Agreed. 12factor guidelines is one of the few really good one I still try to adhere to, even though I full agree that microservices are overhyped. I've seen so many devs to claim how they solve all the problems, without seeing any of the issues they cause (and there are many). Definitely not a free lunch.

I'm a simple guy. I see anything related to Fourier series/transform, me like. To me, it's like solution to at least 16% of all the problems I have. My only gripe is that I can't thank Fourier personally for the genius he had.

> To me, it's like solution to at least 16% of all the problems I have.

What are some examples of problems you've solved with Fourier series/transforms?

There's a good list in Wikipedia for FFT (though it's a bit more specific than just FT, but still application of it):

- fast large-integer and polynomial multiplication, - efficient matrix–vector multiplication for Toeplitz,- circulant and other structured matrices, - filtering algorithms (see overlap–add and overlap–save methods), - fast algorithms for discrete cosine or sine transforms (e.g. fast DCT used for JPEG and MPEG/MP3 encoding and decoding), - fast Chebyshev approximation, - solving difference equations, - computation of isotopic distributions.[47] - modulation and demodulation of complex data symbols using orthogonal frequency division multiplexing (OFDM) for 5G, LTE, Wi-Fi, DSL, and other modern communication systems.

And naturally for any time-based signal about half a gazillion applications due to it's ability to detect "traits" which can be stored/read efficiently due to time => freq transform

Not specific to Fourier but an optimized FFT implementation can be used in many places as a stand-in for convolution.

It's not so much a stand-in as the possibility to compute convolutions (and auto-correlation and cross-correlation) really easily and cheaply in Fourier space.