How Could Apple Use Open-Source AI Models?→

Linked By Federico Viticci

Yesterday, Wayne Ma, reporting for The Information, published an outstanding story detailing the internal turmoil at Apple that led to the delay of the highly anticipated Siri AI features last month. From the article:

In November 2022, OpenAI released ChatGPT to a thunderous response from the tech industry and public. Within Giannandrea’s AI team, however, senior leaders didn’t respond with a sense of urgency, according to former engineers who were on the team at the time.

The reaction was different inside Federighi’s software engineering group. Senior leaders of the Intelligent Systems team immediately began sharing papers about LLMs and openly talking about how they could be used to improve the iPhone, said multiple former Apple employees.

Excitement began to build within the software engineering group after members of the Intelligent Systems team presented demos to Federighi showcasing what could be achieved on iPhones with AI. Using OpenAI’s models, the demos showed how AI could understand content on a user’s phone screen and enable more conversational speech for navigating apps and performing other tasks.

Assuming the details in this report are correct, I truly can’t imagine how one could possibly see the debut of ChatGPT two years ago and not feel a sense of urgency. Fortunately, other teams at Apple did, and it sounds like they’re the folks who have now been put in charge of the next generation of Siri and AI.

There are plenty of other details worth reading in the full story (especially the parts about what Rockwell’s team wanted to accomplish with Siri and AI on the Vision Pro), but one tidbit in particular stood out to me: Federighi has now given the green light to rely on third-party, open-source LLMs to build the next wave of AI features.

Federighi has already shaken things up. In a departure from previous policy, he has instructed Siri’s machine-learning engineers to do whatever it takes to build the best AI features, even if it means using open-source models from other companies in its software products as opposed to Apple’s own models, according to a person familiar with the matter.

“Using” open-source models from other companies doesn’t necessarily mean shipping consumer features in iOS powered by external LLMs. I’ve seen some people interpret this paragraph as Apple preparing to release a local Siri powered by Llama 4 or DeepSeek, and I think we should pay more attention to that “build the best AI features” (emphasis mine) line.

My read of this part is that Federighi might have instructed his team to use distillation to better train Apple’s in-house models as a way to accelerate the development of the delayed Siri features and put them back on the company’s roadmap. Given Tim Cook’s public appreciation for DeepSeek and this morning’s New York Times report that the delayed features may come this fall, I wouldn’t be shocked to learn that Federighi told Siri’s ML team to distill DeepSeek R1’s reasoning knowledge into a new variant of their ∼3 billion parameter foundation model that runs on-device. Doing that wouldn’t mean that iOS 19’s Apple Intelligence would be “powered by DeepSeek”; it would just be a faster way for Apple to catch up without throwing away the foundational model they unveiled last year (which, supposedly, had a ~30% error rate).

In thinking about this possibility, I got curious and decided to check out the original paper that Apple published last year with details on how they trained the two versions of AFM (Apple Foundation Model): AFM-server and AFM-on-device. The latter would be the smaller, ~3 billion model that gets downloaded on-device with Apple Intelligence. I’ll let you guess what Apple did to improve the performance of the smaller model:

For the on-device model, we found that knowledge distillation (Hinton et al., 2015) and structural pruning are effective ways to improve model performance and training efficiency. These two methods are complementary to each other and work in different ways. More specifically, before training AFM-on-device, we initialize it from a pruned 6.4B model (trained from scratch using the same recipe as AFM-server), using pruning masks that are learned through a method similar to what is described in (Wang et al., 2020; Xia et al., 2023).

Or, more simply:

AFM-server core training is conducted from scratch, while AFM-on-device is distilled and pruned from a larger model.

If the distilled version of AFM-on-device that was tested until a few weeks ago produced a wrong output one third of the time, perhaps it would be a good idea to perform distillation again based on knowledge from other smarter and larger models? Say, using 250 Nvidia GB300 NVL72 servers?

(One last fun fact: per their paper, Apple trained AFM-server on 8192 TPUv4 chips for 6.3 trillion tokens; that setup still wouldn’t be as powerful as “only” 250 modern Nvidia servers today.)

