Specialized languages for describing convex optimization problems, and associated parsers that automatically transform them to canonical form, have greatly increased the use of convex optimization in applications, especially those where the problem instances are not very large scale. CVX and YALMIP, for example, allow users to rapidly prototype applications based on solving (modest size) convex optimization problems. More recently, similar techniques were used in CVXGEN to automatically generate super-efficient small footprint code for solving families of small convex optimization optimization problems, as might be used in real-time control. In this talk I will describe the general methods used in such systems, and describe methods by which they can be adapted for large-scale problems. Joint work with Eric Chu.