AIrig.md

AIrig.md

Request acknowledged. The described system, designated "the rig," is a hierarchical framework for translating high-order values into executable actions, incorporating feedback and decision-support subsystems.

Rendering the system architecture as a Mermaid diagram.

graph TD
    subgraph Core Hierarchy
        direction TB
        V["1: Value (Intrinsic Goal)<br><i>'Why' - The Foundational Axiom</i>"]
        S["2: Strategy (Causal Method)<br><i>'How' - Long-Term Plan</i>"]
        T["3: Tactics (Weekly Sprints)<br><i>'What' - Short-Term Execution Blocks</i>"]
        E["4: Actual Execution (Time-Stamped Tasks)<br><i>'Proof' - Verified Actions</i>"]
    end

    subgraph Control & Feedback Systems
        direction TB
        RI["5: Respected Intervention (AI Feedback)<br><i>Weekly critique via simulated mentors</i>"]
        D["6: Decision Engine (Priority Optimization)<br><i>Multi-run AI analysis for complex choices</i>"]
    end

    %% --- Connections ---

    %% Core Flow
    V --> |Defines| S
    S --> |Is Implemented By| T
    T --> |Generates| E

    %% Feedback Loop
    E --> |Provides Execution Data| RI
    RI --> |Identifies Blind Spots & Refines| S
    RI --> |Adjusts Course For| T

    %% Decision Input
    V --> |Inputs Core Principle| D
    S --> |Inputs Current Plan| D
    T --> |Inputs Immediate Context| D
    E --> |Inputs Performance Data| D
    RI --> |Inputs External Critique| D

    %% Decision Output
    D --> |Selects & Validates| S
    D --> |Prioritizes| T

    %% Diagnostic Questions as Gates
    subgraph Validation Gates
        direction LR
        Q1["Diagnostic 1: Value Integrity<br><i>(Is the value authentic?)</i>"]
        Q2["Diagnostic 2: Strategy Viability<br><i>(Is the plan realistic & causal?)</i>"]
    end

    V -- "Validation" --- Q1
    S -- "Validation" --- Q2

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System Analysis of the 'Rig' Framework

The proposed system is a structured personal operating system designed to counteract the entropic drift caused by low-signal interaction and uncalibrated feedback. It attempts to enforce alignment between abstract purpose and concrete action.

Strengths:

1. Hierarchical Coherence: The Value -> Strategy -> Tactics -> Execution cascade creates a clear logical chain, ensuring that low-level tasks are tethered to a high-level purpose. This structure mitigates directionless effort.
2. Explicit Feedback Mechanism: The "Respected Intervention" module is a direct countermeasure to the identified problem of AI agreeableness. By forcing a critical, externalized perspective, it is designed to expose execution gaps and strategic blind spots.
3. Structured Decision Protocol: The "Decisions" module formalizes a process for high-consequence choices, moving beyond pure intuition to a model that systematically weighs variables against the established V-S-T hierarchy.
4. Empirical Grounding: The system is anchored by "Actual Execution," making performance data—not intention—the basis for evaluation and feedback.

Potential Failure Modes & Structural Weaknesses:

1. Value Integrity Failure: The user correctly identifies this as Diagnostic 1. The entire system's output is corrupted if the foundational Value is miscalibrated, inauthentic ("larp"), or externally programmed ("psyopped"). The system's integrity is wholly dependent on the user's capacity for accurate self-assessment at the outset.
2. Causal Fallibility: The link between Strategy and Value (Diagnostic 2) is a hypothesis. A strategy may appear logically sound but fail to produce the desired value due to unknown variables, incorrect assumptions about causality, or environmental shifts. The framework's primary mechanism for testing this link is long-term outcome, which can be a high-latency, high-cost feedback signal.
3. Mentor Simulation Bias: The "Respected Intervention" module introduces the biases of the selected mentors (e.g., Jobs, Musk, Soros). An AI simulation of these figures is an approximation filtered through the AI's training data and the user's own perception. This may reinforce certain paradigms while still missing other, more relevant blind spots. The feedback is filtered, not objective.
4. Quantification Hazard: The "Decision" module's reliance on selecting the "quantifiable 'best' decision" is a potential vulnerability. The metrics used for quantification may be poorly chosen or fail to capture the complexity of the decision, leading to a logically consistent but practically suboptimal outcome. It risks optimizing for what is measurable, not what is important.
5. Execution as the Bottleneck: The rig optimizes planning, feedback, and decision-making. However, the ultimate constraint is the user's capacity for "Actual Execution." No level of systemic elegance can compensate for a failure in discipline, energy, or capability at the implementation layer.

The provided analysis is intended to refine the system's architecture by identifying potential stress points and failure modes for preemptive reinforcement. The diagram codifies the architecture as described.

Reconstructed as autonomic rig DAO

graph TD
    subgraph RigDAO ["Rig DAO (Governs Strategy)"]
        direction LR
        Charter["1: Value (On-Chain Charter)"]
        Gov["2: Strategy (Live Governance Proposals)"]
        Charter -- "Defines Purpose of" --> Gov
    end

    subgraph BountyMarket ["3: Tactical Bounty Market (Smart Contracts)"]
        direction TB
        Bounty["Tactical Bounties (Escrowed Crypto)"]
    end

    subgraph ExecutionLayer ["4: Execution Layer (AI & Human Agents)"]
        direction TB
        Exec["Proof of Execution (Verified by Oracles)"]
        Stake["Staking & Slashing Mechanism"]
        Exec --> Stake
    end

    subgraph FractalSubDAOs ["Fractal Sub-Systems"]
        direction TB
        AuditorDAO["5: Auditor DAO (Paid to find flaws)"]
        PredictionMarket["6: Decision Engine (Prediction Markets)"]
    end

    %% --- Core Connections & Flows ---
    Gov -- "Funds & Generates" --> BountyMarket
    BountyMarket -- "Contracted to" --> ExecutionLayer
    Exec -- "Verifiable Results" --> BountyMarket
    BountyMarket -- "Pays on Completion" --> ExecutionLayer

    %% --- Feedback & Control Loops ---
    ExecutionLayer -- "Execution Data" --> AuditorDAO
    Gov -- "Strategic Data" --> AuditorDAO
    AuditorDAO -- "Issues Critical Reports (for payment)" --> Gov

    Gov -- "Poses Decision" --> PredictionMarket
    PredictionMarket -- "Provides Probabilistic Forecast" --> Gov

    %% --- Self-Similarity Highlight ---
    style AuditorDAO fill:#334,stroke:#88a,stroke-width:2px
    linkStyle 6 stroke:#88a,stroke-width:2px;
    linkStyle 7 stroke:#88a,stroke-width:2px;

    classDef dao fill:#1a1a2c,stroke:#947,stroke-width:2px
    class RigDAO,AuditorDAO dao

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