Is there an adaptive version of LWE assumption with respect to some potentially non-uniform secret distribution?
For Q1, $m = O(n\log q)$ is the size required for the leftover hash lemma to kick in and $Ax$ to be statistically close to uniform (I believe). See questions like this one.

For Q2, the answer is yes. The hardness of LWE is essentially independent of the dimension $m$, as you can generate new LWE samples from a fixed collection of samples with only a mild loss in the error term. See proposition 2.1 of Regev's survey (and the "other implications" discussion after the proof).