Traditional software expects 100% passing tests. In LLM-powered systems, that’s not just unrealistic — it’s a feature, not a bug. Eric Ma leads research data science in Moderna’s data science and AI group, and over breakfast at SciPy we explored why AI products break the old rules, what skills different personas bring (and miss), and how to keep systems alive after the launch hype fades.
You’ll hear the clink of coffee cups, the murmur of SciPy in the background, and the occasional bite of frittata as we talk (hopefully also a feature, not a bug!)
We talk through:
• The three personas — and the blind spots each has when shipping AI systems
• Why “perfect” tests can be a sign you’re testing the wrong thing
• Development vs. production observability loops — and why you need both
• How curiosity about failing data separates good builders from great ones
• Ways large organizations can create space for experimentation without losing delivery focus
If you want to build AI products that thrive in the messy real world, this episode will help you embrace the chaos — and make it work for you.
LINKS
Eric' Website (https://ericmjl.github.io/)
More about the workshops Eric and Hugo taught at SciPy (https://hugobowne.substack.com/p/stress-testing-llms-evaluation-frameworks)
Upcoming Events on Luma (https://lu.ma/calendar/cal-8ImWFDQ3IEIxNWk)
🎓 Learn more:
Hugo's course: Building LLM Applications for Data Scientists and Software Engineers (https://maven.com/s/course/d56067f338) — https://maven.com/s/course/d56067f338 ($600 off early bird discount for November cohort availiable until August 16)

This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit hugobowne.substack.com