Podcasts

Software Evolution with Microservices and LLMs: a Conversation with Chris Richardson

In this podcast, Michael Stiefel spoke with Chris Richardson about using microservices to modernize software applications and the use of artificial intelligence in software architecture. We first discussed the problems of monolithic enterprise software and how to use microservices to evolve them to enable fast flow - the ability to achieve rapid software delivery.

Building a More Appealing CLI for Agentic LLMs Based on Learnings from the Textual Framework

Will McGugan, the maker of Textual and Rich frameworks, speaks about the reasoning of developing the two two libraries and the lesson learned. Also, he shares light on Toad, his current project, which he envisions being a more visually appealing way of interacting with agentic LLMs through command line.

Platform Engineering for AI: Scaling Agents and MCP at LinkedIn

QCon AI New York Chair Wes Reisz talks with LinkedIn’s Karthik Ramgopal and Prince Valluri about enabling AI agents at enterprise scale. They discuss how platform teams orchestrate secure, multi-agentic systems, the role of MCP, the use of foreground and background agents, improving developer experience, and reducing toil.

GenAI Security: Defending Against Deepfakes and Automated Social Engineering

In this episode, QCon AI New York 2025 Chair Wes Reisz speaks with Reken CEO and Google Trust & Safety founder Shuman Ghosemajumder about the erosion of digital trust. They explore how deepfakes and automated social engineering are scaling cybercrime and argues defenders must move beyond default trust, utilizing behavioral telemetry and game theory to counter attacks that simulate human behavior.

If You Can’t Test It, Don’t Deploy It: The New Rule of AI Development?

Magdalena Picariello reframes how we think about AI, moving the conversation from algorithms and metrics to business impact and outcomes. She champions evaluation systems that don't just measure accuracy but also demonstrate real-world business value, and advocates for iterative development with continuous feedback to build optimal applications.