# 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."
Good Stuff 12 - In The Future Work Will Look Like Play
https://blob.satellite.earth/bcfdfff33944f01b89509e4013e419c51119de8fcf9bf3a53c6fffebc02666d2
In Episode 12, 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.
Key Discussion Points: **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 Key Insights: *"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.
Good Stuff 11 - Tastemakers and Zeitgeists
Summary In Episode 11 of The Good Stuff podcast, hosts Pete and Andy welcome their inaugural guest, Joel Pember, brand director and co-founder of Juicebox. The conversation explores Joel's journey from photography to digital marketing, diving into philosophical discussions about AI's impact on creative industries, the evolving nature of brand and taste in an AI-driven world, and the future of agencies in a rapidly changing digital landscape. Key Themes **Introduction and Beach Recording** (00:00-02:00)- The hosts discuss recording at the beach and their unstructured approach to the podcast- They introduce their first-ever guest, Joel Pemba, brand director of Juicebox **Joel's Background in Photography** (02:50-06:30)- Joel discusses his start as a photographer and the philosophical aspects of photo media- Explanation of Magnum Photographers and the artistic side of photography beyond technical skills **AI's Impact on Photography and Creative Industries** (06:30-11:00)- Discussion about how AI might affect artistic expression and photographic storytelling- Reflection on the difference between AI-generated images versus images with lived experience **AI Ethics and Leadership** (11:00-18:30)- Sam Altman's open letter and the weight of responsibility on AI leaders- Debate about governance versus innovation in AI development- The need for more philosophical and ethical conversations around AI **Government Regulation vs. Organic Development** (18:30-25:00)- Discussion about regulation of technology (social media bans for children)- Debate about centralized control versus free markets and bottom-up solutions- How younger generations will adapt to and potentially circumvent restrictions **Economic Pressure and Social Considerations** (25:00-30:40)- How economic pressures limit people's ability to engage with bigger questions- Discussion about inflation, debt, and the impact on younger generations- The need to question existing systems and consider alternatives **Brand Value in an AI World** (30:40-40:00)- The evolution of social media platforms from connection to algorithmic discovery- How brand value might increase in an era of AI-driven abundance- The role of brands as shortcuts for trust and quality **The Nature of Taste and AI as Taste-maker** (40:00-50:00)- Discussion about how taste is formed and whether AI can develop authentic taste- The role of lived experience in developing taste versus algorithmic recommendations- Cultural waves and how brands ride the zeitgeist **The Future of Agencies and Customer Experience** (50:00-01:02:00)- Joel explains how digital agencies have moved from the fringe to the center of brand strategy- How customer experience and technology are replacing traditional advertising- The role of AI in transforming how brands connect with customers **Agency-to-Agency Interactions** (01:02:00-01:15:30)- Exploring how AI agents might interact with each other on behalf of humans- Discussion about hyper-localization and the shift away from global monoculture- Joel's vision for Juicebox as designing meaningful connections between brands, people, and intelligent systems **Conclusion** (01:15:30-01:17:00)- Reflection on the conversation and plans for future episodes- Joking about making all future guests use the "Joel sprite" in their visualization
The Good Stuff 10 - Death of the sunk cost falacy
In this tenth episode of The Good Stuff, hosts Pete and Andy discuss how AI is transforming product development, business creation, and work. We explore how the falling cost of creation enables faster product-market fit testing, the future of venture capital, and the rise of multi-agent AI systems.
