Posts tagged with "artificial intelligence"

On Apple Allowing Third-Party Assistants on iOS→

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→

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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On Apple Offering an Abstraction Layer for AI on Its Platforms

Source: Apple.

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?

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The M3 Ultra Mac Studio for Local LLMs→

Speaking of the new Mac Studio and Apple making the best computers for AI: this is a terrific overview by Max Weinbach about the new M3 Ultra chip and its real-world performance with various on-device LLMs:

The Mac I’ve been using for the past few days is the Mac Studio with M3 Ultra SoC, 32-core CPU, 80-core GPU, 256GB Unified Memory (192GB usable for VRAM), and 4TB SSD. It’s the fastest computer I have. It is faster in my workflows for even AI than my gaming PC (which will be used for comparisons below; it has an Intel i9 13900K, RTX 5090, 64GB of DDR5, and a 2TB NVMe SSD).

It’s a very technical read, but the comparison between the M3 Ultra and a vanilla (non-optimized) RTX 5090 is mind-blogging to me. According to Weinbach, it all comes down to Apple’s MLX framework:

I’ll keep it brief; the LLM performance is essentially as good as you’ll get for the majority of models. You’ll be able to run better models faster with larger context windows on a Mac Studio or any Mac with Unified Memory than essentially any PC on the market. This is simply the inherent benefit of not only Apple Silicon but Apple’s MLX framework (the reason we can efficiently run the models without preloading KV Cache into memory, as well as generate tokens faster as context windows grow).

In case you’re not familiar, MLX is Apple’s open-source framework that – I’m simplifying – optimizes training and serving models on Apple Silicon’s unified memory architecture. It is a wonderful project with over 1,600 community models available for download.

As Weinbach concludes:

I see one of the best combos any developer can do as: M3 Ultra Mac Studio with an Nvidia 8xH100 rented rack. Hopper and Blackwell are outstanding for servers, M3 Ultra is outstanding for your desk. Different machines for a different use, while it’s fun to compare these for sport, that’s not the reality.⁠⁠

There really is no competition for an AI workstation today. The reality is, the only option is a Mac Studio.

Don’t miss the benchmarks in the story.

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Is Apple Shipping the Best AI Computers?→

For all the criticism (mine included) surrounding Apple’s delay of various Apple Intelligence features, I found this different perspective by Ben Thompson fascinating and worth considering:

What that means in practical terms is that Apple just shipped the best consumer-grade AI computer ever. A Mac Studio with an M3 Ultra chip and 512GB RAM can run a 4-bit quantized version of DeepSeek R1 — a state-of-the-art open-source reasoning model — right on your desktop. It’s not perfect — quantization reduces precision, and the memory bandwidth is a bottleneck that limits performance — but this is something you simply can’t do with a standalone Nvidia chip, pro or consumer. The former can, of course, be interconnected, giving you superior performance, but that costs hundreds of thousands of dollars all-in; the only real alternative for home use would be a server CPU and gobs of RAM, but that’s even slower, and you have to put it together yourself. Apple didn’t, of course, explicitly design the M3 Ultra for R1; the architectural decisions undergirding this chip were surely made years ago. In fact, if you want to include the critical decision to pursue a unified memory architecture, then your timeline has to extend back to the late 2000s, whenever the key architectural decisions were made for Apple’s first A4 chip, which debuted in the original iPad in 2010. Regardless, the fact of the matter is that you can make a strong case that Apple is the best consumer hardware company in AI, and this week affirmed that reality.

Anecdotally speaking, based on the people who cover AI that I follow these days, it seems there are largely two buckets of folks who are into local, on-device models: those who have set up pricey NVIDIA rigs at home for their CUDA cores (the vast minority); and – the undeniable majority – those who run a spectrum of local models on their Macs of different shapes and configurations (usually, MacBook Pros). If you have to run high-end, performance-intensive local models for academic or scientific workflows on a desktop, the M3 Ultra Mac Studio sounds like an absolute winner.

However, I’d point out that – again, as far as local, on-device models are concerned – Apple is not shipping the best possible hardware on smartphones.

