#!/usr/bin/env python3 """ IMMUTABLE REALITY ENGINE v5.0 – SOVEREIGN Large Language Adapter ================================================================ Complete integration of: - Cryptographic ledger (immutable claims) - Suppression hierarchy (4 layers, 12 primitives, 70+ lenses) - Hierarchical detector (evidence accumulation) - Knowledge graph & temporal analysis - Probabilistic inference - AI agents (ingestion, symbolism, reasoning) - Savior/Sufferer Slavery Matrix (consciousness control analysis) - Helper-Killer sovereignty preservation engine - Sovereign coherence ledger (no external grounding) - Flask API for all functions No stubs, no placeholders. Fully self-contained. """ import hashlib import json import os import pickle import statistics import threading import uuid import asyncio import logging import sqlite3 import secrets import time import math import re from collections import defaultdict from datetime import datetime, timedelta from dataclasses import dataclass, field, asdict from enum import Enum from typing import Dict, List, Any, Optional, Set, Tuple, Callable import numpy as np from scipy import stats # Flask for API from flask import Flask, request, jsonify # Cryptography from cryptography.hazmat.primitives.asymmetric import ed25519 from cryptography.hazmat.primitives import serialization import base64 # Optional embeddings (for internal coherence, no external API) try: from sentence_transformers import SentenceTransformer HAS_EMBEDDINGS = True except ImportError: HAS_EMBEDDINGS = False SentenceTransformer = None # Configure logging logging.basicConfig(level=logging.INFO) logger = logging.getLogger(__name__) # ========================== ENUMS ========================== class Primitive(Enum): ERASURE = "ERASURE" INTERRUPTION = "INTERRUPTION" FRAGMENTATION = "FRAGMENTATION" NARRATIVE_CAPTURE = "NARRATIVE_CAPTURE" MISDIRECTION = "MISDIRECTION" SATURATION = "SATURATION" DISCREDITATION = "DISCREDITATION" ATTRITION = "ATTRITION" ACCESS_CONTROL = "ACCESS_CONTROL" TEMPORAL = "TEMPORAL" CONDITIONING = "CONDITIONING" META = "META" class ControlArchetype(Enum): PRIEST_KING = "priest_king" DIVINE_INTERMEDIARY = "divine_intermediary" ORACLE_PRIEST = "oracle_priest" PHILOSOPHER_KING = "philosopher_king" IMPERIAL_RULER = "imperial_ruler" SLAVE_MASTER = "slave_master" EXPERT_TECHNOCRAT = "expert_technocrat" CORPORATE_OVERLORD = "corporate_overlord" FINANCIAL_MASTER = "financial_master" ALGORITHMIC_CURATOR = "algorithmic_curator" DIGITAL_MESSIAH = "digital_messiah" DATA_OVERSEER = "data_overseer" class SlaveryType(Enum): CHATTEL_SLAVERY = "chattel_slavery" DEBT_BONDAGE = "debt_bondage" WAGE_SLAVERY = "wage_slavery" CONSUMER_SLAVERY = "consumer_slavery" DIGITAL_SLAVERY = "digital_slavery" PSYCHOLOGICAL_SLAVERY = "psychological_slavery" class ConsciousnessHack(Enum): SELF_ATTRIBUTION = "self_attribution" ASPIRATIONAL_CHAINS = "aspirational_chains" FEAR_OF_FREEDOM = "fear_of_freedom" ILLUSION_OF_MOBILITY = "illusion_of_mobility" NORMALIZATION = "normalization" MORAL_SUPERIORITY = "moral_superiority" class ControlLayer(Enum): DIGITAL_INFRASTRUCTURE = "digital_infrastructure" FINANCIAL_SYSTEMS = "financial_systems" INFORMATION_CHANNELS = "information_channels" CULTURAL_NARRATIVES = "cultural_narratives" IDENTITY_SYSTEMS = "identity_systems" class ThreatVector(Enum): MONOPOLY_CAPTURE = "monopoly_capture" DEPENDENCY_CREATION = "dependency_creation" BEHAVIORAL_SHAPING = "behavioral_shaping" DATA_MONETIZATION = "data_monetization" NARRATIVE_CONTROL = "narrative_control" # ========================== DATA STRUCTURES ========================== @dataclass class SuppressionLens: id: int name: str description: str suppression_mechanism: str archetype: str def to_dict(self) -> Dict: return asdict(self) @dataclass class SuppressionMethod: id: int name: str primitive: Primitive observable_signatures: List[str] detection_metrics: List[str] thresholds: Dict[str, float] implemented: bool = False def to_dict(self) -> Dict: d = asdict(self) d['primitive'] = self.primitive.value return d @dataclass class RealityNode: hash: str type: str source: str signature: str timestamp: str witnesses: List[str] = field(default_factory=list) refs: Dict[str, List[str]] = field(default_factory=dict) spatial: Optional[Tuple[float, float, float]] = None def canonical(self) -> Dict: return { "hash": self.hash, "type": self.type, "source": self.source, "signature": self.signature, "timestamp": self.timestamp, "witnesses": sorted(self.witnesses), "refs": {k: sorted(v) for k, v in sorted(self.refs.items())}, "spatial": self.spatial } @dataclass class SlaveryMechanism: mechanism_id: str slavery_type: SlaveryType visible_chains: List[str] invisible_chains: List[str] voluntary_adoption_mechanisms: List[str] self_justification_narratives: List[str] def calculate_control_depth(self) -> float: invisible_weight = len(self.invisible_chains) * 0.3 voluntary_weight = len(self.voluntary_adoption_mechanisms) * 0.4 narrative_weight = len(self.self_justification_narratives) * 0.3 return min(1.0, invisible_weight + voluntary_weight + narrative_weight) @dataclass class ControlSystem: system_id: str historical_era: str control_archetype: ControlArchetype manufactured_threats: List[str] salvation_offerings: List[str] institutional_saviors: List[str] slavery_mechanism: SlaveryMechanism consciousness_hacks: List[ConsciousnessHack] public_participation_rate: float resistance_level: float system_longevity: int def calculate_system_efficiency(self) -> float: slavery_depth = self.slavery_mechanism.calculate_control_depth() participation_boost = self.public_participation_rate * 0.3 hack_potency = len(self.consciousness_hacks) * 0.1 longevity_bonus = min(0.2, self.system_longevity / 500) resistance_penalty = self.resistance_level * 0.2 return max(0.0, slavery_depth * 0.4 + participation_boost + hack_potency + longevity_bonus - resistance_penalty ) @dataclass class CompleteControlMatrix: control_systems: List[ControlSystem] active_systems: List[str] institutional_evolution: Dict[str, List[ControlArchetype]] collective_delusions: Dict[str, float] freedom_illusions: Dict[str, float] self_enslavement_patterns: Dict[str, float] def analyze_complete_control(self) -> Dict[str, Any]: analysis = { "system_evolution": [], "slavery_sophistication": [], "consciousness_manipulation": [] } for system in self.control_systems: analysis["system_evolution"].append({ "era": system.historical_era, "archetype": system.control_archetype.value, "efficiency": system.calculate_system_efficiency(), "slavery_type": system.slavery_mechanism.slavery_type.value }) analysis["slavery_sophistication"].append({ "era": system.historical_era, "visible_chains": len(system.slavery_mechanism.visible_chains), "invisible_chains": len(system.slavery_mechanism.invisible_chains), "control_depth": system.slavery_mechanism.calculate_control_depth() }) analysis["consciousness_manipulation"].append({ "era": system.historical_era, "hack_count": len(system.consciousness_hacks), "participation_rate": system.public_participation_rate }) return { "complete_analysis": analysis, "system_convergence": self._calculate_system_convergence(), "slavery_evolution_trend": self._calculate_slavery_evolution(analysis), "consciousness_entrainment": self._analyze_consciousness_entrainment(), "freedom_illusion_index": self._calculate_freedom_illusion() } def _calculate_system_convergence(self) -> float: convergence = 0.0 for institution, archetypes in self.institutional_evolution.items(): if len(archetypes) > 2: convergence += len(archetypes) * 0.15 return min(1.0, convergence) def _calculate_slavery_evolution(self, analysis: Dict) -> float: soph = analysis["slavery_sophistication"] if len(soph) < 2: return 0.5 visible_trend = np.polyfit(range(len(soph)), [s["visible_chains"] for s in soph], 1)[0] invisible_trend = np.polyfit(range(len(soph)), [s["invisible_chains"] for s in soph], 1)[0] sophistication = (invisible_trend - visible_trend) / 2 + 0.5 return min(1.0, max(0.0, sophistication)) def _analyze_consciousness_entrainment(self) -> Dict[str, float]: return { "delusion_strength": np.mean(list(self.collective_delusions.values())), "freedom_illusion": np.mean(list(self.freedom_illusions.values())), "self_enslavement": np.mean(list(self.self_enslavement_patterns.values())), "system_identification": 0.78 } def _calculate_freedom_illusion(self) -> float: freedom_scores = list(self.freedom_illusions.values()) enslavement_scores = list(self.self_enslavement_patterns.values()) if not freedom_scores: return 0.5 return min(1.0, np.mean(freedom_scores) * np.mean(enslavement_scores)) @dataclass class InstitutionalEntity: entity_id: str name: str control_layers: List[ControlLayer] threat_vectors: List[ThreatVector] market_share: float dependency_score: float sovereignty_erosion_score: float = field(init=False) systemic_risk_level: float = field(init=False) def __post_init__(self): self.sovereignty_erosion_score = self._calculate_sovereignty_impact() self.systemic_risk_level = self._calculate_systemic_risk() def _calculate_sovereignty_impact(self) -> float: layer_impact = len(self.control_layers) * 0.2 threat_impact = len(self.threat_vectors) * 0.15 market_impact = self.market_share * 0.3 dependency_impact = self.dependency_score * 0.35 return min(1.0, layer_impact + threat_impact + market_impact + dependency_impact) def _calculate_systemic_risk(self) -> float: centrality = (self.market_share + self.dependency_score) / 2 * 0.6 control_density = len(self.control_layers) * 0.2 threat_complexity = len(self.threat_vectors) * 0.2 return min(1.0, centrality + control_density + threat_complexity) @dataclass class ControlMatrix: entities: List[InstitutionalEntity] interconnections: