Contribution and craft
Where competence, clarity, leverage, service, discipline, creation, and feedback become visible.
Growth stack
How do I grow?
The growth model moves from coherence through capacities of action, perception, and care; into courage, curiosity, and compassion; into consciousness; and then into the deep transformative operators: openness, integration, and dissolution.
Centering conditioncohereance
Integrity of the system: thoughts, values, actions, body, relationships, and attention align.
Competence triadcompetence · clarity · caring
Effective action requires ability, perception, and love/care for what the action serves.
Heart-agency triadcourage · curiosity · compassion
The human stance that lets the system move toward truth, uncertainty, and other beings.
Fieldconsciousness
The containing awareness within which perception, agency, growth, and dissolution occur.
Transformationopenness · integration · dissolution
Open to what is; integrate what matters; dissolve what is no longer true or useful.
Life domains
What domains do I grow in?
The model compresses lived growth into three always-present arenas: work, love, and energy. They are not separate projects; each reflects the state of the whole system.
Relationship and care
Where compassion, courage, belonging, intimacy, truth, forgiveness, and responsibility are tested.
Vitality and capacity
Where body, attention, emotion, nervous system, rest, desire, and resilience determine available agency.
Human operating system
What is life?
Life is rendered as five tiers. The first asks what exists; the middle tiers ask how agency and cognition operate; the fourth asks what is becoming through action; the fifth asks where the being is embedded.
1
Existence
What exists?
Substrates
Consciousness • Information • Energy • Matter
2
Agency
What powers & directs me?
Faculties
Awareness • Equanimity • Mindfulness • Focus
Self-Determination
Intentions • Virtues • Beliefs • Identity
3
Cognition
How do I understand it?
Perception
Interpretation • Clarity • Signal
Experience
Feelings • Thoughts • Senses
4
Actualisation
What am I doing & becoming?
Meaning
Calling • Satisfaction • Contribution
Engagement
Action • Participation • Feedback
5
Context
Where am I embedded?
Environment
People • Systems • Places
Situation
Safety • Belonging • Culture
Supercharging layer
How does AI help with the above?
AI helps when it becomes a context-sensitive collaborator across all tiers: it remembers, retrieves, notices, delegates, reasons, publishes, verifies, and learns. The point is not replacement; the point is amplified growth.
What are we asking?
AI helps formulate the right question at the right layer: growth, life, context, intelligence, problem, leverage, collaboration.
What can we delegate?
Research, workflows, verification, synthesis, planning, source inspection, routine judgement, artifact creation, and bounded action.
Signal vs noise
AI can compare context against priorities, memory, patterns, and energy constraints to decide what deserves attention.
Self-improving faculties
What does true intelligence look like?
True intelligence looks like faculties that improve from context. The system becomes more intelligent when it transforms context into clearer signal, priorities, memory, insight, ideas, skill, autonomy, boundaries, leverage, and presence.
More discerning
Fromsignal vs noise
Byclear signal from context
More focused
Fromnow vs later
Byclear priorities from context
More knowledge
Frommemory vs entropy
Byclear memory from context; auto picks up into GBrain
More pattern recognition
Fromconnect vs isolate
Byclear insight from context; auto recalls from GBrain
More creative
Fromgenerate vs imitate
Byclear idea generation from context
More competent
Fromstatic ability vs learned ability
Byclear skills from context; Hermes skill creation
More autonomy
Fromprompted-only vs self-propelled
Byclear self-propelled action from context
More proactive
Frominterruption vs collaboration
Byclear boundaries/collaboration from context
More holistic
Fromlocal optimisation vs whole-system leverage
Byclear high leverage from context
More omnipresent
Fromepisodic chat vs ambient presence
Byclear presence with context
Problem-solving
How can AI truly problem solve?
Problem-solving is not “generate an answer.” It is a staged movement from identity and selection through discernment, comprehension, leverage, envisioning, contribution, and verification.
00I
Who’s solving?
The solver’s identity, incentives, faculties, blind spots, and context shape the problem.
0Selection
Which problem, now?
Scan perspectives, ways of thinking, mental models. Surface pattern recognition, salience, leverage.
1Discernment
What is the real problem?
Filter noise, signal, surface, core.
2Comprehension
What is its shape?
Model complexity, inner workings, surfaces, abstraction.
3Leverage
Where can I operate?
Find levers: points, surfaces, operability.
4EnVision
What replaces it?
Orient among many solutions, emergent end-state, direction.
5Contribution
Who does it serve?
Clarify value, impact, magic.
6Verification
Does it hold?
Test solution, quality, resilience.
Systems leverage
How can systems mimic high-leverage thinking?
The model uses a leverage ladder: shallow intervention changes parameters and structures; deep intervention changes rules, self-organization, goals, paradigms, and the capacity to transcend paradigms.
Low leverage
Shallow
12. Parameters — Constants, taxes, and standards.
11. Buffers — Size of stabilizing stocks.
10. Stocks & Flows — Physical structures.
Medium leverage
Feedback
9. Delays — Feedback response times.
8. Negative Loops — Self-correcting mechanisms.
9. Positive Loops — Driving exponential growth.
6. Information Flows — Access to data.
High leverage
Deep
5. Rules — Incentives and constraints.
4. Self-Organization — Ability to evolve.
3. Goals — System purpose.
Highest leverage
Deepest
2. Paradigms — Mindsets and worldviews.
1. Transcendence — Rising above paradigms.
Human + intelligent system
How do I truly collaborate with an intelligent system?
True collaboration means the human supplies direction, values, taste, body, responsibility, and ultimate judgement; the system supplies memory, context, synthesis, delegation, verification, and continuity. The relationship becomes supercharged when both sides learn.
Connor’s side
- Name the real question.
- Expose the model, values, and constraints.
- Give taste feedback: useful, noisy, wrong, do this.
- Approve or reject risky mutations.
- Use the system to reduce load, not outsource consciousness.
Agent/system side
- Retrieve before answering.
- Separate signal from noise.
- Propose bounded actions.
- Create artifacts and proof.
- Save learnings into GBrain, context assets, and Hermes skills.
The harmony: human consciousness directs the system; the system extends human memory, perception, judgement, and action.
Published from Connor’s 2026-05-30 model notes. Original phrasing preserved where load-bearing, including “cohereance,” “EnVision,” and the leverage ladder numbering.