  In the Euclidean $k$-Means problem we are given a collection of $n$ points
$D$ in an Euclidean space and a positive integer $k$. Our goal is to identify a
collection of $k$ points in the same space (centers) so as to minimize the sum
of the squared Euclidean distances between each point in $D$ and the closest
center. This problem is known to be APX-hard and the current best approximation
ratio is a primal-dual $6.357$ approximation based on a standard LP for the
problem [Ahmadian et al. FOCS'17, SICOMP'20].
  In this note we show how a minor modification of Ahmadian et al.'s analysis
leads to a slightly improved $6.12903$ approximation. As a related result, we
also show that the mentioned LP has integrality gap at least
$\frac{16+\sqrt{5}}{15}>1.2157$.
