Graças a essa versatilidade, o novo modo de voz pode fazer bem mais que apenas conversar. Além de poder ser usado em chats, ele também está integrado ao Work e ao Codex — o que significa que você pode usá-lo para coordenar e acompanhar tarefas, sem usar texto.
ChatGPT Voice is now in the desktop app.
Control your computer and direct multiple agents running in ChatGPT Work or Codex, using just your voice.
It's powered by GPT-Live, so it can speak, listen, and coordinate work in the app at the same time.
Para usar a novidade, é necessário abrir um novo chat ou uma nova tarefa vazia no app e selecionar a opção “Iniciar nova conversa por voz”. As conversas ou tarefas precisam necessariamente ser iniciadas nesse modo — caso contrário, apenas o ditado por voz estará disponível.
O ChatGPT Voice oferece suporte a uma alternância natural entre as falas. Você pode interromper o ChatGPT durante uma resposta, fazer uma pergunta complementar ou mudar de direção. Se o ChatGPT começar a executar uma tarefa, continue falando para verificar o progresso ou orientar o trabalho.
Além disso, a OpenAI destaca que é possível iniciar conversas separadas para conversas mais longas, enviar ao app uma screenshot para apresentar o contexto do que está na tela, bem como usar o recurso pelo Remote do iOS com o iPhone emparelhado ao computador.
O ChatGPT Voice no desktop está sendo disponibilizado gradualmente para os planos Plus, Pro, Business, Edu e Enterprise. Nos dois últimos, há um período inicial de duas semanas com acesso antecipado antes de o recurso ser ativado por padrão.
É possível baixar o novo app do ChatGPT para macOS nessa página.
One of the key advantages of MacWhisper, as it utilizes the Whisper technology, is its superior accuracy in transcriptions compared to other services I have used. I was an early adopter of Rev.com and eventually switched to Otter.ai, for which I paid approximately $100/year. However, I have since canceled my subscription.
[…]
After comparing the audio transcripts from MacWhisper and Otter, I found that while Otter’s formatting is superior, there is hardly any difference in the transcriptions. I started with the free model, which was sufficient for my needs, and eventually upgraded to the large models. Nowadays, I primarily use MacWhisper to transcribe my voice memos, which I then import into Lex.Page.
Whisper, which is made by OpenAI, has the distinct advantage of allowing you to transcribe audio in almost any language you may ever need to. For the time being, Otter can only transcribe English. The benefits of better accuracy and accurate punctuation that the service already holds over Siri hold up across other languages as well. If you’re looking to transcribe non-English audio accurately, Whisper is definitely the best option available on Mac.
By taking advantage of OpenAI’s Whisper framework, Audio Hijack can now take any audio it’s recording and generate a text transcript. While this process was possible before—see Jason’s lengthy post about his workflow—it required a lot of fiddling and several different tools to accomplish; it’s certainly a lot easier to simply drop Audio Hijack’s block into your existing session. If you record multiple sources, it can even appropriately label each one—though if you record multiple people via one input, say the other end of a Zoom call, it can’t distinguish between the various participants.
Apple’s new SpeechAnalyzer is the most accurate on-device speech engine we tested. It beat every Whisper model we ship, including Whisper Small, on both the clean and the noisy half of LibriSpeech, while running roughly three times faster than Small. And the API it replaces, SFSpeechRecognizer, came last on clean speech: behind even Whisper Tiny, a 40MB model.
[…]
The new API cuts word error rate by 3.5 to 4x on the same audio: from 9.02% to 2.12% on clean speech, and from 16.25% to 4.56% on noisy speech. There is no accuracy trade-off to weigh; the new API wins everywhere we measured, and it produces punctuated, cased text where the legacy engine’s output is rougher.
[…]
All five engines ran comfortably faster than real time: between roughly 12x and 40x on the M2 Pro, meaning an hour of audio transcribes in about 1.5 to 5 minutes on-device. SpeechAnalyzer was about 3x faster than Whisper Small per second of audio while beating it on accuracy.
SpeechAnalyzer’s ReportingOption.fastResults reads like a knob you’d reach for when you want to trade power for latency. That framing is what cost me the afternoon.
