Research
Frameworks and position papers on how generative AI reshapes minds, organisations and communities — and what to do about it.
- Frameworks
- Papers
- Thoughts
selfdriven · the AI layer
Generative AI in service of human- and purpose-driven communities.
selfdriven AI combines the selfdriven Framework with the power of generative models — research, technology and practice for a world where AI acts under human governance and cryptographic accountability, not in place of it.
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Open frameworks — built to be used, argued with and improved
30+
Research papers on minds, organisations and communities
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Technology areas tracked, from protocols to hardware
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Open-source repos for running AI on hardware you own
Four ways into the work.
The selfdriven AI layer is organised as a body of research, the technology that puts it into practice, the patterns for daily use, and the tools to build with.
Research · Tech · Usage · Resources
Start with the researchTech
The agent infrastructure: KERI-delegated authority, the SKILL.md and A2A protocols, and the systems that let agents act within human-set bounds.
- Protocols
- Models
- Run it local
Usage
The practice of human/agent collaboration — cognitive assistance, working patterns, and how to put the frameworks to use day to day.
- Cognitive assistance
- Working patterns
Resources
Tools, templates and links across the ecosystem — including octology.io and the wider selfdriven network of intelligence.
- Tools
- Templates
- Ecosystem
Frameworks
Our published thinking.
Open frameworks for navigating a rapidly changing AI landscape — built to be used, argued with, and improved.
- 01 · FrameworkImpact on Mind FrameworkA three-level framework for understanding generative AI's effect on the human mind.
- 02 · OrganisationsSituational Awareness FrameworkA three-level AI maturity model for organisations finding their footing.
- 03 · DecisionWait or Build FrameworkA decision framework for when to build now and when to wait, as the frontier keeps moving.
- 04 · FrameworkInput, Injected-Intelligence, OutputA model for the shape of AI-augmented work — where intelligence enters the flow and where judgment stays human.
Our approach
AI that acts under human governance.
The selfdriven approach is the Human Conductor model: people define intent and hold accountability, while AI agents act under delegated, scoped, time-limited authority. The relationship is verifiable, not assumed.
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01 · People
The Human Conductor
Humans set intent, exercise judgment, and remain accountable. Agents handle volume, consistency and monitoring — never the things that should stay human.
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02 · Proof
Cryptographic accountability
Each agent operates under a KERI-delegated
AID. Actions are anchored to an append-only key event log — independently verifiable, and durable enough to stand up to a regulator. -
03 · Purpose
Purpose over scale
AI in the service of decentralised, human- and purpose-driven communities — measured by what it enables people to do, not by engagement or extraction.