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Governance harness for decision-making systems with structured review, epistemic honesty, and retrospective calibration.

The general-purpose LLM Harness. Wrap your models with a multi-tenant agent runtime, memory, and strict guardrails.

An Agent-native Obsidian wiki + CLAUDE.md skeleton that lets Agent know your project as well as you do.

A reinforcement learning agent that learns to intelligently shape electricity demand, reducing peak loads and optimizing energy consumption in real-time.

Build and run cross-platform event-driven rules and actions with a zero-dependency, lightweight engine for games, web, and mobile apps.

🚀 Process JSON data in batches with `llm-batch`, leveraging sequential or parallel modes for efficient interaction with LLMs.