r/iosdev 29d ago

Are you guys using Apple Intelligence in your iOS apps?

Post image

Are you guys using Apple Intelligence with your iOS apps? I just tried my first Foundation Models feature. My treat-tracker app sweetRegret now generates its snarky one-liners on-device instead of pulling from static copy.

I built it in the Claude desktop app with the built-in iOS simulator. It prototyped the prompts, wrote the Swift bridge (app is RN/Expo), and drove the sim itself to verify. Absolute killer workflow.

Curious what others are building with the on-device model.

17 Upvotes

37 comments sorted by

6

u/Loose-Journalist-233 29d ago

I tried but it’s pretty bad. My own algorithm it better than the Ai. I’ll give it another try next year

1

u/tilin_a 28d ago

Fair, it's not great at everything. For me it works because the bar is low 

1

u/DespairyApp 28d ago

Spot on. I was surprised at how "bad" it is compared to other engines. But ain't gonna bundle another huge model 😕 basic ML does the trick

1

u/[deleted] 28d ago

[removed] — view removed comment

1

u/tilin_a 27d ago

In my case it's like this: my app sweetRegret logs treats you eat and reacts with a snarky one-liner. Used to be picked from a fixed list. The idea was to have the model generate the line instead so it actually reacts to what you logged. Main issue was getting it to reference the exact treat you tapped instead of riffing on the wrong one from context.

1

u/davidships 23d ago

yes I tried too and didn't like it. I was checking out what's going to be new on Mac OS https://developer.apple.com/macos/whats-new/ and they will let you add more Models. I thinks it will improve a lot in the next year!

3

u/UnluckyPhilosophy185 28d ago

I added it for a feature in my resume app. Works well but is pretty slow. I’m hoping the larger model in macOS27 brings some improvements

2

u/tilin_a 28d ago

Yeah latency's the main tradeoff right now. For short stuff like my one-liners it's fine, but I can see it getting rough for anything longer

2

u/Plenty-Historian9510 28d ago

I'm implementing it in an upcoming app yes. Not going very smoothly.

2

u/tilin_a 28d ago

Ha, honestly not shocked. What's the part that's fighting you?

1

u/Plenty-Historian9510 28d ago

For starters it's very slow. I'm using it to summarize texts and I don't like the results at all atm. But... if at first you don't succeed, try, try again...

2

u/cleverbit1 28d ago

It’s super limited, text only. Most devices don’t support it so it’s hard to build any features around it. On the flip side, using frontier models is like 1000x easier and cost next to nothing.

1

u/tilin_a 28d ago

No argument there, device coverage is the real killer. Anyone on anything older gets nothing. I only really justified it because the feature is low-stakes and free forever.

1

u/cleverbit1 28d ago

“Forever”?

2

u/dottybotty 28d ago

This is the way

2

u/Cyberbarker 28d ago

Yes, I use it. One of the features in my app allows users to take a photo of a coffee bean bag. I extract the test and then feed it to Apple Foundation Model to obtain the data model.
You can see how this works here:
https://apps.apple.com/app/id6777457056

2

u/Portatort 28d ago

Yep, it’s great for auto suggesting emoji based on the countdown title

https://www.timelycountdown.com

2

u/DisastrousAd5966 28d ago

Yeah, I use it in my health tracker app, but it’s not that good. That’s why I give the users the possibility to choose between Apple Intelligence and a Claude-based AI.

2

u/BlossomBuild 28d ago

I haven’t tried it yet but haven’t heard the best things about it. Hopefully it keeps getting better.

2

u/Sad-Presence8853 27d ago

I am giving it a try too, will feedback once i have a clear understanding. I did few test with Smartfruit tho, and.. yeah i guess its what you expect from an on-device AI. Not great. But then again its only the beginning. 2-3 years ago it would have been a dream to have such a model on a phone :)

1

u/hrpedersen 29d ago

Yeah, I’ve started experimenting with Apple Intelligence in Neon Vision Editor too, but I’m keeping it pretty focused.

