SE Radio 726: Scott Kingsley on the Swagger Ecosystem

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). They 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.

SE Radio 723: Dave Airlie on Linux Kernel Maintenance

Dave Airlie, a Distinguished Engineer at Red Hat, speaks with host Gregory M. Kapfhammer about Linux kernel maintenance. After over-viewing the scale and structure of the Linux kernel, they dive deep into the review and validation of kernel patches, drawing on examples from the GPU subsystem. After discussing the features and benefits of the Linux kernel's maintenance model, they also explore kernel maintenance best practices and the supporting tools for these practices. Dave and Gregory also discuss topics such as the integration of Rust code in the Linux kernel and the ways in which AI-driven code review are influencing kernel maintenance.

SE Radio 722: Dwayne McDaniel on the Engineering Challenges of Secrets Management

Dwayne McDaniel [https://trail.gitguardian.com/api/t/c/usr_XgQEQPgwFZ78282oE/tsk_CSQx5hsyFYHpAHx2r/enc_U2FsdGVkX19aR9lGtbabCxEhb9Yde_hsokM0Br2H8cO0MuhkXtGOlxqoSa2kzhx9AJEkM4SrYvH4PEzf842ZL9fm-omZUuEVXLdnzhA74ugphvs8lMXgwE63YENVZ9Ax], developer advocate at GitGuardian.com [http://gitguardian.com/], joins host Priyanka Raghavan to talk about the engineering challenges of secrets management. They explore what "secrets" really are in modern systems—far beyond passwords—including API keys, tokens, certificates, and machine identities, and how "secret sprawl" emerges across the SDLC. Drawing on reports from GitGuardian and Verizon, they discuss the growing scale of secret leaks and why credential abuse and phishing remain dominant attack vectors. They examine common leak points—from code repos and logs to CI/CD pipelines, containers, and SaaS integrations—and how cloud, DevOps, and AI tooling are amplifying risks. Priyanka quizzes Dwayne about recent supply chain attacks from pyPi and trivy ecosystems, highlighting recurring root causes like poor access control, long-lived credentials, and weak security hygiene. Finally, they consider detection, response, and modern solutions—short-lived credentials, secret scanning, and identity-based approaches like OWASP NHIR and SPIFFE/SPIRE—ending with practical advice for engineers to reduce blast radius and design for secure secret lifecycle management.