(00:56-07:13) The hosts celebrate reaching episode 10 Shout-outs to podcast supporters including Crispy, BundabergHodl, and BTCShellingPoint. Mention of Bethans upcoming book "The Human Edge" about critical thinking and human skills in an AI world AI and the Changing Nature of Business Creation
(07:13-15:17) Discussion of the Presidio Bitcoin podcast (PBJ)How AI is making engineering less important than product-market fit The ability to rapidly test multiple ideas with minimal cost Product Market Fit and Personal Alignment (15:17-27:53) The importance of "product-founder fit" and choosing problems you genuinely care aboutReduced sunk cost bias when experimenting has lower costsHow energy and interest in a problem are better guides than purely tactical decisionsScott Adams' skill stacking concept applied to the AI era Hyper-Localization and the Future of Business (27:53-40:00)- The rise of hyper-local solutions built by people who understand specific markets"Proof of punch in the mouth" the trust advantage of local businessesPotential "death of B2B SaaS" as local solutions become more viableHow AI might transform interfaces between humans and business services Bitcoin, Capital, and Long-term Value Preservation (40:00-55:43) Discussion of how Bitcoin fits into business strategy in an AI worldThe "up, up, down" methodology (margin up, capital up, risk down)How Bitcoin provides a way to preserve value in an uncertain futureThe trade of the next decade: "Use AI to earn more money now and keep it in Bitcoin" Multi-Agent AI Systems and UI Evolution (55:43-1:15:00) Experiences with Roo Code and orchestrating multiple AI agentsThe limitations of current chat interfaces that require human directionVision for more opinionated AI agents that drive interactions forwardFuture UI as high-agency AI that can maintain context and memory
Good Stuff 04 - The Intelligent Assembly Line (audio)
Pete Winn, Andy David
Pete and Andy explore how AI will transform business processes through "The Intelligent Assembly Line" - breaking down complex knowledge work into smaller, automatable components.
This episode examines how AI is shifting business processes from human-centered to human-at-the-edge, and having a similar impact as Henry Ford's assembly line.
# AI Business Strategy: Build vs Buy Decision Framework
**Hosts:** Pete and Andy (virtually at the beach with their new cinematic backdrop)
## Core Topic Strategic decision-making in the AI era: whether to build new AI-native businesses or acquire and transform existing ones, examining capital allocation strategies and transformation approaches for different industry contexts.
Good Stuff Podcast - Episode 2: The Value Trap
Hosts: Andy and Pete Andy and Pete dive into their "Value Trap" framework a visual framework to explain how AI will transform industries and the approach to escape the value trap.
# The Good Stuff, with Pete and Andy - Episode 4: The Intelligent Assembly Line
**Hosts:** Andy and Pete (recorded in a van at City Beach, Perth, with Tai Chi practitioners visible in the background)
**Episode Overview:** Pete and Andy explore how AI will transform business processes through "The Intelligent Assembly Line" - breaking down complex knowledge work into smaller components that can be automated, similar to how Henry Ford revolutionized manufacturing with the assembly line.
---
## Key Discussion Points
### Opening Chat: Teaching Kids in the AI Era (01:16-07:53)
- Pete describes creating an AI-powered "Teddy Fashion Boutique" business with his 8-year-old daughter
- Discussion about teaching children entrepreneurship and making money online at a young age
- The value of showing kids they can make money on the internet and developing agency
- Using AI to overcome learning barriers in various skills like coding and music
### The Intelligent Assembly Line Concept (12:20-14:44)
- Comparing modern AI implementation to Henry Ford's assembly line revolution (1913)
- Ford transformed car manufacturing by breaking down complex artisan tasks into simple components
- Assembly line reduced car production time from 12.5 hours to 93 minutes
- By 1914, Ford produced more vehicles than all other manufacturers combined
### Historical Impact of the Assembly Line (14:44-18:50)
- Assembly line led to the 5-day work week and 8-hour day work structure
- Ford doubled wages to $5/day while reducing work hours
- Discussion of how these industrial work patterns still influence knowledge work today
- Questioning why these paradigms persist in modern work environments
### The New Paradigm: Units of Intelligence (22:00-24:46)
- **Current paradigm:** humans are the "form factor" for intelligence in business at ~$100k per unit
- **New paradigm:** intelligence can be purchased in smaller units at drastically lower costs (cents)
- Human intelligence is constrained (hours, energy, variability) while AI is not
- Breaking jobs into smaller components allows for more efficient automation
### Bionic Human vs. Human at the Edge (25:57-30:41)
Two models of AI implementation:
- **Bionic human:** humans use AI tools to enhance their capabilities (current mainstream approach)
- **Human at the edge:** AI does core work 24/7, humans only interface at boundaries
- The shift from human-centered to machine-centered processes is key to maximizing efficiency
