Want to attach a debugger to that weird error you got in production yesterday?

With the latest release of the DBOS debugger, you can get pretty close! It lets you take any past trace of a DBOS workflow, execute it locally, and attach a debugger to step through it and see what really happened.

I think the single best decision we made when building DBOS was choosing to build everything on Postgres. Why?

Just put together this new demo–an app that visualizes the execution of a durable workflow, showing you what code executes in its backend and how it seamlessly recovers from failures.

Would love feedback on it! What works and what isn’t clear?

Store Everything In Postgres

An idea on how to radically improve reliability and reduce complexity for stateful apps.

www.dbos.dev/blog/what-is...

I think the most underrated benefit of durable execution is observability.

Durable workflows and steps map 1-to-1 to traces and spans used in OTel and most modern observability frameworks. This makes monitoring easier: how often workflows run, where they spend time, and when they error.

One small ask for the community as we try to make DBOS easier to adopt:

We just refreshed the “Learn DBOS” guides in our documentation. I’d be really grateful if anyone could look at the new guides, try them out, and give feedback on what works and what needs to be explained better. Thanks a lot!

Had a wonderful time presenting DBOS to the Bay Area Python Interest Group last night!

There was a great discussion about how to integrate durable execution into existing apps--what got people excited about DBOS is that it's just a Python library, so you can "pip install" it into any existing app.

A holiday feature release: trace visualization!

DBOS automatically generates OpenTelemetry traces for your workflows. You can now view traces in the cloud console to monitor your apps and see:

- What operations they run
- Where they spend time
- If they error, where and why