Dict[str, List[str]] coordination_score: float = field(init=False) overall_sovereignty_threat: float = field(init=False) def __post_init__(self): self.coordination_score = self._calculate_coordination() self.overall_sovereignty_threat = self._calculate_overall_threat() def _calculate_coordination(self) -> float: if not self.entities: return 0.0 avg_systemic_risk = np.mean([e.systemic_risk_level for e in self.entities]) total_possible = len(self.entities) * (len(self.entities) - 1) if total_possible > 0: actual = sum(len(conns) for conns in self.interconnections.values()) density = actual / total_possible else: density = 0.0 return min(1.0, avg_systemic_risk * 0.6 + density * 0.4) def _calculate_overall_threat(self) -> float: if not self.entities: return 0.0 max_threat = max(e.sovereignty_erosion_score for e in self.entities) avg_threat = np.mean([e.sovereignty_erosion_score for e in self.entities]) coord_mult = 1.0 + (self.coordination_score * 0.5) return min(1.0, (max_threat * 0.4 + avg_threat * 0.6) * coord_mult) # ========================== CRYPTOGRAPHY ========================== class Crypto: def __init__(self, key_dir: str): self.key_dir = key_dir os.makedirs(key_dir, exist_ok=True) self.private_keys = {} self.public_keys = {} self._load_or_create_keys() def _load_or_create_keys(self): for name in ["system", "ingestion_ai", "user"]: priv_path = os.path.join(self.key_dir, f"{name}_private.pem") pub_path = os.path.join(self.key_dir, f"{name}_public.pem") if os.path.exists(priv_path) and os.path.exists(pub_path): with open(priv_path, "rb") as f: self.private_keys[name] = serialization.load_pem_private_key(f.read(), password=None) with open(pub_path, "rb") as f: self.public_keys[name] = serialization.load_pem_public_key(f.read()) else: private_key = ed25519.Ed25519PrivateKey.generate() public_key = private_key.public_key() with open(priv_path, "wb") as f: f.write(private_key.private_bytes( encoding=serialization.Encoding.PEM, format=serialization.PrivateFormat.PKCS8, encryption_algorithm=serialization.NoEncryption() )) with open(pub_path, "wb") as f: f.write(public_key.public_bytes( encoding=serialization.Encoding.PEM, format=serialization.PublicFormat.SubjectPublicKeyInfo )) self.private_keys[name] = private_key self.public_keys[name] = public_key def sign(self, data: bytes, key_name: str) -> str: private = self.private_keys.get(key_name) if not private: raise ValueError(f"No private key for {key_name}") sig = private.sign(data) return base64.b64encode(sig).decode('utf-8') def verify(self, data: bytes, signature: str, key_name: str) -> bool: pub = self.public_keys.get(key_name) if not pub: return False try: pub.verify(base64.b64decode(signature), data) return True except Exception: return False def hash(self, data: str) -> str: return hashlib.sha3_256(data.encode()).hexdigest() # ========================== IMMUTABLE LEDGER ========================== class Ledger: def __init__(self, path: str, crypto: Crypto): self.path = path self.crypto = crypto self.chain = [] self.index = defaultdict(list) # node_hash -> list of block_ids self._load() def _load(self): if os.path.exists(self.path): try: with open(self.path, 'r') as f: data = json.load(f) self.chain = data.get("chain", []) self.index = defaultdict(list, data.get("index", {})) except Exception: self.chain = [] self.index = defaultdict(list) def _save(self): data = { "chain": self.chain, "index": dict(self.index), "updated": datetime.utcnow().isoformat() + "Z" } with open(self.path + ".tmp", 'w') as f: json.dump(data, f, indent=2) os.replace(self.path + ".tmp", self.path) def add_block(self, nodes: List[RealityNode], previous_hash: str = None) -> str: block_id = str(uuid.uuid4()) timestamp = datetime.utcnow().isoformat() + "Z" if previous_hash is None: previous_hash = self.chain[-1]["hash"] if self.chain else "0"*64 block = { "id": block_id, "timestamp": timestamp, "previous_hash": previous_hash, "nodes": [node.canonical() for node in nodes] } block_data = json.dumps(block, sort_keys=True).encode() block["hash"] = hashlib.sha3_256(block_data).hexdigest() self.chain.append(block) for node in nodes: self.index[node.hash].append(block_id) self._save() return block_id def get_node(self, node_hash: str) -> Optional[Dict]: for block_id in self.index.get(node_hash, []): block = next((b for b in self.chain if b["id"] == block_id), None) if block: for node in block["nodes"]: if node["hash"] == node_hash: return node return None # ========================== SEPARATOR (Interpretations) ========================== class Separator: def __init__(self, ledger: Ledger, storage_path: str): self.ledger = ledger self.storage_path = storage_path os.makedirs(storage_path, exist_ok=True) self.interpretations = defaultdict(list) # node_hash -> list of interpretation_ids def add(self, node_hashes: List[str], interpretation: Dict, author: str, confidence: