In this episode of Vanishing Gradients, the tables turn as Hugo sits down with Alex Andorra, host of Learning Bayesian Statistics. Hugo shares his journey from mathematics to AI, reflecting on how Bayesian inference shapes his approach to data science, teaching, and building AI-powered applications.
They dive into the realities of deploying LLM applications, overcoming “proof-of-concept purgatory,” and why first principles and iteration are critical for success in AI. Whether you’re an educator, software engineer, or data scientist, this episode offers valuable insights into the intersection of AI, product development, and real-world deployment.
LINKS
The podcast on YouTube (https://www.youtube.com/watch?v=BRIYytbqtP0)
The original podcast episode (https://learnbayesstats.com/episode/122-learning-and-teaching-in-the-age-of-ai-hugo-bowne-anderson)
Alex Andorra on LinkedIn (https://www.linkedin.com/in/alex-andorra/)
Hugo on LinkedIn (https://www.linkedin.com/in/hugo-bowne-anderson-045939a5/)
Hugo on twitter (https://x.com/hugobowne)
Vanishing Gradients on twitter (https://x.com/vanishingdata)
Hugo's "Building LLM Applications for Data Scientists and Software Engineers" course (https://maven.com/s/course/d56067f338)

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