A hit piece on Nazis?

Well you should subtract out the kids. I’m not going to get irritated about someone under the age of 18 not voting. That’s why it should be eligible voters, not Americans.

Drive through seems like a great proving ground. Record every drive through customer / cashier interaction. Match each recording up with the transaction entered into the register. Train a model by having the model “listen” to the recording to predict what the order should look like, then match it to the items on the transaction receipt.

Then, phase 1 of implementation is to use the model in real time by listening to the live conversation at the drive through, predicting what it thinks the order should be, then prompting the cashier to double-check the order to see if the human made a mistake entering the order if the prediction doesn’t match.

Phase 2 is human-supervised, where the order taking system interacts directly with the customer to take the order, the human checks the result, and is able to step in / take over if there’s a mistake or a special case the order system can’t handle.

Phase 3 is “fuck your entry level employment” and no human is monitoring the system.

All 3 phases seem completely doable to me at this point, depending on how much backlash MCD is willing to deal with.

https://www.store-3d-blurayrental.com/

They outline it pretty well here:

https://annas-blog.org/how-to-run-a-shadow-library.html

One example:

https://learn.microsoft.com/en-us/windows/security/threat-protection/security-policy-settings/maximum-tolerance-for-computer-clock-synchronization

Another use case: when you look at activities that flow across multiple devices and you’re correlating the sequence of events, having every device set to the exact same, ideally correct time makes correlation of events less confusing.

I run wireguard VPN, qbitorrent, most of the *arr apps, and Jellyfin all in containers on a headless Raspberry Pi 4, with storage backed by a NAS. It works surprisingly well, I just ensure that I never need to do transcoding.

It’s a really small dollar investment to try it to see if it meets your needs.

I’ve recoded a bunch of x264 to AV1 and routinely gotten file sizes that are 10-15% of the original file size (a little more than 1/10th the original size)

What I’ve found is that source content often has a lot of key frames. By dropping key frames down to one per 300 or one per 150 frames (one per 10 or 5 seconds for 30fps) and at scene changes, you can save a LOT of space with no loss of quality. You do give up the ability to skip to an arbitrary point in the content, however. You may have to wait a few seconds for rendering to display if you scroll to an arbitrary point in the content.

If you’re just watching the content straight through, no issues. I set CRF to achieve 96 VMAF and I can’t tell any difference in quality between the content with that setup.

I had one corpus of content that I reduced from 1.3 TB down to 250 GB after conversion.

Unfortunately, only the most recent TVs have AV1 playback built in, and the current Fire sticks, Chromecast don’t have support for playback from a LAN source. I’m hoping the next crop of Chromecast and similar devices get full support, I’m assuming it’s just a matter of time until AV1 decoding is included in every hardware decoder since it’s royalyy-free.

Another option for very cheap VM, storage, bandwidth: Oracle Free Forever

https://www.oracle.com/cloud/free/

Enom