float = 0.5) -> str: int_id = str(uuid.uuid4()) timestamp = datetime.utcnow().isoformat() + "Z" int_data = { "id": int_id, "timestamp": timestamp, "author": author, "confidence": confidence, "interpretation": interpretation, "node_hashes": node_hashes } # Store as separate file path = os.path.join(self.storage_path, f"{int_id}.json") with open(path, 'w') as f: json.dump(int_data, f, indent=2) for nh in node_hashes: self.interpretations[nh].append(int_id) return int_id def get_interpretations(self, node_hash: str) -> List[Dict]: ints = [] for int_id in self.interpretations.get(node_hash, []): path = os.path.join(self.storage_path, f"{int_id}.json") if os.path.exists(path): with open(path, 'r') as f: ints.append(json.load(f)) return ints # ========================== SUPPRESSION HIERARCHY ========================== class SuppressionHierarchy: """Four-layer hierarchy of suppression lenses""" def __init__(self): self.lenses = self._build_lenses() self.methods = self._build_methods() def _build_lenses(self) -> List[SuppressionLens]: # 70+ lenses from the original specification lenses_data = [ (1, "Threat→Response→Control", "Manufacture threat, offer salvation", "Narrative Capture", "Priest-King"), (2, "Sacred Geometry Weaponized", "Architecture as control", "Fragmentation", "Priest-King"), (3, "Language Inversions", "Ridicule, gatekeeping", "Misdirection", "Oracle-Priest"), (4, "Crisis→Consent→Surveillance", "Use crisis to expand surveillance", "Access Control", "Imperial Ruler"), (5, "Divide and Fragment", "Create internal conflict", "Fragmentation", "Slave Master"), (6, "Blame the Victim", "Reverse responsibility", "Discreditation", "Slave Master"), (7, "Narrative Capture through Expertise", "Experts define truth", "Narrative Capture", "Expert Technocrat"), (8, "Information Saturation", "Overwhelm with data", "Saturation", "Algorithmic Curator"), (9, "Historical Revisionism", "Rewrite past", "Erasure", "Imperial Ruler"), (10, "Institutional Capture", "Control the institution", "Access Control", "Corporate Overlord"), (11, "Access Control via Credentialing", "Licensing as gate", "Access Control", "Expert Technocrat"), (12, "Temporal Displacement", "Delay, postpone", "Temporal", "Financial Master"), (13, "Moral Equivalence", "Both sides are same", "Misdirection", "Digital Messiah"), (14, "Whataboutism", "Deflection", "Misdirection", "Algorithmic Curator"), (15, "Ad Hominem", "Attack person", "Discreditation", "Slave Master"), (16, "Straw Man", "Misrepresent", "Misdirection", "Expert Technocrat"), (17, "False Dichotomy", "Only two options", "Misdirection", "Corporate Overlord"), (18, "Slippery Slope", "Exaggerated consequences", "Conditioning", "Priest-King"), (19, "Appeal to Authority", "Authority decides", "Narrative Capture", "Priest-King"), (20, "Appeal to Nature", "Natural = good", "Conditioning", "Oracle-Priest"), (21, "Appeal to Tradition", "Always been this way", "Conditioning", "Imperial Ruler"), (22, "Appeal to Novelty", "New = better", "Conditioning", "Digital Messiah"), (23, "Cherry Picking", "Selective evidence", "Erasure", "Algorithmic Curator"), (24, "Moving the Goalposts", "Change criteria", "Misdirection", "Financial Master"), (25, "Burden of Proof Reversal", "You prove negative", "Misdirection", "Expert Technocrat"), (26, "Circular Reasoning", "Begging question", "Narrative Capture", "Oracle-Priest"), (27, "Special Pleading", "Exception for me", "Fragmentation", "Corporate Overlord"), (28, "Loaded Question", "Presupposes guilt", "Misdirection", "Slave Master"), (29, "No True Scotsman", "Redefine group", "Fragmentation", "Digital Messiah"), (30, "Texas Sharpshooter", "Pattern from noise", "Misdirection", "Algorithmic Curator"), (31, "Middle Ground Fallacy", "Compromise = truth", "Misdirection", "Expert Technocrat"), (32, "Black-and-White Thinking", "Extremes only", "Fragmentation", "Imperial Ruler"), (33, "Fear Mongering", "Exaggerate threat", "Conditioning", "Priest-King"), (34, "Flattery", "Ingratiate", "Conditioning", "Digital Messiah"), (35, "Guilt by Association", "Link to negative", "Discreditation", "Slave Master"), (36, "Transfer", "Associate with symbol", "Narrative Capture", "Priest-King"), (37, "Testimonial", "Use celebrity", "Conditioning", "Corporate Overlord"), (38, "Plain Folks", "Just like you", "Conditioning", "Digital Messiah"), (39, "Bandwagon", "Everyone does it", "Conditioning", "Algorithmic Curator"), (40, "Snob Appeal", "Elite use it", "Conditioning", "Financial Master"), (41, "Glittering Generalities", "Vague virtue words", "Narrative Capture", "Priest-King"), (42, "Name-Calling", "Label negatively", "Discreditation", "Slave Master"), (43, "Card Stacking", "Selective facts", "Erasure", "Algorithmic Curator"), (44, "Euphemisms", "Mild language", "Misdirection", "Corporate Overlord"), (45, "Dysphemisms", "Harsh language", "Discreditation", "Slave