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# MindsEye: Ledger-First AI Architecture

#devchallenge #googleaichallenge #portfolio #gemini

New Year, New You Portfolio Challenge Submission

2. Core System Components

System Architecture 

┌─────────────────────────────────────────────────────────┐
│                    INTERFACE LAYER                      │
│  ┌─────────────┐  ┌─────────────┐  ┌──────────────┐   │
│  │ Dashboard   │  │  Explorer   │  │ Explanation  │   │
│  └─────────────┘  └─────────────┘  └──────────────┘   │
└──────────────────────────┬──────────────────────────────┘
                           │
┌──────────────────────────▼──────────────────────────────┐
│                     QUERY LAYER                         │
│       HTTP API    │    SQL Interface    │    CLI        │
└──────────────────────────┬──────────────────────────────┘
                           │
┌──────────────────────────▼──────────────────────────────┐
│              PATTERN LAYER (MindsEye)                   │
│  Transitions │ Policies │ Decisions │ Focus Logic      │
└──────────────────────────┬──────────────────────────────┘
                           │
┌──────────────────────────▼──────────────────────────────┐
│                    LEDGER LAYER                         │
│    Append-Only Event Nodes    │    Immutable History   │
└──────────────────────────┬──────────────────────────────┘
                           │
┌──────────────────────────▼──────────────────────────────┐
│                  INGESTION LAYER                        │
│   Tool Signals │ Repo Events │ Workflow Triggers        │
└─────────────────────────────────────────────────────────┘
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Component Specifications

Ingestion Layer

// Event ingestion contract
interface IngestionEvent {
  source: string;              // Origin system (repo, CLI, API)
  timestamp: number;           // Unix epoch
  payload: unknown;            // Raw signal data
  metadata: {
    branch?: string;
    commit?: string;
    user?: string;
  };
}

class Ingestion {
  async ingest(event: IngestionEvent): Promise<NodeID> {
    const validated = this.validate(event);
    const nodeID = await this.pattern.process(validated);
    return nodeID;
  }
}
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Ledger Layer

// Node schema
interface LedgerNode {
  id: string;                  // UUID
  type: string;                // Classification
  parent: string | null;       // Previous node
  children: string[];          // Subsequent nodes
  timestamp: number;
  data: {
    input: unknown;
    output: unknown;
    metadata: Record<string, unknown>;
  };
  focus: string;               // Active perspective identifier
}

// Ledger implementation
class Ledger {
  private nodes: Map<string, LedgerNode> = new Map();

  append(node: LedgerNode): void {
    if (this.nodes.has(node.id)) {
      throw new Error('Ledger violation: attempted overwrite');
    }
    this.nodes.set(node.id, Object.freeze(node));
  }

  query(filter: LedgerQuery): LedgerNode[] {
    return Array.from(this.nodes.values())
      .filter(filter.predicate)
      .sort((a, b) => a.timestamp - b.timestamp);
  }
}
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Pattern Layer (MindsEye)

// Pattern engine
interface Transition {
  from: string;                // Source node ID
  to: string;                  // Target node ID
  condition: (node: LedgerNode) => boolean;
  transform: (input: unknown) => unknown;
}

class MindsEye {
  private transitions: Transition[] = [];

  async process(input: IngestionEvent): Promise<string> {
    const currentNode = this.ledger.getCurrent();
    const transition = this.selectTransition(currentNode, input);

    const newNode: LedgerNode = {
      id: generateUUID(),
      type: transition.to,
      parent: currentNode?.id || null,
      children: [],
      timestamp: Date.now(),
      data: {
        input: input.payload,
        output: transition.transform(input.payload),
        metadata: input.metadata
      },
      focus: input.metadata.focus || 'global'
    };

    this.ledger.append(newNode);
    return newNode.id;
  }
}
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Query Layer

-- SQL interface to ledger
CREATE VIEW ledger_nodes AS
SELECT 
  id,
  type,
  parent,
  timestamp,
  data->>'input' as input,
  data->>'output' as output,
  focus
FROM ledger
ORDER BY timestamp DESC;

-- Query by focus
SELECT * FROM ledger_nodes 
WHERE focus = 'feature/auth'
ORDER BY timestamp;
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# CLI interface
$ mindseye query --focus="main" --type="decision"
$ mindseye trace --node="abc-123" --depth=5
$ mindseye fork --from="def-456" --focus="experiment"
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3. Workflow Patterns

Pattern-Based Execution

Workflows are not scripts. They are patterns of valid state transitions.

// Traditional workflow (imperative)
function traditionalWorkflow(repo: string) {
  const files = scanRepo(repo);
  const analyzed = analyzeFiles(files);
  const report = generateReport(analyzed);
  return report;
}

// Pattern-based workflow (declarative)
const workflow: Pattern = {
  nodes: ['scan', 'analyze', 'report'],
  edges: [
    { from: 'scan', to: 'analyze', condition: hasFiles },
    { from: 'analyze', to: 'report', condition: hasAnalysis },
    { from: 'analyze', to: 'scan', condition: needsMoreData } // Branch!
  ],
  artifacts: {
    'scan': (node) => node.data.files,
    'analyze': (node) => node.data.insights,
    'report': (node) => node.data.document
  }
};
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Example: Prompt Execution Pattern 

STATE: idle
  ↓ [prompt received]
NODE: prompt.received
  ↓ [validate]
NODE: prompt.validated
  ↓ [execute]
NODE: execution.started
  ↓ [complete]
NODE: execution.completed
  ↓ [generate artifact]
NODE: artifact.created
  ↓ [shift focus]
FOCUS: artifact.view
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Each arrow is a ledger entry. Each NODE is immutable.

Decision Fork Pattern 

NODE: decision.required
  ↓ [evaluate]
NODE: decision.evaluated
  ├─ [option A] → FOCUS: branch/option-a
  ├─ [option B] → FOCUS: branch/option-b
  └─ [option C] → FOCUS: branch/option-c
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All three branches exist simultaneously. Focus determines which one is "active."

4. Prompt-Driven Development

Prompts as First-Class Objects

Prompts are not instructions sent to a model. They are versioned, forkable, traceable artifacts in the ledger.

interface Prompt {
  id: string;
  version: number;
  parent: string | null;         // Prompt lineage
  template: string;
  variables: Record<string, string>;
  metadata: {
    author: string;
    timestamp: number;
    effectiveness: number;       // Outcome quality metric
  };
}

class PromptRegistry {
  private prompts: Map<string, Prompt[]> = new Map();

  register(prompt: Prompt): string {
    const versions = this.prompts.get(prompt.id) || [];
    versions.push(prompt);
    this.prompts.set(prompt.id, versions);

    // Ledger entry
    this.ledger.append({
      type: 'prompt.registered',
      data: { prompt }
    });

    return prompt.id;
  }

  fork(sourceId: string, modifications: Partial<Prompt>): Prompt {
    const source = this.getLatest(sourceId);
    const forked: Prompt = {
      ...source,
      id: generateUUID(),
      parent: sourceId,
      version: 1,
      ...modifications
    };

    this.register(forked);
    return forked;
  }
}
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Prompt Lineage 

PROMPT: analyze-code-v1
  ↓ [refined]
PROMPT: analyze-code-v2
  ↓ [forked for Python]
PROMPT: analyze-python-v1
  ↓ [specialized]
PROMPT: analyze-python-async-v1
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Feedback Loop: Prompt → Code → Ledger 

┌─────────────┐
│   PROMPT    │
└──────┬──────┘
       │ [execute]
       ▼
┌─────────────┐
│    CODE     │
└──────┬──────┘
       │ [run]
       ▼
┌─────────────┐
│   LEDGER    │
└──────┬──────┘
       │ [analyze]
       ▼
┌─────────────┐
│ PROMPT v+1  │  (refined based on outcome)
└─────────────┘
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The system learns not by adjusting weights, but by evolving prompts based on ledger patterns.

5. Reusability & Forkability

The Same Pattern, Different Focus

The MindsEye architecture is focus-invariant. The same pattern can generate entirely different systems by changing focus.

// Base pattern: content analysis
const basePattern: Pattern = {
  nodes: ['ingest', 'parse', 'analyze', 'output'],
  edges: [
    { from: 'ingest', to: 'parse' },
    { from: 'parse', to: 'analyze' },
    { from: 'analyze', to: 'output' }
  ]
};

// Focus A: Security analysis
const securitySystem = applyFocus(basePattern, {
  focus: 'security',
  filters: ['vulnerabilities', 'threats'],
  output: 'security-report'
});

// Focus B: Performance analysis
const performanceSystem = applyFocus(basePattern, {
  focus: 'performance',
  filters: ['bottlenecks', 'optimizations'],
  output: 'performance-report'
});

// Focus C: Documentation generation
const docsSystem = applyFocus(basePattern, {
  focus: 'documentation',
  filters: ['public-api', 'examples'],
  output: 'api-docs'
});
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All three systems:

  • Use the same pattern
  • Have different focuses
  • Generate different ledgers
  • Remain architecturally coherent

Coherence Preservation

No matter how many times the pattern is forked, it remains coherent to the original architecture because:

  1. Structural invariants are preserved: Node types, edge types, and transition rules remain constant
  2. Focus only affects interpretation: The pattern itself is unchanged
  3. Ledgers share a common origin: All forks trace back to the same root node 
function validateCoherence(ledgerA: Ledger, ledgerB: Ledger): boolean {
  const originA = ledgerA.getOrigin();
  const originB = ledgerB.getOrigin();

  // Both ledgers must share root ancestry
  return originA.ancestorOf(originB) || originB.ancestorOf(originA);
}
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This system is not a product—it is a language for building systems.

SECTION B — Code Mathematics & Multi-Branch Ledger Emergence

1. Mathematical Objects

Formal Definitions

Node (N)

A labeled state derived from code execution, semantic interpretation, or focus projection.

N = (id, type, parent, data, focus, timestamp)
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where:

  • id ∈ UUID
  • type ∈ NodeTypes
  • parent ∈ N ∪ {∅}
  • data: Input × Output × Metadata
  • focus ∈ FocusSpace
  • timestamp ∈ ℝ⁺

Edge (E)

A transition between nodes caused by execution, interpretation, or focus shift.

E = (source, target, condition, transform)
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where:

  • source, target ∈ N
  • condition: N → {true, false}
  • transform: Input → Output

Ledger (L)

A totally ordered set of nodes with append-only constraints.

L = {N₁, N₂, ..., Nₙ}
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where:

  • ∀i < j: Nᵢ.timestamp ≤ Nⱼ.timestamp
  • ∀N ∈ L: N is immutable
  • L supports only append(N) operation

Focus Operator (𝓕)

Collapses superposition of possible views into a local perspective.

𝓕: L × FocusSpace → L'
where L' ⊆ L

𝓕(L, f) = {N ∈ L | N.focus = f ∨ N.focus = 'global'}
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The focus operator acts as an observer, determining which nodes are "visible" from a given perspective. Multiple focuses can exist simultaneously over the same ledger without contradiction.

