atJIT is an LLVM-powered system that provides the ability to automatically performance-tune annotated C++ programs via machine learning and other techniques. We are trying to improve on lackluster results in related work by taking advantage of the precise control over optimization that is available in LLVM and Polly. For example, our online tuner is able to control and observe the effects of transformations applied to individual loops. So far, we have tried tuning one real computational kernel, matrix multiplication, and saw a 1.48x speedup (without Polly) over the baseline of ordinary JIT compilation. Additional speedup is expected as the integration with Polly continues. At the core of atJIT is a careful cooperation between Clang and LLVM that brings us another step closer toward enabling lifelong program optimization (an idea central to motivating LLVM's development). The "fat binary" approach used in atJIT can be improved and generalized into a new feature that supports front-ends other than Clang.