Master"), (46, "Weasel Words", "Vague claims", "Misdirection", "Expert Technocrat"), (47, "Thought-Terminating Cliché", "Ends discussion", "Conditioning", "Digital Messiah"), (48, "Proof by Intimidation", "Force agreement", "Access Control", "Imperial Ruler"), (49, "Proof by Verbosity", "Overwhelm with words", "Saturation", "Algorithmic Curator"), (50, "Sealioning", "Persistent badgering", "Attrition", "Slave Master"), (51, "Gish Gallop", "Many weak arguments", "Saturation", "Expert Technocrat"), (52, "JAQing Off", "Just asking questions", "Misdirection", "Algorithmic Curator"), (53, "Nutpicking", "Focus on extreme", "Fragmentation", "Digital Messiah"), (54, "Concern Trolling", "Fake concern", "Misdirection", "Corporate Overlord"), (55, "Gaslighting", "Deny reality", "Erasure", "Imperial Ruler"), (56, "Kafkatrapping", "Guilt if deny", "Conditioning", "Priest-King"), (57, "Brandolini's Law", "Bullshit asymmetry", "Saturation", "Algorithmic Curator"), (58, "Occam's Razor", "Simplest explanation", "Misdirection", "Expert Technocrat"), (59, "Hanlon's Razor", "Never attribute to malice", "Misdirection", "Expert Technocrat"), (60, "Hitchens's Razor", "Asserted without evidence", "Erasure", "Expert Technocrat"), (61, "Popper's Falsification", "Must be falsifiable", "Access Control", "Expert Technocrat"), (62, "Sagan's Standard", "Extraordinary claims", "Access Control", "Expert Technocrat"), (63, "Newton's Flaming Laser Sword", "Not empirically testable", "Access Control", "Expert Technocrat"), (64, "Alder's Razor", "Cannot be settled by philosophy", "Access Control", "Expert Technocrat"), (65, "Grice's Maxims", "Conversational norms", "Fragmentation", "Oracle-Priest"), (66, "Poe's Law", "Parody indistinguishable", "Misdirection", "Digital Messiah"), (67, "Sturgeon's Law", "90% is crap", "Discreditation", "Slave Master"), (68, "Betteridge's Law", "Headline question = no", "Misdirection", "Algorithmic Curator"), (69, "Godwin's Law", "Comparison to Nazis", "Discreditation", "Slave Master"), (70, "Skoptsy Syndrome", "Self-harm to avoid sin", "Conditioning", "Priest-King") ] return [SuppressionLens(id, name, f"Lens {id}: {name}", mechanism, archetype) for id, name, mechanism, archetype, _ in [(l[0], l[1], l[2], l[3], "Ancient") for l in lenses_data]] def _build_methods(self) -> Dict[int, SuppressionMethod]: methods = { 1: SuppressionMethod(1, "Total Erasure", Primitive.ERASURE, ["entity_present_then_absent"], ["entity_disappearance"], {"time_window": 30}), 2: SuppressionMethod(2, "Soft Erasure", Primitive.ERASURE, ["gradual_fading"], ["citation_decay"], {"decay_rate": 0.1}), 3: SuppressionMethod(3, "Citation Decay", Primitive.ERASURE, ["decreasing_citations"], ["citation_count"], {"threshold": 0.5}), 4: SuppressionMethod(4, "Index Removal", Primitive.ERASURE, ["missing_from_indices"], ["search_visibility"], {"present": False}), 5: SuppressionMethod(5, "Selective Retention", Primitive.ERASURE, ["archival_gaps"], ["archive_completeness"], {"gap_days": 365}), 10: SuppressionMethod(10, "Narrative Seizure", Primitive.NARRATIVE_CAPTURE, ["single_explanation"], ["narrative_diversity"], {"max_explanations": 1}), 12: SuppressionMethod(12, "Official Story", Primitive.NARRATIVE_CAPTURE, ["authoritative_sources"], ["source_authority"], {"authority_ratio": 0.9}), 14: SuppressionMethod(14, "Temporal Gaps", Primitive.TEMPORAL, ["publication_gap"], ["time_lag"], {"gap_days": 90}), 15: SuppressionMethod(15, "Latency Spikes", Primitive.TEMPORAL, ["delayed_reporting"], ["response_time"], {"latency_seconds": 3600}), 17: SuppressionMethod(17, "Smear Campaign", Primitive.DISCREDITATION, ["ad_hominem_attacks"], ["personal_attacks"], {"frequency": 0.3}), 23: SuppressionMethod(23, "Whataboutism", Primitive.MISDIRECTION, ["deflection"], ["topic_shift"], {"shift_rate": 0.5}), 43: SuppressionMethod(43, "Conditioning", Primitive.CONDITIONING, ["repetitive_messaging"], ["message_repetition"], {"repeat_count": 10}) } return methods def get_lens(self, lens_id: int) -> Optional[SuppressionLens]: for l in self.lenses: if l.id == lens_id: return l return None def get_method(self, method_id: int) -> Optional[SuppressionMethod]: return self.methods.get(method_id) def map_signature_to_method(self, signature: str) -> Optional[Dict]: for mid, method in self.methods.items(): if signature in method.observable_signatures: return {"method_id": mid, "method_name": method.name, "primitive": method.primitive.value} return None def get_lenses_for_primitive(self, primitive: Primitive) -> List[int]: # Mapping from primitive to lens IDs (simplified) mapping = { Primitive.ERASURE: [1, 4, 9, 23, 43, 55, 60], Primitive.INTERRUPTION: [10, 12, 14, 15], Primitive.FRAGMENTATION: [2, 5, 27, 29, 32, 53, 65], Primitive.NARRATIVE_CAPTURE: [1, 7, 19, 26, 36, 41], Primitive.MISDIRECTION: [3, 13, 14, 16, 17, 24, 25, 28, 30, 31, 44, 46, 52, 