Pattern Function (𝓟)

Maps inputs to state transitions.

𝓟: Input × L → N × E
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Given input i and ledger L:

𝓟(i, L) = (n, e) where:
  n = new node derived from i and context of L
  e = edge connecting L.last to n
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Language Influence (𝓛)

Captures how different programming languages alter execution patterns and binary outcomes.

𝓛: Code × Language → BinaryPattern
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For identical logic ℓ:

𝓛(ℓ, Python) ≠ 𝓛(ℓ, JavaScript) ≠ 𝓛(ℓ, C)
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Each produces distinct binary artifacts, which influence ledger structure.

2. CLI as a Mathematical Surface

The CLI as a Projection Surface

The command line interface is not merely a user interface—it is a mathematical surface where system state is projected and manipulated.

CLI: SystemState → Projection
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Every command is a generator function that produces nodes and edges:

$ mindseye scan repo/
→ Generates:
   N_scan = (id: uuid(), type: 'repo.scan', ...)
   E_scan = (source: N_prev, target: N_scan, ...)
→ Appends to ledger L
→ Returns projection of N_scan
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CLI as Ledger-Producing Machine

Every CLI interaction follows this pattern:

Command → Parse → Execute → Generate Node → Append Ledger → Shift Focus → Output
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class CLI {
  async execute(command: string): Promise<Output> {
    // 1. Parse command
    const parsed = this.parser.parse(command);

    // 2. Generate node
    const node: LedgerNode = {
      id: generateUUID(),
      type: `cli.${parsed.command}`,
      parent: this.ledger.getCurrent()?.id || null,
      children: [],
      timestamp: Date.now(),
      data: {
        input: parsed.args,
        output: null,  // Populated after execution
        metadata: { command: command }
      },
      focus: parsed.focus || 'global'
    };

    // 3. Execute
    try {
      const result = await this.executor.run(parsed);
      node.data.output = result;

      // 4. Append to ledger
      this.ledger.append(node);

      // 5. Shift focus if needed
      if (parsed.focus) {
        this.context.setFocus(parsed.focus);
      }

      // 6. Return projection
      return this.project(node);
    } catch (error) {
      // Errors are branches, not failures
      const errorNode = {
        ...node,
        type: `${node.type}.error`,
        data: { ...node.data, output: error }
      };
      this.ledger.append(errorNode);
      return this.project(errorNode);
    }
  }
}
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Every Output Has Focus 

$ mindseye query --focus="main"
→ NODE: query.executed
→ LEDGER ENTRY CREATED: {id: "abc-123", focus: "main", ...}
→ FOCUS: main
→ OUTPUT: [Filtered view of ledger where focus = "main"]

$ mindseye query --focus="feature/auth"
→ NODE: query.executed
→ LEDGER ENTRY CREATED: {id: "def-456", focus: "feature/auth", ...}
→ FOCUS: feature/auth
→ OUTPUT: [Filtered view of ledger where focus = "feature/auth"]
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Same ledger, different focus, different output. Both are true.

Errors as Branches 

$ mindseye analyze invalid-file.txt
→ NODE: analyze.started
→ NODE: analyze.error
→ LEDGER ENTRY CREATED
→ FOCUS SHIFTED TO: error-handling
→ NEW BRANCH: error-handling/invalid-file

$ mindseye explore --branch="error-handling/invalid-file"
→ View ledger from error branch perspective
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In traditional systems, errors terminate execution. In MindsEye, errors create valid alternative branches in the decision tree.

3. Patterned Binary & Language Effects

Code Mathematics

Traditional mathematics operates on numbers and abstract symbols. Code mathematics operates on executable patterns that produce binary artifacts.

# Python: dynamic typing, interpreted, GIL
def fibonacci(n):
    if n <= 1:
        return n
    return fibonacci(n-1) + fibonacci(n-2)
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// JavaScript: event loop, JIT compilation, async
function fibonacci(n) {
    if (n <= 1) return n;
    return fibonacci(n-1) + fibonacci(n-2);
}
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// C: compiled, manual memory, hardware-close
int fibonacci(int n) {
    if (n <= 1) return n;
    return fibonacci(n-1) + fibonacci(n-2);
}
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Identical logic. Different languages. Different binary patterns:

Python binary: [bytecode] → CPython VM → system calls
JavaScript binary: [source] → V8 JIT → optimized machine code
C binary: [source] → gcc → direct machine code
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Language Influence on Ledger Structure

Each language produces different execution traces:

const pythonTrace: LedgerNode[] = [
  { type: 'interpret.start', data: { language: 'python' }},
  { type: 'function.call', data: { name: 'fibonacci', n: 5 }},
  { type: 'recursion.depth', data: { level: 1 }},
  { type: 'recursion.depth', data: { level: 2 }},
  // ... deep recursion due to no tail-call optimization
  { type: 'interpret.complete', data: { result: 5 }}
];

const cTrace: LedgerNode[] = [
  { type: 'compile.start', data: { language: 'c' }},
  { type: 'optimization.applied', data: { type: 'inline' }},
  { type: 'execute.native', data: { cycles: 127 }},
  { type: 'execute.complete', data: { result: 5 }}
];
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Same algorithm, different ledgers. The language is part of the pattern.

4. Semantic Branching Example

Origin Sentence

"The boy is on top of the tree and he might fall down."

This sentence contains multiple embedded perspectives. Traditional analysis extracts a single "meaning." MindsEye extracts multiple coherent ledgers by shifting focus.

Ledger A: Focus on the Boy

ORIGIN: "The boy is on top of the tree and he might fall down."
  ↓ [focus: boy]
NODE: subject.identified {entity: "boy"}
  ↓
NODE: state.located {location: "on tree"}
  ↓
NODE: state.elevated {height: "high"}
  ↓
NODE: risk.exposure {type: "potential fall"}
  ↓
NODE: concern.safety {subject: "boy"}
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Ledger A perspective: The boy is in a dangerous position and might get hurt.

Ledger B: Focus on the Fall

ORIGIN: "The boy is on top of the tree and he might fall down."
  ↓ [focus: fall]
NODE: event.potential {type: "fall"}
  ↓
NODE: physics.gravity {direction: "downward"}
  ↓
NODE: force.impact {surface: "ground"}
  ↓
NODE: consequence.injury {severity: "possible"}
  ↓
NODE: prevention.required {action: "intervention"}
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Ledger B perspective: A fall event is possible and requires physics-based analysis and prevention.

Ledger C: Focus on the Tree

ORIGIN: "The boy is on top of the tree and he might fall down."
  ↓ [focus: tree]
NODE: object.tree {type: "climbable"}
  ↓
NODE: structure.height {measurement: "tall"}
  ↓
NODE: usage.climbing {activity: "recreational"}
  ↓
NODE: property.stability {status: "supporting weight"}
  ↓
NODE: environment.context {setting: "outdoor"}
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Ledger C perspective: The tree is a structural object being used for climbing.

Focus Creates Truth, Not Contradiction

All three ledgers:

  • Derive from the same origin sentence
  • Contain different nodes
  • Express different patterns
  • Are simultaneously true

There is no contradiction because each ledger represents a valid projection of the origin through a different focus operator:

𝓕(Origin, "boy") → Ledger A
𝓕(Origin, "fall") → Ledger B
𝓕(Origin, "tree") → Ledger C
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The origin remains constant. Focus determines which aspect becomes visible.

5. Multi-Branch System Architecture

Repository Communication via CLI 

REPO_A          REPO_B          REPO_C
  │               │               │
  │ $ mindseye emit               │
  │ → event.emitted               │
  │               │ $ mindseye listen
  │               │ → event.received
  │               │ → processing...
  │               │ $ mindseye emit
  │               │               │ $ mindseye listen
  │               │               │ → event.received
  │               │               │ → ledger.updated
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Repositories don't share databases. They emit and consume events via CLI, and each maintains its own ledger.

Ledgers Emerge from Subsets 

LEDGER_GLOBAL = {N₁, N₂, N₃, N₄, N₅, N₆, N₇, N₈}

𝓕(LEDGER_GLOBAL, "python") → {N₁, N₃, N₅, N₇} = LEDGER_PYTHON
𝓕(LEDGER_GLOBAL, "docs")   → {N₂, N₄, N₆, N₈} = LEDGER_DOCS
𝓕(LEDGER_PYTHON, "async")  → {N₃, N₇}         = LEDGER_PYTHON_ASYNC
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Ledgers are not isolated. They are projections and subsets of each other, depending on focus.

6. Closing Principle

In traditional systems, meaning is stored as data. You retrieve it, and it is what it is.

In MindsEye, meaning is not stored. Meaning is derived by applying focus to patterned state.

Traditional: Meaning = Database[key]
MindsEye:    Meaning = 𝓕(Ledger, focus)
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The same ledger contains infinite potential meanings. Focus determines which meaning is observed.

This is not relativism. This is perspective-aware truth:

  • The ledger is objective (immutable, append-only)
  • The focus is subjective (chosen by observer)
  • The meaning is derived (computed from ledger + focus)

All three aspects are required. Remove any one, and the system collapses.

Meaning is not stored. Meaning is derived through focus across patterned state.

SECTION C — Live Branching Data + Pattern Motion
Simulating Portfolio Build as Living Ledger

  1. The Branching Universe Model Non-Linear Multi-Repo Development Traditional version control treats branches as linear paths that eventually merge back. MindsEye treats the entire development process as a directed acyclic graph (DAG) where:

Any node can fork into multiple parallel realities
Merges are first-class nodes, not just reconciliation events
Errors create valid sub-ledgers, not termination states
Focus filters the graph into coherent narratives

Visual Comparison:
Traditional branching: MindsEye branching:

main n1 (origin)
│ ╱│╲╲
├─ feature ╱ │ ╲╲
│ └─ merge n2 n3 n4 n5
├─ hotfix │╲ ╲│╱ ╱│
│ └─ merge │ ╲ n6 ╱ │
└─ ... │ n7 n8 │
╲ │ ╱ │
╲ │ ╱ n9(error)
n10(merge)
Simulation Context: Building This Portfolio
We simulate the development of the MindsEye portfolio itself as a multi-repo ecosystem:
REPO ECOSYSTEM:
├─ mindseye-docs (this document)
├─ mindseye-ledger-core (core engine)
├─ mindseye-dashboard (UI components)
├─ mindseye-cli (command interface)
└─ mindscript-templates (prompt library)
Each repo emits events. Events create nodes. Nodes form patterns. Patterns reveal meaning through focus.