[…]
If a recording matters — and in a notes app, a lost meeting is the worst thing that can happen to a user — then the container you write while recording has to be PCM in CAF. Not m4a, and not AAC-in-CAF either.
[…]
AVAudioEngine’s inputNode silently ignores your device selection
[…]
I wanted summarization to run on-device. Everything else in Nib’s transcription path does, the audio never leaves the machine, and it would have been a much cleaner story to tell.
But 4,096 tokens for the Foundation Models isn’t enough.
At the heart of this architecture is our third generation of Apple Foundation Models (AFM), a family of five foundation models custom-built in collaboration with Google. These span from on-device models to server-based models running on Private Cloud Compute.
Apple Foundation Models are built to unlock a wide range of helpful experiences for our users, like an entirely new Siri and intelligent tools that make everyday apps smarter and more useful.
“The amount of the Google Assistant we use is none,” Federighi said, explaining that Apple uses none of the Gemini models deployed to Google’s customers, none of Google’s client-side code, and no Google Search infrastructure as the knowledge backbone.
The cloud models hosted on Private Cloud Compute are interesting: they’re not “Gemini” models; they’re Apple Foundation Models trained using proprietary data via RL, which were then “refined” with data from Google’s “frontier” models.
[…]
I’ve been covering the AFM family of models for a while now, and AFM Core Advanced is one of the most interesting on-device models I’ve read about in a while, especially in the context of model size for mobile devices and built-in multimodality with support for text, images, and audio. I’m very keen to play around with this model and understand how it holds up in practice. I wonder if the new CLI (!) for AFM will let you test this one.
It’s very cool that Apple shipped a 20B parameter on-device.
You can’t put 20B parameters in RAM at any reasonable precision. To make it work they are using pretty exotic architecture by today’s standards.
A small model predicts from the query (or prompt) which experts to load from Nand into RAM. The key distinction from a typical MoE is that you do this once per query and then generate all the tokens with the same experts (instead of switching the experts for every token).
FoundationModels is one of the highlights of this WWDC; I think it’s now finally what we hoped we were getting two years ago, and thought we were getting last year (just the models themselves were barely up for it then). I finally feel like I can build features with it, though I really don’t like the Sword of Damocles Apple is holding above Private Cloud Compute and how it can permanently lock you out of using the models forever across all your apps if you hit a download threshold. Poison pill
These strict limits don’t seem to be getting as much attention as they should. It’s nice that for small developers who meet the above criteria, access to PCC has no cost. But there’s no way (yet?) to buy your way out of these limits.
[…]
The “fewer than 2 million first-time app downloads from any of their apps” restriction is particularly notable. It’s not 2 million installations for apps that are using PCC, but 2 million downloads for any app the developer has ever released.
Is there a real time count somewhere of total lifetime downloads for a developer account, so one can plan if they at risk of the switch tripping? Reminds me of the small business qualifications. I had to try to figure out if an employers developer account was going to exceed the revenue threshold for the year. I was left trying to determine it from sales reports, not to mention sales date versus pay out date confusion. I think I botched the calculation in the end.
Claude for Foundation Models is a Swift package that makes Claude available as a server-side language model in Apple’s Foundation Models framework. The package conforms Claude to the framework’s LanguageModel protocol, so you drive it with the same LanguageModelSession API you use for Apple’s on-device model: respond(to:), streaming, guided generation, and tool calling all work the same way.
Requests go directly from your app to the Claude API; Apple is not in the request path and does not see prompts or responses. Usage is billed to your Anthropic account at standard API pricing.
Apple is literally stopping me from building a brand new app using Private Cloud Compute because I made a successful app a few years ago.
The new app might be a success, or it might be a complete flop. Nobody knows yet.
I would understand it if Apple were to limit developers once an app reaches a certain number of downloads or active users, but that’s not what’s happening here.
Instead, they’re blocking developers before they’ve even had a chance to build and ship the app.
Siri AI is an entirely new version of Siri deeply integrated into iPhone, iPad, Mac, Apple Watch, and Apple Vision Pro. It can draw on personal context understanding to search across messages, emails, photos, and more, and get things done across apps with even more systemwide app actions. Additionally, Siri AI can answer questions related to the content on a user’s screen or go out to the web to get up-to-date information using broad world knowledge and generate a helpful answer. A dedicated Siri app allows users to revisit a past conversation or kick off a new one — all in one place — and uses iCloud to privately sync conversational history across a user’s products.
