# 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