count_duckula

joined 1 year ago
[–] [email protected] 2 points 1 month ago (1 children)

I've been using KOReader on my phone now, ever since my Kindle one day decided to be unrecognisable on my computer. Couldn't find a solution to fix it so it became a glorified paperweight.

The screen real estate is slightly degraded, but fuck if I give Amazon any more of my money. Besides, I get to store epubs as epubs instead of converting to that god awful mobi format.

[–] [email protected] 2 points 5 months ago

I switched to Pipepipe from Newpipe because I wasn't sure Newpipe was being maintained. Pipepipe has SponsorBlock.

[–] [email protected] 16 points 7 months ago (13 children)

I have a similar setup and decided to install it on my degoogled phone because I definitely wanted to use a VPN to connect to Whatsapp and my other phone is an older Android without the global VPN option.

I have it completely isolated from my main account by using Shelter from F-droid, installing Aurora store in that sandbox and then installing Whatsapp from Aurora into the work profile created by Shelter.

This way, my main contacts and media are not accessed by Whatsapp. It does its own separate thing and I have no other apps interacting with it.

[–] [email protected] 3 points 9 months ago* (last edited 9 months ago)

The reason they are blackboxes is because they are function approximators with billions of parameters. Theory has not caught up with practical results. This is why you tune hyperparameters (learning rate, number of layers, number of neurons ina layer, etc.) and have multiple iterations of training to get an approximation of the distribution of the inputs. Training is also sensitive to the order of inputs to the network. A network trained on the same training set but in a different order might converge to an entirely different function. This is why you train on the same inputs in random order over multiple episodes to hopefully average out such variations. They are blackboxes simply because you can't yet prove theoretically the function it has approximated or converged to given the input.