Powered by the next generation of Apple Intelligence, Siri AI is a completely reimagined version of Siri that is more helpful, more capable, and more intelligent. With detailed, engaging responses and natural back-and-forth conversation, Siri AI helps users get more done than ever.
[…]
Siri has been rebuilt from the ground up with powerful AI at its core. It takes full advantage of the bold new architecture for Apple Intelligence, including the next generation of Apple Foundation Models that run on device and on servers using Private Cloud Compute. When Private Cloud Compute is handling users’ requests, their personal data is not stored nor made accessible to Apple or anyone else. Outside experts can continue to verify this privacy promise at any time. Additionally, Siri AI uses the system orchestrator to tap into core capabilities like the Spotlight index and App Toolbox, which work entirely on device to keep users in control of their data.
[…]
For products that support Apple’s most advanced on-device model ever, Siri AI offers even more expressive voices, as well as a major boost in accuracy with systemwide dictation. Users have the ability to customize the expressiveness and pace of Siri’s voice so it’s just right for them. Dictation now captures what users say as polished text with greater precision, automatically handling capitalization, punctuation, and formatting as they speak. With improved speech understanding, users can speak naturally and trust that their words will appear clearly, accurately, and as intended.
I remain uninterested in these more advanced Siri features. They matter in that Apple needs to show that they’re not “behind” and that they can actually deliver on what they announce and advertise. But I don’t find the use cases compelling and wouldn’t trust the results.
If you really care about a particular concert, would you believe Siri about how the tickets work and that it will auto-create a reminder at the right time? Why should I believe that it can figure out whether a particular irregularly shaped object can fit inside an irregularly shaped container when said shapes are probably not even published on the Web? Can it really dig into my e-mail to find the right flight info? An answer without being able to see the sources is not useful. And I certainly don’t want it to pick for itself which vacation photos to send out.
I just want the basic features—from a decade or so ago, like music controls, reminders, and access to my purchases—to work quickly, reliably, and offline to the extent possible. I did not hear anything to suggest that this was a focus. Instead, everyone is celebrating how slow the demos were because that proves they weren’t fake.
The other interesting thing to note from the keynote: watching the demos, there is ZERO doubt these are real, recorded demonstrations on live devices. There will be no confusion about what is real or fake like there was at WWDC 2024 and the initial attempt at the new Siri.
And, if your standards are the state of the art in AI circa June 2024, when Apple took their first crack at answering the question, they did quite well.
[…]
What’s fascinating about this specific demo is that it also showed just how far behind Apple is.
[…]
Still, the bubble was strong enough to convince OpenAI to make the exact same mistake Dropbox did: the company somehow convinced itself that it could make enough money selling subscriptions to consumers; Anthropic, meanwhile, realized that it was enterprises who were willing to pay for AI’s massive productivity benefits, even as OpenAI failed to capitalize on their consumer market penetration by refusing to build an advertising product.
This is a long-winded way of saying that I don’t think that Apple’s agentic shortcomings are a big deal, at least for now. Agents help you do work and be more productive, and consumers don’t want to work or care about being productive. What they do want to do is watch short-form video, and an iPhone is simply much better at that than any other device ever will be; in that context, Siri being good enough is enough, and it appears that Apple crossed that bar.
They need to execute, they need to prove this can scale, and most of all, they need to get third-party apps on board with App Intents and App Schemas. But it seems like they’re doing all of that. This is not a done deal but it is very realistic.
Siri doesn’t have access to the filesystem or tool-calling, so it’s completely unable to do the kinds of tasks you might easily do with Codex or Claude. It’s a good start, but the state of the art has already dramatically shifted over the past year. The question is will Siri move in this direction, or stick to providing answers from the web?
