The Foundation models keep a surprisingly decent set of data available for fast filtering by a local LLM.
I’ve found a slower, second pass, is needed by Qwen to clean it up. (I am running on the new Mac Mini m5 pro 64gb.)
I’ve run my own tests using a date range, a short description of a red cedar planter box build I did last year, and a set of known correct photos.
There is some cool stuff you can do without sending your family’s pics up to any of the frontier model providers.
I switched to Gemma 4 instead.
Nothing about iCloud Photos is private.
I recently installed App Tamer which is helping a bit, but it doesn't allow me to tame everything. I just checked now and duetexpertd was trying to use nearly two full cores. I have all iCloud integrations turned off, I do not use iCloud at all, and I do not have an iPhone, so I am flabbergasted that this iCloud desktop-phone syncing process runs at such high core usage in the background with extreme regularity. Proof: https://imgur.com/VkBh9bi
Unfortunately, this does nothing for services such as WindowServer which now often takes up 50-100% of a core just on its own.
I have reinstalled and upgraded to the latest version. This MacBook is only a few months old.
While typing this message and literally just having open Codex with one running task that is not doing anything substantial, and Firefox, with no high CPU usage from any tab, my laptop got so hot that I had to use another installed program, Stats, to manually bump up the fan since macOS also has absolutely garbage native system fan controls.
I have fibromyalgia and seem to be sensitive to hot keys; my Thinkpad regularly getting to hot to touch with even light usage is EXACTLY why I went and bought a MacBook one morning in '23. One morning I touched my keyboard and realized I was tired of singeing the nerves on my fingertips and having them hurt for hours. So now in '26, finding myself in the same exact position but with a much more expensive computer, I feel like I am living in a corporate hellscape nightmare.
One thing I have noticed is all of my cores can sit idle except a single one and that will still heat up the machine a lot. If the hardware is designed as such, the software MUST be designed to work around this and not blast a core for 10 minutes straight every time one of a dozen or two misbehaving system processes wants to complete a background task. If the software is not designed for this, the hardware is incomplete and faulty. This would be true if it was shipped as firmware; software doesn't get a free pass here.
But now Pages and Numbers recommend document titles for me, so.... worth it?
I do feel like my complaints go to the void, and honestly I have seen the kind of responses people get on the Apple support forums and it gives me the same ick that Microsoft's does. And lately I'm just going to get someone's canned, patronizing LLM-generated resume-building response. My only hope out of this mess is Gravity Linux catching up and adding full SEP and thunderbolt support. Once that happens, I'm gone.
I do wonder about the prevalence of my experience vs yours. I can quickly find hundreds of threads across various platforms which confirm my experiences. From what I have been able to ascertain, my experience is increasingly common if not more common than yours. Hard to tell.
They make and sell the hardware that will actually make AI something everyone can used without needing to rely on OpenAI/Anthropic et al.
Apple has never had a moat, anyone else could have built this sort of hardware but they haven't.
So they don't care which model wins, because it will always move out to the edge of the network, and they own a large chunk of that edge.
Hint: money is not the only reason.
Jeez.
I think both points are very incorrect. Apple has a massive moat. And Apple is making hardware (specifically, CPUs) that are two years ahead.
Apples largest competitive advantage has always been and always will be - trust combined with epic marketing to make the vast majority of people aware of it.
Trust of quality, privacy w.r.t data etc.
I’m sorry but I’ve seen so many bad takes re. Apple over the years. Many of you think you are so smart on topics outside of your domain - but you’re not.
They spent decades learning to eliminate a 1000 headaches for themselves, and many for their customers, by accumulating vertical integration:
The co-design of their own comms, processors, devices and OS variants. Their own integrated supply chain and sales channels.
The better the results, the more invisible or clock-like their delivery.
Very easy to overlook. But nobody can match them on their terms.
(Nintendo also applied a unique set of internal advantages to generations of successful devices: vertically integrating humble components, highly scoped software, a strong theory of fun, and deep branding with novel polished interfaces.)
Lmao
> Many of you think you are so smart on topics outside of your domain
Turn the mirror around
Nvidia's own model does not have to beat OpenAI or any SOTA model. Nvidia just need to build up the in house expert and knowledge to make a better chip since hyperscalers and none hyperscaler like Meta is building their own AI chip now.
Apple can chose not to train their own model to learn how to sell a better shovel.
just wish their containerization / linux compat wasn't so nerfed. Just moved my homelab's inference engine over to a GB10 from a Mac MTP setup to have first class k8s support, as well as open up access to the broader CUDA ecosystem (cuOpt etc.).
And usually, most of a company's revenue comes from the heaviest spenders.
So figure there are some out there who will by tens of these because they have some "idea to change the world". (Whether true or not.) Some startups, etc.
I'd wager, across planet earth right now, it should be trivial to push 100,000 units per year. Probably closer to 500,000 units per year. So that's an easy $10 billion a year.
$2-10 billion is meaningful for any company. Even one as large is Apple.
Especially if it's a first step towards a stronger foothold in the AI inference space.
They can then expand down market to grow the pie further.
For example, with truly dedicated inference boxes.
And given the demand for $7,000 Sparks and sold out $5,000 GPUs, the market for these $20,000 machines is likely much higher than my 100,000 unit lower bound. (Earth is a big place. It doesn't take much excitement to generate massive sales in a space with currently insatiable demand.)
Muse Spark Open would require more than 1.6TB of RAM in its native precision (BF16) for the model parameters alone. In a 4-bit quantized (NVFP4) format, the model memory footprint is lowered to about 493GB.In any case, I'd much rather run GLM 5.3 Flash on that sort of hardware. Better fit for the performance profile, and will probably beat Muse Spark Open in most uses.
I think the iOS App Store is going to be in steep decline in a few years. People will generally have a few apps: ChatGPT, Instagram, messaging, and something like a Photos/Camera app.
Everything else will be done in your AI app.
I've been hooking up more and more data and plugins into ChatGPT/Claude. I only want to use it. I don't want to use other apps if I don't have to. For example, research, buy, plan. I prefer not to use other apps.
I think all it takes is for OpenAI to make a phone from the ground up for this and Apple is going to have serious competition.
All of their competition has real inference and training stacks. Apple ships an iGPU and a smartphone NPU, they're reliant on Nvidia and Google hardware to deliver Apple Intelligence.
They aren’t
Apple is a high net worth consumer technology integrator that focuses on highest coherence user experience. They pick the most efficient stable technology, integrate it and sell the package
Apple only needs to lead in precisely that they don’t need to lead in literally anything else
in fact if they tried to lead in those other things they would give up their integration capacity
The vertical integration argument relies on Apple having a good architecture for AI. First it was the ANE, which everyone (mistakenly) thought would differentiate Apple Silicon from other NPU platforms. Then everyone parroted the "unified memory" talking point without checking to see if professionals actually used it. Now people are pivoting to say the next-gen Apple GPUs will finally blow the last-gen GPGPUs out of the water. Which is good, because there are 4-year-old GPUs that beat M5 Ultra in compute efficiency.
I know that "Apple is behind" is a tough pill to swallow, but they literally have no analog to CUDA, CDNA or even TPU technology. Nvidia SOCs like Tegra prove that Apple could absolutely bring GPGPU tech to low-power hardware like the iPad and iOS without giving up any real features. Apple just refuses to invest in it.
As open weight models continue to match frontier models, the race to $0 will continue to benefit supporters of running open weight models locally.