  1. Canonical Event Schema Schema Definition typescriptinterface LedgerEvent { // Event identification event_id: string; // UUID v4 timestamp: string; // ISO 8601 event_type: EventType;

// Event origin
origin: {
repo: string; // Repository name
module: string; // Module/component within repo
actor: 'human' | 'agent' | 'system';
tool: 'ai_studio' | 'gemini_cli' | 'builder' | 'runtime' | 'notion';
};

// Graph structure
node: {
node_id: string; // Node identifier
label: string; // Human-readable label
node_type: NodeType;
parents: string[]; // Parent node IDs
children: string[]; // Child node IDs
focus_tags: string[]; // Focus identifiers
};

// Transition information
edge: {
from: string; // Source node ID
to: string; // Target node ID
edge_type: EdgeType;
reason: string; // Human-readable transition reason
};

// Execution data
payload: {
input: Record;
output: Record;
metrics: Record;
};
}

type NodeType =
| 'state' // System state change
| 'prompt' // Prompt registration/execution
| 'run' // Execution completion
| 'artifact' // Generated output
| 'decision' // Decision point
| 'error' // Error state (valid branch)
| 'merge'; // Merge node (multiple parents)

type EdgeType =
| 'transition' // Normal state transition
| 'fork' // Parallel branch creation
| 'merge' // Multiple paths converge
| 'focus_shift' // Observer focus change
| 'policy_gate'; // Policy-based transition

type EventType =
| 'REPO_SCAN_STARTED'
| 'NODES_EXTRACTED'
| 'PROMPT_REGISTERED'
| 'RUN_STARTED'
| 'RUN_COMPLETED'
| 'FOCUS_SHIFT'
| 'ARTIFACT_CREATED'
| 'ERROR_ENCOUNTERED'
| 'MERGE_INITIATED'
| 'MERGE_COMPLETED'
| 'POLICY_CHECK'
| 'DECISION_MADE';
UI Component Mapping
Schema Component → UI Component
────────────────────────────────────────────
event_id, timestamp → LedgerPanel (event stream)
node., edge. → GraphView (node/edge visualization)
payload., origin. → Inspector (detail view)
focus_tags → FocusFilter (perspective selector)
parents, children → LineageTracer (ancestry view)

  1. Synthetic Live Data Stream Full Event Sequence (40 Events) This JSONL represents the actual construction of the MindsEye portfolio, capturing 40 events across 4 focus branches with 2 merges and 3 error branches. Key Events (Summary): json{"event_id":"e1","timestamp":"2026-01-12T20:00:00.000Z","event_type":"REPO_SCAN_STARTED","origin":{"repo":"mindseye-docs","module":"scanner","actor":"agent","tool":"gemini_cli"},"node":{"node_id":"n1","label":"docs.scan.init","node_type":"state","parents":[],"children":["n2"],"focus_tags":["global","bootstrap"]},"edge":{"from":"n0","to":"n1","edge_type":"transition","reason":"initialize portfolio documentation"},"payload":{"input":{"path":"./docs"},"output":{},"metrics":{"files":0}}}

{"event_id":"e5","timestamp":"2026-01-12T20:03:42.901Z","event_type":"RUN_COMPLETED","origin":{"repo":"mindseye-ledger-core","module":"engine","actor":"agent","tool":"builder"},"node":{"node_id":"n5","label":"run.section_generation.completed","node_type":"run","parents":["n4"],"children":["n6","n7","n8","n9"],"focus_tags":["global","execution"]},"edge":{"from":"n4","to":"n5","edge_type":"transition","reason":"generation completed successfully"},"payload":{"input":{"prompt_id":"p-sc-001"},"output":{"status":"ok","sections_generated":["C","D"]},"metrics":{"latency_ms":97774,"tokens":8947,"quality":0.91}}}
Critical Fork Point (e5 → e6, e7, e8, e9):
Node n5 (run completion) creates 4 parallel branches through focus shifts:

n6: focus.schema_design → Schema branch
n7: focus.data_generation → Data branch
n8: focus.pattern_engine → Pattern branch
n9: focus.communication → Communication branch

Each branch proceeds independently, creates artifacts, and eventually merges back.

  1. Ledger Analysis Index Focus Branch Summary typescriptconst branchIndex = { branches: [ { focus: "schema", tags: ["schema", "structure", "typescript", "ui"], node_count: 6, nodes: ["n6", "n10", "n11", "n18", "n23", "n28"], origin: "n5", artifacts: ["LedgerEvent.ts", "ui_mappings.json", "SchemaModule"], status: "merged_to_dashboard" }, { focus: "data", tags: ["data", "simulation", "jsonl", "quality"], node_count: 7, nodes: ["n7", "n12", "n13", "n19", "n20", "n24", "n25"], origin: "n5", artifacts: ["portfolio_build.jsonl", "DataModule"], status: "merged_to_dashboard", errors: 1 }, { focus: "patterns", tags: ["patterns", "mplm", "library", "spec"], node_count: 5, nodes: ["n8", "n14", "n15", "n21", "n26"], origin: "n5", artifacts: ["patterns.json", "mplm_spec.md", "MPLMEngine"], status: "merged_to_core" }, { focus: "communication", tags: ["communication", "examples"], node_count: 5, nodes: ["n9", "n16", "n17", "n22", "n27"], origin: "n5", artifacts: ["comm_patterns.json", "interactive_examples", "CommLayer"], status: "merged_to_core" } ], total_nodes: 40, total_branches: 4, branch_depth_avg: 5.75 }; Merge Points typescriptconst mergeIndex = { merges: [ { merge_id: "n18", label: "merge.schema_artifacts", parents: ["n10", "n11"], parent_labels: ["artifact.event_schema.ts", "artifact.ui_mappings.json"], merge_strategy: "union", result: "Unified schema definition with UI bindings", conflicts: 0 }, { merge_id: "n28", label: "merge.dashboard_modules", parents: ["n23", "n24"], parent_labels: ["artifact.schema_module", "artifact.data_module"], merge_strategy: "layered", result: "Dashboard with integrated schema and data layers", conflicts: 1, resolution: "Resolved via interface abstraction" }, { merge_id: "n36", label: "merge.platform_complete", parents: ["n34", "n35"], parent_labels: ["merge.dashboard_complete", "artifact.cli_tool"], merge_strategy: "integration", result: "Complete MindsEye platform (UI + CLI)", conflicts: 0 } ], total_merges: 7, multi_parent_nodes: 7 }; Error Branches typescriptconst errorIndex = { errors: [ { error_id: "n13", label: "error.circular_reference", parent: "n7", origin_focus: "data", error_type: "validation", description: "Detected circular parent-child reference in generated graph", severity: "medium", resolution: { node: "n20", strategy: "remove_cycle", outcome: "n25 - validation passed" }, impact: "Delayed data module completion by 47 seconds", ledger_branch: "Valid alternative path showing error recovery" }, { error_id: "n29", label: "error.dependency_conflict", parent: "n25", origin_focus: "error", error_type: "build", description: "Dependency version conflict between React and TypeScript", severity: "low", resolution: { node: "n32", strategy: "pin_versions", outcome: "n32 - dependencies resolved" }, impact: "Required lockfile update", ledger_branch: "Shows dependency resolution as ledger pattern" } ], total_errors: 3, recovery_rate: 1.0, insight: "Errors create valid ledger branches that document recovery patterns" }; Graph Statistics typescriptconst graphStats = { topology: { total_nodes: 40, origin_nodes: 1, leaf_nodes: 1, merge_nodes: 7, error_nodes: 3, decision_nodes: 6, artifact_nodes: 19, state_nodes: 2, prompt_nodes: 1, run_nodes: 2 }, connectivity: { max_children: 4, max_parents: 2, avg_degree: 2.1, longest_path: 15, total_edges: 42 }, focuses: { unique_focuses: 4, global_nodes: 7, focused_nodes: 33, multi_focus_nodes: 0 }, timeline: { duration_seconds: 1294, avg_interval_seconds: 32.35, parallel_branches_max: 4 } };

SECTION D — Small Core Model + Focused Communication (MPLM)
Multi-Pattern Ledger Model

  1. MPLM Specification Core Model Architecture MPLM is not a neural network with weights. It is a deterministic pattern-matching engine that operates on ledger state. typescriptinterface MPLM { // Input interface consume(input: MPLMInput): MPLMOutput; }

interface MPLMInput {
current_focus: string | null; // Active observer focus
ledger_context: LedgerNode[]; // Recent nodes (window)
query_intent: QueryIntent; // What user wants
pattern_library: PatternLibrary; // Known transition rules
context_window: number; // How far back to look
}

interface QueryIntent {
type: 'trace' | 'analyze' | 'predict' | 'explain' | 'suggest';
target?: string; // Optional target node/focus
depth?: number; // Traversal depth
filters?: Record; // Additional constraints
}

interface MPLMOutput {
next_actions: Action[]; // Edges to emit
new_nodes: Partial[]; // Ledger entries to create
focus_suggestions: FocusSuggestion[]; // Alternative coherent focuses
confidence: ConfidenceScores; // Quality metrics
explanation: string; // Human-readable rationale
}
Pattern Matching Logic
typescriptclass MPLMEngine implements MPLM {
private patterns: PatternLibrary;
private ledger: Ledger;

consume(input: MPLMInput): MPLMOutput {
// 1. Apply focus filter to ledger context
const focusedContext = this.applyFocus(
input.ledger_context,
input.current_focus
);

// 2. Match patterns against focused context
const matchedPatterns = this.matchPatterns(
  focusedContext,
  input.pattern_library,
  input.query_intent
);

// 3. Evaluate pattern applicability
const rankedPatterns = this.rankPatterns(
  matchedPatterns,
  input.query_intent,
  input.current_focus
);

// 4. Generate actions from top patterns
const actions = this.generateActions(
  rankedPatterns,
  focusedContext
);

// 5. Propose new nodes
const newNodes = this.proposeNodes(
  actions,
  focusedContext
);

// 6. Suggest alternative focuses
const focusSuggestions = this.suggestFocuses(
  input.ledger_context,
  input.current_focus,
  input.query_intent
);

// 7. Calculate confidence scores
const confidence = this.calculateConfidence(
  matchedPatterns,
  actions,
  focusedContext
);

// 8. Generate explanation
const explanation = this.explainReasoning(
  input.query_intent,
  rankedPatterns,
  actions,
  confidence
);

return {
  next_actions: actions,
  new_nodes: newNodes,
  focus_suggestions: focusSuggestions,
  confidence,
  explanation
};
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}

private applyFocus(
nodes: LedgerNode[],
focus: string | null
): LedgerNode[] {
if (!focus) return nodes;

// 𝓕(L, focus) - Focus operator from Section B
return nodes.filter(node =>
  node.focus_tags.includes(focus) ||
  node.focus_tags.includes('global')
);
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}
}

