conventional versions of the same model. Apple has reportedly held early discussions with the startup about possible uses for the technology.
Neither company has announced a partnership, acquisition or product plan. PrismML’s claims also need to be judged through independent testing, sustained performance and the compromises introduced by heavy compression. Even so, the demonstration points directly at one of Apple’s largest AI challenges: fitting stronger models inside devices without destroying battery life, consuming too much memory or sending every complex request to the cloud.
PrismML AI Compression Changes the Size Problem
Large AI models are difficult to run on phones because their parameters require substantial storage and working memory. A standard model may perform well in a data center equipped with powerful accelerators, large memory pools and constant electricity, then become impractical inside a device designed to last all day on one battery.
PrismML approaches that problem by representing model weights with far fewer bits. The startup has developed what it calls Bonsai models, including 1-bit versions designed to preserve much of the original model’s capability while sharply reducing size and energy use.
The reported iPhone demonstration used a compressed version of Alibaba’s Qwen model with 27 billion parameters. That is far larger than the compact language models normally associated with smartphones. Running it locally suggests that model size may no longer be controlled only by the number of parameters. How efficiently those parameters are represented becomes just as relevant.
Compression is not new. Developers already use quantization, pruning and other methods to make models smaller. PrismML’s claim is that its approach pushes that process much further while retaining useful reasoning and language performance.
That claim is exactly why Apple would be interested.
Why Siri AI Needs Smaller Models
Apple’s AI strategy depends on dividing work between the device and the cloud. Simpler Apple Intelligence requests can run locally, while more demanding tasks may move to Private Cloud Compute. That hybrid structure allows Apple to offer stronger capabilities without forcing the iPhone to carry every model it may ever need.
The cloud layer, however, introduces cost, latency and availability requirements. A request must leave the device, reach a server, be processed and return. Apple has designed Private Cloud Compute around strong privacy protections, but local processing remains faster and easier to explain.
A larger on-device model could improve Siri AI’s language understanding, reasoning and ability to complete multi-step tasks without a network connection. It could also reduce the number of requests sent to Apple’s servers.
That does not mean cloud processing would disappear. Server models can remain larger and support especially demanding requests. PrismML AI compression could instead move the dividing line. Tasks that currently need Private Cloud Compute might become possible directly on a future iPhone.
The benefits could include quicker responses, stronger offline operation and less server demand as Apple Intelligence reaches more users.
Image Credit: Apple Inc.
Privacy Fits Apple’s Preferred Direction
On-device processing has always been central to Apple’s AI positioning. Personal information can remain on the iPhone rather than moving through a conventional cloud service. The device can interpret a request, use local context and produce an answer without exposing the underlying data beyond the hardware.
A model capable of handling more work locally would reinforce that approach. Siri AI could potentially understand messages, notes, schedules or onscreen content while reducing the need to transfer information elsewhere.
Apple already uses Private Cloud Compute when local processing is insufficient, and the company has built significant safeguards around that system. Yet every task completed on the iPhone removes one additional processing step.
Privacy is not the only advantage. Local models can continue operating when connectivity is poor or unavailable. That could help Siri AI during flights, rural travel, crowded events or other situations where cloud access becomes unreliable.
A capable offline assistant would also feel more like part of the operating system and less like a remote service accessed through an interface.
The iPhone Still Has Physical Limits
Shrinking a model to less than 4GB does not automatically make it ideal for everyday iPhone use. The model must also run quickly enough, avoid excessive heat and leave enough memory for iOS and active apps.
A technical demonstration may complete tasks successfully while consuming more energy or time than users would accept from Siri. Apple would need to optimize the model for its Neural Engine, memory architecture and software frameworks rather than simply loading PrismML’s existing build onto every supported device.
Storage is another consideration. A model occupying several gigabytes would compete with apps, photos, offline media and system files. Apple could reserve more storage for intelligence features, deliver models selectively or use several specialized models instead of one large general system.
