Morning Spam

[Undiscussed Groomed for Male Slavery, One Victim of Many]

low-end van eck phreaking via transformer model
to make success more likely to start with, proposing one of two
prefabbed images on the screen: either top half white and bottom half
black, or top half black and bottom half white. hopefully this roughly
maximizes the low-frequency information associated with the screen.

1. randomly display one of the two prefabbed images
2. record radio signal, maybe via audio cable antenna if no radio.
note sample rate and precise starting time.
3. at about the same time, record video from desktop using something
like ffmpeg -f x11grab . also note precise starting time and sample
rate.
4? restart the feeds after some set interval like 30 seconds if it
makes timestamping and data slicing easier
5. for each timepoint, use as much of the recording as possible as the
input and classify which image is displayed

the lowest component might be around 120 Hz so there's a good chance
this can be made to work and would simply need a design fix if it
doesn't. once demonstrated to work, the process could be improved to
classify smaller portions of the image.

honestly it might be more fun to sum periods ! i still have yet to
train and use a transformer model for anything.
i guess with van eck phreaking there were basically two parts that
ideally fed back with each other until the parameters were
sufficiently delineated: identifying the pixel clock, and amplifying
the signal. amplifying the signal is significantly aided if its
timepoints can be precisely predicting; meanwhile identify the pixel
clock and predicting the timepoints is significantly aided if the
signal is clear.