SE Radio 728: Clare Liguori on AWS Strands SDK for AI Agents

SE Radio 728: Clare Liguori on AWS Strands SDK for AI Agents

Clare Liguori, a Senior Principal Engineer who works on developer tooling and agentic AI at Amazon Web Services, speaks with host Sri Panyam about the Amazon Strands Agents SDK. This episode explores the philosophy, design decisions, and emerging patterns behind building production-grade AI agents. Clare frames any agent as three core components: a model, a set of tools, and a prompt. During this interview, she describes the origin story of Strands, the model-driven approach vs. workflows and custom orchestration, steering hooks, tools and MCP, sub-agents and multi-agents, memory layers, production readiness, testing and evaluation starting with use cases where trajectories can be evaluated deterministically, and anti-patterns for newcomers. She describes what's next for Strands, and offers some closing advice for getting results from working with agents

SE Radio 727: Jeroen Janssens and Thijs Nieuwdorp on Using Polars

Jeroen Janssens, a senior developer relations engineer at Posit, and Thijs Nieuwdorp, a developer relations engineer at Polars, speak with host Gregory M. Kapfhammer about Polars, a Python package for transforming, analyzing, and visualizing data. After discussing the key features, they explore the implementation and use of the expressions data type provided by Polars. Along with comparing Polars to other data-manipulation packages like Pandas, they also share best practices for performing data analysis in Python with Polars. Jeroen, Thijs, and Gregory also discuss topics such as how to interface Polars with a SQL database.

SE Radio 726: Scott Kingsley on the Swagger Ecosystem

Scott Kingsley, a VP of Engineering at SmartBear, speaks with host Gregory Kapfhammer about the Swagger ecosystem. They discuss the user interface, editor, and Swagger CodeGen and how these tools support the creation and documentation of OpenAPI-compatible APIs. Scott describes how Swagger fits into frameworks like FastAPI, as well as how Swagger APIs can be exposed through the Model Context Protocol (MCP). The discussion closes with best practices for designing and testing APIs and the role that APIs play in a landscape in which AI agents are building and interacting with APIs.

SE Radio 725: Danny Yang and Sam Goldman on the Pyrefly Type Checker

Danny Yang and Sam Goldman, both Software Engineers at Meta, speak with host Gregory M. Kapfhammer about the Rust-based Pyrefly type checker for Python. After a look at the foundational concepts for annotating and checking types for Python programs, Danny and Sam present a deep dive of the implementation of Pyrefly. While comparing and contrasting against various type checkers, they also describe how Pyrefly implements the language server protocol (LSP) for Python. The episode explores a range of other topics, including how to balance the features, performance, and language integrations of a type checker.

SE Radio 724: Jure Leskovec on Relational Graph and Foundational Models

Jure Leskovec, Professor of Computer Science at Stanford University and Chief Scientist at Kumo.ai, speaks with host Sriram Panyam about relational and graph language models and their transformative impact on enterprise decision-making and predictive modeling. Jure begins by establishing the critical importance of predictive modeling across industries - from fraud detection in financial institutions to customer churn prediction, lifetime value estimation, product recommendations, and healthcare risk assessment. He notes that while AI has made remarkable advances in natural language understanding and computer vision, predictive modeling over enterprise operational data stored in relational databases has been largely left behind, still relying on 30-year-old machine learning approaches that are expensive, time-consuming, and require manual feature engineering. His proposed solution to the fundamental problem with current approaches is relational deep learning and relational transformers. The discussion explores how this approach differs from traditional graph neural networks (GNNs), which Jure pioneered and deployed successfully at Pinterest. Jure concludes with practical guidance for software engineers and data scientists interested in exploring this technology.