### Why People Think AI Won't Replace Their Jobs (30:41-38:52)
- People often test AI with their entire job and find it lacking, giving false security
- Framework of AI implementation stages
- Current resistance to AI often based on LLM-only experience
### Memory and Context in AI Systems (38:52-48:00)
- Key to effective AI is solving the "memory problem"
- Combining semantic knowledge with contextual memory and examples
- The power of providing examples into AI systems dramatically improves output
- Using knowledge graphs and databases to enhance AI capabilities
### Process Mapping and Enumeration (48:50-55:06)
- Many business processes are poorly documented or understood
- Breaking down processes reveals they're often far more complex than perceived
- AI implementation requires better enumeration of tasks
- Enterprise memory is lost when people leave organizations
### Capital Allocation and Market Disruption (01:15:06-01:19:04)
- Capital allocators can bypass traditional product-market fit models
- Traditional service businesses with established markets are prime for disruption
### Future of Work and Human Value (01:22:35-01:27:54)
- Shift in working identity as humans move from center to edge of processes
- Potential for humans to pursue higher-value creative work
- Rethinking the 9-to-5 work structure in an AI-powered world
### Conspiracy Corner (01:28:44-01:34:39)
- Discussion about human intuition and creativity
---
## Core Concepts
### The Assembly Line Analogy
Just as Henry Ford broke down complex car manufacturing into simple, repeatable tasks that dramatically increased efficiency and reduced costs, AI enables breaking down knowledge work into smaller components that can be automated at scale.
### Intelligence as a Commodity
The fundamental shift from viewing human intelligence as the primary unit of business capability (~$100k/year) to purchasing intelligence in much smaller, more cost-effective units through AI systems.
### Process Transformation Models
- **Human-Centered:** Traditional approach where humans remain at the center with AI as tools
- **Machine-Centered:** Revolutionary approach where AI handles core processes and humans operate at decision boundaries
### The Memory Problem
Effective AI implementation requires solving how systems remember, contextualize, and apply knowledge - combining semantic understanding with specific examples and organizational memory.
# AI Business Strategy: Build vs Buy Decision Framework
**Hosts:** Pete and Andy (virtually at the beach with their new cinematic backdrop)
## Core Topic
Strategic decision-making in the AI era: whether to build new AI-native businesses or acquire and transform existing ones, examining capital allocation strategies and transformation approaches for different industry contexts.
## Two Primary Strategies
### Build Strategy: AI-Native from Scratch
Creating new businesses without legacy constraints, leveraging AI capabilities from inception.
**When to Build:**
- Incumbent organizations are trapped in "inertia traps" and slow to adopt AI
- A beginner's mindset can lead to radically different approaches
- Customer acquisition costs can be significantly reduced with AI-native solutions
- Service delivery can be fundamentally transformed through AI
- Speed to market with AI-native solutions outweighs existing asset value
### Buy Strategy: Acquire and Transform
Purchasing established businesses with existing customers and transforming them through AI integration.
**When to Buy:**
- Customer acquisition and trust-building are expensive or time-consuming
- Significant regulatory or compliance barriers exist
- Brand and credibility serve as critical differentiators
- Distribution networks represent high-value, difficult-to-replicate assets
- Existing customer contracts create substantial switching costs
## Key Decision Factors
- Industry characteristics and competitive dynamics
- Customer switching costs and acquisition expenses
- Trust and credibility requirements
- Regulatory and compliance complexity
- Capital requirements and resource availability
## Strategic Frameworks and Concepts
### The "Truck Size" Analogy
*"If I can buy a bucket of cognition for $1 instead of $100,000, why is the truck that big? What changes?"*
Historical business processes were designed around humans as the sole source of intelligence. AI enables complete reimagining of processes without human constraints, questioning why systems are sized and structured as they are.
### Chesterton's Gate Principle
Understanding the rationale behind legacy systems before redesigning them—recognizing why processes exist in their current form before transformation.
### The "Netflix Model"
Incubating new AI-native businesses alongside existing operations, allowing for innovation without disrupting core business functions.
## Transformation Challenges
### Organizational Dynamics
- Embedded resistance to change in established businesses
- Complex system transitions with interdependent components
- Managing stakeholder expectations during transformation
- Balancing innovation with operational continuity
### The "Intelligent Assembly Line" Methodology
Practical framework for systematic business transformation through AI integration.
