lighthouse_pm VS ios

The motors for the 2.0 Basestations can be loud, but it depends on where you put them in your room but also how much stuff that reduces echoing (like couches, beds or carpets). In my case, they're very loud when I'm laying in bed. But I use an application to put them into sleep mode (sleep mode turns off the motors and standby mode turns off the lasers but keeps the motor spinning). I use an application called Lighthouse PM, it's an open source project that's on the android store, but you'll need to side load it if you use an iPhone.

Lighthouse PM app for Android https://github.com/jeroen1602/lighthouse_pm

https://github.com/jeroen1602/lighthouse_pm#ios

In the vive base station file you can see that the command consists of 20 bytes.

Although I don't need to dial a VPN before accessing my NAS outside, but expose NextCloud on public network is not safe. There are already some people request NextCloud add support for client-side certificate authentication on their app, I am also very interested in this feature, it will improve the security of public network access.

I recently setup my first unraid homelab and ended up going with a cloudflare tunnel + warp setup for accessing docker/VMs like Nextcloud remotely. Everything seems to be working well through the browser, but the nextcloud desktop and mobile apps give me "unknown" errors when I try to connect. I've seen a few other posts and issues submitted on github in regards to this (https://github.com/nextcloud/ios/issues/1565), but I haven't seen anyone mention a work around besides creating another connection workflow (ie. separate VPN). Any suggestions?

With multiple android phones come and go in the past 8 years or so, the background upload seems to stand its ground.

The real problem here is iOS and its lack of proper background tasks. See: https://github.com/nextcloud/ios/issues/215 -- they tried every possible way to persuade iOS into running background sync, but still hit and miss.

I have to request access to my wife's iPhone and manually trigger some :)

One small suggestion here -- PhotoPrism went with `tensorflow.js` to load up classification models, and I recommend a "real" TF or PyTorch installation to properly leverage the computation resources. The difference is huge even running cpu-only because it's wasm vs. proper BLAS library.

I worked on a nodejs binding for native ONNX runtime (not publicly) so that's also a possible way out.