Mark Seemann

Mark Seemann, a former bad economist turned programmer, shares his insights on the concerns surrounding AI's impact on software development. He discusses the perceived reliability of tools like ChatGPT, stressing the importance of having a solid understanding to validate AI-generated code. The conversation touches on ethical considerations and the challenges faced by junior developers in an AI-driven environment. Seemann also emphasizes the need for accountability and trust as technology evolves, urging caution in incorporating AI into critical systems.

Mark Seemann, a seasoned software developer and blogger, shares his critical insights on the impact of AI tools like ChatGPT on the development community. He warns against the illusion of AI as a reliable source of truth, highlighting the need for developers to possess sufficient knowledge to evaluate AI-generated code. The conversation touches on the risks of inexperienced developers misusing these tools, underscoring the importance of accountability and critical thinking in software development. Mark emphasizes that as AI evolves, so must our approach to its implications and oversight.

Mark Seemann, a programmer with an economics background and blogger at blog.ploeh.dk, shares his insights on the impacts of AI, particularly large language models, on software development. He addresses the pitfalls of viewing AI as an ultimate source of truth and the essential knowledge required to evaluate AI-generated code. The conversation touches on the challenges faced by junior developers, the ethical implications of AI, and the importance of critical thinking in an era increasingly reliant on technology. Practical discussions around integrating AI tools highlight the need for careful oversight.

Guests on the show discuss Apple's new AI features, rebuilding nanoGPT in a spreadsheet, the risks of using natural keys in database design, and a grand unified theory of the AI hype cycle

Guests discuss Apple's new AI system, Piku deployment tool, rebuilding nanoGPT in a spreadsheet, pitfalls of natural keys in database design, and a grand unified theory of the AI hype cycle.