Software Engineering Radio - the podcast for professional software developers

Wesley Beary of Anchor speaks with host Sam Taggart about designing APIs with a particular emphasis on user experience. Wesley discusses what it means to be an “API connoisseur”— paying attention to what makes the APIs we consume enjoyable or frustrating and then taking those lessons and using them when we design our own APIs. Wesley and Sam also explore the many challenges developers face when designing APIs, such as coming up with good abstractions, testing, getting user feedback, documentation, security, and versioning. They address both CLI and web APIs. This episode is sponsored by Fly.io. [https://www.fly.io?utm=seradio]

Chris Love, co-author of the book Core Kubernetes [https://www.manning.com/books/core-kubernetes], joins host Robert Blumen [https://se-radio.net/team/robert-blumen/] for a conversation about kubernetes security. Chris identifies the node layer, secrets management, the network layer, contains, and pods as the most critical areas to be addressed. The conversation explores a range of topics, including when to accept defaults and when to override; differences between self-managed clusters and cloud-service provider-managed clusters; and what can go wrong at each layer -- and how to address these issues. They further discuss managing the node layer; network security best practices; kubernetes secrets and integration with cloud-service provider secrets; container security; pod security, and Chris offers his views on policy-as-code frameworks and scanners. Brought to you by IEEE Computer Society [https://www.computer.org/] and IEEE Software magazine [https://www.computer.org/software].

Jacob Visovatti and Conner Goodrum of Deepgram speak with host Kanchan Shringi about testing ML models for enterprise use and why it's critical for product reliability and quality. They discuss the challenges of testing machine learning models in enterprise environments, especially in foundational AI contexts. The conversation particularly highlights the differences in testing needs between companies that build ML models from scratch and those that rely on existing infrastructure. Jacob and Conner describe how testing is more complex in ML systems due to unstructured inputs, varied data distribution, and real-time use cases, in contrast to traditional software testing frameworks such as the testing pyramid. To address the difficulty of ensuring LLM quality, they advocate for iterative feedback loops, robust observability, and production-like testing environments. Both guests underscore that testing and quality assurance are interdisciplinary efforts that involve data scientists, ML engineers, software engineers, and product managers. Finally, this episode touches on the importance of synthetic data generation, fuzz testing, automated retraining pipelines, and responsible model deployment—especially when handling sensitive or regulated enterprise data. Brought to you by IEEE Computer Society [https://www.computer.org/] and IEEE Software magazine [https://www.computer.org/software].

Samuel Colvin, the CEO and founder of Pydantic, speaks with host Gregory M. Kapfhammer [https://se-radio.net/team/gregory-kapfhammer/] about the ecosystem of Pydantic’s Python frameworks, including Pydantic, Pydantic AI, and Pydantic Logfire. Along with discussing the design, implementation, and use of these frameworks, they dive into the refactoring of Pydantic and the follow-on performance improvements. They also explore ways in which Python programmers can use these three frameworks to build, test, evaluate, and monitor their own applications that interact with both local and cloud-based large language models. Brought to you by IEEE Computer Society [https://www.computer.org/] and IEEE Software magazine [https://www.computer.org/software].

Brian Demers, Developer Advocate at Gradle, speaks with host Giovanni Asproni [https://se-radio.net/team/giovanni-asproni/] about the importance of having observability in the toolchain. Such information about build times, compiler warnings, test executions, and any other system used to build the production code can help to reduce defects, increase productivity, and improve the developer experience. During the conversation they touch upon what is possible with today’s tools; the impact on productivity and developer experience; and the impact, both in terms of risks and opportunities, introduced by the use of artificial intelligence. Brought to you by IEEE Computer Society [https://www.computer.org/] and IEEE Software magazine [https://www.computer.org/software].