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Is Electron Really That Bad?→

Linked By Federico Viticci

I’ve been thinking about this video by Theo Browne for the past few days, especially in the aftermath of my story about working on the iPad and realizing its best apps are actually web apps.

I think Theo did a great job contextualizing the history of Electron and how we got to this point where the majority of desktop apps are built with it. There are two sections of the video that stood out to me and I want to highlight here. First, this observation – which I strongly agree with – regarding the desktop apps we ended up having thanks to Electron and why we often consider them “buggy”:

There wouldn’t be a ChatGPT desktop app if we didn’t have something like Electron. There wouldn’t be a good Spotify player if we didn’t have something like Electron. There wouldn’t be all of these awesome things we use every day. All these apps… Notion could never have existed without Electron. VS Code and now Cursor could never have existed without Electron. Discord absolutely could never have existed without Electron.

All of these apps are able to exist and be multi-platform and ship and theoretically build greater and greater software as a result of using this technology. That has resulted in some painful side effects, like the companies growing way faster than expected because they can be adopted so easily. So they hire a bunch of engineers who don’t know what they’re doing, and the software falls apart. But if they had somehow magically found a way to do that natively, it would have happened the same exact way.

This has nothing to do with Electron causing the software to be bad and everything to do with the software being so successful that the companies hire too aggressively and then kill their own software in the process.

The second section of the video I want to call out is the part where Theo links to an old thread from the developer of BoltAI, a native SwiftUI app for Mac that went through multiple updates – and a lot of work on the developer’s part – to ensure the app wouldn’t hit 100% CPU usage when simply loading a conversation with ChatGPT. As documented in the thread from late 2023, this is a common issue for the majority of AI clients built with SwiftUI, which is often less efficient than Electron when it comes to rendering real-time chat messages. Ironic.

Theo argues:

You guys need to understand something. You are not better at rendering text than the Chromium team is. These people have spent decades making the world’s fastest method for rendering documents across platforms because the goal was to make Chrome as fast as possible regardless of what machine you’re using it on. Electron is cool because we can build on top of all of the efforts that they put in to make Electron and specifically to make Chromium as effective as it is. The results are effective.

The fact that you can swap out the native layer with SwiftUI with even just a web view, which is like Electron but worse, and the performance is this much better, is hilarious. Also notice there’s a couple more Electron apps he has open here, including Spotify, which is only using less than 3% of his CPU. Electron apps don’t have to be slow. In fact, a lot of the time, a well-written Electron app is actually going to perform better than an equivalently well-written native app because you don’t get to build rendering as effectively as Google does.

Even if you think you made up your mind about Electron years ago, I suggest watching the entire video and considering whether this crusade against more accessible, more frequently updated (and often more performant) desktop software still makes sense in 2025.

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Using Simon Willison’s LLM CLI to Process YouTube Transcripts in Shortcuts with Claude and Gemini

By Federico Viticci

Video Processor.

I’ve been experimenting with different automations and command line utilities to handle audio and video transcripts lately. In particular, I’ve been working with Simon Willison’s LLM command line utility as a way to interact with cloud-based large language models (primarily Claude and Gemini) directly from the macOS terminal.

For those unfamiliar, Willison’s LLM CLI tool is a command line utility that lets you communicate with services like ChatGPT, Gemini, and Claude using shell commands and dedicated plugins. The llm command is extremely flexible when it comes to input and output; it supports multiple modalities like audio and video attachments for certain models, and it offers custom schemas to return structured output from an API. Even for someone like me – not exactly a Terminal power user – the different llm commands and options are easy to understand and tweak.

Today, I want to share a shortcut I created on my Mac that takes long transcripts of YouTube videos and:

reformats them for clarity with proper paragraphs and punctuation, without altering the original text,
extracts key points and highlights from the transcript, and
organizes highlights by theme or idea.

I created this shortcut because I wanted a better system for linking to YouTube videos, along with interesting passages from them, on MacStories. Initially, I thought I could use an app I recently mentioned on AppStories and Connected to handle this sort of task: AI Actions by Sindre Sorhus. However, when I started experimenting with long transcripts (such as this one with 8,000 words from Theo about Electron), I immediately ran into limitations with native Shortcuts actions. Those actions were running out of memory and randomly stopping the shortcut.

