In February 2026, Anders Endsa expanded numpy operation support in the keras-team/keras repository’s OpenVINO backend, enabling seamless integration of mathematical and neural network components across backends. He implemented utilities such as gcd using the Euclidean algorithm, hanning, histogram, lcm, allclose, and kaiser windows, addressing type and shape robustness for numerical workflows. Anders also added dropout, binomial sampling, and RNN primitives, broadening neural network support. His work involved Python and numpy, with a focus on backend development and numerical computing. Collaborative, co-authored commits improved code quality, documentation, and maintainability, reflecting a deep, methodical approach to backend consistency.