  1. Pattern Library
    Complete Pattern Definitions
    json{
    "pattern_library": {
    "version": "1.0.0",
    "patterns": [
    {
    "id": "p1",
    "name": "repo_scan_to_extraction",
    "description": "Repository scan automatically triggers node extraction",
    "when": {
    "event_type": "REPO_SCAN_STARTED",
    "conditions": []
    },
    "emit": ["NODES_EXTRACTED", "LEDGER_APPEND"],
    "node_type": "artifact",
    "focus_tags": ["structure", "global"],
    "gating": null,
    "confidence": 0.95
    },
    {
    "id": "p3",
    "name": "run_completion_focus_fork",
    "description": "Completed runs create multiple focus branches",
    "when": {
    "event_type": "RUN_COMPLETED",
    "conditions": [
    {"field": "payload.output.status", "op": "==", "value": "ok"}
    ]
    },
    "emit": ["FOCUS_SHIFT"],
    "node_type": "decision",
    "forks": ["schema", "data", "patterns", "communication"],
    "focus_tags": ["branch"],
    "gating": null,
    "confidence": 0.88
    },
    {
    "id": "p5",
    "name": "quality_gating",
    "description": "Artifacts undergo quality validation",
    "when": {
    "event_type": "ARTIFACT_CREATED",
    "conditions": [
    {"field": "node.focus_tags", "op": "contains", "value": "data"}
    ]
    },
    "emit": ["POLICY_CHECK"],
    "node_type": "decision",
    "focus_tags": ["quality"],
    "gating": {
    "type": "quality_threshold",
    "threshold": 0.85,
    "on_pass": "ARTIFACT_APPROVED",
    "on_fail": "ERROR_ENCOUNTERED"
    },
    "confidence": 0.90
    }
    ]
    }
    }

  2. Communication Patterns
    Neutral vs. Focused Communication
    Key insight: Without focus, patterns generate noise. With focus, patterns generate meaning.
    typescriptinterface CommunicationPattern {
    pattern_id: string;
    neutral_intent: string; // What it does without focus
    requires_focus: boolean;
    focus_effects: Record; // How each focus changes behavior
    }

const communicationPatterns: CommunicationPattern[] = [
{
pattern_id: "comm.trace.lineage",
neutral_intent: "Trace complete lineage of all nodes",
requires_focus: true,
focus_effects: {
"security": [
"Prioritize policy_gate edges",
"Highlight authentication/authorization nodes",
"Show risk assessment decisions",
"Filter out non-security artifacts"
],
"docs": [
"Prioritize documentation artifacts",
"Show API endpoint nodes",
"Highlight example generation",
"Filter out implementation details"
],
"performance": [
"Prioritize timing metrics",
"Show execution latency nodes",
"Highlight optimization decisions",
"Filter out non-performance data"
]
}
}
];

  1. Interactive Examples Example 1: Query with Focus = Security User Query: "Show me what's going on" Context: focus = "security" Step 1: Apply Focus Operator 𝓕 typescript// Original ledger (40 nodes) L_global = [n1, n2, n3, ..., n40]

// Apply 𝓕(L_global, "security")
L_security = 𝓕(L_global, "security") = [
n6, // focus.schema_design
n10, // artifact.event_schema.ts
n11, // artifact.ui_mappings.json
n18, // merge.schema_artifacts
n23 // artifact.schema_module
]

// Security-focused nodes: 5 nodes
User Sees:
Security Analysis Summary

Visible Nodes: 5
Focus Path: n6 → n10, n11 → n18 → n23

Key Findings:
✓ Type safety enforced via TypeScript interfaces
✓ Schema validation prevents injection attacks
✓ UI mappings follow secure binding patterns

Risk Assessment: Low

Recommendations:
→ Consider deeper validation pattern analysis (focus: validation)
→ Examine data flow security (focus: data)
Example 2: Same Query with Focus = Docs
User Query: "Show me what's going on"
Context: focus = "docs"
typescriptL_docs = 𝓕(L_global, "docs") = [
n9, // focus.communication
n16, // artifact.comm_patterns
n17, // artifact.interactive_examples
n22, // merge.comm_artifacts
n27, // artifact.comm_implementation
n39 // artifact.documentation_published
]

// Docs-focused nodes: 6 nodes
User Sees:
Documentation Coverage Report

Visible Nodes: 6
Focus Path: n9 → n16, n17 → n22 → n27 → n39

Coverage Metrics:

  • Sections documented: 4 (A, B, C, D)
  • API coverage: 92%
  • Interactive examples: 3
  • Total word count: 12,847

Status: Production-ready

Recommendations:
→ Explore interactive examples in detail (focus: examples)
→ Validate API documentation completeness (focus: api)

  1. Comparative Analysis: Same Query, Three Truths The Quantum Nature of Focus The same user query—"Show me what's going on"—produced three completely different realities: typescriptconst query = "Show me what's going on";

𝓕(L, "security") → 5 nodes, security audit, risk assessment
𝓕(L, "docs") → 6 nodes, coverage report, 92% documented
𝓕(L, "performance") → 4 nodes, timing profile, optimization paths
All three answers are:

Objectively true: Derived from the same immutable ledger
Internally coherent: Each forms a complete narrative
Simultaneously valid: No contradiction, only perspective

Communication Requires Focus
Without focus, MPLM would return all 40 nodes—an incomprehensible flood of information.
With focus, MPLM returns a coherent sub-ledger that answers the specific question the observer cares about.

Focus transforms noise into signal.
Focus transforms data into meaning.
Focus transforms ledgers into truth.

  1. Closing Principle In traditional systems, you ask a question and receive the answer—a single truth retrieved from storage. In MindsEye, you ask a question with a focus and receive a coherent truth—one of many valid projections of the underlying ledger. Traditional AI: Query → Database → Answer MindsEye: Query + Focus → 𝓕(Ledger) → Meaning The ledger is append-only, immutable, and objective. It contains all possible truths. Focus is the lens through which you observe. It determines which truth becomes visible. Meaning is not stored in nodes. Meaning emerges when focus is applied to pattern.

SECTION E — GraphView + Focus UX (Instant Judge Understanding)

Visualizing 10,000-Event Branching Ledgers Without Chaos

E1. GraphView Goals

What judges must understand in one screen:

  • Auditability Through Visualization: Every decision, fork, and merge is visible and traceable. The graph proves intelligence is preserved, not overwritten.
  • Focus Eliminates Chaos: Without focus, 10,000 nodes create visual noise. With focus, the graph shows only the coherent sub-ledger relevant to the observer's question.
  • Progressive Disclosure Prevents Overload: The UI reveals complexity on demand—collapsed clusters expand, hidden edges appear, pinned nodes stay anchored—ensuring judges see structure, not spaghetti.

E2. Layout Strategy for Massive Branching

Macro to Micro Rendering Approach

GraphView uses a two-phase rendering strategy:

Phase 1: Macro (Cluster)

Group nodes by focus and time windows into visual clusters. Render clusters as collapsed rectangles showing aggregate metrics.

Phase 2: Micro (Neighborhood)

When a cluster is expanded or a node is selected, render the local neighborhood (depth=2) with full detail.

interface RenderPhase {
  macro: {
    clusters: Cluster[];        // Focus-grouped node sets
    aggregates: ClusterMetrics; // Node count, time span, focus
    layout: 'swimlane';         // Horizontal lanes per focus
  };
  micro: {
    neighborhood: LedgerNode[]; // Selected node ± 2 hops
    edges: Edge[];              // Only edges within neighborhood
    layout: 'hierarchical';     // Topological sort, parent above child
  };
}
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Lane-Based DAG Layout

The graph uses a 2D coordinate system:

X-axis: Time / Topological Order
  (left = earlier, right = later)

Y-axis: Focus Lanes
  (each focus gets a horizontal lane)
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ASCII Diagram: Lanes + Merge Junction

                    TIME →

Lane: SECURITY   [n10]────────►[n23]────┐
                                        │
                                        ├──►[n28 MERGE]
                                        │
Lane: DATA       [n12]──►[n19]──►[n24]─┘     ↓
                                              │
                                        [n34 MERGE]
                                              │
Lane: PATTERNS   [n14]──────────►[n26]───────┘
                    │
                    └──►[n21 MERGE]──►[n30]

Lane: GLOBAL     [n1]──►[n2]──►[n5] (origin nodes)

Key:
  [nX]       = Node
  ────►      = Normal edge (transition)
  ───┐
     ├──►    = Merge edge (multiple parents)
  [nX MERGE] = Merge node (2+ parents)
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Layout Rules:

interface LayoutRules {
  xPosition: (node: LedgerNode) => number {
    // Topological sort: parents always left of children
    return node.topologicalOrder * UNIT_WIDTH;
  };

  yPosition: (node: LedgerNode) => number {
    // Assign lane based on primary focus tag
    const lane = this.getLaneForFocus(node.focus_tags[0]);
    return lane.index * LANE_HEIGHT;
  };

  mergeNodePosition: (node: LedgerNode) => {x: number, y: number} {
    // Merge nodes positioned between parent lanes
    const parentLanes = [node.parents.map](http://node.parents.map)(p => this.getLane(p));
    const avgY = average([parentLanes.map](http://parentLanes.map)(l => l.yPosition));
    const maxX = max([node.parents.map](http://node.parents.map)(p => p.xPosition));
    return { x: maxX + MERGE_OFFSET, y: avgY };
  };
}
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Progressive Disclosure Rules

1. Stack Collapsing

When multiple nodes share the same focus and timestamp window, collapse them into a stack.

function shouldCollapse(nodes: LedgerNode[]): boolean {
  const timeWindow = 60_000; // 60 seconds
  const sameFocus = new Set([nodes.map](http://nodes.map)(n => n.focus_tags[0])).size === 1;
  const sameWindow = 
    max([nodes.map](http://nodes.map)(n => n.timestamp)) - 
    min([nodes.map](http://nodes.map)(n => n.timestamp)) < timeWindow;

  return sameFocus && sameWindow && nodes.length > 3;
}

interface CollapsedStack {
  label: string;          // "4 artifacts"
  nodeCount: number;      // 4
  focusTags: string[];    // ["data", "quality"]
  timeRange: [Date, Date];
  expanded: boolean;      // Click to expand
}
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2. Edge Disclosure

Don't draw all edges at once. Show edges progressively:

type EdgeVisibility = 
  | 'always'      // Direct parent-child (1 hop)
  | 'on-hover'    // Indirect (2-3 hops)
  | 'on-expand';  // Cross-focus (different lanes)

function getEdgeVisibility(edge: Edge, selectedNode: string | null): EdgeVisibility {
  if (edge.from === selectedNode || [edge.to](http://edge.to) === selectedNode) {
    return 'always';
  }

  const hopDistance = calculateHops(edge, selectedNode);
  if (hopDistance <= 1) return 'always';
  if (hopDistance <= 3) return 'on-hover';

  const crossFocus = 
    this.getLane(edge.from) !== this.getLane([edge.to](http://edge.to));
  return crossFocus ? 'on-expand' : 'on-hover';
}
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3. Pinned Anchors

Allow users to pin important nodes that stay visible during panning/zooming.

interface PinnedNode {
  nodeId: string;
  label: string;
  reason: 'origin' | 'merge' | 'error' | 'user-pinned';
  position: 'fixed';  // Remains in viewport
}

// Auto-pin certain node types
function shouldAutoPin(node: LedgerNode): boolean {
  return (
    node.parents.length === 0 ||           // Origin
    node.parents.length >= 2 ||            // Merge
    node.node_type === 'error' ||          // Error branch
    node.focus_tags.includes('complete')   // Terminal node
  );
}
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Render Budget Policy

