Loops, Goals, and Token Bonfires
Introduction and Context of Fable Five Release
Unpacking the Fable Five Experience
Political Implications and AI Development
Comparative Analysis of Fable and Opus Models
User Experience and Model Selection
Cost Considerations in AI Usage
Conclusion and Future of AI Models
Exploring New AI Workflows
Understanding Loops and Goals in AI
The Power of Workflows in AI Development
Pros and Cons of AI Autonomy
Navigating AI's Limitations and Challenges
Building Trust in AI Tools
Innovative vs. Maintenance Tasks in AI
Episode 12: Loops, Goals, and Token Bonfires
Justin and Kellan are back from a stretch of client workshops to break down the strangest model launch yet — Fable Five, which shipped and got pulled inside of three days — and what it says about how you actually choose a model. Then they get into the vocabulary everyone's suddenly using: loops, goals, and workflows, and when each one is worth it.
The Drip:
- Fable Five ships, gets jailbroken in a government demo, and is pulled three days later
- The irony of the pullback landing right after Dario's case for slowing AI down
- What three days with the model actually felt like — faster, fewer mistakes, less fake agreement
Inside The Bottle:
- Model choice as a binary: just _using_ AI vs. _building_ agentic AI into software
- Why unit cost (price per token) matters less than the cost to achieve the objective
- A plain-English glossary: loops, goals, workflows, ultra plan, ultra code
- When workflows earn their keep — and when loops turn into a token bonfire
- Knowing whether you're in maintenance mode or invention mode before you automate
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Music licensed through Soundstripe.
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