I think this sort of guessing is intended to be combined with additional data the marketers already have, like purchase history, location, social media posts, and so on. Basically the VLM output is treated as another data point rather than the sole source, or the existing data could be fed into the model's prompt before reading the image.

Currently services like Github Actions painfully and inefficiently rack thousands of Mac Minis and run 2 VMs on each to stay within the limits. They probably wouldn't mind paying a fee to run more VMs on Mac Studios instead.

If you have an AVR with composite or s-video in and HDMI out that could also work in place of the converter. In either case you'll downscale the footage back to 640x480 before encoding.

You have to monitor the process start to finish if the tapes are bad, there's nothing around that.

For MiniDV and Digital8 you should straight up get a lossless copy using a cheap Firewire card.

This would incur an added cost in licensing and possibly hardware but this would also be the cleanest way to do it. Also on the security side this would be safer than escalating a legacy ActiveX app on the secure client.

Having multiple instances of IE running remotely on Windows Server and then served using Citrix or something similar should work as well if you don't need full VM isolation between clients, and I've seen this used in real companies with legacy apps that can't run on the standard employee machines. Again though this has a licensing cost.

You can probably guess what happened, and that was that no one remembered their passwords and people wrote it down on their pads or sticky notes instead.

Also I just thought of this but it should be possible to design a chip where the second processor runs a couple cycles behind the first one, with all the inputs and outputs stashed in fifos. This would basically make any power glitches affect the two CPUs differently and any disrepancies would be easily detected.

So, the only way to manipulate it is to actually screw with the internals of the CPU itself by "glitching", meaning tampering with the power supply to the chip at exactly the right moment to corrupt the state of the internal electronics. Glitching a processor has semi-random effects and you don't control what happens exactly, but sometimes you can get lucky and the CPU will skip instructions. By creating a device that reboots the machine over and over again, glitching each time, you can wait until one of those attempts gets lucky and makes a tiny mistake in the execution process.

Considering that the PSP is a small ARM processor that presumably takes up little die space, would it make sense for it to them employ TMR with three units in lockstep to detect these glitches? I really doubt that power supply tampering would cause the exact same effect in all three processors (especially if there are differences in their power circuitry to make this harder) and any disrepancies would be caught by the system.

Seems some people have done this already with a PC app: https://timeandtidewatches.com/how-to-make-your-own-timegrap...

This wasn't something I really dug into in great detail but I remember my surprise back then at how all those merged models and those "expanded" models like Goliath still generated coherent output. IMO those were more community models made by small creators for entertainment rather than work, and only really of interest to the local LLM groups on Reddit, 4chan, and Discord. People might briefly discuss it on the board and say "that's cool" but papers aren't being written and it's less likely for academics or corpo researchers to notice it.

That being said I wonder if it's possible to combine the layers of completely different models like say a Llama and a Qwen and still get it to work.

Even with math probes, I hit unexpected problems. LLMs fail arithmetic in weird ways. They don’t get the answer wrong so much as get it almost right but forget to write the last digit, as if it got bored mid-number. Or they transpose two digits in the middle. Or they output the correct number with a trailing character that breaks the parser.

Would using grammar parsing help here by forcing the LLM to only output the expected tokens (i.e. numbers)? Or maybe on the scoring side you could look at the actual probabilities per token to see how far the correct digit is.

Now there's one thing with AR/VR that might need this kind of infrastructure though and that's basically AI driven games or Holodeck like stuff. Basically have the frames be generated rather than modeled and rendered traditionally.

Their apps also regularly nag you to allow access to stuff like contacts and the photo gallery when you've already said no the first time.

And for a personal anecdote: I was recently helping a senior setup Whatsapp Desktop on her Windows computer. It could chat fine but refused to join calls, displaying an error that said there was no microphone connected. I mean, there is a mic connected and it could record voice notes fine. Turns out that error actually meant that there was no webcam connected, and a webcam is required to join calls. I think it's the same way in the mobile app where you need to give it the camera permission to join a video call even if you turn the video off. Meanwhile Zoom, Teams, Webex, and others allow you to join any call without a mic or camera.

As she didn't have a webcam I first tried the OBS virtual camera but Whatsapp refused to recognize that despite all other apps working fine with it. Somehow Droidcam with no phone connected worked fine, displaying a black screen in the virtual camera feed, and that got Whatsapp to join the call successfully. Absolutely ridiculous and it's clear to me how desperately they want that camera access and that sweet data.

With improved cellular and possibly future satellite connectivity I feel that this would also be more of a thin client than a local first device, since companies want that recurring cloud subscription revenue over a single lump sum.

I love my ZFS server as it handles all that transparently but that's really not an option for everyone.

Maybe this will push more places towards self-hosting?