Show HN: Shotoku – Local-first action governance and budget caps for AI agents (www.shotoku.dev)

A new project titled Shotoku has emerged, introducing a local-first governance framework designed specifically for AI agents. This innovative approach allows users to impose budget limits and obtain approvals for the spending of AI agents without requiring third-party oversight. By implementing a tamper-evident audit trail directly on users' machines, Shotoku ensures that budget adherence is enforced before any transaction occurs, positioning itself as a secure and deterministic solution for managing AI expenditures. This development is significant for the AI/ML community as it addresses pressing concerns regarding the autonomy of AI agents and their capacity to incur costs without human intervention. With Shotoku, users gain greater control over AI operations while enhancing transparency and accountability in AI spending. By prioritizing local management and foregoing custody requirements, Shotoku not only mitigates potential risks associated with uncontrolled AI actions but also establishes a model for responsible AI governance that could inspire broader applications across various industries.