How many continuous measurements are needed to learn a vector?

One can recover vectors from ${\mathbb{R}}^m$ with arbitrary precision, using only $\lceil {\log }_2(m+1)\rceil +1$ continuous measurements that are chosen adaptively. This surprising result is explained and discussed, and we present applications to infinite-dimensional approximation problems.