[1] https://tech-insider.org/ca/steam-deck-price-increase-2026/
Your letter will help feed the boiler that heats the building and is much appreciated.
What makes you think that? There's a lot of data centers that sell you access to colocated Mac Mini's, they have added FileVault unlock via SSH in the boot process which also makes things easier. There's not that many reasons to run a Mac in the cloud unless you have some very specific Mac related workload.
https://wccftech.com/apples-private-cloud-compute-server-m5-...
There’s articles about them, Apple uses them internally for AI services. https://forums.macrumors.com/threads/photos-of-apples-own-ne...
[0]https://pmarchive.com/guide_to_startups_part4.html: "In a great market—a market with lots of real potential customers—the market pulls product out of the startup... The product doesn’t need to be great; it just has to basically work."
My vibes were that Apple wound down the “actual work” side of their operations (including machines like Xserve), because Ives couldn’t handle the unsexiness and unpredictability of business requirements in hardware.
He was self-indulgent and only wanted to work on things that “vibed” with him, rather than what the customers needed. It’s easy to be creative when you get to do what you want to do, it’s hard when you have hard constraints.
I realize I’m somewhat limited (16GB RTX 9070), but still, it seems really far off from the kind of experience even a basic $20/month subscription gets me.
Any tips anyone might have are appreciated! I’d love to be local first and would be willing to buy hardware to get there.
The $20/month subs are much stronger than the local models you can run, even with how far local models have advanced lately.
The appeal of local models is that the data never leaves your network so you can feel safer putting sensitive content into it. It also feels “free” to use when you’ve already paid for the hardware.
But it doesn’t perform better and if you do the math you’re probably not saving money either. It’s helpful for things that you can’t or don’t want to outsource to a 3rd party.
IDK if that might be a concern for Apple or their AI partners.
It's a niche market but it's a market that overlaps heavily with professionals in the AI space and lead developers, so it's a market that gets them customers in those roles.
If I were running Apple I'd call the RAM price bubble for what it is and temporarily eat some margin to offer machines with more RAM than competitors, especially these models that are great for edge AI, and capture market share.
It's true, most people don't run models, but being the default platform for running open weights seems like it has plenty of advantages right now. Just like sales benefited from developers defaulting to MacOS for most open source languages like Ruby, Go, Rust, and TypeScript.
32GB is not enough RAM. I don't even own a device with less than 36GB at this point, and that device I only have because my employer is being cheap. 64GB is a reasonable starting point for running local LLMs + normal tasks. 128GB let's you really run most smaller models like Qwen 27B and 35BA3B with good context. Even Qwen3.8-Flash-Next runs in 128GB with a 4-bit quant.
32GB would be limited to running models like Gemma4 12B and smaller dense Qwen versions like 9B unless you were using very small quants which damages quality of response.
64GB+ or dedicated 48GB (2x24 on GPUs) is IMHO absolute minimum.
I hope Apple can take all this cash and do some stability releases like they used to do, bugs around things like Family Sharing, the painful "update" to Settings App, etc could all use a lot of love.
Is that what others read ?
Here's a leaked / rumor image of Apple servers themselves.
https://www.macrumors.com/2026/08/26/leaked-images-of-apple-...
https://www.macrumors.com/2026/08/26/leaked-images-of-apple-...
https://www.reuters.com/business/apple-begins-shipping-ai-se...
> [still claims there is no reason]
https://forums.macrumors.com/threads/photos-of-apples-own-ne...
So yes, they are genuinely very useful, but they are not yet a full replacement unless you have more powerful hardware and or don't need more intelligent ai.
It's still frustrating as hell to come down in the morning, having given it a list of tasks to do overnight, with tests to pass before they're "done" and find that it worked for about 20 minutes after I went to bed, and decided that it would stop at "3am" (it wasn't) and "not do significant work this at this late hour". Like WTF ? You're an LLM. You don't sleep.
Bloody training data full of humans demanding sleep. I tells ya...
I think that's Anthropic trying to get you to not extract as much value out of that subsidized subscription as possible.
Local llms don't suffer from cloud availability issues. Anyone that used Google models know that sometimes they just don't have capacity whatsoever, at least that was the state of things some months back when I used them. Just bear in mind if needed, cloud providers will prioritise API and corporate customers over subscriptions if availability degrades more.
Also they don't have the same guardrails as the other models, so for hacking, reverse engineering and black coding (piracy etc...) these local models might be the only options.
But currently it's really hard to beat anything offered by the cloud companies. And the cost and complexity of setting it all up, just to barely (if at all) touch on Opus-level intelligence makes it seem like we're not quite there for the common man (enthusiasts are a different story.)
I am very excited for open source local models, and we're nearly there, but it's still too complex and expensive to be my daily driver (yet).
What you'll learn pretty quickly from said engineering is that there's a lot more to a good LLM than just the weights themselves. You need a good search provider (also self-hostable, but sounds easier than it really is). You need (well, it's debatable) a memory system. You need a good system for up-to-date library references like a Context7 (also self-hostable but the options are surprisingly not that good). You need a good set of specialized subagents that can perform various tasks well -- for the sake of "doing things well" but also managing context efficiently.
When you've got all that, local models can be _extremely_ useful. But there's one other important thing and that's decent hardware, unfortunately. A lot of people try out local models using small consumer GPUs or Macs and are rightfully unimpressed with the performance. And if the performance doesn't get them, usually they have expectations that they'll perform at Claude levels out of the box. Getting in that neighborhood, like I said, definitely requires some work.
Though keep in mind not being beholden to shenanigans from said cloud companies (and interference from government entities!) is definitely worth something intangible.
ceejayoz•58m ago