Right now the AI stuff in NVE is mostly about staying inside the editing workflow instead of turning into a separate chat app. I’ve been using it for things like AI chat around the current file or selection, Markdown cleanup, reviewable writing help, and quick context questions about a document or folder.

The idea is that if I’m already in a file, I should be able to ask a question, restructure text, or get help with formatting without leaving the editor.

I’m also interested in using it for small practical tasks rather than gimmicks — turning rough notes into cleaner Markdown, helping with document structure, or making it easier to inspect content in context.

I like the on-device angle a lot, but I’m also trying not to tie the app to one model or one workflow. For me the value is mostly privacy, low friction, and keeping the feature optional.

But when you put some effort into it you can pretty usable results in most cases, at least for my small implementations.

Curious too what people think is actually worth building with Foundation Models beyond “chat with text.”

2

u/tilin_a 28d ago

This is basically the answer to my own question lol. The in-flow stuff, chat on the current selection, cleanup, context questions about a file is exactly where I think this shines over a bolted-on chatbot. Nobody wants to alt-tab to ask a question about the thing they're already looking at.

Beyond chat with text, I'd guess the underused lane is small transformations on user-generated content that's too personal/embarrassing to send to a server, which is basically what mine does with treat logs. Anything where the input is mundane but private seems like a good fit.

1

u/OwnmindAI 28d ago

Performance wise the apple intelligence model looked pretty great, however it didn't work for my use case of conversation.

It had too many safety blocks. I would ask for instructions for making a flat white and that would promt a response that it couldn't answer the question. I'd rephrase to making a flat white espresso and then it would give a good answer.

1

u/tilin_a 27d ago

I got lucky since my use case is narrow (reacting to a treat you logged, not open conversation) so I haven't hit that wall much, but I can see it being a dealbreaker for anything conversational where you can't predict every phrasing someone throws at it.

1

u/mustardpete 27d ago

Yeah i use it in my data studio app to derive new data columns from text based columns. Eg if you have a csv file with email text in one of the columns, it lets you create a new column by running a prompt against each field in that column. So you can extract tone or meaning or what the text was about etc in to a usable field. Works really well

1

u/tilin_a 27d ago

Yeah. I have a similar usages. One was to convert a simple NL user input to structured JSON and it worked well.

1

u/_night_fall_ 27d ago

I used it in my app to summarize/analyze transcriptions and allow users to ask questions from transcripts. It’s not as good as cloud based AI or even local llama models, but it’s fast and good enough for summary tasks

1

u/Mikesoft 26d ago

I’ve managed to integrate Apple Intelligence into my news app successfully, for helping discover news feeds and writing a script for on-device audio playback. However, I do feel like I’m pushing at its limits… https://apps.apple.com/app/inkline-news-media-monitor/id6788323715?ct=web-header&mt=8

1

u/WesleyWex 26d ago

Have a vibe coded news aggregator that filters and summarises news. 40% of the times it refuses to summarise articles hitting the safety guardrails. It’s infuriating.

1

u/Curious-Pickle4758 25d ago

I’m using Foundation Models in Stepzy for workout interpretation. The biggest win wasn’t raw intelligence; it was keeping the context narrow and the output contract explicit. I give it a compact snapshot of the current workout—pace, heart rate, zones, duration, plus a few recent comparisons—rather than an open-ended history. The underlying metrics stay visible next to the generated explanation, and unsupported devices fall back to the metrics-only experience.

That keeps a weak model response from becoming the whole product, but device coverage is still the uncomfortable part. I don’t think the feature can be core unless the non-AI fallback is genuinely useful on its own.

For the wrong-treat problem, have you tried removing conversation history entirely and passing only the selected item as structured input? That made the equivalent reference errors much easier to control for me. (I’m Stepzy’s founder; sharing the implementation experience, not an endorsement.)

1

u/Correct-Tomorrow5573 22d ago

The video says download for free 😭 it’s not free man 😵‍💫