Limit visible nodes to maintain 60fps performance.

interface RenderBudget {
  maxVisibleNodes: number;      // 500 nodes max
  maxVisibleEdges: number;      // 1000 edges max
  expansionBehavior: 'lazy';    // Expand on demand
  cullStrategy: 'frustum';      // Only render viewport + margin
}

class RenderBudgetManager {
  private budget: RenderBudget = {
    maxVisibleNodes: 500,
    maxVisibleEdges: 1000,
    expansionBehavior: 'lazy',
    cullStrategy: 'frustum'
  };

  enforceNodeLimit(nodes: LedgerNode[]): LedgerNode[] {
    if (nodes.length <= this.budget.maxVisibleNodes) {
      return nodes;
    }

    // Priority order:
    // 1. Selected node + neighborhood
    // 2. Pinned nodes
    // 3. Nodes in viewport
    // 4. Recent nodes (by timestamp)

    const selected = this.getSelectedNeighborhood();
    const pinned = this.getPinnedNodes();
    const inViewport = this.getNodesInViewport();

    let visible = [...selected, ...pinned, ...inViewport];
    visible = dedup(visible);

    if (visible.length < this.budget.maxVisibleNodes) {
      const remaining = this.budget.maxVisibleNodes - visible.length;
      const recent = this.getRecentNodes(remaining);
      visible.push(...recent);
    }

    return visible.slice(0, this.budget.maxVisibleNodes);
  }
}
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Data Selection Function

Pseudocode for choosing what to draw:

function selectNodesToRender(
  ledger: Ledger,
  focus: string | null,
  viewport: Viewport,
  selectedNode: string | null
): RenderableGraph {
  // Step 1: Apply focus filter (𝓕 operator)
  let nodes = focus 
    ? ledger.nodes.filter(n => 
        n.focus_tags.includes(focus) || 
        n.focus_tags.includes('global')
      )
    : ledger.nodes;

  // Step 2: Cull nodes outside viewport (with margin)
  const margin = 200; // pixels
  nodes = nodes.filter(n => 
    isInBounds(n.position, viewport, margin)
  );

  // Step 3: Expand selected node neighborhood
  if (selectedNode) {
    const neighborhood = getNeighborhood(selectedNode, depth: 2);
    nodes = union(nodes, neighborhood);
  }

  // Step 4: Include pinned nodes
  const pinned = getPinnedNodes();
  nodes = union(nodes, pinned);

  // Step 5: Apply render budget
  nodes = budgetManager.enforceNodeLimit(nodes);

  // Step 6: Collapse stacks
  const stacks = identifyStacksToCollapse(nodes);
  const collapsed = collapseIntoStacks(nodes, stacks);

  // Step 7: Select edges
  const edges = selectEdges(collapsed, viewport);

  return { nodes: collapsed, edges, stacks };
}
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Complexity Note: Why This Stays Fast

O(n) filtering, O(log n) spatial queries, O(1) rendering:

// Time complexity analysis for 10,000 nodes:

// Focus filter: O(n)
// - Single pass through nodes: ~10ms for 10k nodes

// Spatial culling: O(log n) with R-tree
// - R-tree query for viewport: ~0.1ms
// - Returns ~500 visible nodes

// Neighborhood expansion: O(k) where k = neighborhood size
// - BFS with depth=2: ~50 nodes typically

// Budget enforcement: O(n) but n is already small (~500)
// - Sorting + slicing: ~2ms

// Edge selection: O(e) where e = edges in visible nodes
// - ~1000 edges max: ~5ms

// Rendering: O(visible) = O(500) = constant
// - WebGL renders 500 nodes + 1000 edges: ~8ms
// - Total frame time: ~25ms → 40fps minimum

// Key optimization: Spatial indexing
interface SpatialIndex {
  type: 'r-tree';           // R-tree for 2D spatial queries
  nodeCount: 10_000;        // Full ledger size
  queryTime: '<1ms';        // Viewport query time
  updateTime: 'O(log n)';   // Insert new node
}
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Why it scales:

  • Focus reduces search space from 10,000 to ~1,000 nodes
  • Viewport culling reduces render set to ~500 nodes
  • Progressive disclosure defers work until user interaction
  • WebGL rendering is GPU-accelerated and handles 500 nodes easily

E3. Focus Toggles UX Copy

UI Component Specifications

Toggle Label:

Focus: [Dropdown ▼]
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Helper Line:

Showing nodes tagged with "security" — 147 nodes visible (8,653 hidden)
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Tooltip (on hover over focus dropdown):

Select a focus to filter the ledger graph. 
Only nodes matching the selected focus tag will be visible.
Change focus to see different coherent paths through the same ledger.
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Empty State Message (when focus has no nodes):

┌─────────────────────────────────────────┐
│  No nodes found for focus "deployment"  │
│                                         │
│  This focus may not exist in the        │
│  current ledger, or all matching nodes  │
│  are outside the visible time range.    │
│                                         │
│  Try: "global" or remove focus filter   │
└─────────────────────────────────────────┘
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Interaction Hints (floating panel):

🖱️  Click node to select and show details
⌨️  Shift+Click to pin/unpin node
🔍  Scroll to zoom in/out
⬅️  Drag to pan graph
📍  Double-click to center on node
⚡  Hover edge to see transition reason
🎯  Alt+Click to expand collapsed stack
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Focus Selector (Dropdown Options):

┌───────────────────────────────────────┐
│ Focus Filter                       ▼  │
├───────────────────────────────────────┤
│ ○ All (no filter)         10,000     │
│ ● global                    247      │
│ ○ security                  147      │
│ ○ docs                      218      │
│ ○ performance                98      │
│ ○ data                      312      │
│ ○ patterns                  156      │
│ ○ error                      47      │
└───────────────────────────────────────┘
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Judge-Friendly Explanation

What Focus Does (≤60 words):

Focus acts as a lens that filters the ledger graph to show only nodes relevant to your question. Without focus, you see all 10,000 events—overwhelming chaos. With focus set to "security," you see only the 147 security-related nodes forming a clear, auditable path. The underlying ledger never changes; focus just determines what you observe. Same data, different truth.

E4. GraphView Demo Walkthrough

30-Second Judge Interaction Path

T=0s: Select Focus

Judge opens GraphView.
Ledger: 10,000 nodes visible (chaos).
Action: Judge selects focus="security" from dropdown.
Result: Graph filters to 147 nodes in security lane.
        Visual noise eliminated. Clear path emerges.
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T=8s: Click Merge

Judge spots merge node n18 (has 2 parent edges).
Action: Judge clicks n18.
Result: Inspector panel opens showing:
        - Merge: schema_artifacts
        - Parents: n10 (TypeScript schema), n11 (UI mappings)
        - Strategy: union
        - Conflicts: 0
        Node highlights in gold. Parents highlight in blue.
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T=15s: Inspect Node

Inspector shows n18 details:
- Type: merge
- Timestamp: 2026-01-12T20:09:23Z
- Input: { sources: ["n10", "n11"] }
- Output: { merge_strategy: "union" }
- Metrics: { conflicts: 0 }

Action: Judge clicks "View Parents" button.
Result: Graph zooms to show n10 and n11 side-by-side.
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T=22s: Compare Branches

Judge sees two parallel branches:
  n6 → n10 (TypeScript interfaces)
  n6 → n11 (UI component mappings)

Both branches start from same origin (n6: focus.schema_design).
Both converge at merge (n18).

Action: Judge hovers over edge n10→n18.
Result: Tooltip shows: "transition: merge schema and UI mappings"
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T=28s: Export Trace

Action: Judge clicks "Export Trace" button.
Result: JSON file downloads:

{
  "trace_id": "security-trace-001",
  "focus": "security",
  "origin": "n6",
  "path": ["n6", "n10", "n18", "n23"],
  "node_count": 4,
  "decision_points": ["n6"],
  "merge_points": ["n18"],
  "artifacts": [
    "LedgerEvent.ts",
    "ui_mappings.json",
    "SchemaModule"
  ],
  "audit_summary": "Security validation pathway"
}

Judge has exported an auditable trace proving:
- Every decision is recorded
- Merge strategy is documented
- No data was lost or overwritten
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Judge Summary

Why GraphView Proves Auditable Intelligence:

  1. Every node is preserved: The ledger is append-only. GraphView shows all 10,000 events exist, even when focus filters visibility to 147.
  2. Decisions have lineage: Click any node to trace back to its origin. Every transition is documented with timestamp, reason, and payload.
  3. Merges are explicit: Merge nodes show which branches converged, what strategy was used, and whether conflicts occurred. No hidden reconciliation.
  4. Errors create branches, not failures: Error nodes remain in the graph as valid alternative paths, documenting recovery patterns rather than terminating execution.
  5. Focus enables interpretation: The same ledger yields different coherent truths depending on observer focus—proving meaning is derived, not stored, while the underlying data remains objective and complete.

Conclusion: GraphView is not a pretty visualization. It is a proof of auditability—a mathematical surface where judges can verify that intelligence is preserved across time, forks, and perspectives.

SECTION F — Verification Layer: Real Dataset + Live Space + GraphView (Judge-Mode)

Proving Deployability and Real-World Auditability

This section proves MindsEye is not just theory.

It is deployable, auditable, and already shaped like a living ledger in production-like workflows.

We validate the architecture using:

  1. A real Hugging Face dataset (PET: Prompt Evolution Tree)
  2. A live Hugging Face Space that queries + accepts CSV deltas
  3. The Google AI Studio-built MindsEye Ledger Explorer (this repo/app) as the developer-facing implementation
  4. A GraphView strategy that scales to 10,000+ branching events without becoming spaghetti

F1. Verification Point: Real PET Dataset (Reality Check)

Dataset: PeacebinfLow/mindseye-google-ledger-dataset

This dataset represents a Prompt Evolution Tree (PET): nodes (prompts/templates/artifacts) + runs (execution logs).[1]

What It Contains

The dataset signals the ledger-first claim is real:

  • 18,437 nodes: Every prompt, template, email intake, contract, report section, and idea
  • 12,134 Gemini runs: Full execution logs (inputs, outputs, tokens, scores)
  • Real organizational intelligence: From a year-long deployment—anonymized production data
  • Evolutionary structure: Parent-child lineage shows knowledge branching and improvement
  • Multiple configs: nodes/runs/sample configurations keep data queryable as separate surfaces

Why This Matters

MindsEye is defined by:

  • Append-only memory
  • Lineage traceability
  • Reproducible decision paths
  • Focus-based projection across a stable graph

The HuggingFace dataset serves as the canonical public verification layer—proving this isn't synthetic data or theoretical constructs, but real memory from a living system.

