Over the past several decades, the compiler research community has developed a number of sophisticated and powerful algorithms for a variety of code improvements. While there are still promising directions for particular optimizations, research on new or improved optimizations is reaching the point of diminishing returns and new approaches are needed to achieve significant performance improvements beyond traditional optimizations. In this paper, we describe a new strategy based on a continuous compilation system that constantly improves application code by applying aggressive and adaptive code optimizations at all times, from static optimization to online dynamic optimization. In this paper, we describe our general approach and process for continuous compilation of application code. We also present initial results from our research with continuous compilation. These initial results include a new prediction framework that can estimate the benefit of applying code transformations without actually doing the transformation. We also describe results that demonstrate the benefit of adaptively changing application code for embedded systems to make trade-offs between code size, performance, and power consumption.