In this episode, we chat with Yaron Hadad, a fascinating individual who transitioned from theoretical physics to entrepreneurship.

We explore his groundbreaking work on black holes and gravitational waves, and learn about the Ramanujan Machine - an algorithmic system he helped develop that discovers new mathematical formulas and democratizes mathematical research. We'll hear about the scientific community's mixed reactions to this innovative approach.

The conversation then shifts to his work with Neutrino, a company he founded that uses AI and continuous monitoring devices to understand how food affects individual health. We delve into the complexities of nutrition science, the challenges of processing multiple data streams, and the future of personalized health monitoring.

Throughout the episode, Yaron shares insights on bridging theoretical research with practical applications, and the role of AI in advancing both pure mathematics and healthcare.

00:00 Yaron Hadad's Journey: From Physics to AI in Healthcare
04:50 The Complexity of Einstein's Equations and Their Solutions
10:12 AI in Mathematics: The Ramanujan Machine and Conjectures
15:41 Navigating Criticism: The Scientific Community's Response to Innovation
29:24 The Impact of Algorithms in Mathematics
35:30 The Planck Machine: A New Approach
41:15 Neutrino: A Personal Journey in Nutrition
50:11 Connecting Food Complexity to Health Metrics

In this episode, we chat with Philippe Noël, founder of ParadeDB, about building an Elasticsearch alternative natively on PostgreSQL.

We explore the challenges and benefits of extending PostgreSQL versus building a separate system, diving into topics like full-text search, faceted analytics, and why organizations need these capabilities.

We discuss the emerging bring-your-own-cloud deployment model, the state of the PostgreSQL extension ecosystem, and what makes a truly production-ready database extension.

Philippe shares insights on the future of search technology and how recent AI developments are actually increasing the demand for traditional search capabilities.

The conversation also covers the misconceptions around PostgreSQL's scalability and the trade-offs between multi-tenant and single-tenant architectures in modern data infrastructure.

Chapters

00:00 Introduction to ParadeDB and Its Mission
06:35 User-Facing Search and Analytics
11:45 The Role of Postgres in Modern Data Solutions
17:30 Future of Multimodal Databases
31:04 The Rise of Fintech and Data Integrity
36:36 Deployment Models: BYOC and Control Plane
43:41 The Evolution of Cloud Infrastructure and Serverless Databases
49:38 The Future of Search and Community Engagement

Click here to view the episode transcript.

In this episode, we chat with Ben, founder of Espresso AI, about his journey from building Excel Python integrations to optimizing data warehouse compute costs.

We explore his experience at companies like Uber and Google, where he worked on everything from distributed systems to ML and storage infrastructure.

We learn about the evolution of his latest venture, which started as a C++ compiler optimization project and transformed into a system for optimizing Snowflake workloads using ML.

Ben shares insights about applying LLMs to SQL optimization, the challenges of verified code transformation, and the importance of formal verification in ML systems. Finally, we discuss his practical approach to choosing ML models and the critical lesson he learned about talking to users before building products.

Chapters

00:00 Ben's Journey: From Startups to Big Tech
13:00 The Importance of Timing in Entrepreneurship
19:22 Consulting Insights: Learning from Clients
23:32 Transitioning to Big Tech: Experiences at Uber and Google
30:58 The Future of AI: End-to-End Systems and Data Utilization
35:53 Transitioning Between Domains: From ML to Distributed Systems
44:24 Espresso's Mission: Optimizing SQL with ML
51:26 The Future of Code Optimization and AI

Click here to view the episode transcript.