During a three-month period, Blurylee contributed to the AdvancedCompiler/FlagGems and FlagOpen/FlagGems repositories by developing core neural network operations and backend features. They implemented 2D average pooling with both forward and backward passes, enabling autograd-compatible pooling layers for improved model training. Blurylee also refactored the ELU backward pass to align with PyTorch’s gradient semantics, introducing kernel variants to ensure gradient correctness and interoperability. Additionally, they delivered bitwise shift operations using Triton kernels, complete with performance benchmarks and accuracy tests. Their work demonstrated depth in Python and C++ development, GPU programming, and performance optimization, addressing both correctness and extensibility.