While the entire iPhone 16 lineup is stuck on 8 GB of RAM (and we know how memory-hungry these models can be), Android phones with at least 12 GB or 16 GB of RAM are becoming pretty much the norm now, especially in flagship territory. Even better in Android land, what are being advertised as “gaming phones” with a whopping 24 GB of RAM (such as the ASUS ROG Phone 9 Pro or the RedMagic 10 Pro) may actually make for compelling pocket computers to run smaller, distilled versions of DeepSeek, LLama, or Mistral with better performance than current iPhones.

Interestingly, I keep going back to this quote from Mark Gurman’s latest report on Apple’s AI challenges:

There are also concerns internally that fixing Siri will require having more powerful AI models run on Apple’s devices. That could strain the hardware, meaning Apple either has to reduce its set of features or make the models run more slowly on current or older devices. It would also require upping the hardware capabilities of future products to make the features run at full strength.

Given Apple’s struggles, their preference for a hybrid on-device/server-based AI system, and the market’s evolution on Android, I don’t think Apple can afford to ship 8 GB on iPhones for much longer if they’re serious about AI and positioning their hardware as the best consumer-grade AI computers.

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Notes on the Apple Intelligence Delay

Simon Willison, one of the more authoritative independent voices in the LLM space right now, published a good theory on what may have happened with Apple’s delay of Apple Intelligence’s Siri personalization features:

I have a hunch that this delay might relate to security.

These new Apple Intelligence features involve Siri responding to requests to access information in applications and then perform actions on the user’s behalf.

This is the worst possible combination for prompt injection attacks! Any time an LLM-based system has access to private data, tools it can call and potentially malicious instructions (like emails and text messages from untrusted strangers) there’s a risk that an attacker might subvert those tools and use them to damage or exfiltration a user’s data.

Willison has been writing about prompt injection attacks since 2023. We know that Mail’s AI summaries were (at least initially?) sort of susceptible to prompt injections (using hidden HTML elements), as were Writing Tools during the beta period. It’s scary to imagine what would happen with a well-crafted prompt injection when the attack’s surface area becomes the entire assistant directly plugged into your favorite apps with your data. But then again, one has to wonder why these features were demoed at all at Apple’s biggest software event last year and if those previews – absent a real, in-person event – were actually animated prototypes.

On this note, I disagree with Jason Snell’s idea that previewing Apple Intelligence last year was a good move no matter what. Are we sure that “nobody is looking” at Apple’s position in the AI space right now and that Siri isn’t continuing down its path of damaging Apple’s software reputation, like MobileMe did? As a reminder, the iPhone 16 lineup was advertised as “built for Apple Intelligence” in commercials, interviews, and Apple’s website.

If the company’s executives are so certain that the 2024 marketing blitz worked, why are they pulling Apple Intelligence ads from YouTube when “nobody is looking”?

On another security note: knowing Apple’s penchant for user permission prompts (Shortcuts and macOS are the worst offenders), I wouldn’t be surprised if the company tried to mitigate Siri’s potential hallucinations and/or the risk of prompt injections with permission dialogs everywhere, and later realized the experience was terrible. Remember: Apple announced an App Intents-driven system with assistant schemas that included actions for your web browser, file manager, camera, and more. Getting any of those actions wrong (think: worse than not picking your mom up at the airport, but actually deleting some of your documents) could have pretty disastrous consequences.

Regardless of what happened, here’s the kicker: according to Mark Gurman, “some within Apple’s AI division” believe that the delayed Apple Intelligence features may be scrapped altogether and replaced by a new system rebuilt from scratch. From his story, pay close attention to this paragraph:

There are also concerns internally that fixing Siri will require having more powerful AI models run on Apple’s devices. That could strain the hardware, meaning Apple either has to reduce its set of features or make the models run more slowly on current or older devices. It would also require upping the hardware capabilities of future products to make the features run at full strength.

Inference costs may have gone down over the past 12 months and context windows may have gotten bigger, but I’m guessing there’s only so much you can do locally with 8 GB of RAM when you have to draw on the user’s personal context across (potentially) dozens of different apps, and then have conversations with the user about those results. It’ll be interesting to watch what Apple does here within the next 1-2 years: more RAM for the same price on iPhones, even more tasks handed off to Private Cloud Compute, or a combination of both?