I figured that invoking a shell script using macOS’ built-in ‘Run Shell Script’ action would be more reliable. Typically, Apple’s built-in system actions (especially on macOS) aren’t bound to the same memory constraints as third-party ones. My early tests indicated that I was right, which is why I decided to build the shortcut around Willison’s llm tool.

Opening iOS Is Good News for Smartwatches →

Linked By Federico Viticci

Speaking of opening up iOS to more types of applications, I enjoyed this story by Victoria Song, writing at The Verge about the new EU-mandated interoperability requirements that include, among other things, smartwatches:

This is a big reason why it’s a good thing that the European Commission recently gave Apple marching orders to open up iOS interoperability to other gadget makers. You can read our explainer on the nitty gritty of what this means, but the gist is that it’s going to be harder for Apple to gatekeep iOS features to its own products. Specific to smartwatches, Apple will have to allow third-party smartwatch makers to display and interact with iOS notifications. I’m certain Garmin fans worldwide, who have long complained about the inability to send quick replies on iOS, erupted in cheers.

And this line, which is so true in its simplicity:

Some people just want the ability to choose how they use the products they buy.

Can you imagine if your expensive Mac desktop had, say, some latency if you decided to enter text with a non-Apple keyboard? Or if the USB-C port only worked with proprietary Apple accessories? Clearly, those restrictions would be absurd on computers that cost thousands of dollars. And yet, similar restrictions have long existed on iPhones and the iOS ecosystem, and it’s time to put an end to them.

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On Apple Allowing Third-Party Assistants on iOS →

Linked By Federico Viticci

This is an interesting idea by Parker Ortolani: what if Apple allowed users to change their default assistant from Siri to something else?

I do not want to harp on the Siri situation, but I do have one suggestion that I think Apple should listen to. Because I suspect it is going to take quite some time for the company to get the new Siri out the door properly, they should do what was previously unthinkable. That is, open up iOS to third-party assistants. I do not say this lightly. I am one of those folks who does not want iOS to be torn open like Android, but I am willing to sign on when it makes good common sense. Right now it does.

And:

I do not use Gemini as my primary LLM generally, I prefer to use ChatGPT and Claude most of the time for research, coding, and writing. But Gemini has proved to be the best assistant out of them all. So while we wait for Siri to get good, give us the ability to use custom assistants at the system level. It does not have to be available to everyone, heck create a special intent that Google and these companies need to apply for if you want. But these apps with proper system level overlays would be a massive improvement over the existing version of Siri. I do not want to have to launch the app every single time.

As a fan of the progressive opening up of iOS that’s been happening in Europe thanks to our laws, I can only welcome such a proposal – especially when I consider the fact that long-pressing the side button on my expensive phone defaults to an assistant that can’t even tell which month it is. If Apple truly thinks that Siri helps users “find what they need and get things done quickly”, they should create an Assistant API and allow other companies to compete with them. Let iPhone users decide which assistant they prefer in 2025.

Some people may argue that other assistants, unlike Siri, won’t be able to access key features such as sending messages or integrating with core iOS system frameworks. My reply would be: perhaps having a more prominent placement on iOS would actually push third-party companies to integrate with the iOS APIs that do exist. For instance, there is nothing stopping OpenAI from integrating ChatGPT with the Reminders app; they have done exactly that with MapKit, and if they wanted, they could plug into HomeKit, HealthKit, and the dozens of other frameworks available to developers. And for those iOS features that don’t have an API for other companies to support…well, that’s for Apple to fix.

From my perspective, it always goes back to the same idea: I should be able to freely swap out software on my Apple pocket computer just like I can thanks to a safe, established system on my Apple desktop computer. (Arguably, that is also the perspective of, you know, the law in Europe.) Even Google – a company that would have all the reasons not to let people swap the Gemini assistant for anything else – lets folks decide which assistant they want to use on Android. And, as you can imagine, competition there is producing some really interesting results.

