Show notes
Episode Notes
When AI generates work faster, who checks it before customers depend on it? Cody talks with Nate Stewart of Runtype about AI agent reliability and the responsibilities that remain when a model can write code or take action.
Nate describes how his expectations of automation changed when faster generation created more work to review. The conversation explores how to build reliable AI products through testing, simulations, reviewed building blocks, and clear limits on destructive access.
Cody shares his own mistakes with production databases, while Nate describes safeguards and ways to test changes without affecting production. The point is not to stop experimenting. It's to understand what an agent can change before expanding its authority.
They also discuss AI automation mistakes, model selection, and why AI product development is about more than the model: cost, latency, failure handling, integrations, and supporting the people who use the result. The later conversation covers local-model experiments and what honest building in public can teach other builders.
In this episode:
- The review workload behind faster generation
- Where to draw the line on agent access
- Simulating changes before production
- Turning prototypes into products through Runtype
- Matching models to tasks rather than chasing every release
- Local models and sharing the real work behind a build
Guest and project:
Runtype: https://www.runtype.com/
Nate on X: https://x.com/becomevocal
Nate on LinkedIn: https://www.linkedin.com/in/becomevocal/
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This conversation shares experience and opinions, not a security audit, implementation guarantee, or promise of business results. Contains explicit language.
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