Battery use may create the hardest constraint. An AI model can fit in memory and still require too much sustained computation for frequent mobile use. The ideal system needs to be small, fast and efficient enough to disappear into normal iPhone behavior.
Apple’s chip design gives it an advantage here. The company controls the processor, Neural Engine, operating system and AI software stack. PrismML could provide the compression technique while Apple handles the deeper hardware integration.
Image Credit: Apple Inc.
Better AI Without Replacing the Cloud
The strongest use of PrismML’s technology may not be putting one enormous model on every iPhone. Apple could use compression across a family of models designed for different devices and tasks.
A smaller model could handle routine language requests. A more capable local model could be downloaded only to recent Pro devices with sufficient memory. Specialized models could support writing, image generation, app actions or Visual Intelligence. Private Cloud Compute could remain available when the request exceeds local capability.
That would give Apple more flexibility than an all-local or all-cloud design. Each device could carry the intelligence appropriate for its chip, memory and battery capacity.
The approach could also help Macs, iPads and future home products. A compressed model may run more comfortably on a MacBook Air, HomePod or smart home hub without requiring constant server access. Apple could scale the same intelligence foundation across products while adjusting model size to the hardware.
This is especially relevant as Siri AI moves beyond answering questions. App actions, personal context and ambient smart home control require intelligence that remains available throughout the day. Lower memory and energy requirements could make those experiences more practical.
Compression Always Includes Trade-Offs
A 93% reduction is a striking headline, but compression can affect output quality. A smaller representation may lose precision, weaken reasoning or struggle with uncommon information. The real test is not whether the model runs. It is whether it performs well enough across the tasks Apple expects Siri AI to handle.
PrismML says its technology preserves strong performance compared with full-precision models. Apple would still need extensive evaluation across languages, safety categories, tool use and personal-assistant scenarios.
Reliability is especially sensitive for an operating-system assistant. A model used for casual brainstorming can tolerate occasional weakness. A model handling messages, appointments, directions or app actions needs a higher level of consistency.
Apple may therefore use aggressive compression selectively. Some tasks can accept a smaller model, while others may remain on a higher-quality server model. The user would not need to know which model is active as long as the response remains fast and dependable.
Image Credit: Apple Inc.
Apple Wants the Technology, Not Necessarily the Company
Reports indicate Apple has spoken with PrismML, but early conversations can lead in several directions. Apple could license the startup’s technology, work on a technical integration, hire members of the team or explore an acquisition.
The company has a long history of purchasing smaller firms for specialized technology and engineering talent. Model compression would fit that pattern because it supports Apple’s existing AI strategy rather than creating a separate consumer product.
PrismML could also remain independent and provide its tools to multiple hardware companies. Its technology has potential uses across phones, computers, robotics, vehicles and industrial equipment where cloud access is expensive or unreliable.
Apple’s interest alone gives the startup attention. The larger significance is that efficient models are becoming as valuable as larger ones. The AI industry spent years treating scale as the primary route to improvement. Mobile computing forces a different question: how much intelligence can fit inside the power and memory limits of one personal device?
A Smaller Model Could Make Siri Feel Larger
Apple does not need to place the biggest available model on an iPhone. It needs a model capable enough to improve the interactions users repeat every day.
PrismML AI compression could help Siri understand more context, complete more requests locally and respond without waiting for a server. It could strengthen Apple’s privacy argument while reducing the infrastructure load created by millions of AI-enabled devices.
The technology remains early, and a startup demonstration is not the same as a shipping Apple feature. The 93% figure will mean little if quality, battery life or speed suffer under daily use.
But the direction fits Apple unusually well. The company has spent years designing smaller, more efficient hardware systems that perform beyond their apparent limits. PrismML is proposing a similar strategy for AI models.
If the startup’s claims hold up, the next major Siri improvement may not come from sending a larger model into the cloud. It may come from fitting one into the iPhone without making the iPhone feel the weight.
Siri Wolfram helped define the original promise of Apple’s voice assistant. When Siri debuted on the iPhone 4S in 2011, it could answer factual and computational questions without sending users through a list of search results. Ask about a mathematical calculation, scientific quantity, historical statistic or unit conversion, and Wolfram Alpha could provide a structured answer.
