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