I like the thing, but it misses what I enjoyed teaching about these most: phase. Many people know white noise is nominally 'all frequencies at the same intenisty', yet those taught Fourier mathematics are also taught that the same recipe makes a pulse. The difference is all in the phase information, and why I maintain to this day the Nyquist-Shannon sampling theorem, as typically applied, is incorrect.
1. Fourier Transform an Image 2. Set all magnitues the the spectrum to 1.0, but do not change the phase 3. Inverse Transform and look at the result 4. Now try the same, but this time keep the maginutes unchanged but change all phases to 0°
Spoiler: When changing all amplitudes the image is still regocognizable, when changing all phases, it is not. See example: [1]
But in what sense are you saying the Nyquist-Shannon theorem is incorrect (when applied)? It only says something about the most general case of perfectly reconstructing a signal.
For getting an playful and intuitive understanding of time/frequency transformations my fourier-cube visualization might be useful [2]
[1]: https://static.laszlokorte.de/phase.png [2]: https://static.laszlokorte.de/frft-cube/