It seems to me they've had enough foresight to prevent their openclaw credentials from being stolen via copy-fail

openclaw fills the gap left by Siri and was built with tight integration with Apple ecosystem.

this how i understand the hype for something like openclaw vs the capabilities provided by zapier or n8n for years now.

i would say a majority of users are tech and non tech roles, who (1) have an iPhone with all their contacts and data on iCloud (2) have a macbook at work and use macOS daily (3) use ChatGPT or Claude daily and trust it with their personal data (4) aren't familiar with Linux or a VPS and don't trust themselves with setting it up through the terminal (5) feel more at ease with a "a second macOS that i can debug visually on my monitor at home" rather than a remote linux VPS

you could still rent a mac mini but cloud providers will ask you $119 a month for a Mac mini M4 with 16GB of RAM. $599 is unbelievably cheap for a second computer with which you can do anything you can do on your usual Macbook

The AI techniques that fit on the mac mini and are accessible due to Apple's hci advantage are worth it to people who pay to solve their niche problem patterns.

Ai can even run in raspberry pi

I think the main reason people are buying mac minis is because of how much user friendly it is.

You can expect a software engineer or a devops guy to run stuff in a VPS but a slightly less technical person won't ever go there. In contrast, people are familiar with macOS and that's way less scary to setup.

The added benefit of the mac mini is that it can also double down as a second device one could use for something else too

They probably hope to run a LLM locally on the Mac mini but they don't realize that decent models require much more computational power

I’ll let you know when I get mine. Ordered April 1 and not expected until August.

The premise that 'barely any decent size models can run on it' misses the biggest advantage of Apple Silicon: Unified Memory. Where else can you get a machine with 64GB or 128GB of VRAM for running quantized models at this price point? Buying the equivalent VRAM in Nvidia GPUs (like multiple RTX 3090s/4090s) would cost thousands of dollars, draw massive power, and sound like a jet engine. The Mac Mini is dead silent, sips power, and lets you run 70B+ parameter models locally via llama.cpp. It's currently the undisputed king of VRAM-per-dollar for local inference.