- Using AI for deterministic tasks like data formatting is inefficient, costly, and error-prone compared to simple scripts.
- Prompting is fast for one-off tasks but becomes expensive and non-deterministic when used for recurring jobs, leading to high token costs and context bloat.
- Tasks should be categorized as probabilistic (requiring judgment) for AI agents or deterministic (exact and repeatable) for apps to optimize costs and performance.
- Avoid using AI memory notes as a database for structured data; instead, use real databases for structured, high-volume data and notes for unstructured, interpretive context.
- Vybe's architecture integrates AI agents for reasoning with apps for deterministic tasks, reducing token usage and improving efficiency.
- Most tools lack the dual-layer structure of reasoning agents and deterministic apps, forcing all tasks through token-based models and increasing costs.
- A checklist helps identify tasks that should not be tokenized, such as scheduled jobs, API calls, or structured data storage.
- The goal is to apply AI intelligence only where it adds value, using deterministic code for other tasks to minimize expenses and improve reliability.