How do you build quality software with LLMs? Carl and Richard talk to Den Delimarsky about the GitHub Spec Kit, which uses specifications to help LLMs generate code for you. Den discusses the iterative process of refining specifications to produce better code, and then being able to add your own code without disrupting the process. The conversation delves into this new style of software development, utilizing specifications to break down tasks sufficiently for LLMs to be successful, and explores the limitations that exist today.
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CSLA 9 with Rocky Lhotka
The next version of CSLA is out! Carl and Richard talk to Rocky Lhotka about his business objects framework that pre-dates .NET itself! Rocky discusses the surge in development that occurred for version 9, where a company heavily dependent on CSLA contracted developers to clear some of the backlog. The result is a few new long-term contributors, resulting in an increased development candence and a substantial modernization of the code base. The conversation also turns to AI and its role in development, as well as Rocky's experiments with making an MCP server for CSLA!
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Digging Deeper into .NET Aspire with Chris Klug
Aspire has been around for almost two years. How do you use it effectively? Carl and Richard talk to Chris Klug about his experience with .NET Aspire. Chris discusses thinking cloud natively, whether you are going to the cloud or not - it's not just a place, but also an architecture. The conversation digs into the role of containers and Kubernetes, deployment strategies, telemetry, security, testing, and more. You can use as much or as little Aspire as you wish!
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Valuable Testing with Egil Hansen
You write tests - but are they valuable tests? Carl and Richard talk to Egil Hansen about his approach to creating tests for applications. Egil discusses the types of testing and who they impact. Testing isn't only for you! Valuable tests are also durable, being able to persist between changes where it makes sense, and help to understand when updates are going to create problems. The role of LLMs in generating code comes into play: should AI write your tests, evaluate them, or do both? Lots of great thinking from someone who's been helping developers build better tests for years!
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Local AI Models with Joe Finney
AI in the cloud dominates, but what can you run locally? Carl and Richard speak with Joe Finney about his work in setting up local machine learning models. Joe discusses the non-LLM aspects of machine learning, including the vast array of models available at sites like Hugging Face. These models can help with image recognition, OCR, classifiers, and much more. Local LLMs are also a possibility, but the hardware requirements become more significant - a balance must be found between cost, security, and productivity!
France