Episode 14: Vibe Coding that works!

In this episode, the hosts discuss their development of "slow code," a more intentional approach to coding with AI that contrasts with "vibe coding." They explore how this methodology creates a structured workflow combining human planning with AI execution, resulting in higher quality code while maintaining the speed benefits of AI assistance.

Key Themes:

Understanding Vibe Coding (00:02:20 - 00:08:00)* Vibe coding involves speaking instructions to AI and ignoring the underlying code* Democratizes coding by allowing anyone to build in natural language* Major downside: potential security issues and poor code quality Evolution to Slow Code (00:08:00 - 00:19:40)* Hosts developed methodologies through experimentation with AI tools* Initial frustrations with existing AI coding tools led to refinement* Slow code combines intentional planning with AI execution Three-Phase Methodology (00:19:40 - 00:25:30)* Ideation: Using Claude Desktop for exploration and brainstorming* Planning: Converting ideas to structured documents and roadmaps in Obsidian* Execution: Having AI implement based on detailed specifications Tools and Implementation (00:25:30 - 00:40:00)* Using Obsidian for planning and documentation* Connecting to AI tools via MCP (Model Context Protocol)* Root Code for implementation with orchestration capabilities* Benefits of separating environments for different phases Multi-Agent Conversations (00:40:00 - 00:51:00)* Creating pipelines where multiple AI agents discuss ideas* Using different models (Claude, Grok) to avoid single-model biases* Value in seeing how ideas develop through agent conversations Benefits of Slow Code (00:51:00 - 01:01:30)* Creates reusable, higher quality code versus one-shot vibe coding* Maintains human intentionality and design control* Still much faster than traditional development* Parallels the "write drunk, edit sober" approach to creative work Future Applications (01:01:30 - 01:15:00)* Integration with Goose for continuous development and scheduling* Creating dedicated AI development environments* Applications beyond coding (writing, research, analysis)* Plans to share methodology through tutorials and community Contrasts with No-Code Tools (01:15:00 - 01:26:00)* Visual no-code tools add unnecessary complexity* Slow code leverages AI's strengths while maintaining human oversight* More flexible than constrained visual interfaces