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In this section we discuss our implementation of the approach described in \mysection\ref{se:overview} in \tinyvm, a proof-of-concept virtual machine we developed as a playground to exercise our OSR techniques. TinyVM is based on LLVM's MCJIT compiler and supports interactive invocation of LLVM IR functions either generated at run-time or loaded from disk. The main design goal behind TinyVM is the creation of an interactive environment for IR manipulation and JIT-compilation of functions: for instance, it allows the user to insert OSR points in loaded functions, run optimization passes on them or display their CFGs, repeatedly invoke a function for a specified amount of times and so on. TinyVM supports dynamic library loading and linking, and comes with a helper component for MCJIT that simplifies tasks such as handling multiple IR modules, symbol resolution in presence of multiple versions of a function, and tracking native code and other machine-level generated object such as Stackmaps.  To explain how OSR can be encoded in IR, we consider the toy example of \myfigure\ref{fi:isord-example}. Function {\tt isord} checks whether an array of numbers is sorted according to some ordering given by a comparator. If the number of iterations exceeds a certain threshold, The scenario  we explore how to is profile-driven optimization, where we  dynamically divert control to a faster version where if  the comparator is inlined. number of iterations exceeds a certain threshold.  To achieve this goal, we use deferred compilation by instrumenting {\tt isord} in \tinyvm\ with an open OSR, as shown in \myfigure\ref{fig:isordfrom}. The generated stub calls a function inliner that generates a version of {\tt isord} where the comparator's body body, pointed to by live variable {\tt c_osr},  is inlined in the loop body. The optimized version {\tt isordto} is shown in \myfigure\ref{fig:isordascto}.  \ifdefined\noauthorea  \begin{figure}[t]