Send us a text

When identical prompts yield different outputs and a request for "a cat running across the screen" generates four cylinders and a bobbing balloon, the non-deterministic nature of generative AI becomes impossible to ignore. Despite the power of these sophisticated models, we discovered that creating consistent, high-quality results demanded substantial human intervention – not less. Our journey moved from refining prompts to building extensive code example libraries, requiring the very software engineering expertise these systems supposedly reduce.

This experience directly contradicts the popular "vibe programming" approach where developers mindlessly shuttle between AI suggestions and error messages. While this might suffice for weekend projects (as its originator noted), it produces unmaintainable spaghetti code lacking the architectural vision essential for professional software. Real engineering demands creating systems that scale, maintain, and transfer to other developers – not just code that temporarily functions.

The lessons extend beyond development to any organization implementing AI in decision-making processes. These systems lack the contextual understanding to independently determine business priorities or handle nuanced human factors. The future belongs not to AI replacing humans, but to thoughtful partnerships harnessing each party's unique strengths.

Ready to dive deeper into AI's practical realities? Listen now and join the conversation about how we can responsibly integrate these powerful tools while maintaining human oversight and expertise. Share your experiences with us at [email protected] or @CloudOutLoudPod.

Jon Gallagher on LinkedIn

Logan Gallagher on LinkedIn

The Animation App on Github

Andrej Karpathy on vibe programming

*Extra!* Andrej Karpathy on "Privacy Hygiene" - protecting yourself online now