We present Aptly, an extension of the MIT App Inventor platform enabling mobile app development via natural language powered by code-generating large language models (LLMs). Aptly complements App Inventor's block language with a text language designed to allow visual code generation via text-based LLMs. We detail the technical aspects of how the Aptly server integrates LLMs with a realtime collaboration function to facilitate the automated creation and editing of mobile apps given user instructions. The paper concludes with insights from a study of a pilot implementation involving high school students, which examines Aptly's practicality and user experience. The findings underscore Aptly's potential as a tool that democratizes app development and fosters technological creativity.

As a part of Expedia Group's partnership with AWS we recently took an amazing opportunity to host a DeepRacer competition in our Brisbane office. DeepRacer is designed to introduce people of all backgrounds to Machine Learning. The goal of the competition is to engineer a control loop for an autonomous toy racing car that enables the car to complete a full circuit of a physical race track in the shortest amount of time. This control loop is constructed using a Machine Learning technique called Reinforcement Learning. Reinforcement Learning encourages an autonomous machine to perform certain actions.