AI chief of staff framework with invisible shadow predictions
The article discusses a new framework for implementing an AI Chief of Staff called Giovanni. This framework focuses on memory architecture, predictive layers, and governance to enhance the functionality of AI assistants. It emphasizes the importance of invisible shadow predictions to avoid self-fulfilling outcomes in stakeholder interactions.
- ▪Giovanni provides a domain-agnostic methodology for running an AI Chief of Staff.
- ▪The framework includes features like daily digests, stakeholder modeling, and adversarial review.
- ▪Invisible shadow hypotheses are used to prevent predictions from contaminating stakeholder interactions.
Opening excerpt (first ~120 words) tap to expand
Giovanni Extended hand and second brain. A methodology framework for running an AI Chief of Staff inside Claude Code — memory architecture, daily digest, predictive layer, governance discipline, custom subagents, slash commands, living constitution, per-stakeholder modeling, adversarial-default review. Distilled from a working domain-specific implementation, then sanitized into a domain-agnostic framework you can fork and fill with your own context. In 10 seconds Most "AI assistant" repos hand you prompts.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at GitHub.