That partnership gave early Siri something many modern AI assistants still struggle to guarantee: a source designed to compute answers rather than produce plausible sentences. Wolfram Alpha was not a chatbot. It was a computational knowledge engine built around curated data, symbolic mathematics and algorithms.
Siri AI enters a far more ambitious era. Apple now wants its assistant to hold natural conversations, understand personal context, interpret images, find information across apps and complete actions. Yet the old Siri Wolfram relationship remains useful for understanding what Apple gained, what was lost as assistants became more conversational and why verified computation may become even more valuable in the generative AI age.
Siri Wolfram Was Built Around Answers
The original Siri Wolfram integration reflected a straightforward idea: users wanted direct answers without manually searching, opening websites and comparing sources.
Wolfram Alpha was particularly effective for questions with a computable result. It could solve equations, compare financial data, convert measurements, retrieve astronomical information, calculate dates and present structured scientific facts. Siri provided the voice interface, while Wolfram supplied the knowledge engine behind selected responses.
A user could ask how many days remained until a date, calculate a percentage, compare populations or request information about a mathematical concept. Siri translated the spoken request and delivered the result in a format suitable for the iPhone.
The experience was narrow compared with modern generative AI, but it had a valuable discipline. Wolfram Alpha attempted to determine an answer from structured information. It did not need to improvise an explanation every time.
That made Siri Wolfram especially useful for mathematics, science, education and quick factual calculations. The assistant could sound intelligent because it was connected to a system built to compute.
Image Source: Google
What Siri Wolfram Alpha Could Do Well
Siri Wolfram Alpha worked best when the request had a definable structure. Arithmetic, percentages, conversions, equations and statistics were natural categories. The system could also retrieve information about weather history, geography, nutrition, astronomy, transportation and other data-rich subjects.
The integration helped make Siri feel different from ordinary web search. Instead of showing several links, the assistant could present one answer with supporting figures or charts.
That approach had limitations. Wolfram Alpha could only respond effectively when it understood the request and had the relevant data or computational method. It was not designed for open-ended conversation, personal writing, emotional nuance or the countless informal questions users ask assistants.
Siri also relied on several external services, with Wolfram Alpha handling only certain knowledge categories. The experience could therefore feel inconsistent. One request produced a polished computational response, while another returned a web search or a limited canned answer.
Still, the partnership demonstrated a principle Apple has never fully abandoned: the best assistant does not need to create every answer itself. It needs to route each request to the system most qualified to handle it.
Generative AI Changed the Definition of an Assistant
Modern AI assistants are expected to do far more than retrieve facts. Users want them to explain ideas, write messages, summarize documents, analyze images, brainstorm projects and remember the direction of a conversation.
Siri AI is Apple’s response to that expanded expectation. Powered by Apple Intelligence and Apple Foundation Models, the new assistant can handle open-ended requests and continue natural conversations. It can use personal context from supported apps, understand onscreen content and connect requests with app actions.
This is a dramatic expansion from the Siri Wolfram era. The old model was largely transactional: ask a question, receive an answer. Siri AI is intended to interpret intent, understand relationships between information and help complete a larger task.
A user may ask Siri AI to find a restaurant recommendation buried in Messages, locate a hotel confirmation in Mail and add the details to a travel note. That request involves personal context and several system services rather than one public factual answer.
The assistant becomes less like a talking search box and more like an operating-system layer.
Image Credit: Apple Inc.
Conversation Creates a Reliability Problem
Generative AI makes assistants more flexible, but it also introduces uncertainty. A language model can produce an elegant response even when the underlying information is incomplete or wrong. The same fluency that makes the assistant enjoyable can make an inaccurate answer sound convincing.
The old Siri Wolfram model avoided much of that risk within its strongest categories. A mathematical calculation came from a computational engine. A unit conversion followed a defined relationship. A data query could be tied to structured information.
