2025 has been a wild year for DBOS. Huge thanks to you all for the support, feedback, and trust along the way. Looking forward to making reliable software even easier to build in 2026 🚀

Simplicity wins. I really appreciated the shoutout: "Integrating durable workflows through DBOS solved our distributed system headaches without adding ops overhead." www.decodingai.com/p/building-v...

People used to complain that the DBOS UI wasn't available locally. Good news: it is now, and it's free for open-source local dev. Run a local DBOS UI right on your laptop and actually see your workflows while you build. Debugging hits different when it's visual. docs.dbos.dev/production/s...

Cool new feature: explicit queue listening! You can now configure exactly which queues each of your workers listens to. This is especially helpful if you have heterogeneous workers (such as some workers for GPU tasks and others for CPU-intensive tasks) as you can easily divide work between them.

Want to hear what two database professors Mike Stonebraker and @andypavlo.bsky.social really think about AI and data systems, plus a recap of 2025 and predictions for 2026? In case you missed the webinar, watch the full recording here: www.youtube.com/watch?v=RnQK...

What happens when applications are written almost entirely by LLMs? @andypavlo.bsky.social explains what’s already happening, and why future enterprise apps may start with vibe coding.

Ever wondered how AI teams avoid burning cash every time they run an image model?

When you combine patching + versioning, you get: - Multiple workflow versions for large refactors (blue/green deployment). - Inline hotfixes for long-running workflows that need to be corrected. Would love feedback or war stories from anyone who's wrestled with workflow versioning in production.

I was once skeptical about workflow patching, mostly because of all the horror stories I heard from other developers. But this implementation looks nice: a patch point is just another workflow step. That shift makes the patching interface more intuitive than I expected. docs.dbos.dev/python/tutor...

Goblins moved from external workflow services to DBOS for faster execution, a unified stack, and full control over debugging and uptime. Alp said "when your database is essentially one millisecond away from your servers, every workflow is just blazing fast.”

Looking forward to this! Fun fact: Andy generated those Christmas-themed headshots himself (pretty sure via Nano Banana).

Good memories from #QConSF Conversations about system design tradeoffs, and someone told me they learned about SKIP LOCKED from my talk. That's why I love QCon: the technical exchange, not product pitches. I now have my second Postgres elephant plushie thanks to @yugabytedb.bsky.social

For Ontologize, reliably spinning up enterprise software environments at scale required durability and simplicity. DBOS let them scale their deployment pipeline with Python and zero rearchitecting.

In the upcoming webinar, we’re hosting a conversation with two leaders in the database world, Mike Stonebraker and Andy Pavlo. They will discuss the trends most likely to influence the software you build and the architectures you choose. 🗓️ Weds, December 10, 2025 | 1 PM EST

Happy Thanksgiving! Our dev is already shipping code before dinner.

As teams move toward agentic AI, systems need to handle long running, independent tasks with very little supervision. The challenge is keeping these workflows stable, consistent, and easy to recover when something goes wrong.

Big hugs to friends and family working overtime this time of the year to keep systems alive during the surge. I'm reminded how many people quietly keep the world running. Thank you for everything you do!

Thanks so much for hosting the podcast! Really enjoyed our conversation.

Yesterday was our final @southbaysystems.xyz talk event of 2025 (what a year!). Huge thanks to @julianhyde.bsky.social and Yuanyuan for their awesome talks! A big shout-out to our amazing host, @startreedata.bsky.social, for the venue and food!

Youtube: youtu.be/h4FrkIf2Poo Spotify: open.spotify.com/episode/1SxH... Apple podcasts: podcasts.apple.com/us/podcast/e...

The latest episode of the #BugBash podcast has @qianli.dev from DBOS talking about the hard truths of ergonomics. As always, if it ain't easy, it probably won't happen. Full episode links below.

Gunnar is such an incredible writer! He explains durable execution better than I do. I love seeing more lightweight, database-backed approaches getting attention, and really appreciate the thoughtful shout-out for @dbos.dev. Always grateful for folks who push the space forward.

Durable execution is underrated. In real systems, any step can fail at any time. Peter breaks down why reliability is such a challenge.

Build durable, exactly-once workflows in your favorite language. DBOS Go ✅ DBOS Java ✅ DBOS Python ✅ DBOS TypeScript ✅ docs.dbos.dev

Here is a nice article summarizing the talk (thanks stiller.bsky.social) 🔗 www.infoq.com/news/2025/11...

Thanks QCon for having @qianli.dev and @jedberg.net. Their talk covered how durable computing and DBOS Transact boost reliability, remove heavyweight workflow coordinators, and reduce both complexity and cost.