Will Apple introduce its own distinct Codex-style agentic programming model that can do all this, and work with Xcode? You would hope so…
The revamped version of Siri that is officially named “Siri AI” will be available to test in the iOS 27, iPadOS 27, macOS 27, and visionOS 27 developer betas starting today, and it is coming to the Apple Watch in a future watchOS 27 beta. “Siri AI” will also be included in the public betas of each platform that are launching in July.
“Siri AI” requires a device that is compatible with Apple Intelligence.
Apple senior vice president of software engineering Craig Federighi has explained why the company launched a standalone Siri app in iOS 27, after previously characterizing a dedicated chatbot as contrary to its Apple Intelligence strategy.
[…]
“We see Siri not as a separate chatbot, just an unintegrated place you go and chit-chat, but rather as an integral, conversational tool that you use in the moment, deeply integrated into your experience.”
…And playlists, contacts, settings and preferences. Peel off the agentic slop bandwagon and focus on core performance, smarter caching, and resilience under adverse network conditions.
Though this is a test conducted on the very first version of Siri A.I. from a fixed point in Calgary, it seems to work quite well. So far, it is better than any version of Siri Apple has released yet and, as you can see above, it is almost as good as the original 2010 demo, before Apple acquired the company.
On the one hand, if Siri has indeed become this reliable at understanding queries like those demoed so far, it’s indeed impressive. On the other hand, I can’t help but ask myself, once again, whether people at Apple even know how regular people actually use their phones and devices.
Apple executives downplay and obfuscate the role of Google in Siri AI. The Siri AI announcement didn’t even mention Google or Gemini once! Does anyone actually believe Apple’s BS? The argument seems to be that since Google made a bespoke implementation for Apple, Google is not really involved, and it’s all Apple. Ridiculous.
It’s really odd to me that Apple is getting so much credit for Google’s work. Siri now “works” to the extent it does because Siri is to a very large extent Gemini, and Apple has wrapped that in a bow.
Apple didn’t “nail” anything. Apple failed at its own internal efforts and was forced to outsource.
This is the advantage of being a platform: you can fail and still win.
Apple’s Siri team, led by Craig Federighi, held a post-WWDC keynote tech talk with members of the press this afternoon to talk through iOS 27 and the new Siri AI.
During the talk, Federighi shared more details about Apple’s collaboration with Google.
Apple looks like it will use some combination of Google Gemini models, combined with Google’s Confidential Inference and Apple’s own Private Cloud Compute for private hosting. These systems will process both your queries and evaluate private data from your devices.
[…]
Apple has since “expanded” PCC to encompass Google’s hardware as well. I will confess that I find the details of the new “expanded” PCC just a bit vague. It sounds a lot like Apple is primarily going to rely on Google’s existing confidential compute (running in Google datacenters) to process this data, but they’re bolting on a new layer of technical security to control which models are actually running. In any case: security experts can argue about whether this is good enough to keep Cozy Bear away from your data. What I will grant is that it’s probably good enough to keep Google and Apple from accessing your stuff, which is what most people are worried about in the first place.
So why am I so nervous?
[…]
So far we’ve discussed two adversaries: the misaligned designers of private agents (such as search operators), and the possibility of remote prompt injection attacks. But of course, in any discussion of technical privacy systems we need to talk about the last elephant in the room: your government.
Ontem, falamos aqui sobre o rumor de que a Apple estaria prestes a lançar, nos Estados Unidos, um programa de assinatura de iPhones, iPads, Macs e Apple Watches, dando aos consumidores mais formas de acessar seus dispositivos.
Logo depois, o 9to5Mac encontrou, no código da beta do iOS 27, menções a um novo framework que serviria para gerenciar dispositivos alugados por meio de assinaturas como essa.
Chamado App Managed Features, ele permitirá, ao que tudo indica, que um aplicativo autorizado de financiamento ou de prestação de serviços — e não apenas o “Apple Upgrade”, como a solução proprietária está sendo chamada — registre um iPhone e realize verificações contínuas de status.