**Key Insight:** *"This ability to branch at that point always required a human, so you have to have a person in a chair doing that. This implies that you no longer need to put somebody in a chair."*
## Case Studies and Applications
### Duolingo Analysis
Exploration of how language learning applications might evolve with AI integration and immersive experience technologies.
### Distribution vs. Technology Value
Balancing the worth of existing customer bases against new technical capabilities and AI-driven innovations.
## Market and Investment Considerations
### Competition Dynamics
The potential for individual entrepreneurs to create competitive AI applications that challenge established players.
### Long-term Value Creation
- Where to build sustainable equity as technical moats erode rapidly
- Shifting company lifecycles in public markets (from 60+ years to 15-20 years)
- Bitcoin as potential value preservation during industry transformations
## Strategic Implementation
### Acquisition Considerations
*"The actual overall cost of that business would include all of those things, effectively as assets. The people involved, the employees are all part of the business that you're buying."*
### Transformation Steps
Practical methodologies for companies implementing AI transformation while avoiding the "value trap" of unsuccessful change management.
## Key Takeaway
The fundamental question for any business in the AI era is understanding how dramatically reduced cognitive costs change optimal business structure, process design, and competitive positioning.
Good Stuff Podcast - Episode 2: The Value Trap
Hosts: Andy and Pete
Andy and Pete dive into their "Value Trap" framework a visual framework to explain how AI will transform industries and the approach to escape the value trap.
Introduction and reflection on their lo-fi podcast approachExplanation of the "Value Trap" concept
Phase 1: Cost reduction through AI implementation
Phase 2: Revenue growth and pricing power
Phase 3: Competition and mean reversion
- Capital allocation strategies for the AI transition
- Business characteristics that fare better through this transition
- The paradox of technology laggards benefiting most from AI
- Strategies for incumbents: The "Netflix model" of business transformation
- Buy vs. build approaches for traditional businesses
- Risk management during AI transformation
- Potential macroeconomic impacts of widespread job displacement
- Monetary policy implications and inflation concerns
- Buy Bitcoin
"This is a renaissance for entrepreneurs. If you're entrepreneurial minded, this is just a huge, an amazing time to be alive."
Hosts: Andy and Pete (recorded at City Beach, Perth)
Episode Overview: The inaugural episode explores how AI will transform business models, where value will accrue, and strategic approaches for businesses adapting to AI.
Key Discussion Points:
Value Shift in AI: The hosts argue that value in AI won't primarily accrue to companies like OpenAI but to traditional service businesses that leverage AI to transform their operations.
Transformation of Traditional Businesses: Businesses with language-heavy workflows and high human labor costs can use AI to shift the "unit of intelligence" from humans to scalable AI systems, potentially achieving software-style margins.
Hyper-Localization: Pete predicts a future where power and control shift to small businesses that can leverage commodity intelligence, rather than large centralized players.
SaaS Evolution: Discussion about whether SaaS business models will decline as AI enables more custom-built solutions specific to individual business needs, reducing dependency on one-size-fits-all platforms.
App Players vs. Agentic Workflows: The hosts debate whether there will be an "app player renaissance" or if agentic workflows will eliminate the need for traditional application interfaces.
First Principles Thinking: Businesses need to reimagine their processes from first principles rather than simply adding AI tools to existing workflows.
Human Role Transformation: A key insight is the shift of humans from being central to business processes to working "at the edge" - where humans become interfaces with the real world while AI handles core processes.
The Value Trap: Andy and Pete introduce the concept of the "value trap" - where initial AI efficiency gains create massive value, but competition eventually erodes pricing power, potentially creating challenging transitions.
Transformation Strategies: Discussion of whether businesses should create "digital twins" (like Netflix did when moving from DVDs to streaming) or transform their existing operations.
Capital Allocation Opportunity: Private equity and venture capital firms are already raising funds to acquire businesses specifically to implement AI transformation strategies.
Looking Ahead: The hosts tease a deeper discussion of the "value trap" concept for episode two, promising to show listeners how to navigate this transitional period.Closing Thought: "We spent the last two decades searching for product market fit, and it turned out the valuable thing was just to stick with the companies that already had it."