Siri AI needs both capabilities. It needs conversational intelligence for requests that cannot be reduced to a formula, while retaining access to reliable tools for tasks that demand precision.
Apple can use specialized systems for calculations, calendars, maps, weather, app actions and personal information rather than asking one generative model to invent every result. Foundation models can understand the request, then direct the work to an appropriate source.
That structure would preserve the strongest lesson from Siri Wolfram: intelligence also means knowing when not to guess.
Personal Context Is the New Knowledge Engine
Wolfram Alpha gave Siri access to public computational knowledge. Siri AI adds a second knowledge source: the user’s own information.
Apple says Siri AI can understand personal context across messages, emails, photos, notes and other supported content. It can help retrieve a detail without requiring the user to remember which app contains it.
This changes the assistant’s value. Wolfram Alpha could explain the distance between two cities. Siri AI could find the hotel reservation, identify the arrival time and retrieve the message where a friend recommended a nearby restaurant.
That personal layer also requires stricter privacy. Apple uses on-device processing for many requests and Private Cloud Compute when more processing power is required. The company has designed the system so personal data does not become part of an ordinary cloud AI profile.
Siri Wolfram answered questions about the world. Siri AI is being designed to answer questions about the user’s place within it.
Image Credit: Apple Inc.
Apps Complete the Transition
Siri AI also moves beyond knowledge by connecting directly with apps through App Intents. Developers can expose content and actions so the assistant can help users complete tasks instead of only describing how to do them.
That could include editing a photo, retrieving a document, starting a workout, updating a task or sending information through a supported app. Onscreen awareness adds another route, allowing users to ask about content already visible without explaining every detail again.
Wolfram Alpha remains valuable because some questions require specialized computation. App Intents extend the same routing philosophy into actions. Siri does not need to contain every capability internally. It needs to understand the request and connect it with the correct tool.
This may become the defining structure of Siri AI: one conversational interface backed by models, apps, services, personal context and specialized knowledge engines.
The Old Siri Still Has a Lesson for the New One
Siri Wolfram belonged to a more limited assistant era, but its underlying approach was disciplined. The system used a specialized source for questions that demanded structured knowledge and computation.
Siri AI has a wider assignment. It must converse naturally, interpret images, understand personal information and complete actions across Apple devices. That breadth can make Siri far more useful, but only if Apple preserves dependable pathways for calculations and factual tasks.
The future assistant should not choose between Wolfram-style precision and generative flexibility. It should combine them. A conversational model can understand what the user wants. A computational engine can provide the numerical answer. Personal context can supply relevant details. App Intents can complete the task.
The old Siri Wolfram integration showed that a voice assistant could make expert knowledge accessible through one spoken request. Siri AI now aims to place conversation, personal context and action around that foundation.
The assistant has become larger, more personal and more capable. Its credibility will still depend on something the 2011 version understood well: when a question has a correct answer, intelligence should calculate before it speaks
Photo editing is getting some of the most interesting Apple Intelligence features this fall. At WWDC, Tyler Stalman interviewed Jon McCormack, Apple’s Vice President of Camera and Photos Software Engineering, and Della Huff, the company’s Senior Manager of Camera and Photos Product Marketing, in a video that covers Spatial Reframing, Extend, and an improved Clean Up tool, along with demos of each:
The interview goes into depth on each feature and how they’re enhanced by the integration with Apple’s improved Private Cloud Compute models, sharing nuggets like the fact that Spatial Reframing of photos is possible even if an image is taken with a single-lens iPhone and the fact that Apple includes metadata in generatively edited photos identifying the use of AI and will add SynthID watermarking, a technology developed by Google, to AI-edited photos later this year.
I’m not a fan of wholesale image generation, which is why I appreciate the thoughtful approach Apple is taking with these photo editing features. The changes are at the margins of the photo and identified as edited using AI.
Os developers já vão poder experimentar a Siri AI a partir de hoje, com a mais recente versão da assistente a chegar mais tarde ao público. No entanto, por enquanto, a nova Siri não vai chegar nem à União Europeia nem à China.
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