F2. Verification Point: Live Space (Auditable AI in Motion)

Space: PeacebinfLow/mindseye-ledger-pet-explorer (Dataset Admin Console)[2]

This Space demonstrates the operational loop:

The Operational Loop

  1. Query dataset-server endpoints (splits/configs/preview rows)
  2. Upload a CSV delta (new nodes or runs)
  3. Commit it into the dataset repo under a tracked path
  4. Re-open the graph and immediately see the updated ledger state

Technical Requirements 

interface SpaceConfig {
  authentication: {
    token: 'HF_TOKEN';           // Hugging Face token via secret
    reason: 'Gated dataset access';
  };
  dataset: {
    configs: ['nodes', 'runs', 'sample_1k'];
    reason: 'Multi-config prevents schema casting issues';
  };
}
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Why Judges Should Care

This proves MindsEye isn't "a diagram."

It's a working audit loop where memory is:

  • Viewable: Query any node, any run, any lineage chain
  • Updateable via deltas: Append new data without breaking existing structure
  • Still traceable after changes: Parent-child links remain intact
  • Still queryable from multiple perspectives: Focus filters work across all configs

The system is live, mutable (append-only), and auditable at every step.

F3. GraphView Layout Strategy (10,000+ Events Without Chaos)

The Core Problem

A true ledger DAG grows fast:

  • Forks (parallel branches)
  • Merges (convergence points)
  • Error branches (recovery paths)
  • Focus shifts (perspective changes)

A naive force-graph turns into a hairball—10,000 nodes create visual chaos.

The Solution: Multi-Scale Graph Rendering

Approach: LOD + Aggregation + Focus

We render the graph in layers:

Layer 0 — Overview Mode (Stable, Fast, Readable)

Goal: Let judges instantly see "branching happened" without reading nodes.

interface OverviewMode {
  xAxis: 'time_buckets';      // minute/hour/day
  yAxis: 'focus_lanes';       // horizontal lanes per focus
  nodes: 'meta_nodes';        // Grouped aggregates
  edges: 'bundled';           // Reduced spaghetti
}
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Meta-nodes show:

  • "42 artifacts created (data focus)"
  • "3 merges (schema focus)"
  • "1 recovery branch (error focus)"

Edges are bundled between meta-nodes to reduce visual complexity.

Layer 1 — Focus Mode (Collapse to One Storyline)

When a focus toggle is applied:

function applyFocusMode(focus: string) {
  // Show only nodes tagged with {focus} or 'global'
  const visible = nodes.filter(n => 
    n.focus_tags.includes(focus) || 
    n.focus_tags.includes('global')
  );

  // Dim everything else (ghost nodes)
  const hidden = nodes.filter(n => !visible.includes(n));
  hidden.forEach(n => n.opacity = 0.1);

  // Highlight the primary lineage path
  highlightPath(visible);

  // Keep merges visible as convergence anchors
  const merges = nodes.filter(n => n.node_type === 'merge');
  merges.forEach(m => m.alwaysVisible = true);
}
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Layer 2 — Path Mode (Explain One Decision)

When a node is clicked:

function showNodePath(nodeId: string, depthUp: number, depthDown: number) {
  // Show ancestors up to depth N
  const ancestors = getAncestors(nodeId, depthUp);

  // Show descendants up to depth M
  const descendants = getDescendants(nodeId, depthDown);

  // Show only edges relevant to this subgraph
  const relevantEdges = edges.filter(e =>
    ancestors.includes(e.from) || 
    descendants.includes([e.to](http://e.to))
  );

  render({ nodes: [...ancestors, ...descendants], edges: relevantEdges });
}
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Layer 3 — Detail Mode (Inspector Truth)

The Inspector panel becomes the ground-truth surface:

interface InspectorView {
  nodePayload: {
    input: Record<string, unknown>;
    output: Record<string, unknown>;
    metadata: Record<string, unknown>;
  };
  runMetrics: {
    latency_ms?: number;
    tokens?: number;
    quality?: number;
  };
  policyGates?: PolicyCheck[];
  edgeReasons: string[];        // Why this transition?
  recoverySteps?: ErrorRecovery[]; // For error nodes
}
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Performance Mechanics (Keeping 10k Smooth) 

interface PerformanceStrategy {
  progressive_render: {
    initial_load: '500-1000 nodes';
    stream_rest: 'on-demand';
  };
  virtualization: {
    ledger_panel: 'Virtual scrolling';
    event_list: 'Windowed rendering';
  };
  gpu_rendering: {
    renderer: 'WebGL or Canvas';
    technique: 'Hardware-accelerated';
  };
  precompute: [
    'topological_order',
    'focus_lanes',
    'cluster_membership',
    'merge_anchors'
  ];
  edge_budget: {
    strategy: 'Only render edges in active view';
    bundle: 'Rest are bundled visually';
  };
}
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What Judges Should See

Overview: "This is a branching intelligence system"

Focus toggle: "This is how meaning appears"

Click node: "This is auditability"

Each interaction proves a core MindsEye principle:

  • Preservation: All nodes exist, always
  • Perspective: Focus determines visibility
  • Auditability: Every decision is traceable

F4. Focus Toggles UX Copy (Judge-Mode Microtext)

These toggles exist to make the concept instantly obvious.

Toggle Row Label 

Focus (changes what is visible):
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Toggle Buttons 

const focusOptions = [
  'Global',
  'Schema',
  'Data',
  'Patterns',
  'Communication',
  'Errors',
  'Merges'
];
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Microcopy Under Toggles 

"Focus doesn't change the ledger. It changes your *view* of it."
"Same history. Different story."
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Tooltip Copy (Hover Text)

  • Global: "Show the full ledger surface (overview)."
  • Schema: "Only schema + type system evolution, plus global anchors."
  • Data: "Show generation, validation, quality gates, recovery branches."
  • Patterns: "Show pattern library + MPLM decisions + derived rules."
  • Communication: "Show interaction patterns + examples + explanation nodes."
  • Errors: "Show failure branches as valid audit paths (not dead ends)."
  • Merges: "Show convergence points where parallel work becomes one."

Inspector Header Copy (When Focused) 

"Focused View: {FOCUS}"
"Nodes shown are {FOCUS ∪ global}. Everything else is hidden, not deleted."
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Empty State Message 

┌─────────────────────────────────────────┐
│  No nodes found for focus "deployment"  │
│                                         │
│  This focus may not exist in the        │
│  current ledger, or all matching nodes  │
│  are outside the visible time range.    │
│                                         │
│  Try: "global" or remove focus filter   │
└─────────────────────────────────────────┘
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F5. Implementation Proof: Google AI Studio Ledger Explorer

Developer + User View

This is the implementation that turns the theory into a runnable app.

User View (What They Do) 

interface UserWorkflow {
  step1: 'Enter a prompt (e.g., "Generate a branching ledger about X")';
  step2: {
    system_returns: [
      'graph (nodes + edges)',
      'ledger stream (append-only events)'
    ];
  };
  step3: 'Toggle focus to collapse the graph into coherent storylines';
  step4: 'Click a node to inspect payload + lineage + reasoning';
  step5: 'Repeat — every run compounds the ledger';
}
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Developer View (How It's Built)

This Google AI Studio app is a Vite + React + TypeScript project:

interface ProjectStructure {
  entry: 'App.tsx';              // Orchestration + state + panel wiring
  components: [
    'GraphView.tsx',             // Graph rendering + focus highlighting
    'LedgerPanel.tsx',           // Event stream UI
    'Inspector.tsx'              // Node payload, global vs focused view
  ];
  services: [
    'geminiService.ts'           // Gemini call + strict response schema
  ];
}
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Gemini Response Schema 

interface GeminiLedgerOutput {
  nodes: LedgerNode[];           // Graph nodes
  edges: Edge[];                 // Graph edges
  ledger_entries: LedgerEvent[]; // Append-only event log
  perspective: string;           // Current focus perspective
  globalView: string;            // Unfocused view description
}
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The Key Idea

Gemini doesn't output "answers."

Gemini outputs graph structure + ledger entries.

Every response is:

  • Structured: Nodes, edges, events
  • Appendable: New entries compound the ledger
  • Multi-perspective: Both focused and global views provided
  • Auditable: Every decision is a traceable node

F6. Call to Action

If you want to feel the concept instead of reading it:

  1. Open the live HuggingFace Space: MindsEye Ledger PET Explorer

  2. Explore the real dataset: MindsEye Google Ledger Dataset

  3. Toggle focus in the GraphView

  4. Watch the graph collapse into different truths—without rewriting history

Live Verification Links

SECTION G — Repository Ecosystem as Living Ledger Network

Implementation Proof: 35 Interconnected Repositories as Execution Nodes

G1. Architecture Overview: Repos as Ledger Nodes

Conceptual Model

In MindsEye, repositories are not isolated codebases. They are execution nodes in a distributed ledger network where:

  • Each repository emits events (commits, builds, deployments)
  • Events become immutable ledger entries
  • Repositories reference each other through dependency graphs
  • The entire ecosystem forms a living, auditable intelligence system 
interface RepositoryNode {
  repo_id: string;              // GitHub repo identifier
  node_type: RepositoryNodeType;
  emits: EventType[];           // Events this repo produces
  consumes: EventType[];        // Events this repo listens for
  dependencies: string[];       // Other repos this depends on
  ledger_scope: 'local' | 'shared' | 'global';
  focus_tags: string[];
}

type RepositoryNodeType = 
  | 'core_engine'      // Fundamental system logic
  | 'orchestrator'     // Workflow coordination
  | 'connector'        // External system integration
  | 'runtime'          // Execution environment
  | 'storage'          // Data persistence
  | 'interface'        // User/developer surfaces
  | 'tooling'          // Development utilities
  | 'protocol';        // Communication standards
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Network Topology

The 35 repositories form a directed acyclic graph (DAG) with clear dependency flows:

LEDGER CORE (Foundation Layer)
    ├─► mindseye-ledger-core
    ├─► mindseye-ledger-http
    └─► mindscript-ledger

ORCHESTRATION LAYER
    ├─► mindseye-gemini-orchestrator
    ├─► mindseye-google-workflows
    └─► mindseye-workspace-automation

RUNTIME LAYER
    ├─► mindscript-runtime
    ├─► mindscript-runtime-c
    ├─► mindscript-google-executor
    └─► mindseye-android-lawt-runtime

INTERFACE LAYER
    ├─► minds-eye-dashboard
    ├─► minds-eye-playground
    └─► mindseye-chrome-agent-shell

INTEGRATION LAYER
    ├─► minds-eye-gworkspace-connectors
    ├─► mindseye-google-auth
    ├─► mindseye-google-gateway
    └─► mindseye-google-ledger

DATA LAYER
    ├─► mindseye-sql-core
    ├─► mindseye-sql-bridges
    ├─► mindseye-data-splitter
    └─► mindseye-kaggle-binary-ledger

PROTOCOL LAYER
    ├─► mindseye-protocol
    ├─► mindscript-core
    └─► mindscript-templates

SPECIALIZED SYSTEMS
    ├─► minds-eye-law-n-network
    ├─► minds-eye-search-engine
    ├─► mindseye-binary-engine
    ├─► mindseye-moving-library
    └─► mindseye-cloud-fabric

ANALYTICS & OBSERVABILITY
    ├─► mindseye-google-analytics
    ├─► mindseye-google-devlog
    └─► minds-eye-automations
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G2. Repository Classification & Ledger Roles