Caso haja algum problema com o registro (como pagamentos atrasados), o iPhone será colocado automaticamente em um estado intitulado “Restricted Mode” (“Modo Restrito”, em tradução livre), que bloqueia o acesso a uma série de apps do iOS, incluindo:
Leitor de Acessibilidade (Accessibility Reader)
App Store
Saúde (Health)
Lupa (Magnifier)
Telefone (Phone)
Relógio (Clock)
Ajustes (Settings)
Carteira (Wallet)
Senhas (Passwords)
A interface do “Restricted Mode” em si
Como é possível notar, alguns apps mais “críticos”, como o Mensagens (Messages) e o Casa (Home), poderão continuar disponíveis mesmo quando esse bloqueio estiver ativo. Segundo o 9to5Mac, no entanto, o provedor do aparelho será capaz de escolher quais apps ficarão (ou não) de fora dessa lista.
Embora restrinja o acesso aos apps do aparelho, o recurso não parece modificar assinaturas da App Store, o que significa que o usuário continuará sendo cobrado pelos seus apps e serviços mesmo após ter sido colocado no “Restricted Mode”.
Além disso, o código do iOS 27 não lista um número limite de pagamentos atrasados como condição para o bloqueio dos apps, o que significa que ficará a cargo dos serviços de assinatura decidir quando impor essas restrições de acordo com os seus termos e condições.
Há, no entanto, menções a um novo tipo de Bloqueio de Ativação (Activation Lock) chamado “Partner Finance Lock”, criado com o objetivo de impedir que usuários apaguem, vendam ou desmontem um aparelho alugado para revender suas peças.
Ainda segundo as informações, o App Managed Features faz parte do app Buscar (Find My), embora o provedor não tenha acesso à localização do dispositivo. Ao que tudo indica, essa integração existe apenas para garantir que o bloqueio continue de pé mesmo que o usuário apague ou redefina o aparelho.
Embora essas referências não tenham sido encontradas na versão Release Candidate (RC) do iOS 26.6, é possível que elas sejam adicionadas à versão final do sistema, já que, segundo a Bloomberg, o “Apple Upgrade” será anunciado na semana que vem.
Enables direct image editing in Pixelmator Pro for Apple Creator Studio subscribers. ($129.99 annual Apple Creator Studio subscription, various sizes, macOS 15.6+)
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.
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.
Hyundai workers in South Korea strike over pay and job protections as the automaker plans to deploy Atlas humanoid robots across Hyundai and Kia factories.
Microsoft fixed a record 570 vulnerabilities in July's Patch Tuesday as AI-powered tools play a growing role in vulnerability discovery and cybersecurity.
Call forwarding on iPhone is a simple feature with a specific job: it sends incoming calls from your iPhone number to another phone number. That can be useful when you are traveling, using a temporary phone, separating work and personal calls, covering a business line or making sure important calls reach someone else when you cannot answer.
The feature is built into iOS, but it depends on carrier support. Apple notes that call forwarding can be set up on iPhone if cellular service is through a GSM network. If the service uses a CDMA network, users may need to contact the carrier for setup details. That means the option may not appear for every user, every carrier or every plan.
For most supported iPhones, the path is quick:
Settings > Apps > Phone > Call Forwarding
Turn on Call Forwarding, then enter the phone number where calls should be sent. When call forwarding is active, an icon appears in the status bar so the user can see that incoming calls are being routed elsewhere.
Image Credit: AppleMagazine
Call Forwarding on iPhone Sends Calls Elsewhere
Call forwarding on iPhone does not move messages, FaceTime calls, app notifications or voicemail settings by itself. It only redirects standard cellular phone calls. If someone calls your iPhone number, the carrier routes that call to the number you entered.
That distinction is important. If you forward calls to another phone, text messages still arrive on the original iPhone unless other settings or services are involved. FaceTime calls may still ring on Apple devices signed into the same Apple Account. Messaging apps such as WhatsApp, Signal, Telegram or Messenger are unaffected.
Call forwarding is also different from Silence Unknown Callers, Focus modes or Do Not Disturb. Those features control how calls appear or interrupt you. Call forwarding changes where the call goes.
This makes the feature useful in a few common situations. A business owner can send calls to an office line. A traveler can forward calls to a local number. Someone with a damaged iPhone can temporarily send calls to another device. A freelancer can route calls to a partner during time off. A parent can forward calls to a spouse for a short period.
