The Stack Overflow Podcast The Stack Overflow Podcast

In this episode we chat with Saumil Patel, co-founder and CEO of Squire AI. The company uses an agentic workflow to automatically review your code, write your pull requests, and even review and provide opinions on other people’s PRs. Different AI systems with specific capabilities work together as a mixture of experts, following a chain of thought approach to provide recommendations on security, code quality, error handling, performance, scalability, and more.

This week we chat with Kamakshi Narayan, Director of Product Management at SnapLogic, who is focused on how APIs can apply fine-grained controls for privacy and governance to the LLM-powered AI apps vacuuming up our data.

Today's episode is a chat with Benjamin Shestakofsky, an assistant professor of sociology at the University of Pennsylvania with a focus on the ways in which digital technologies are affecting work and employment, organizations, and economic exchange.
We discuss research from his new book which dives into the venture capital business and explores the cooperative model that some software startups are taking instead.

Temporal is an open-source project focused on durable execution and workflow orchestration. Cofounder and CTO Maxim Fateev tells Ben and Ryan about the challenges of building a cloud service based on an open-source project and how Temporal is helping teams simplify their code and build more features more quickly.

Ben and Ryan are joined by Robin Gupta for a conversation about benchmarking and testing AI systems. They talk through the lack of trust and confidence in AI, the inherent challenges of nondeterministic systems, the role of human verification, and whether we can (or should) expect an AI to be reliable.

Ben and Ryan talk with Vikram Chatterji, founder and CEO of Galileo, a company focused on building and evaluating generative AI apps. They discuss the challenges of benchmarking and evaluating GenAI models, the importance of data quality in AI systems, and the trade-offs between using pre-trained models and fine-tuning models with custom data.