Notice that at any given point in time we have a certain amplitude, plus there are infinite points of time. This signal can be captured via a microphone and stored in something like a gramophone record. But we want to store them digitally right? We no longer have gramophones. Or in other words, we need this signal to be discrete rather than continues in order for us to store it in a computer. So we have to sample this signal at a frequency. This raises a few questions:
- At what frequency we should sample our continues signal (sample rate)
- How many bits we should use for storing each sample, or rather what is the amplitude range we are willing to cover (bit depth)
p.s: Bit-rate is basically sample rate multiplied by bit depth

For the first question, the answer lies in Nyquist–Shannon sampling theorem. Which states that to reconstruct a signal via samples, we have to at least sample at double the frequency of the original signal. This makes a lot of sense if we just look at visualization:

You can see if we sample double the frequency of our audio signal we have a chance for a good reconstruction later on. Note that this is the most optimistic view. For example in the pictures above we started sampling with a little bit delay to capture the peak of our signal. In reality this is not feasible. If we started sampling at 0s, we would've ended up with a straight line, even if we sampled double the frequency.
So its fair to say that most signals will require even more than double for a good reconstruction

I will continue writing on signals in the future. More so on signal-compression

Continuing the signal processing thread, we can look at Fourier transforms. In signal compression we like to transform the signal from time-domain to frequency-domain. For example using DCT(A fourier-related transform) we can transform a periodic signal into sum of different cosine functions; therefore, representing it in frequency-domain. Or in other words, DCT simply yields n cosine coefficients for n signal samples. This usually results in a sparse representation of the signal. For example:

Signal in time-domain

Same signal in frequency-domain

Notice that there aren't many points in frequency-domain that have a notable value. Many of them are near 0. We could simply consider them as 0s and achieve a sparse representation of the original signal. Knowing the data is sparse we can compress it using techniques like run-length encoding, Or in case of DCT, we could simply slice the data since the 0s are usually towards the end (e.g throwing off the last 45% of the coefficients).
Then while decompressing we simply pass the sparse data to the Inverse of the transform we used, in this case it would be DCT inverse. It worths highlighting that this will be a lossy compression since we considered near-0 coefficients to be absolute 0s

A neat way of processing C arguments.

for (argc--, argv++; argc > 0; argc--, argv++)

I found this while going through ncompress code
#C

Moodist | Compose your own ambient sounds to focus
You can share your presets with others or even self-host the project since it's open-source.
https://moodist.mvze.net

Github repository

https://posting.sh/ | In terminal API client
It's keyboard driven and supports vim keys as well. Not the smoothest experience but still better than constantly switching between coding and fiddling with postman

Drizzle sponsorship plans

"A little copying is better than a little dependency."

- Rob Pike

This is such a nice take. We mostly tend to be so strict about the DRY principle. Duplication is not always evil, nor it is always the root of evil. In fact a little redundancy in exchange for dependency is totally worth it.
Let's say you have multiple components that have their own build processes and operate separately with loose couplings between them. And now you can't stand that you have implemented a logic on both component A and component B. So you decide that there should be a dependency between these components.
You are slowly moving towards tight coupling, and while there are ways to avoid it (introducing a third component ,...), they tend to just consume more of your time and effort so you'd probably just let them be.
This will not only make changes have their effects distributed throughout your entire system, but will impact the build-process/run-time of your components.

#DRY #components

More on componenets here:

Path of exile, Running on nixos + i3 + lightdm

I can now confidently say gaming has been made possible in linux. Thanks to proton you can now play most of your favorite AAA games out of the box (or with minimal tweaks).
A common issue for me was screen-tearing which was solved by using a compositor called picom and enabling its vsync feature.

If you are a dual-boot user like me and can't let go of your windows, may this be a sign

Cognitive load is what matters

https://minds.md/zakirullin/cognitive