Producing a small deployment version of an application is a challenge because static abstractions such as packages cannot anticipate the use of their parts at runtime. Thus, an application often occupies more memory than actually needed. Tailoring is one of the main solutions to this problem i.e., extracting used code units such as classes and methods of an application. However, existing tailoring techniques are mostly based on static type annotations. These techniques cannot efficiently tailor applications in all their extent (e.g., runtime object graphs and metadata) nor be used in the context of dynamically-typed languages. We propose a run-fail-grow technique to tailor applications using their runtime execution. Run-fail-grow launches (a) a reference application containing the original application to tailor and (b) a nurtured application containing only a seed with a minimal set of code units the user wants to ensure in the final application. The nurtured application is executed, failing when it founds missing objects, classes or methods. On failure, the necessary elements are installed into the nurtured application from the reference one, and the execution resumes. The nurtured application is executed until it finishes, or until the developer explicitly finishes it, for example in the case of a web application. resulting in an object memory (i.e., a heap) with only objects, classes and methods required to execute the application. To validate our approach we implemented a tool based on Virtual Machine modifications, namely Tornado. Tornado succeeds to create very small memory footprint versions of applications e.g., a simple object-oriented heap of 11kb. We show how tailoring works on application code, base and third-party libraries even supporting human interaction with user G. interfaces. These experiments show memory savings ranging from 95% to 99%.