We’ll see how this will play out at WWDC 2025 and beyond. I continue to think that Apple and Google have the most exciting takes on AI in terms of applying the technology to user’s phones and apps they use everyday. The only difference is that one company’s announcements were theoretical, and the other’s are shipping today. It seems clear now that Apple got caught off guard by LLMs while they were going down the Vision Pro path, and I’ll be curious to see how their marketing strategy will play out in the coming months.

Gemini for iOS Gets Lock Screen Widgets, Control Center Integration, Basic Shortcuts Actions

Gemini for iOS.

Gemini for iOS.

When I last wrote about Gemini for iOS, I noted the app’s lackluster integration with several system features. But since – unlike others in the AI space – the team at Google is actually shipping new stuff on a weekly basis, I’m not too surprised to see that the latest version of Gemini for iOS has brought extensive support for widgets.

Specifically, Gemini for iOS now offers a collection of Lock Screen widgets that also appear as controls in iOS 18’s Control Center, and there are barebones Shortcuts actions to go along with them. In both the Lock Screen’s widget gallery and Control Center, you’ll find Gemini widgets to:

  • type a prompt,
  • Talk Live,
  • open the microphone (for dictation),
  • open the camera,
  • share an image (with a Photos picker), and
  • share a document (with a Files picker).

It’s nice to see these integrations with Photos and Files; notably, Gemini now also has a share extension that lets you add the same media types – plus URLs from webpages – to a prompt from anywhere on iOS.

The Shortcuts integration is a little less exciting since Google implemented old-school actions that do not support customizable parameters. Instead, Gemini only offers actions to open the app in three modes: type, dictate, or Talk Live. That’s disappointing, and I would have preferred to see the ability to pass text or images from Shortcuts directly to Gemini.

While today’s updates are welcome, Google still has plenty of work left to do on Apple’s platforms. For starters, they don’t have an iPad version of the Gemini app. There are no Home Screen widgets yet. And the Shortcuts integration, as we’ve seen, could go much deeper. Still, the inclusion of controls, basic Shortcuts actions, and a share extension goes a long way toward making Gemini easier to access on iOS – that is, until the entire assistant is integrated as an extension for Apple Intelligence.

“Everyone Is Caught Up, Except for Apple”→

Good post by Parker Ortolani (who’s blogging more frequently now; I recommend subscribing to his blog) on the new (and surprisingly good looking?) Alexa+ and where Apple stands with Siri:

So here we are. Everyone is caught up, except for Apple. Siri may have a pretty glowing animation but it is not even remotely the same kind of personal assistant that these others are. Even the version of Siri shown at WWDC last year doesn’t appear to be quite as powerful as Alexa+. Who knows how good the app intents powered Siri will even be at the end of the day when it ships, after all according to reports it has been pushed back and looks like an increasingly difficult endeavor. I obviously want Siri to be great. It desperately needs improvement, not just to compete but to make using an iPhone an even better experience.

I continue to think that Apple has immense potential for Apple Intelligence and Siri if they get both to work right with their ecosystem. But at this point, I have to wonder if we’ll see GTA 6 before Siri gets any good.

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Beyond ChatGPT’s Extension: How to Redirect Safari Searches to Any LLM

xSearch for Safari.

xSearch for Safari.

Earlier this week, OpenAI’s official ChatGPT app for iPhone and iPad was updated with a native Safari extension that lets you forward any search query from Safari’s address bar to ChatGPT Search. It’s a clever approach: rather than waiting for Apple to add a native ChatGPT Search option to their list of default search engines (if they ever will), OpenAI leveraged extensions’ ability to intercept queries in the address bar and redirect them to ChatGPT whenever you type something and press Return.

However, this is not the only option you have if you want to redirect your Safari search queries to a search engine other than the one that’s set as your default. While the solution I’ll propose below isn’t as frictionless as OpenAI’s native extension, it gets the job done, and until other LLMs like Claude, Gemini, Perplexity, and Le Chat ship their own Safari extensions, you can use my approach to give Safari more AI search capabilities right now.

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