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Battery-free sensing devices harvest energy from their surrounding environment to perform sensing, computation, and communication. A core challenge for these devices is maintaining usefulness despite erratic, random, or irregular energy availability, which causes inconsistent execution, loss of service, and power failures. Adapting execution (degrading or upgrading) based on available or predicted power/energy seems promising to stave off power failures, meet deadlines, or increase throughput. However, due to constrained resources and limited local information, deciding what and when exactly to adapt is challenging. This article explores the fundamentals of energy-aware adaptation for intermittently powered computers and proposes heuristic adaptation mechanisms to dynamically modulate the program complexity at run-time to enable higher sensor coverage and throughput. While we target battery-free, intermittently powered, resource-constrained sensors, we see a general application to all energy harvesting devices.