While early software command recommender systems drew negative user reaction, recent studies show that users of unusually complex applications will accept and utilize command recommendations. Given this new interest, more than a decade after first attempts, both the recommendation generation (backend) and the user experience (frontend) should be revisited. In this work, we focus on recommendation generation. One shortcoming of existing command recommenders is that algorithms focus primarily on mirroring the short-term past — i.e., assuming that a developer who is currently debugging will continue to debug endlessly. We propose an approach to improve on the state of the art by modeling future task context to make better recommendations to developers. That is, the approach can predict that a developer who is currently debugging may continue to debug OR may edit their program. To predict future development commands, we applied Temporal Latent Dirichlet Allocation, a topic model used primarily for natural language, to software development interaction data (i.e., command streams). We evaluated this approach on two large interaction datasets for two different IDEs, Microsoft Visual Studio and ABB Robot Studio. Our evaluation shows that this is a promising approach for both predicting future IDE commands and producing empirically-interpretable observations.