  We revisit strong approximation theory from a new perspective, culminating in
a proof of the Koml\'os-Major-Tusn\'ady embedding theorem for the simple random
walk. The proof is almost entirely based on a series of soft arguments and easy
inequalities. The new technique, inspired by Stein's method of normal
approximation, is applicable to any setting where Stein's method works. In
particular, one can hope to take it beyond sums of independent random
variables.