How to Turn On Call Forwarding
To turn on call forwarding:
Settings > Apps > Phone > Call Forwarding
Turn on Call Forwarding
Tap Forward To
Enter the phone number
Go back to save the setting
Use the full phone number, including area code. If forwarding to an international number, include the correct country code. After setting it up, test the feature from another phone. Do not assume it works until a test call rings at the forwarded number.
To turn call forwarding off:
Settings > Apps > Phone > Call Forwarding >Turn off
After turning it off, test again. A quick call from another phone confirms whether incoming calls are back to normal.
If the Call Forwarding menu spins, fails to load or does not save the number, restart the iPhone and check cellular service. If the problem continues, contact the carrier because the setting depends on the carrier network.
Why the Setting May Be Missing
Some users look for call forwarding and cannot find it. That usually means one of three things: the carrier does not expose the feature in iPhone settings, the plan does not support it, or the network requires carrier codes or account-level setup.
Apple’s guide says CDMA users should contact their carrier for call forwarding information. Some carriers also use special dialing codes for forwarding all calls, forwarding when busy, forwarding when unanswered or forwarding when unreachable. Those codes vary by carrier and country, so guessing is not a good idea.
The best path is to check the carrier’s support page or app. Many carriers let users manage forwarding from the account dashboard, especially for business lines or multi-line plans.
If the option is missing, the iPhone is not necessarily broken. The carrier may simply handle forwarding differently.
What Happens to Voicemail
Call forwarding can affect voicemail behavior depending on the carrier and destination number. If the forwarded phone does not answer, the call may go to the voicemail of the forwarded number instead of the original iPhone voicemail. In some cases, carrier rules may still send it back to the original mailbox, but users should not assume that.
This is another reason to test. Call the iPhone from another number, let it ring, and see where the missed call lands. If the goal is to preserve the original iPhone voicemail, carrier support may be needed.
For work numbers, this can be especially important. A forwarded business call going to a personal voicemail greeting can look unprofessional. A personal call going to a shared office voicemail can create privacy problems. Check before relying on it.
Image Credit: Apple Inc.
Call Forwarding and Dual SIM
iPhone users with Dual SIM or eSIM setups should pay attention to which line is being forwarded. A personal line and business line can have separate carrier settings. Forwarding one does not automatically forward the other.
To check lines:
Settings > Cellular >Select the line you want to manage
Then return to:
Settings > Apps > Phone > Call Forwarding
The available settings may vary by carrier and line. If one line supports call forwarding and the other does not, that is usually a carrier difference, not an iPhone problem.
Dual SIM users should also test both numbers separately. Call the personal number, then call the business number, and confirm each behaves as expected.
When Call Forwarding Is Useful
Call forwarding works best when the goal is temporary coverage. It is useful during travel, device repairs, vacations, business handoffs, office moves, event coverage or times when another person needs to answer calls.
It can also help people who use a landline or desk phone during work hours. Calls can be forwarded from the iPhone to the office line, then turned off at the end of the day. The reverse can also work if the carrier supports it.
For small businesses, call forwarding can help avoid missed calls without adding a full phone system. A company can route calls to whoever is available. For individuals, it can be a quick fix when an iPhone battery is failing, a device is being repaired, or cellular reception is better on another phone.
The feature is less ideal as a permanent business phone solution. If multiple people need to answer calls, if calls need menus, if calls need recordings, or if voicemail needs to stay organized, a dedicated phone service may be better.
Privacy and Cost Considerations
Call forwarding may create charges depending on the carrier, plan and destination number. Forwarding calls to an international number can be expensive. Forwarding to another domestic mobile number is usually simpler, but users should still check the plan.
Privacy also deserves attention. Once calls are forwarded, the person or device receiving them may hear personal, business or sensitive conversations meant for the original number. Do not forward calls to a shared phone unless that is intentional.
Before forwarding a work line to a personal number, check company policy. Some organizations do not allow business calls to be routed to unmanaged personal devices.
Image Credit: Freepik
How to Troubleshoot Call Forwarding
If call forwarding does not work, start with the basics. Confirm the iPhone has cellular service. Restart the device. Check that the forwarding number is entered correctly. Turn call forwarding off, wait a few seconds, then turn it on again.
Then test from another phone. A test call is the fastest way to know whether the carrier accepted the setting.
