  A Martin-L\"of test $\mathcal U$ is universal if it captures all
non-Martin-L\"of random sequences, and it is optimal if for every ML-test
$\mathcal V$ there is a $c \in \omega$ such that $\forall n(\mathcal{V}_{n+c}
\subseteq \mathcal{U}_n)$. We study the computational differences between
universal and optimal ML-tests as well as the effects that these differences
have on both the notion of layerwise computability and the Weihrauch degree of
LAY, the function that produces a bound for a given Martin-L\"of random
sequence's randomness deficiency. We prove several robustness and idempotence
results concerning the Weihrauch degree of LAY, and we show that layerwise
computability is more restrictive than Weihrauch reducibility to LAY. Along
similar lines we also study the principle RD, a variant of LAY outputting the
precise randomness deficiency of sequences instead of only an upper bound as
LAY.