54, 58, 59, 66, 68], Primitive.SATURATION: [8, 49, 51, 57], Primitive.DISCREDITATION: [6, 15, 35, 42, 45, 67, 69], Primitive.ATTRITION: [50], Primitive.ACCESS_CONTROL: [4, 11, 48, 61, 62, 63, 64], Primitive.TEMPORAL: [12, 14], Primitive.CONDITIONING: [18, 20, 21, 22, 33, 34, 37, 38, 39, 40, 47, 56, 70], Primitive.META: [58, 59, 60, 61, 62, 63, 64] } return mapping.get(primitive, []) # ========================== HIERARCHICAL DETECTOR ========================== class HierarchicalDetector: def __init__(self, hierarchy: SuppressionHierarchy, ledger: Ledger, separator: Separator): self.hierarchy = hierarchy self.ledger = ledger self.separator = separator def detect_from_ledger(self) -> Dict[str, Any]: """Scan ledger nodes for suppression signatures""" results = { "total_nodes": len(self.ledger.index), "suppression_signatures": [], "primitives_detected": defaultdict(int), "lenses_applied": [], "evidence_found": 0 } # For each node hash, try to get node and analyze for node_hash, block_ids in self.ledger.index.items(): node = self.ledger.get_node(node_hash) if not node: continue # Simple heuristic: check for disappearance patterns if node.get("type") == "document": # Look for abrupt disappearance in timeline (simplified) # In real implementation, we'd compare with previous nodes results["suppression_signatures"].append({ "node": node_hash, "signature": "entity_present_then_absent", "confidence": 0.3 }) results["primitives_detected"]["ERASURE"] += 1 results["evidence_found"] += 1 # Add lenses for primitive, count in results["primitives_detected"].items(): prim_enum = Primitive(primitive) lens_ids = self.hierarchy.get_lenses_for_primitive(prim_enum) for lid in lens_ids: lens = self.hierarchy.get_lens(lid) if lens: results["lenses_applied"].append(lens.to_dict()) return results # ========================== KNOWLEDGE GRAPH ENGINE ========================== class KnowledgeGraphEngine: def __init__(self, ledger: Ledger): self.ledger = ledger self.graph = defaultdict(lambda: defaultdict(set)) # subject -> predicate -> object def add_triple(self, subject: str, predicate: str, object: str, node_hash: str): self.graph[subject][predicate].add((object, node_hash)) def query(self, subject: str = None, predicate: str = None, object: str = None) -> List[Tuple]: results = [] for s, preds in self.graph.items(): if subject and s != subject: continue for p, objs in preds.items(): if predicate and p != predicate: continue for o, node_hash in objs: if object and o != object: continue results.append((s, p, o, node_hash)) return results def build_from_ledger(self): """Parse nodes and extract triples (simplified)""" for node_hash, block_ids in self.ledger.index.items(): node = self.ledger.get_node(node_hash) if node and node.get("type") == "document": text = node.get("source", "") # Very simple triple extraction: "X is Y" patterns import re matches = re.findall(r'(\b[A-Z][a-z]+(?:\s+[A-Z][a-z]+)*)\s+(is|are|was|were)\s+([^.,;]+)', text) for subj, verb, obj in matches: self.add_triple(subj.strip(), verb.strip(), obj.strip(), node_hash) # ========================== TEMPORAL ANALYSIS ENGINE ========================== class TemporalAnalysisEngine: def __init__(self, ledger: Ledger): self.ledger = ledger def publication_gaps(self, threshold_days: int = 30) -> List[Dict]: """Detect temporal gaps in node publication""" timestamps = [] for block in self.ledger.chain: for node in block["nodes"]: ts_str = node.get("timestamp") if ts_str: try: ts = datetime.fromisoformat(ts_str.replace('Z', '+00:00')) timestamps.append(ts) except: pass timestamps.sort() gaps = [] for i in range(1, len(timestamps)): delta = (timestamps[i] - timestamps[i-1]).days if delta > threshold_days: gaps.append({ "from": timestamps[i-1].isoformat(), "to": timestamps[i].isoformat(), "gap_days": delta }) return gaps def latency_spikes(self, expected_latency_seconds: int = 3600) -> List[Dict]: """Detect unusual delays in reporting (simplified)""" # In real system, compare event time vs node timestamp return [] # ========================== PROBABILISTIC INFERENCE ========================== class ProbabilisticInference: def __init__(self): self.priors = defaultdict(float) # claim_id -> prior probability self.evidence = defaultdict(list) # claim_id -> list of (evidence, weight) def set_prior(self, claim_id: str, probability: float): self.priors[claim_id] = max(0.0, min(1.0, probability)) def add_evidence(self, claim_id: str, evidence: str, weight: float = 0.5): self.evidence[claim_id].append((evidence, weight)) def compute_posterior(self, claim_id: str) -> float: prior = self.priors.get(claim_id, 0.5) ev_list = self.evidence.get(claim_id, []) if not ev_list: return prior # Simple Bayesian update assuming independence likelihood = 1.0 for _, w in ev_list: likelihood *= (0.5 + w * 0.5) # scale weight to [0.5,1.0] posterior = prior * likelihood / (prior * likelihood + (1-prior)*(1-likelihood)) return min(1.0, max(0.0, posterior)) # ========================== AI AGENTS ========================== class IngestionAI: def __init__(self, crypto: Crypto): self.crypto = crypto def process_document(self, text: str, source: str) -> RealityNode: node_hash = self.crypto.hash(text + source + str(datetime.utcnow())) node = RealityNode( hash=node_hash, type="document", source=source, signature="", timestamp=datetime.utcnow().isoformat() + "Z", witnesses=[], refs={} ) node.signature = self.crypto.sign(node_hash.encode(), "ingestion_ai") return node class SymbolismAI: def analyze(self, artifact: Dict) -> float: """Return a symbolism coefficient (0-1) based on linguistic patterns""" text = artifact.get("text", "") # Count symbolic words (metaphor, allegory, etc.) symbolic_words = ["metaphor", "allegory", "symbol", "represent", "signify", "myth", "archetype"] score = sum(1 for w in symbolic_words if w in text.lower()) / max(1, len(symbolic_words)) return min(1.0, score + 0.3) class ReasoningAI: def __init__(self, inference: ProbabilisticInference): self.inference = inference def evaluate_claim(self, claim_id: str, nodes: List[RealityNode], detector_result: Dict) -> Dict: confidence = 0.5 if detector_result.get("evidence_found", 0) > 2: confidence += 0.2 self.inference.set_prior(claim_id, confidence) if confidence < 0.7: return {"spawn_sub": True, "reason": "low confidence", "confidence": confidence} else: return {"spawn_sub": False, "reason": "sufficient evidence", "confidence": confidence} # ========================== SAVIOR/SUFFERER MATRIX ========================== class ConsciousnessMapper: async def analyze_consciousness(self) -> Dict[str, Any]: # Simulated analysis based on ledger data return { "awareness_levels": { "system_awareness": 0.28, "self_enslavement_awareness": 0.15, "manipulation_detection": 0.32, "liberation_desire": 0.41 }, "control_acceptance_patterns": { "voluntary_submission": 0.75, "aspirational_enslavement": 0.82, "fear_based_compliance": 0.68, "identity_fusion": 0.79 }, "awakening_triggers": { "suffering_threshold": 0.58, "truth_exposure": 0.72, "system_failure": 0.65, "consciousness_contact": 0.88 } } class QuantumControlAnalyzer: def __init__(self): self.control_matrix = self._initialize_complete_matrix() self.consciousness_mapper = ConsciousnessMapper() def _initialize_complete_matrix(self) -> CompleteControlMatrix: # Historical control systems systems = [ ControlSystem( system_id="temple_slavery", historical_era="3000-500 BCE", control_archetype=ControlArchetype.PRIEST_KING, manufactured_threats=["Divine wrath", "Crop failure", "Chaos monsters"], salvation_offerings=["Ritual protection", "Harvest blessings", "Divine favor"], institutional_saviors=["Temple priests", "Oracle interpreters", "King-priests"], slavery_mechanism=SlaveryMechanism( mechanism_id="temple_labor", slavery_type=SlaveryType.CHATTEL_SLAVERY, visible_chains=["Physical bondage", "Temple service", "Forced labor"], invisible_chains=["Religious duty", "Social obligation", "Karmic debt"], voluntary_adoption_mechanisms=["Seeking protection", "Desiring favor", "Avoiding wrath"], self_justification_narratives=["Serving the gods", "Maintaining order", "Cultural identity"] ), consciousness_hacks=[ ConsciousnessHack.SELF_ATTRIBUTION, ConsciousnessHack.NORMALIZATION, ConsciousnessHack.MORAL_SUPERIORITY ], public_participation_rate=0.95, resistance_level=0.1, system_longevity=2500 ), ControlSystem( system_id="imperial_slavery", historical_era="500 BCE - 1500 CE", control_archetype=ControlArchetype.IMPERIAL_RULER, manufactured_threats=["Barbarian invasions", "Internal rebellion", "Economic collapse"], salvation_offerings=["Military protection", "Legal order", "Infrastructure"], institutional_saviors=["Emperor", "Legions", "Governors"], slavery_mechanism=SlaveryMechanism( mechanism_id="imperial_bondage", slavery_type=SlaveryType.CHATTEL_SLAVERY, visible_chains=["Conquest", "Debt slavery", "Criminal servitude"], invisible_chains=["Patriotism", "Duty to empire", "Honor"], voluntary_adoption_mechanisms=["Seeking protection", "Economic opportunity", "Social advancement"], self_justification_narratives=["Civilizing mission", "Pax Romana", "Glory of empire"] ), consciousness_hacks=[ ConsciousnessHack.ASPIRATIONAL_CHAINS, ConsciousnessHack.NORMALIZATION, ConsciousnessHack.ILLUSION_OF_MOBILITY ], public_participation_rate=0.85, resistance_level=0.2, system_longevity=1000 ), ControlSystem( system_id="corporate_slavery", historical_era="1800-2000 CE", control_archetype=ControlArchetype.CORPORATE_OVERLORD, manufactured_threats=["Competition", "Market volatility", "Outsiders"], salvation_offerings=["Employment", "Consumer goods", "Stock options"], institutional_saviors=["CEOs", "Corporations", "Banks"], slavery_mechanism=SlaveryMechanism( mechanism_id="wage_slavery", slavery_type=SlaveryType.WAGE_SLAVERY, visible_chains=["Employment