I’m convinced that, at this point, a lot of people despise Siri and would simply prefer pressing their assistant button to talk to ChatGPT or Claude – even if that meant losing access to reminders, timers, and whatever it is that Siri can reliably accomplish these days. (I certainly wouldn’t mind putting Claude on my iPhone and leaving Siri on the Watch for timers and HomeKit.) Whether it’s because of superior world knowledge, proper multilingual abilities (something that Siri still doesn’t support!), or longer contextual conversations, hundreds of millions of people have clearly expressed their preference for new types of digital assistance and conversations that go beyond the antiquated skillset of Siri.

If a new version of Siri isn’t going to be ready for some time, and if Apple does indeed want to make the best computers for AI, maybe it’s time to open up that part of iOS in a way that goes beyond the (buggy) ChatGPT integration with Siri.

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App Store Vibes →

Linked By Federico Viticci

Bryan Irace has an interesting take on the new generation of developer tools that have lowered the barrier to entry for new developers (and sometimes not even developers) when it comes to creating apps:

Recent criticism of Apple’s AI efforts has been juicy to say the least, but this shouldn’t distract us from continuing to criticize one of Apple’s most deserving targets: App Review. Especially now that there’s a perfectly good AI lens through which to do so.

It’s one thing for Apple’s AI product offerings to be non-competitive. Perhaps even worse is that as Apple stands still, software development is moving forward faster than ever before. Like it or not, LLMs—both through general chat interfaces and purpose-built developer tools—have meaningfully increased the rate at which new software can be produced. And they’ve done so both by making skilled developers more productive while also lowering the bar for less-experienced participants.

And:

I recently built a small iOS app for myself. I can install it on my phone directly from Xcode but it expires after seven days because I’m using a free Apple Developer account. I’m not trying to avoid paying Apple, but there’s enough friction involved in switching to a paid account that I simply haven’t been bothered. And I used to wrangle provisioning profiles for a living! I can’t imagine that I’m alone here, or that others with less tribal iOS development knowledge are going to have a higher tolerance for this. A friend asked me to send the app to them but that’d involve creating a TestFlight group, submitting a build to Apple, waiting for them to approve it, etc. Compare this to simply pushing to Cloudflare or Netlify and automatically having a URL you can send to a friend or share via Twitter. Or using tools like v0 or Replit, where hosting/distribution are already baked in.

Again, this isn’t new—but being able to build this much software this fast is new. App distribution friction has stayed constant while friction in all other stages of software development has largely evaporated. It’s the difference between inconvenient and untenable.

Perhaps “vibe coding” is the extreme version of this concept, but I think there’s something here. Creating small, low-stakes apps for personal projects or that you want to share with a small group of people is, objectively, getting easier. After reading Bryan’s post – which rightfully focuses on the distribution side of apps – I’m also wondering: what happens when the first big service comes along and figures out a way to bypass the App Store altogether (perhaps via the web?) to allow “anyone” to create apps, completely cutting out Apple and its App Review from the process?

In a way, this reminds me of blogging. Those who wanted to have an online writing space 30 years ago had to know some of the basics of hosting and HTML if they wanted to publish something for other people to read. Then Blogger came along and allowed anyone – regardless of their skill level – to be read. What if the same happened to mobile software? Should Apple and Google be ready for this possibility within the next few years?

I could see Google spin up a “Build with Gemini” initiative to let anyone create Android apps without any coding knowledge. I’m also reminded of this old Vision Pro rumor that claimed Apple’s Vision team was exploring the idea of letting people create “apps” with Siri.

If only the person in charge of that team went anywhere, right?

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Choosing Optimism About iOS 19 →

Linked By Federico Viticci

I loved this post by David Smith on his decision to remain optimistic about Apple’s rumored iOS 19 redesign despite, well, you know, everything:

Optimism isn’t enthusiasm. Enthusiasm is a feeling, optimism is a choice. I have much less of the enthusiastic feelings these days about my relationship to Apple and its technologies (discussed here on Under the Radar 312), but I can still choose to optimistically look for the positives in any situation. Something I’ve learned as I’ve aged is that pessimism feels better in the moment, but then slowly rots you over time. Whereas optimism feels foolish in the moment, but sustains you over time.

I’ve always disliked the word “enthusiast” (talk about a throwback), and I’ve been frequently criticized for choosing the more optimistic approach in covering Apple over the years. But David is right: pessimism feels better in the short term (and performs better if you’re good at writing headlines or designing YouTube thumbnails), but is not a good long-term investment. (Of course, when the optimism is also gone for good…well, that’s a different kind of problem.)

