Earlier quoted context omitted.
In spite of their deeper pockets, massive datacenters, colosal amounts of user data, and hundreds of thousands of top developers, even Amazon, Meta, Microsoft, and Google are well behind. I think Evans is completely wrong. There are only 2 truly frontier models. (at least for now). And Anthropic seems to be leaving OpenAI behind so there might be only 1 in the near future. (which is scary/dangerous)
> I think Evans is completely wrong. There are only 2 truly frontier models. (at least for now). And Anthropic seems to be leaving OpenAI behind so there might be only 1 in the near future. (which is scary/dangerous) Truly fascinating ecosystem and community in general, as experiences differ so wildly. Anthropic's models seems far behind OpenAI to me, especially when you get into "Pro" territory, and there doesn't se…
Apple Foundation Models
111–120 of 244 posts
Re: Apple Foundation Models
#112This is Apple commoditizing LLMs while keeping control of the UX. They are a hardware company and will keep selling the best machine for AI use. Well done.
Re: Apple Foundation Models
#113What I'm curious about is whether this is actually on-device. Apple's framework caps local models around 3B params last I looked, and Claude is way bigger than that. So either there's some hybrid setup I haven't seen documented, or this is mostly a Claude SDK in FM clothing. Anyone tried it on a plane?
so Claude via FM dies offline while Apple's on-device SystemLanguageModel (the ~3B one) keeps working. It isn't a hybrid really: the framework just has both implement the same LanguageModelSession protocol so "local 3B" and "remote frontier model" become a one-argument swap.
IMHO what's worth internalising is that the two share an API but nothing else: the on-device path runs on Apple's Neural Engine and costs battery (you can watch ANE power ramp while it works) while the cloud path costs API credits/tokens and does zero local compute. Same code, opposite cost model.
Re: Apple Foundation Models
#114I think this is just Apple planning for their on-device models getting better, which makes sense given they have access to Gemini now. If developers use this for all their code calling an external LLM, then as Apple's model becomes more capable and covers more use cases it'll be easy to switch to it at individual call sites. That'll give apps better UX and save developers money on a bill that Apple doesn't get a cut…
How does using Gemini lead to better on-device models?
Re: Apple Foundation Models
#115This is Apple commoditizing LLMs while keeping control of the UX. They are a hardware company and will keep selling the best machine for AI use. Well done.
Does “the best machine for AI use” apply here considering these models are still server-side?
But we can imagine that the balance of what's on-device vs what's remote will move continuously towards the former as time, improved HW and improved local models keep progressing
Re: Apple Foundation Models
#116Is this Apple encouraging developers to go through their api abstraction layer to use LLMs so that when they launch their own (which I think we’ve heard they’ve been spending lots of money on training and might be somehow involved with Siri or current Apple AI?) that they can easily help devs make a seamless transition? Or is it just a developer nicety or something else?
Apple has some clever mechanics to protect user data. I had to work with App tracking stuff lately and their approach to keeping user details private with anonymized cohorts (SKAN, Differential Privacy) before reporting tracking events to third party platforms was surprisingly well thought out. There is value in having them in your loop if you care about privacy.
Re: Apple Foundation Models
#117This is Apple commoditizing LLMs while keeping control of the UX. They are a hardware company and will keep selling the best machine for AI use. Well done.
Re: Apple Foundation Models
#118Re: Apple Foundation Models
#119Re: Apple Foundation Models
#120Ahh I was hoping for the opposite: all of the existing features of Claude Code but somehow running locally on my laptop's neural engine. A pipe dream on an M2 with 8 GB of RAM, but I had a flicker of hope there.