Super rough summary of the first half: in order to pick out random vectors with a given shape (where the "shape" is determined by the covariance matrix), MASS::mvrnorm() computes some eigenvectors, and eigenvectors are only well defined up to a sign flip. This means tiny floating differences between machines can result in one machine choosing v_1, v_2, v_3,... as eigenvectors, while another machine chooses -v_1, v_3, -v_3,... The result for sampling random numbers is totally different with the sign flips (but still "correct" because we only care about the overall distribution--these are random numbers after all). The section around "Q1 / Q2" is the core of the article.

There's a lot of other stuff here too: mvtnorm::rmvnorm() also can use eigendecomp to generate your numbers, but it does some other stuff to eliminate the effect of the sign flips so you don't see this reproducibility issue. mvtnorm::rmvnorm also supports a second method (Cholesky decomp) that is uniquely defined and avoids eigenvectors entirely, so it's more stable. And there's some stuff on condition numbers not really mattering for this problem--turns out you can't describe all possible floating point problems a matrix could have with a single number.