If calls still do not forward, check whether the carrier supports call forwarding through iPhone settings. Some carriers require account-level activation, carrier codes or a business calling feature. If the iPhone setting is missing or stuck, carrier support is usually the right next step.
Also check Focus modes and blocked numbers if the issue is not forwarding but missed calls. A call may fail to ring because of Focus, Silence Unknown Callers, a blocked contact, poor signal or voicemail settings rather than call forwarding.
A Simple Feature Worth Testing
Call forwarding on iPhone is easy to use when the carrier supports it. Open iPhone settings, turn on Call Forwarding, enter the destination number and test it. Turn it off when the temporary need is over.
The feature is most useful when it is deliberate and short-term. It can keep calls moving during travel, repairs, work coverage or busy periods, but it should be checked carefully because voicemail, costs and privacy can change once calls are routed to another number.
The iPhone makes the setting simple. The carrier decides how far that simplicity goes.
A newly published study in Nature Metabolism reports that brown fat can act as a hub of molecular messaging, using secreted microRNAs to coordinate physiology across the body. Researchers focused on brown adipose tissue—well known for its role in thermogenesis—and asked whether it also participates in long-distance communication via RNA signals.
To visualize and track these messages, the team employed brown-fat–specific RNA labeling in living systems. This strategy allowed them to connect RNA production within adipocytes to downstream effects elsewhere, moving beyond correlations toward a clearer map of inter-organ RNA traffic.
The central finding is the presence of a communication network mediated by microRNAs released from brown fat. These small regulatory RNAs appear to travel beyond the tissue of origin and engage gene-regulatory pathways in other organs, effectively relaying information about metabolic state.
Functionally, the work supports a model in which microRNA “dispatch” from thermogenic fat tunes cellular programs in remote targets. Such programs likely involve post-transcriptional regulation, a process microRNAs are known to influence by pairing with complementary sequences in messenger RNAs.
Importantly, the approach does not treat microRNAs as generic circulating molecules. Instead, the labeling confirms their brown-fat origin and strengthens the link between secretory RNA dynamics and systemic physiological outcomes.
The study also highlights the specificity of the messaging: not all RNAs are equally represented, and the labeled microRNA repertoire points to organized, tissue-defined signaling rather than random leakage.
Beyond mapping pathways, the results carry implications for metabolic disease research. If brown fat microRNAs can reshape gene expression in other organs, manipulating these signals could offer a route to modulate energy balance, insulin sensitivity, or inflammatory tone.
For now, the findings provide a blueprint for how RNA labelling combined with tissue specificity can uncover hidden layers of endocrine-like communication. As techniques improve, the field may move toward a broader “RNA connectome” linking organ behavior through secreted regulatory molecules.
Researchers describe their results as evidence that brown fat is not only a metabolic engine but also an information broadcaster. In the evolving landscape of viral-style science news, this discovery underscores how tiny RNA molecules can orchestrate large-scale biological conversations.
Subject of Research: Inter-organ communication via secreted microRNAs from brown adipose tissue.
Article Title: Brown fat-specific RNA labelling reveals a network of inter-organ communication by secreted microRNAs.
Article References: Lino, M., Palermo-Ruiz, G., Efthymiou, V. et al. Brown fat-specific RNA labelling reveals a network of inter-organ communication by secreted microRNAs. Nat Metab (2026). https://doi.org/10.1038/s42255-026-01558-0
Major publishers and author Scott Turow accuse Google of using copyrighted books and articles without permission to train Gemini AI models in a new lawsuit.
Twenty-six current and former Meta employees allege AI-assisted productivity metrics unfairly influenced layoff decisions affecting workers on protected leave. Meta denies the claims.
Also, in an exclusive feature, University of Toronto bioethicist KM Bowman, Ph.D., writes for The Debrief about his firsthand experience with uncontacted indigenous tribes: “Through the haze stood a man. Barely clothed, carrying what appeared to be a weapon of some form, he remained motionless for only a few seconds before silently disappearing back into the forest.” In his essay, Bowman shares his experience and asks whether governments can protect “the uncontacted.”
Finally, here’s a look at more stories The Debrief is tracking right now…
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