PM_ME_VINTAGE_30S [he/him]

Anarchist, autistic, engineer, and Certified Professional Life-Regretter. I mosty comment bricks of text with footnotes, so don’t be alarmed if you get one.

You posted something really worrying, are you okay?

No, but I’m not at risk of self-harm. I’m just waiting on the good times now.

Alt account of PM_ME_VINTAGE_30S@lemmy.sdf.org. Also if you’re reading this, it means that you can totally get around the limitations for display names and bio length by editing the JSON of your exported profile directly. Lol.

  • 3 Posts
  • 132 Comments
Joined 1 year ago
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Cake day: July 9th, 2023

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  • Amazon Fire Tablet 7in. I bought it literally just to read PDFs, and it was so slow that it was basically unusable. I tried switching out the launcher to something more minimal (Niagara launcher I think), and I figured out how to disable the ads that were all over the place. It helped a bit, but not enough to overcome the hardware and Fire OS. (I think I needed ADB for both of those fixes; I had to put in some real work to unfuck that tablet.) Plus the screen was too small for my pathetic human eyeballs.

    Was it worth $30? At the time, yeah, because I literally couldn’t afford anything else, but I now have an $80 10in generic Android tablet that’s wildly faster.










  • DSP (digital signal processing) is the field of applied mathematics and engineering dedicated to transforming and manipulating digital signals.

    Examples of real digital signals include audio files, image files, video files, and digitized recordings of various physical quantities by computers like the configuration of a robot as it moves in time, measurements of the processes in a factory, the trajectory of a spacecraft — almost anything that can be periodically sampled and take on a finite set of values [1] can be seen as a digital signal.

    DSP includes using tools like the Discrete Fourier Transform (DFT), the Z-transform, wavelet analysis, probability, statistics, and linear algebra to do things such as filter a signal (example: audio equalizer), predict future values (example: weather forecasting), data compression (example: JPEGs), system identification (example: fit a model of the earth to predict seismic activity), control (example: make a DC motor to respond to position commands), and stabilization (example: keep plane from “wanting” to smash into the ground). Particularly, it requires a careful consideration of the effect of sampling a signal (example: if done carelessly, you can make the sampled system unstable [read: explode]), as well as an interpolation process of some kind if you plan on using that signal outside your computer (example: you want to hear an audio signal stored on your computer).

    I got into DSP because I was an audio engineer and musician [2], and I wanted to design my own audio plugins. IMO I think almost everyone would benefit from some knowledge of DSP, but the math is really intense. Personally, I found out late in life that I have a nearly infinite appetite for math, so it’s a good fit for me.

    Here’s a playlist about DSP if you’re interested.

    [1] Actually, a lot of basic DSP books don’t restrict the signal to be in a finite set because it makes the math easier if the signal could be any real number. However, certain structures that would be exactly equivalent in theory are not equivalent on a real computer because ordinary computer arithmetic is approximate.

    [2] I still play music, but not as much as before engineering school.









  • Thanks for replying. It sounds like you basically get two (or some number well below one keys per character) keys and the set of possible characters gets somehow distributed between the two “real” keys, then the keyboard uses a predictive algorithm based on previous input to guess which keys were meant to be pressed.

    IMO I’d be willing to try out an implementation of such an idea so long as I could run the predictive algorithm locally on my phone. I do think that current autocorrect + predicting which keys were pressed would require a lot more training data than just a generic autocorrect to get it working sensibly, and I think it would take a lot longer to converge to the user’s “style” if it ever does.