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There's a different thread if you want to wax about Fluid Glass etc [1], but there's some really interesting new improvements here for Apple Developers in Xcode
by gdubs 1y ago
There's a different thread if you want to wax about Fluid Glass etc [1], but there's some really interesting new improvements here for Apple Developers in Xcode 26.
The new foundation frameworks around generative language model stuff looks very swift-y and nice for Apple developers. And it's local and on device. In the Platforms State of the Union they showed some really interesting sample apps using it to generate different itineraries in a travel app.
The other big thing is vibe-coding coming natively to Xcode through ChatGPT (and other) model integration. Some things that make this look like a nice quality-of-life improvement for Apple developers is the way that it tracks iterative changes with the model so you can rollback easily, and the way it gives context to your codebase. Seems to be a big improvement from the previous, very limited GPT integration with Xcode and the first time Apple Developers have a native version of some of the more popular vibe-coding tools.
Their 'drag a napkin sketch into Xcode and get a functional prototype' is pretty wild for someone who grew up writing [myObject retain] in Objective-C.
Are these completely ground-breaking features? I think it's more what Apple has historically done which is to not be first into a space, but to really nail the UX. At least, that's the promise – we'll have to see how these tools perform!
1: https://news.ycombinator.com/item?id=44226612 https://news.ycombinator.com/item?id=44226612
- givemeethekeys 1y ago> And it's local and on device. Does that explain why you don't have to worry about token usage? The models run locally?
- hbcondo714 1y ago> You don’t have to worry about the exact tokens that Foundation Models operates with, the API nicely abstracts that away for you [1] I have the same question. Their Deep dive into the Foundation Models framework video is nice for seeing code using the new `FoundationModels` library but for a "deep dive", I would like to learn more about tokenization. Hopefully these details are eventually disclosed unless someone else here already knows? [1] https://developer.apple.com/videos/play/wwdc2025/301/?time=124 https://developer.apple.com/videos/play/wwdc2025/301/?time=1...
- refulgentis 1y agoI guess I'd say "mu", from a dev perspective, you shouldn't care about tokens ever - if your inference framework isn't abstracting that for you, your first task would be to patch it to do so. To parent, yes this is for local models, so insomuch worrying about token implies financial cost, yes
- IanCal 1y agoIsh - it always depends how deep in the weeds you need to get. Tokenisation impacts performance, both speed and results, so details can be important.
- refulgentis 1y agoI maintain a llama.cpp wrapper, on everything from web to Android and cannot quite wrap my mind around if you'd have any more info by getting individual token IDs from the API, beyond what you'd get from wall clock time and checking their vocab.
- lqstuart 1y agoI don’t really see a need for token IDs alone, but you absolutely need per-token logprob vectors if you’re trying to do constrained decoding
- refulgentis 1y agoInteresting point, my first reaction was "why do you need logprobs? We use constrained decoding for tool calls and don't need them"...which is actually false! Because we need to throw out those log probs then find the highest log prob of a token meeting the constraints.
- lqstuart 1y agoHaha yeah. I’ve seen you mention the llama cpp wrapper elsewhere, it sounds cool! I’ve worked enough with vLLM and sglang to get angry at xgrammar, which I believe has some common ancestry with the GGML stack (GBNF if I’m not mistaken, which I may be). The constrained decoding part is as simple as you’d expect, just applies a bitmask to the logprobs during the “logit processing” and continuing as normal.
- pzo 1y agoI might we wrong but I guess this will only works on iphone 16 devices and iphone 15 pro - thus drastically limits your user base and you would still have to use online API for most apps. I was hoping they provide free ai api on their private cloud for other devices even if also running small models
- jjcob 1y agoIf you start writing an app now, by the time it's polished enough to release it, the iPhone 16 will already be a year old phone, and there will be plenty potential customers. If your app is worthwhile, and gets popular in a few years, by that time iPhone 16 will be an old phone and a reasonable minimum target. Skate to where the puck is going...
- geodel 1y agoExactly, it can take at least a couple of years to get big/important apps to use iOS, macOS features. By that Iphone 16 would be quite common.
- rs186 1y agoDevelopers could be adding a feature utilizing LLMs to their existing app that already has a large user base. This could be a matter of a few weeks from an idea ti shipping the feature. While competitors use API calls to just "get things done", you are trying to figure out how to serve both iPhone 16 and older users, and potentially Android/web users if your product is also available elsewhere. I don't see how an iPhone 16 only feature helps anyone's product development, especially when the quality still remains to be seen.
- edude03 1y agoBasically this - network effects are huge. People will definitely by hardware if it solves a problem for them - so many people bought blackberries just for BBM.
- dangus 1y agoDrastically limits your user base for like 3 years. Phones still get replaced often, and the people who don’t replace them are the type of people who won’t spend a lot of money on your app.
- charliebwrites 1y agoIf the new foundation models are on device, does that mean they’re limited to information they were trained on up to that point? Or do have the ability to reach out to the internet for up to the moment information?
- dwaite 1y agoIn addition to context you provide, the API lets you programmatically declare tools
- rubicon33 1y agoThe direction the software engineering is going in with this whole "vibe coding" thing is so depressing to me. I went into this industry because I grew up fascinated by computers. When I learned how to code, it was about learning how to control these incredible machines. The joy of figuring something out by experimenting is quickly being replaced by just slamming it into some "generative" tool. I have no idea where things go from here but hopefully there will still be a world where the craft of hand writing code is still valued. I for one will resist the "vibe coding" train for as long as I possibly can.
- tansan 1y agoThis sounds like a boomer trying to resist using Google in favor of encyclopedias. Vibe coding can be whatever you want to make of it. If you want to be prescriptive about your instructions and use it as a glorified autocomplete, then do it. You can also go at it from a high-level point of view. Either way, you still need to code review the AI code as if it was a PR.
- hooverd 1y agoYou can take an augmented approach, a sort of capability fusion, or you can spam regenerate until it works.
- fragmede 1y agoNo, that's what's separates the vibecoding from the glorified autocomplete. as originally defined, vibe coding doesn't include the final code review of the generated code, just a quick spot check, and then moving on to the next prompt.
- tansan 1y agoThe definition is broad and can include testing. Refining requires you to review the code for iterations. > Vibe coding (or vibecoding) is an approach to producing software by using artificial intelligence (AI), where a person describes a problem in a few natural language sentences as a prompt to a large language model (LLM) tuned for coding. The LLM generates software based on the description, shifting the programmer's role from manual coding to guiding, testing, and refining the AI-generated source code.[1][2][3] https://en.wikipedia.org/wiki/Vibe_coding https://en.wikipedia.org/wiki/Vibe_coding