Hi! How does it perform under heavy load and with thousands of workflows trying to run concurrently since it relies on Postgres for a lot of things (including using a transaction)? In the end it seems that if I have an application with lots of distributed workers trying to run workflows, I'll still be limited by the CPU/memory of the DB.

Hi there, I think I might have found a typo in your example class in the github README. In the class's `workflow` method, shouldn't we be `await`-ing those steps?

Can you change the workflow code for a running workflow that already advanced some steps? What support DBOS have for workflow evolution?

I know this this might sound scripted or can be considered cliche but what is the use case for DBOS.

How do you persist execution state? Does it hook into the Python interpreter to capture referenced variables/data structures etc, so they are available when the state needs to be restored?

Hai, really cool project! This is something I can actually use.

About workflow recovery: if I'm running multiple instance of my app that uses DBOS and they all crash, how do you divide the work of retrying pending workflows?

FYI the “Build Crashproof Apps” button in your docs doesn’t do anything.