What made attention mechanism click to me was the video https://www.youtube.com/watch?v=0PjHri8tc1c by Sebastian Raschka. Another good video on attention that tries to give a bit more intuition on the query/key/value stuff is https://www.youtube.com/watch?v=OyFJWRnt_AY

In hindsight, what helped me most is understanding the dimensionality at each step of the attention computation (explained well in Raschka's video). Another think I simply did not get from the original Transformer paper is that the learning of self-attention happens in the linear layers. (At least, that's my current understanding. If that's wrong, I appreciate any correction :)