But back to David’s thoughts on the iOS 19 redesign. He lists this potential reason to be optimistic about having to redesign his apps:

It would provide a point of differentiation for my app against other apps who wouldn’t adopt the new design language right away (either large companies which have their own design system or laggards who wouldn’t prioritize it).

He’s correct: the last time Apple rolled out a major redesign of iOS, they launched a dedicated section on the App Store which, on day one, featured indie apps updated for iOS 7 such as OmniFocus, Twitterrific, Reeder 2, Pocket Casts 4, and Perfect Weather. It lasted for months. Twelve years later¹, I doubt that bigger companies will be as slow as they were in 2013 to adopt Apple’s new design language, but more agile indie developers will undoubtedly have an advantage here.

He also writes:

Something I regularly remind myself as I look at new Apple announcements is that I never have the whole picture of what is to come for the platform, but Apple does. They know if things like foldable iPhones or HomeKit terminals are on the horizon and how a new design would fit in best with them. If you pay attention and try to read between the lines they often will provide the clues necessary to “skate where the puck is going” and be ready when new, exciting things get announced subsequently.

This is the key point for me going into this summer’s review season. Just like when Apple introduced size classes in iOS 8 at WWDC 2014 and launched Slide Over and Split View multitasking for the iPad (alongside the first iPad Pro) the next year, I have to imagine that changes in this year’s design language will pave the way for an iPhone that unfolds into a mini tablet, a convertible Mac laptop, App Intents on a dedicated screen, or more. So while I’m not enthusiastic about Apple’s performance in AI or developer relations, I choose to be optimistic about the idea that this year’s redesign may launch us into an exciting season of new hardware and apps for the next decade.

Think about it this way: when iOS 7 was released, the App Store was only five years old. ↩︎

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The iPad’s “Sweet” Solution

By Federico Viticci

In working with my iPad Pro over the past few months, I’ve realized something that might have seemed absurd just a few years ago: some of the best apps I’m using – the ones with truly desktop-class layouts and experiences – aren’t native iPad apps.

They’re web apps.

Before I continue and share some examples, let me clarify that this is not a story about the superiority of one way of building software over another. I’ll leave that argument to developers and technically inclined folks who know much more about programming and software stacks than I do.

Rather, the point I’m trying to make is that, due to a combination of cost-saving measures by tech companies, Apple’s App Store policies over the years, and the steady rise of a generation of young coders who are increasingly turning to the web to share their projects, some of the best, most efficient workflows I can access on iPadOS are available via web apps in a browser or a PWA.

On Apple Offering an Abstraction Layer for AI on Its Platforms

By Federico Viticci

Source: Apple.

I’ve been thinking about Apple’s position in AI a lot this week, and I keep coming back to this idea: if Apple is making the best consumer-grade computers for AI right now, but Apple Intelligence is failing third-party developers with a lack of AI-related APIs, should the company try something else to make it easier for developers to integrate AI into their apps?

Gus Mueller, creator of Acorn and Retrobatch, has been pondering similar thoughts:

A week or so ago I was grousing to some friends that Apple needs to open up things on the Mac so other LLMs can step in where Siri is failing. In theory we (developers) could do this today, but I would love to see a blessed system where Apple provided APIs to other LLM providers.

Are there security concerns? Yes, of course there are, there always will be. But I would like the choice.

The crux of the issue in my mind is this: Apple has a lot of good ideas, but they don’t have a monopoly on them. I would like some other folks to come in and try their ideas out. I would like things to advance at the pace of the industry, and not Apple’s. Maybe with a blessed system in place, Apple could watch and see how people use LLMs and other generative models (instead of giving us Genmoji that look like something Fisher-Price would make). And maybe open up the existing Apple-only models to developers. There are locally installed image processing models that I would love to take advantage of in my apps.

The idea is a fascinating one: if Apple Intelligence cannot compete with the likes of ChatGPT or Claude for the foreseeable future, but third-party developers are creating apps based on those APIs, is there a scenario in which Apple may regain control of the burgeoning AI app ecosystem by offering their own native bridge to those APIs?