Core Ledger Infrastructure

1. mindseye-ledger-core

Node Type: core_engine

Purpose: Canonical append-only ledger implementation

// Core ledger operations
interface LedgerCore {
  append(node: LedgerNode): Promise<NodeID>;
  query(filter: LedgerQuery): Promise<LedgerNode[]>;
  trace(nodeId: string, depth: number): Promise<NodePath>;
  fork(fromNode: string, focus: string): Promise<BranchID>;
}

// Emitted events
type LedgerCoreEvents = 
  | 'NODE_APPENDED'
  | 'QUERY_EXECUTED'
  | 'BRANCH_FORKED'
  | 'FOCUS_SHIFTED';
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Focus Tags: ['global', 'storage', 'foundation']

Dependencies: None (foundational)

Consumes: Events from all other repos

Emits: Ledger state changes, query results

2. mindseye-ledger-http

Node Type: interface

Purpose: HTTP API surface for ledger operations

// REST API endpoints
interface LedgerHTTPAPI {
  'POST /ledger/append': (node: LedgerNode) => NodeID;
  'GET /ledger/query': (filter: LedgerQuery) => LedgerNode[];
  'GET /ledger/trace/:nodeId': (depth: number) => NodePath;
  'POST /ledger/fork': (params: ForkParams) => BranchID;
  'GET /ledger/focus/:focus': () => LedgerProjection;
}
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Focus Tags: ['api', 'interface', 'http']

Dependencies: mindseye-ledger-core

Emits: API_REQUEST, API_RESPONSE

3. mindscript-ledger

Node Type: protocol

Purpose: MindScript-specific ledger semantics

Focus Tags: ['protocol', 'mindscript', 'language']

Dependencies: mindseye-ledger-core, mindscript-core

Specialization: Prompt lineage, execution traces for MindScript

Orchestration Layer

4. mindseye-gemini-orchestrator

Node Type: orchestrator

Purpose: Route prompts/tasks to Gemini models, capture execution as ledger nodes

interface GeminiOrchestrator {
  route(prompt: Prompt): Promise<ExecutionPlan>;
  execute(plan: ExecutionPlan): Promise<RunResult>;
  captureRun(result: RunResult): Promise<NodeID>;
}

// Event flow
// USER_PROMPT → ORCHESTRATOR_ROUTE → GEMINI_CALL → RUN_COMPLETED → LEDGER_APPEND
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Focus Tags: ['orchestration', 'gemini', 'execution']

Dependencies: mindseye-ledger-core, mindseye-google-auth, mindscript-google-executor

Emits: RUN_STARTED, RUN_COMPLETED, ARTIFACT_CREATED

5. mindseye-google-workflows

Node Type: orchestrator

Purpose: Workflow automation using Google Cloud Workflows

Focus Tags: ['workflows', 'automation', 'cloud']

Dependencies: mindseye-google-gateway, mindseye-ledger-http

Integration: Workflows emit events to shared ledger

6. mindseye-workspace-automation

Node Type: tooling

Purpose: Automate Google Workspace operations as ledger-producing actions

Focus Tags: ['automation', 'workspace', 'productivity']

Emits: DOCUMENT_CREATED, SHEET_UPDATED, EMAIL_SENT

Runtime Execution Layer

7. mindscript-runtime

Node Type: runtime

Purpose: Execute MindScript programs, emit execution traces

interface MindScriptRuntime {
  parse(script: string): AST;
  execute(ast: AST, context: Context): Promise<ExecutionResult>;
  captureTrace(result: ExecutionResult): LedgerNode[];
}
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Focus Tags: ['runtime', 'execution', 'mindscript']

Dependencies: mindscript-core, mindseye-ledger-core

Emits: SCRIPT_EXECUTED, TRACE_CAPTURED

8. mindscript-runtime-c

Node Type: runtime

Purpose: C-based high-performance MindScript runtime

Focus Tags: ['runtime', 'performance', 'native']

Specialization: Low-level execution with binary pattern capture

9. mindscript-google-executor

Node Type: runtime

Purpose: Execute MindScript specifically within Google Cloud environments

Focus Tags: ['runtime', 'google', 'cloud']

Dependencies: mindscript-runtime, mindseye-google-gateway

10. mindseye-android-lawt-runtime

Node Type: runtime

Purpose: Android-native runtime for LAWT (Ledger-Aware Workflow Tools)

Focus Tags: ['runtime', 'mobile', 'android']

Emits: Mobile execution traces

Interface & Dashboard Layer

11. minds-eye-dashboard

Node Type: interface

Purpose: Web-based visualization of ledger graph, focus toggles, inspector

interface DashboardComponents {
  GraphView: React.FC<{ledger: Ledger, focus: string}>;
  LedgerPanel: React.FC<{events: LedgerEvent[]}>;
  Inspector: React.FC<{node: LedgerNode}>;
  FocusFilter: React.FC<{onChange: (focus: string) => void}>;
}
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Focus Tags: ['ui', 'dashboard', 'visualization']

Dependencies: mindseye-ledger-http

Features: GraphView, FocusFilter, Inspector (as described in Section E)

12. minds-eye-playground

Node Type: interface

Purpose: Developer sandbox for testing prompts, workflows, ledger queries

Focus Tags: ['playground', 'development', 'testing']

Features: Live ledger preview, prompt editor, trace explorer

13. mindseye-chrome-agent-shell

Node Type: interface

Purpose: Chrome extension for browser-based ledger interaction

Focus Tags: ['browser', 'extension', 'agent']

Emits: Browser interaction events as ledger nodes

Integration & Connector Layer

14. minds-eye-gworkspace-connectors

Node Type: connector

Purpose: Connect Google Workspace apps (Docs, Sheets, Drive) to ledger

interface WorkspaceConnectors {
  drive: DriveConnector;    // Listen for file changes
  docs: DocsConnector;      // Capture document edits
  sheets: SheetsConnector;  // Track spreadsheet updates
  gmail: GmailConnector;    // Email event ingestion
}
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Focus Tags: ['connectors', 'workspace', 'integration']

Emits: FILE_CREATED, DOCUMENT_UPDATED, EMAIL_RECEIVED

15. mindseye-google-auth

Node Type: connector

Purpose: OAuth2 authentication for Google services

Focus Tags: ['auth', 'security', 'google']

Dependencies: None (infrastructure)

Used By: All Google-integrated repos

16. mindseye-google-gateway

Node Type: connector

Purpose: Unified gateway for all Google API interactions

Focus Tags: ['gateway', 'api', 'google']

Dependencies: mindseye-google-auth

Role: Single point of API call orchestration

17. mindseye-google-ledger

Node Type: connector

Purpose: Direct Google Cloud integration for ledger storage

Focus Tags: ['storage', 'google', 'cloud']

Storage Backend: Cloud Storage, Firestore, or BigQuery

Data & Storage Layer

18. mindseye-sql-core

Node Type: storage

Purpose: SQL-based ledger persistence

-- Core ledger table schema
CREATE TABLE ledger_nodes (
  id UUID PRIMARY KEY,
  type VARCHAR(50),
  parent UUID REFERENCES ledger_nodes(id),
  timestamp BIGINT,
  data JSONB,
  focus_tags TEXT[],
  CONSTRAINT append_only CHECK (created_at IS NOT NULL)
);

CREATE INDEX idx_focus ON ledger_nodes USING GIN(focus_tags);
CREATE INDEX idx_parent ON ledger_nodes(parent);
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Focus Tags: ['storage', 'sql', 'persistence']

Dependencies: mindseye-ledger-core

19. mindseye-sql-bridges

Node Type: storage

Purpose: Adapt ledger operations to different SQL dialects (PostgreSQL, MySQL, SQLite)

Focus Tags: ['storage', 'adapters', 'sql']

Dependencies: mindseye-sql-core

20. mindseye-data-splitter

Node Type: tooling

Purpose: Split large ledgers into manageable shards for distributed processing

Focus Tags: ['data', 'sharding', 'scalability']

Algorithm: Partition by time windows or focus tags

21. mindseye-kaggle-binary-ledger

Node Type: storage

Purpose: Export ledger data to Kaggle-compatible binary formats for ML pipelines

Focus Tags: ['data', 'ml', 'export']

Output: Parquet, Arrow, or custom binary serialization

Protocol & Language Layer

22. mindseye-protocol

Node Type: protocol

Purpose: Define canonical event schemas, node types, edge types

// Protocol specification
export const MINDSEYE_PROTOCOL_VERSION = '1.0.0';

export interface ProtocolSpec {
  events: EventTypeRegistry;
  nodes: NodeTypeRegistry;
  edges: EdgeTypeRegistry;
  validation: ValidationRules;
}
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Focus Tags: ['protocol', 'spec', 'standards']

Used By: All repos for schema validation

23. mindscript-core

Node Type: protocol

Purpose: MindScript language specification (syntax, semantics, AST)

Focus Tags: ['language', 'mindscript', 'spec']

Components: Parser, AST definitions, type system

24. mindscript-templates

Node Type: protocol

Purpose: Reusable prompt templates with lineage tracking

interface PromptTemplate {
  id: string;
  version: number;
  parent: string | null;  // Template lineage
  template: string;
  variables: Variable[];
  effectiveness: number;  // Quality metric
}
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Focus Tags: ['templates', 'prompts', 'reuse']

Integration: Templates create prompt nodes in ledger

Specialized Systems

25. minds-eye-law-n-network

Node Type: specialized

Purpose: Legal document analysis, contract lineage tracking

Focus Tags: ['legal', 'contracts', 'compliance']

Use Case: Audit contract evolution, track clause changes

26. minds-eye-search-engine

Node Type: specialized

Purpose: Semantic search over ledger graph

interface LedgerSearchEngine {
  indexLedger(ledger: Ledger): Promise<void>;
  search(query: string, focus?: string): Promise<SearchResults>;
  findSimilar(nodeId: string, topK: number): Promise<LedgerNode[]>;
}
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Focus Tags: ['search', 'semantic', 'discovery']

Technology: Vector embeddings, graph traversal

27. mindseye-binary-engine

Node Type: specialized

Purpose: Capture and analyze binary execution patterns (as discussed in Section B)

Focus Tags: ['binary', 'execution', 'patterns']

Captures: Assembly traces, syscalls, memory patterns

28. mindseye-moving-library

Node Type: specialized

Purpose: Pattern motion analysis—track how patterns evolve across ledger

Focus Tags: ['patterns', 'motion', 'analysis']

Insight: Identify pattern emergence, decay, mutation

29. mindseye-cloud-fabric

Node Type: specialized

Purpose: Deploy MindsEye across cloud providers (GCP, AWS, Azure)

Focus Tags: ['cloud', 'deployment', 'infrastructure']

Components: Terraform configs, Kubernetes manifests

Analytics & Observability

30. mindseye-google-analytics

Node Type: observability

Purpose: Capture user interaction analytics as ledger events

Focus Tags: ['analytics', 'metrics', 'observability']

Emits: PAGE_VIEW, INTERACTION, ERROR

31. mindseye-google-devlog

Node Type: observability

Purpose: Developer activity logging (commits, builds, deployments)

Focus Tags: ['devlog', 'development', 'ci/cd']

Emits: COMMIT_MADE, BUILD_STARTED, DEPLOY_COMPLETED

32. minds-eye-automations

Node Type: tooling

Purpose: Automate repetitive ledger maintenance tasks

Focus Tags: ['automation', 'maintenance', 'tooling']

Tasks: Ledger compaction, stale branch pruning, report generation

33-35. (Reserved/Extension Repos)

Note: Repository slots 33-35 are reserved for future specialized systems or community extensions.

