In distributed systems, data doesn't travel instantly. Eventual Consistency is the guarantee that if you stop updating data, all servers will eventually have the same value.
Imagine three servers in different parts of the world. When you write data to one (e.g., "New York"), it takes time to copy that data to "London" and "Tokyo".
During this delay (latency), the system is inconsistent. A user in Tokyo might see old data even after the New York user has updated it.
The time gap between the initial write and the final sync. Your goal as a developer is to hide this gap from the user.
You can't fix the speed of light, but you can fix how the user feels about it. Choose the right pattern for the job.
Assume the request will succeed. Update the interface immediately. Sync in background.
Block interaction until the server confirms success. Shows loading states.
Notify the user when data is ready or updated. Don't block flow.
Try adding items to this list with different Consistency Models turned on.