Vulcan Wet Dress Rehearsal (WDR)✔️ Yesterday, ULA conducted a Wet Dress Rehearsal (WDR), at Space Launch Complex-41. This full day-of-launch demonstration loaded propellant into the Vulcan rocket and new LEO-optimized Centaur V upper stage, and tested functions of the new Vulcan Launch Platform (VLP)-A, the second Vulcan platform to serve @AmazonLeo missions on our heavy-lift rocket.

Leo Vulcan 1 (LV-01) update: Our first mission on @ulalaunch's Vulcan rocket is set for Friday, Nov. 13, pending range approval. All 40 Leo satellites—our largest payload to date—have been integrated with Vulcan inside Amazon's vertical integration facility (VIF-A) at the Cape, and teams are working through final processing ahead of launch from SLC-41. Follow along with upcoming Leo missions:

Leo Vulcan 1 (LV-01) payload on the move. LV-01 is the first of 38 Leo missions on @ulalaunch’s heavy-lift Vulcan rocket, and last week, teams successfully transported the 40-satellite payload—encapsulated inside a 70-foot-tall fairing—to the new Amazon Vertical Integration Facility (VIF-A) at Cape Canaveral.

🎉 VulcanBench v0.9.0 is out, and the focus this release is fewer dependencies. The harness dropped 7 direct dependencies and the 16 packages they needed. This means a plain install is now 15 packages, down from 37. A reduction of over 50%. I think we are moving into a time where you want to rely on as few packages as possible. Every package is code you did not write, running on your machine. I think all of us, as software developers, need to think about this, not just for ourselves but for our users. It has been an honor to see more and more people using VulcanBench to run their own benchmarks. I want to keep the list of things they have to trust as short as I can. Now live on GitHub: Live long and benchmark 🖖

New on VulcanBench, our Analysis section debuts with a look into the traces for Muse Wonder. At Low effort, Muse Wonder seems to have an overthinking problem. To better understand what is happening here, we compare Muse Wonder with GPT-6.1 Sol on the same tasks and compare how each model thinks differently. Overall Muse Wonder takes more than 4x the time to complete a task vs. GPT-6.1 Sol. Full trace analysis below. Live long and benchmark 🖖