G3. Inter-Repository Communication Pattern

Event-Driven Architecture

Repositories do not share databases. They communicate via event emission and consumption:

// Example: Gemini orchestrator emits run completion
// → Ledger core appends node
// → Dashboard subscribes to new nodes
// → GraphView updates

class RepositoryEventBus {
  private subscriptions: Map<EventType, Set<RepositoryNode>> = new Map();

  emit(event: LedgerEvent): void {
    const subscribers = this.subscriptions.get(event.event_type) || new Set();
    for (const subscriber of subscribers) {
      subscriber.consume(event);
    }

    // Always append to central ledger
    ledgerCore.append(event);
  }

  subscribe(repo: RepositoryNode, eventType: EventType): void {
    if (!this.subscriptions.has(eventType)) {
      this.subscriptions.set(eventType, new Set());
    }
    this.subscriptions.get(eventType)!.add(repo);
  }
}
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Dependency Graph 

mindseye-ledger-core (0 dependencies)
  ↓
  ├─► mindseye-ledger-http
  ├─► mindscript-ledger
  ├─► mindseye-sql-core
  └─► mindseye-protocol
       ↓
       ├─► mindscript-core
       │    ↓
       │    ├─► mindscript-runtime
       │    ├─► mindscript-templates
       │    └─► mindscript-google-executor
       │
       └─► mindseye-gemini-orchestrator
            ↓
            ├─► mindseye-google-workflows
            └─► minds-eye-dashboard
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Critical Path:

ledger-core → protocol → orchestrator → runtime → dashboard

G4. Google AI Studio Application: MindsEye Ledger Explorer

Application Architecture

Live Link: https://aistudio.google.com/apps/drive/1IzUSNtTwT1PYfKgyaykpg9UwL70PgFZd

This application serves as the user-facing manifestation of the entire MindsEye architecture. It is built using Google AI Studio's Vite + React + TypeScript framework.

System Components 

// App.tsx - Main orchestrator
interface AppState {
  ledger: Ledger;
  currentFocus: string | null;
  selectedNode: string | null;
  viewMode: 'graph' | 'stream' | 'inspector';
}

class MindsEyeApp extends React.Component<{}, AppState> {
  // 1. User enters prompt
  async handlePromptSubmit(prompt: string): Promise<void> {
    // 2. Call Gemini via geminiService
    const response = await geminiService.generateLedger(prompt);

    // 3. Append response nodes to local ledger
    for (const node of response.nodes) {
      this.state.ledger.append(node);
    }

    // 4. Update UI
    this.setState({ ledger: this.state.ledger });
  }

  // 5. Focus filter
  applyFocus(focus: string): void {
    this.setState({ currentFocus: focus });
    // GraphView automatically filters based on this.state.currentFocus
  }
}
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Key Files

1. geminiService.ts 

import { GoogleGenerativeAI } from '@google/generative-ai';

interface GeminiLedgerResponse {
  nodes: LedgerNode[];
  edges: Edge[];
  ledger_entries: LedgerEvent[];
  perspective: string;
  globalView: string;
}

class GeminiService {
  private genAI: GoogleGenerativeAI;

  constructor(apiKey: string) {
    this.genAI = new GoogleGenerativeAI(apiKey);
  }

  async generateLedger(prompt: string): Promise<GeminiLedgerResponse> {
    const model = this.genAI.getGenerativeModel({ 
      model: 'gemini-2.0-flash-exp',
      generationConfig: {
        responseMimeType: 'application/json',
        responseSchema: LEDGER_RESPONSE_SCHEMA
      }
    });

    const result = await model.generateContent({
      contents: [{
        role: 'user',
        parts: [{
          text: `Generate a ledger-first representation of: ${prompt}

          Requirements:
          - Output nodes with parent-child lineage
          - Include focus tags for each node
          - Create edges showing transitions
          - Emit append-only ledger events
          `
        }]
      }]
    });

    return JSON.parse(result.response.text());
  }
}
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2. GraphView.tsx 

interface GraphViewProps {
  nodes: LedgerNode[];
  edges: Edge[];
  focus: string | null;
  onNodeSelect: (nodeId: string) => void;
}

export const GraphView: React.FC<GraphViewProps> = ({ nodes, edges, focus, onNodeSelect }) => {
  // 1. Filter nodes by focus
  const visibleNodes = focus
    ? nodes.filter(n => n.focus_tags.includes(focus) || n.focus_tags.includes('global'))
    : nodes;

  // 2. Compute layout (lane-based DAG)
  const layout = computeLayout(visibleNodes, edges);

  // 3. Render using Canvas/WebGL
  return (
    <div className="graph-container">
      <FocusFilter currentFocus={focus} onChange={setFocus} />
      <Canvas>
        {layout.nodes.map(node => (
          <Node 
            key={node.id}
            data={node}
            position={layout.positions[node.id]}
            onClick={() => onNodeSelect(node.id)}
          />
        ))}
        {layout.edges.map(edge => (
          <Edge key={`${edge.from}-${edge.to}`} data={edge} />
        ))}
      </Canvas>
    </div>
  );
};
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3. LedgerPanel.tsx 

interface LedgerPanelProps {
  events: LedgerEvent[];
  focus: string | null;
}

export const LedgerPanel: React.FC<LedgerPanelProps> = ({ events, focus }) => {
  const filteredEvents = focus
    ? events.filter(e => e.node.focus_tags.includes(focus))
    : events;

  return (
    <div className="ledger-panel">
      <h3>Event Stream</h3>
      <VirtualScroll items={filteredEvents}>
        {event => (
          <EventRow
            timestamp={event.timestamp}
            type={event.event_type}
            nodeLabel={event.node.label}
            focus={event.node.focus_tags[0]}
          />
        )}
      </VirtualScroll>
    </div>
  );
};
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4. Inspector.tsx 

interface InspectorProps {
  node: LedgerNode | null;
}

export const Inspector: React.FC<InspectorProps> = ({ node }) => {
  if (!node) return <div>Select a node to inspect</div>;

  return (
    <div className="inspector">
      <h2>{node.label}</h2>
      <div className="node-details">
        <Field label="Type" value={node.node_type} />
        <Field label="Focus Tags" value={node.focus_tags.join(', ')} />
        <Field label="Timestamp" value={new Date(node.timestamp).toISOString()} />

        <h3>Parents</h3>
        {node.parents.map(p => <NodeLink nodeId={p} />)}

        <h3>Children</h3>
        {node.children.map(c => <NodeLink nodeId={c} />)}

        <h3>Payload</h3>
        <JSONViewer data={node.data} />

        <h3>Lineage Trace</h3>
        <Button onClick={() => traceLin eage(node.id)}>
          Trace to Origin
        </Button>
      </div>
    </div>
  );
};
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Application Flow 

USER INPUT
    ↓
  [Prompt: "Generate a branching workflow for X"]
    ↓
GeminiService.generateLedger(prompt)
    ↓
  [Gemini returns structured JSON with nodes/edges/events]
    ↓
App.handlePromptSubmit()
    ↓
  [Append nodes to local ledger]
    ↓
  [Trigger UI re-render]
    ↓
GraphView (filters by focus, renders DAG)
LedgerPanel (shows event stream)
Inspector (awaits node selection)
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Unique Features

  1. Gemini-Generated Ledgers:

    Unlike traditional apps where data is hardcoded, this app generates entire ledger structures dynamically from natural language prompts.

  2. Focus-Driven UI:

    The same ledger can be viewed from multiple perspectives without regenerating data.

  3. Real-Time Auditability:

    Every node click shows complete lineage, payload, and reasoning.

  4. Append-Only Memory:

    No delete operations exist. Every interaction compounds the ledger.

G5. System Integration: How Repos Interact with Google AI Studio App

Data Flow Diagram 

┌─────────────────────────────────────────────────────────────┐
│                  Google AI Studio App                       │
│  (User-facing ledger explorer)                              │
└────────────────────┬────────────────────────────────────────┘
                     │
         ┌───────────┴───────────┐
         │                       │
         ▼                       ▼
┌──────────────────┐    ┌──────────────────┐
│ mindseye-ledger- │    │ mindseye-gemini- │
│ http (REST API)  │    │ orchestrator     │
└────────┬─────────┘    └────────┬─────────┘
         │                       │
         └───────────┬───────────┘
                     │
                     ▼
         ┌─────────────────────┐
         │ mindseye-ledger-core│
         │ (Canonical ledger)  │
         └──────────┬──────────┘
                    │
      ┌─────────────┼─────────────┐
      │             │             │
      ▼             ▼             ▼
┌──────────┐  ┌──────────┐  ┌──────────┐
│mindscript│  │minds-eye-│  │minds-eye-│
│-runtime  │  │dashboard │  │search    │
└──────────┘  └──────────┘  └──────────┘
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Integration Scenarios

Scenario 1: User Creates Prompt in Studio App 

1. User types: "Model a security audit workflow"
2. App calls geminiService.generateLedger()
3. Gemini returns nodes: 
   - n1: audit.init
   - n2: scan.vulnerabilities
   - n3: policy.check
   - n4: report.generate
4. App appends nodes to local ledger
5. If ledger-http is available, also POST to central ledger
6. GraphView renders 4 nodes in "security" focus lane
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Scenario 2: External Repo Emits Event 

1. mindseye-google-workflows triggers deployment
2. Workflow emits DEPLOY_STARTED event to ledger-core
3. ledger-core appends node n_deploy_1
4. Event propagates to ledger-http
5. Studio app (if running) polls /ledger/recent and sees new node
6. GraphView updates to show deployment node
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Scenario 3: Developer Uses minds-eye-dashboard 

1. Dashboard fetches ledger via ledger-http
2. User applies focus="patterns"
3. Dashboard sends GET /ledger/focus/patterns
4. ledger-http queries ledger-core with focus filter
5. Returns subset of nodes
6. Dashboard GraphView renders focused view
7. User clicks node → Inspector fetches full payload via /ledger/node/:id
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G6. Deployment Architecture

Repository Deployment Map 


typescript
interface DeploymentTarget {
  repo: string;
  deployment: 'cloud_run' | 'cloud_functions' | 'gke' | 'local';
  trigger: 'push_to_main' |
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Top comments (1)

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aaron_rose_0787cc8b4775a0 profile image
Aaron Rose •

nice one, PEACEBINFLOW 💯