Implementing the forward-forward algorithm in Jax can be a good idea because Jax provides several benefits over other libraries, such as TensorFlow. Some of the benefits of using Jax include:

• Jax is designed for high-performance machine learning applications, so it can be faster and more efficient than other libraries.
• Jax has a simpler API than other libraries, which can make it easier to use and understand.
• Jax uses automatic differentiation to compute gradients, which can make it easier to implement and debug machine learning algorithms.
• Jax has support for parallel and GPU acceleration, which can make it easier to scale machine learning applications to large datasets.

Overall, using Jax to implement the forward-forward algorithm can be a good choice if you want to build high-performance machine learning applications that are easy to use and understand.

Note: My original Implementation in here: https://github.com/sleepingcat4/Forward-Forward-Algorithm