ArrayFire was a great way to bootstrap Julia’s GPU compute support, but it is not what you want to use for a mature, flexible language like Julia in the long term. Since Julia+GPUCompiler can generate arbitrary (fused) kernels with competitive performance to CUDA C++/ROCm HIP, there’s not really a good reason to use ArrayFire (unless your hardware isn’t supported by CUDA.jl/AMDGPU.jl/oneAPI.jl).
If you want to reach more people who have unsupported GPUs or use Windows with an AMD GPU, the better approach is to consider reviving CLArrays.jl (warning: not a small or easy task) and making it fully GPUArrays-compatible.