In February 2026, Singleheart developed and integrated the Sigmoid Gated Linear Unit (GLU) activation function into the NVIDIA/TransformerEngine repository. This work involved implementing the activation in CUDA and Python, ensuring seamless integration with existing activation frameworks in PyTorch. Singleheart focused on test-driven development, validating the new feature with comprehensive tests and tuning for performance. The addition of Sigmoid GLU expanded the range of activation options available to Transformer models, enabling them to learn more complex patterns and improving training efficiency. The work demonstrated depth in deep learning activations, neural network optimization, and cross-component collaboration within a production codebase.