Building open-source solutions for my 100 Days of AI Agents challenge meant I needed to start looking at frameworks that scale better than standard NumPy and PyTorch. That inevitably led me to JAX.
Transitioning to JAX requires a bit of a paradigm shift. If you are used to the standard Python data science stack, JAX forces you to rewire how you think about array operations, memory, and hardware execution.
I spent today digging into the core mechanics, and I want to share my top 3 takeaways and the exact code snippets that made it click for me.
1. Immutability is a Feature, Not a Bug
This was my first major roadblock. In standard NumPy, if you want to change an element in an array, you just reassign it in place.
