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 # BatchNLPKernels.jl
 
-[![Dev](https://img.shields.io/badge/docs-dev-blue.svg)](https://klamike.github.io/BatchNLPKernels.jl/dev/)
-[![Build Status](https://github.com/klamike/BatchNLPKernels.jl/actions/workflows/CI.yml/badge.svg?branch=main)](https://github.com/klamike/BatchNLPKernels.jl/actions/workflows/CI.yml?query=branch%3Amain)
+[![Dev](https://img.shields.io/badge/docs-dev-blue.svg)](https://LearningToOptimize.github.io/BatchNLPKernels.jl/dev/)
+[![Build Status](https://github.com/LearningToOptimize/BatchNLPKernels.jl/actions/workflows/CI.yml/badge.svg?branch=main)](https://github.com/LearningToOptimize/BatchNLPKernels.jl/actions/workflows/CI.yml?query=branch%3Amain)
 [![Coverage](https://codecov.io/gh/LearningToOptimize/BatchNLPKernels.jl/branch/main/graph/badge.svg)](https://codecov.io/gh/LearningToOptimize/BatchNLPKernels.jl)
 
 `BatchNLPKernels.jl` provides [`KernelAbstractions.jl`](https://github.com/JuliaGPU/KernelAbstractions.jl) kernels for evaluating problem data from a (parametric) [`ExaModel`](https://github.com/exanauts/ExaModels.jl) for batches of solutions (and parameters). Currently the following functions (as well as their non-parametric variants) are exported: