This work presents (a) Queenie, an application-level tool that can automatically learn 10 internal properties of block-level SSDs, (b) Kelpie, the learning and analysis results of running Queenie on 21 different SSD models from 7 major SSD vendors, and (c) Newt, a set of storage performance optimization examples that use the learned properties. By bringing numerous observations and unique findings, this work exposes substantial improvement spaces for both SSD users and vendors, enlightening possibilities of unleashing more SSD performance potential and highlighting the necessity of further exploring SSD internals.