  We show that there are sampling projections on arbitrary $n$-dimensional
subspaces of $B(D)$ with at most $2n$ samples and norm of order $\sqrt{n}$,
where $B(D)$ is the space of complex-valued bounded functions on a set $D$.
This gives a more explicit form of the Kadets-Snobar theorem for the uniform
norm and improves upon Auerbach's lemma. We discuss consequences for optimal
recovery in $L_p$.
