Don’t Think Vibe Coding. Think Just-in-Time Modeling.
Source: Mike Caulfield Substack
Author: Mike Caulfield
Original source: https://mikecaulfield.substack.com/p/dont-think-vibe-coding-think-just
Private backup: the full article text is archived in the private repository at archives/articles/mikecaulfield-substack-com-dont-think-vibe-coding-think-just.source.md. It is not published on the public Quartz site.
Summary
Mike Caulfield argues that the educational significance of LLM-assisted coding is not software production alone, but just-in-time modeling as a way to think. Using Claude Code to investigate why Seattle’s earliest sunset occurs before the winter solstice, he builds and revises interactive models that test competing explanations about solar noon, latitude, time zones, orbital speed, and axial tilt. The article frames AI-generated models as learning objects that can expose gaps in understanding, invite verification, and make complex phenomena manipulable for learners who could not otherwise build the tools themselves.
Big ideas
- AI simulations need clear boundaries for learning
- Voice AI may make learning support easier to access
Claims
- AI-built models can help students show conceptual understanding
- Vibe coding is an early sign of broader knowledge-work change
Key evidence and examples
- Caulfield begins with a confusing AI-generated explanation of Seattle’s earliest sunset and realizes that apparent understanding collapses under closer questioning.
- He uses Claude Code to build a Flask-based interactive solar-time model without writing the code himself.
- Comparing cities such as Seattle, San Francisco, Houston, Bangor, Anchorage, and Santiago helps test explanations involving latitude, time zones, and orbital effects.
- The model helps separate day length from solar-noon shift, making the equation of time more visible.
- The learning value comes partly from checking the model and the AI’s assumptions rather than accepting either as authoritative.
Education relevance
This is directly relevant to AI literacy, STEM learning, computational thinking, and inquiry-based pedagogy because it treats AI as a scaffold for model-building and conceptual testing rather than as a shortcut around understanding.