The design of LLVM and Clang enables them to be used as libraries, and has led to the creation of an entire compiler-assisted ecosystem of tools. The relatively friendly codebase of Clang and advancements in the JIT infrastructure in LLVM further enable research into different methods for processing C++ by blurring the boundary between compile time and runtime. Challenges include incremental compilation and fitting compile/link time optimizations into a more dynamic environment. Incremental compilation pipelines process code chunk-by-chunk by building an ever-growing translation unit. Code is then lowered into the LLVM IR and subsequently run by the LLVM JIT. Such a pipeline allows creation of efficient interpreters. The interpreter enables interactive exploration and makes the C++ language more user friendly. The incremental compilation mode is used by the interactive C++ interpreter, Cling, initially developed to enable interactive high-energy physics analysis in a C++ environment. Cling is now used for interactive development in Jupyter Notebooks (via xeus-cling), dynamic python bindings (via cppyy) and interactive CUDA development. In this talk we dive into the details of implementing incremental compilation with Clang. We outline a path forward for `Clang-Repl` which is built with the experience gained in Cling and is now available in mainstream llvm. We describe how the new Orc JIT infrastructure allows us to naturally perform static optimizations at runtime, and enables linker voodoo to make the compiler/interpreter boundaries disappear. We explain the potential of having a compiler-as-a-service architecture in the context of automatic language interoperability for Python and beyond."