This repository aims to map the ecosystem of artificial intelligence guidelines, principles, codes of ethics, standards, regulation and beyond.

Agent orchestration & security template featuring MCP tool building, agent2agent workflows, mechanistic interpretability on sleeper agents, and agent integration via CLI wrappers

The comprehensive, community-maintained index of Australian AI Security standards, policies, and frameworks across all 11 jurisdictions.

The left hemisphere. Frameworks, logic, and certainty architecture. Home of FSVE, AION, LAV, ASL, GENESIS, TOPOS, and 60+ epistemically validated frameworks built to make AI systems reliable, not just capable.

Centralized AI IDE rules management for Cursor and GitHub Copilot.

Artificial Intelligence Regulation Interface & Agreements

Official Technical Stack & Economic Engine for the NUPA Framework. Authored by Brandon Anthony Bedard (Nov 2025). Featuring the 40/40/20 Recursive Reinvestment Model and FASL Protocol

This repository provides comprehensive guidelines, frameworks, and sample policies for the ethical and effective integration of AI in progressive organizations. It serves as a platform for discussion and collaboration on AI governance and ethics.

Generate a "How we use AI" policy for your software dev team.

Curated dataset and tools for tracking global AI legislation — US federal, state, and international frameworks.

Curated AI policy resources for children, families and schools, maintained by SafeAI.School

Non-Human Identity Disclosure Standard for Healthcare Voice Workflows

The standard protocol for defining runtime guardrails for your enterprise agents with a mission of trustworthy and reliable agentic systems 🛡️

The Uncomfortable Coexistence of Job Destruction and Labor shortages

The presentation layer. Structure, format, and register conversion. The last layer before output — deciding how the brain speaks, not just what it says. Prose or list. Dense or clear. Report or reply.

Customizable AI Acceptable Use Policy and governance framework for US enterprises. MIT licensed. Covers compliance, HR, infosec, and legal.

The security layer. Every output clears here before it exits. Threat detection, adversarial pattern recognition, red-team archive, and the Go/No-Go authority that can halt the entire system. Nothing bypasses it.

Architecture research exploring capability-tiered AI governance and compute-linked enforcement protocols.

The refinement layer. Precision after clearance. Every output passes through here for LAV gate validation, density calibration, and roughness preservation — the layer that makes the output worthy of the input.

Independent research on human-centered AI and LLMs | Policy frameworks for responsible AI | A collaborative space for researchers, innovators, and policymakers advancing ethical, inclusive AI