Building reliable AI systems is hard. In this video, @petereliaskraft.net walks through a deep research agent that many of us want to build and the kind that fails in all the surprising ways. Failures are inevitable and a durable workflow is what will make your agent production ready.

I will be at #QConSF next week and co-presenting with @jedberg.net! We're speaking on Monday at 11:45 am on "Compiling Workflows into Databases: The Architecture That Shouldn't Work (But Does)". DM me if you want to catch up or grab coffee. 🔗 qconsf.com/presentation...

80% of backend code is retry and error-handling logic. With durable execution, that disappears. Every input, output, and error is saved, giving you full observability and debugging out of the box.

We're back on HN! This time with our Postgres-backed durable execution library for Java. It plugs into existing Spring apps with minimal changes and gives you durable workflows, queues, retries, and scheduling without extra infrastructure. Feedback very welcome! github.com/dbos-inc/dbo...

Build durable, exactly-once workflows in your favorite language. DBOS Go ✅ DBOS Java ✅ DBOS Python ✅ DBOS TypeScript ✅ docs.dbos.dev

When Yutori looked into Temporal, the cost was clear. It would mean rearchitecting their workflows. With DBOS, they could orchestrate durable workflows using just a few Python decorators and a Postgres database.

Moreover, their execution is fully observable in the DBOS console (workflow graphs + management, shown in the screenshot) and instrumented with Logfire to trace token usage and cost per step. Link to the original PR: github.com/pydantic/pyd...

I added two new demos showing durable agent patterns (parallel vs sequential) with @pydantic.dev AI + @dbos.dev: - Deep Research: large fan-out parallel workflows - Twenty Questions: long sequential subagent chaining Both are long-running, so durable execution is necessary to survive crashes.

Great sample app from the dbos team of an inbox for durable agentic workflows- group executions by pending/approved/denied, with state all stored in a PostgreSQL database. https://github.com/dbos-inc/dbos-demo-apps/tree/main/python/agent-inbox

Check out this example for building crash proof human-in-the-loop agents.

p.s. We have quite a few LOTR fans here. One colleague joked, "DBOS might have saved Gondolin". Earlier this year, I finished both The Hobbit and LOTR books (and the movies, of course), so I'll allow it.

Great to see more people exploring lightweight, Postgres-based approaches to durable workflows. We're chasing the same goal at DBOS: making reliable systems simple, without extra infrastructure or complex setup. Always happy to chat and learn from others working in this space, and keep improving DX!

Celery’s familiar. But once tuning and load management start piling up, things get messy fast. DBOS changes that.

If your durable execution solution makes you rebuild your stack, it’s not a solution. Embedded libraries keep your architecture intact.

If you’ve ever wondered why your queue is filling up, this one’s for you. Cameron shares how DBOS’s Grafana dashboard helps them see every job, every queue, and fix stuck workflows before things go sideways.

I also came up with a Halloween idea: instead of "trick or treat", I'll knock on doors saying "trick or try DBOS"? 👻

Our user group virtual meetup will be in half an hour! What's special today is that you'll hear how Goblins uses DBOS + @planetscale.com for agent AI math tutoring - and Holly Guevara will talk about PlanetScale Postgres. Last call for registration: luma.com/vgcn4847

When reliability is built in, devs can focus on solving real problems instead of managing infra. Hear what Cameron Spiller from Soria Analytics has to say about it.

Just launched the official @supabase.com integration with DBOS! Power your workflows with Supabase Postgres: - Add workflows in just a few lines of code - No new dependencies: just your app and Supabase - See your workflow data from the Supabase dashboard Here's a quick start guide 👇

I wrote a tutorial on integrating DBOS with Logfire. It's super easy to set up (kudos to the @pydantic.dev team!) -> just a few lines to configure the exporter and your Logfire write token. Once it's running, you'll get a unified view of your app with logs + traces in one place.

Just released new minor versions of DBOS TypeScript and DBOS Python 🚀 What's new: - Improved performance for rate-limited queues (thanks to our user Matt Reed!) - Added Kysely datasources in TypeScript - Better compatibility with custom Otel exporters - Minor fixes for warnings and type definitions

The October DBOS User Group is happening this Thursday! Hear from the CTO of Goblins, as he shares how they equipped their AI Math Tutor with DBOS durable execution in just one day. 🧵 🗓️ Thurs, October 31, 2025 | 11AM PDT 📍 RSVP: luma.com/vgcn4847

I care more about helping developers build reliable systems than chasing vanity metrics such as GitHub stars. Still, seeing the DBOS Python repo reach 1K 🌟 makes me smile. Thanks for building durable workflows with @dbos.dev. Full speed ahead - Java release coming soon!

More systems groups 🎉🎉