# 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

Episode 13 - Creativity and Summoning AI like a Shaman

Hosts: Andy and Pete Guest: Gav Fielding (Digital Marketing & Brand Specialist, Artist)

Exploring AI's creative potential, the art of prompt engineering as digital shamanism, and how traditional creative processes translate to the age of artificial intelligence.

AI Development Evolution (00:00-08:22)

• Cursor IDE billing changes and the shift from "slow coding" to "vibe coding"
• Return to "super fast waterfall" development with AI agents
• Context window limitations: quality vs quantity

Human vs AI Context (08:22-15:55)

• The challenge of replicating human contextual understanding in AI
• Multi-personality aspects of identity and AI agent development
• "Things you know" vs "things you are" in AI persona creation
• Vision and sensory data as the next AI frontier

Learning Transformation (15:55-24:12)

• Khan Academy's personalized AI math tutoring
• Tailoring communication styles to individual learning preferences
• ChatGPT's tutor mode: guided discovery vs direct answers
• Voice interface challenges in natural conversation

Creativity as Process (24:12-35:20)

• Creativity as a "volumes game"
• generating many ideas to find exceptional ones
• AI raises both floor and ceiling of creative output
• The "doorman fallacy" - losing tacit knowledge through naive automation
• Personal AI tools outperform organisational implementations

Purpose in an Abundant World (35:20-46:48)

• When everything is automated, purpose becomes the key differentiator
• Brand strategy increasingly important with commoditized intelligence- Balancing automation benefits against loss of meaning
• David Graeber's "bullshit jobs" in AI context

Creative Problem-Solving (46:48-55:06)

• Engineering vs creative solutions (Rory Sutherland's elevator mirror example)
• Market research challenges: people can't articulate true needs
• The difference between mechanical and psychological solutions

Decentralization & Community (55:06-58:14)

• AI enabling hyper-localisation and community based solutions
• Shift from centralised to localised innovation
• Evolution toward gig economy with community co-working hubs

Shamanic Prompt Engineering (58:14-1:18:43)

• Prompt engineering as modern "shamanism" summoning digital entities
• "Set and setting" for AI interactions, borrowed from psychedelic methodology
• Multi-agent conversations for enhanced ideation
• Tools like Mind Hive for collaborative AI workshops

Creative Methodology (1:18:43-1:27:54)

• Hemingway's "write drunk, edit sober" framework
• Separating ideation from judgment in creative processes
• Underwater brainstorming for forced creative breakthroughs
• Divergent vs convergent thinking states

Digital Summoning (1:27:54-1:39:34)

• AI requiring careful "birthing" and context setting
• Traditional shamanic practices informing modern AI interaction
• The art of "enchanting" AI with proper incantations

Standout Quotes

"Creativity is just a volumes game. You have more ideas. Some of them are good, some of them are bad, and you filter them all out."

"It's almost like layering in pre-modern medicine... the clash between those applied to AI now versus current life versus future AI life."

"We're all shamaning this thing. And we're just giving it a bad trip."

"There probably is like a way of... maybe it's all in occult books around summoning demons. It's actually just got mis-translated over the years. And it was actually all about context engineering."

The Good Stuff, with Pete and Andy - Episode 12: AI Myths and the Future of Work as Play

Hosts: Pete and Andy (recorded at City Beach, Perth)

Episode Overview: Pete and Andy explore common AI myths and misconceptions, diving deep into interface design, the productivity vs creativity paradigm, and how work might evolve to resemble play in an AI-enabled future.

Reflections on Guest Episodes (00:00-03:20)

• Dynamic of having guests vs. just the two hosts
• Preference for discussion format over structured interviews
• Organic conversation flow versus scripted content

The "I Trained the Model" Myth (03:20-10:30)

• Misconception between fine-tuning vs. adding context/documents
• Most "training" is actually just attaching PDFs or system prompts
• LLMs should handle interface, not factual recall
• Context engineering as the superior approach over model training

Small vs. Large Language Models (10:30-16:30)

• The "Ferrari for grocery shopping" mentality - overusing frontier models
• Small language models as the better choice for repetitive commercial workflows
• Cost and speed advantages of smaller models for specific tasks
• Modular approach: using right-sized models for different pipeline steps

Interface Design Myths (16:30-27:30)

• Chat as the default AI interface limiting potential
• Need for adaptive interfaces suited to different working styles
• Microservices architecture finally becoming economically viable with AI
• Moving beyond monolithic "big model for everything" approach

Flow State and Adaptive Interfaces (27:30-39:00)

• Spreadsheets as example of adaptive, durable tools
• Visual vs. text-based collaboration preferences
• The ramp-up/ramp-down challenge when returning to complex projects
• Multiple input/output modalities for different contexts

Human Collaboration Patterns (39:00-48:00)

• Engineers gravitating to whiteboards for collaboration
• The canvas as shared workspace vs. individual thinking space
• Voice, visual, and collaborative interfaces serving different needs
• Balancing real-time interaction with persistent documentation

Creativity vs. Productivity Paradigm (48:00-58:00)

• AI as creative enabler rather than just productivity booster
• The scary prospect of agency - having to decide what to work on
• Embodied human experience as irreplaceable for insight generation
• Examples from Rory Sutherland: mirrors in elevators, train comfort over speed

The Future of Work as Play (58:00-1:08:00)

• Moving from medieval peasant schedules to office work and back to leisure
• Work resembling exploration and experimentation
• The role of craft and embodied skills in an AI world
• Victorian gentlemen as preview of future leisure class

Error Tolerance Double Standards (1:08:00-1:14:00)

• Unrealistic expectations for AI accuracy vs. human error rates
• Need for same systems and processes, just faster iteration cycles
• Human mistakes tolerated due to context; AI mistakes seen as fundamental flaws

"It's not the job of the model to know stuff... the best way to get good factual core from these things is context engineering."

"Why are you in a hurry? Take your time. Be really comfortable. We'll get rid of the plebs." - On reframing problems

"The future's here, it's just not evenly distributed" - Applied to leisure and creative work

Bottom Line: AI myths persist because people experience AI through limited interfaces and apply unrealistic error expectations. The real opportunity lies in modular, adaptive systems that enable work to become more play-like, with humans focusing on embodied creativity and meaning-making while AI handles decomposed tasks.