Duncan McGregor and Nat Pryce, co-authors of Java to Kotlin: Refactoring Guidebook, speak with host Giovanni Asproni about their hands-on experiences migrating Java codebases. The episode starts by highlighting Kotlin’s seamless interoperability with Java, allowing teams to incrementally adopt Kotlin without disrupting existing Java code.

Duncan and Nat then describe some of the benefits of using Kotlin — including stronger type safety, non-nullable types, and better support for immutability — and some of the gotchas when refactoring from Java to Kotlin due to the different idioms supported by the two languages. Finally, they discuss the importance of testing and tooling, and the evolving role of AI-assisted tools in complex and large-scale refactorings — in the context of work done by teams, as opposed to individuals.

This episode is sponsored by Monday Dev

Qian Li of DBOS, a durable execution platform born from research by the creators of Postgres and Spark, speaks with host Kanchan Shringi about building durable, observable, and scalable software systems, and why that matters for modern applications. They discuss database-backed program state, workflow orchestration, real-world AI use cases, and comparisons with other workflow technologies.

Li explains how DBOS persists not just application data but also program execution state in Postgres to enable automatic recovery and exactly-once execution. She outlines how DBOS uses workflow and step annotations to build deterministic, fault-tolerant flows for everything from e-commerce checkouts to LLM-powered agents. Observability features, including SQL-accessible state tables and a time-travel debugger, allow developers and business users to understand and troubleshoot system behavior. Finally, she compares DBOS with tools like Temporal and AWS Step Functions.

Brought to you by IEEE Computer Society and IEEE Software magazine.

Luke Hinds, CTO of Stacklok and creator of Sigstore, speaks with SE Radio's Brijesh Ammanath about the privacy and security concerns of using AI coding agents. They discuss how the increased use of AI coding assistants has improved programmer productivity but has also introduced certain key risks. In the area of secrets management, for example, there is the risk of secrets being passed to LLMs. Coding assistants can also introduce dependency-management risks that can be exploited by malicious actors. Luke recommends several tools and behaviors that programmers can adopt to ensure that secrets do not get leaked.

Brought to you by IEEE Computer Society and IEEE Software magazine.