The project looks fairly active, based on the commit history:

https://github.com/tensorflow/tensorflow/commits/master/

It could be the code still stay there, but the direction has been changed.

Still active, but many fewer resources than in the past. Many backends like CUDA for Windows have been dropped and others pushed off to partners with varying levels of support. TensorFlow 2.19 is going to release soon without Python 3.13 support, it's hard not to imagine that resource constraints are at play.

What about embedded/mobile environments?

Are these other APIs as good as leveraging the hardware accelerators via hardware vendor provided drivers as tensorflow these days? (For nnapi etc ...).

I haven't touched these apis recently but back in 2020 or so the easiest way to use models like yolo was via tflite on those systems.

it's not abandoned nor deprecated, google still depends on it to run the models in production. Google just splits the model development into JAX. They're companions to each other and equally important down the road.

Did you read the last sentence?