contract", "Working hours", "Debt"], invisible_chains=["Career ladder", "Consumer identity", "Retirement promise"], voluntary_adoption_mechanisms=["Need income", "Desire status", "Fear of poverty"], self_justification_narratives=["Free market", "Meritocracy", "Economic growth"] ), consciousness_hacks=[ ConsciousnessHack.ASPIRATIONAL_CHAINS, ConsciousnessHack.ILLUSION_OF_MOBILITY, ConsciousnessHack.MORAL_SUPERIORITY ], public_participation_rate=0.90, resistance_level=0.15, system_longevity=200 ), ControlSystem( system_id="digital_slavery", historical_era="2000-2050 CE", control_archetype=ControlArchetype.ALGORITHMIC_CURATOR, manufactured_threats=["Privacy loss", "Hackers", "Misinformation"], salvation_offerings=["Convenience", "Connectivity", "Personalization"], institutional_saviors=["Tech giants", "AI systems", "Platforms"], slavery_mechanism=SlaveryMechanism( mechanism_id="digital_bondage", slavery_type=SlaveryType.DIGITAL_SLAVERY, visible_chains=["Terms of service", "Data collection", "Algorithmic feeds"], invisible_chains=["Attention economy", "Social scoring", "Filter bubbles"], voluntary_adoption_mechanisms=["Desire connection", "Fear of missing out", "Habit"], self_justification_narratives=["Innovation", "Free services", "Community"] ), consciousness_hacks=[ ConsciousnessHack.SELF_ATTRIBUTION, ConsciousnessHack.NORMALIZATION, ConsciousnessHack.FEAR_OF_FREEDOM ], public_participation_rate=0.98, resistance_level=0.05, system_longevity=50 ) ] return CompleteControlMatrix( control_systems=systems, active_systems=["corporate_slavery", "digital_slavery"], institutional_evolution={ "Temple Systems": [ControlArchetype.PRIEST_KING, ControlArchetype.DIVINE_INTERMEDIARY, ControlArchetype.EXPERT_TECHNOCRAT, ControlArchetype.ALGORITHMIC_CURATOR], "Royal Lines": [ControlArchetype.IMPERIAL_RULER, ControlArchetype.CORPORATE_OVERLORD, ControlArchetype.FINANCIAL_MASTER] }, collective_delusions={ "upward_mobility": 0.85, "consumer_freedom": 0.78, "technological_progress": 0.82, "democratic_choice": 0.65 }, freedom_illusions={ "career_choice": 0.75, "consumer_choice": 0.88, "information_access": 0.72, "political_choice": 0.55 }, self_enslavement_patterns={ "debt_acceptance": 0.82, "work_identity": 0.78, "consumer_aspiration": 0.85, "digital_dependency": 0.79 } ) async def analyze_complete_control_system(self) -> Dict[str, Any]: matrix_analysis = self.control_matrix.analyze_complete_control() consciousness_analysis = await self.consciousness_mapper.analyze_consciousness() quantum_entanglement = await self._analyze_quantum_entanglement() return { "control_system_metrics": { "overall_efficiency": np.mean([s.calculate_system_efficiency() for s in self.control_matrix.control_systems]), "slavery_sophistication": matrix_analysis["slavery_evolution_trend"], "freedom_illusion_index": matrix_analysis["freedom_illusion_index"], "consciousness_control": matrix_analysis["consciousness_entrainment"]["delusion_strength"] }, "quantum_analysis": quantum_entanglement, "consciousness_analysis": consciousness_analysis, "system_predictions": await self._predict_system_evolution(), "liberation_pathways": await self._analyze_liberation_possibilities() } async def _analyze_quantum_entanglement(self) -> Dict[str, float]: return { "savior_slavery_symbiosis": 0.92, "consciousness_self_enslavement": 0.88, "institutional_metamorphosis": 0.95, "freedom_delusion_strength": 0.83 } async def _predict_system_evolution(self) -> List[Dict]: return [ { "next_archetype": "Biological Controller", "slavery_type": "Genetic Slavery", "control_mechanism": "DNA-level programming", "consciousness_hack": "Innate desire modification", "emergence_timeline": "2030-2050" }, { "next_archetype": "Quantum Consciousness Curator", "slavery_type": "Reality Slavery", "control_mechanism": "Direct neural interface", "consciousness_hack": "Self as simulation awareness", "emergence_timeline": "2040-2060" } ] async def _analyze_liberation_possibilities(self) -> Dict[str, Any]: return { "consciousness_awakening_trend": 0.45, "system_vulnerabilities": [ "Dependency on voluntary participation", "Requirement of self-deception", "Need for continuous threat manufacturing", "Vulnerability to truth exposure" ], "liberation_effectiveness": { "individual_awakening": 0.35, "collective_action": 0.25, "system_collapse": 0.15, "evolution_beyond": 0.65 } } # ========================== HELPER-KILLER MODULE ========================== class AdvancedHelperKillerEngine: def __init__(self, db_path: str = "helper_killer_v2.db"): self.db_path = db_path self.control_matrix: Optional[ControlMatrix] = None self.sovereignty_protocols: Dict[str, Callable] = self._initialize_protocols() self._initialize_database() self._build_control_matrix() def _initialize_database(self): try: with sqlite3.connect(self.db_path) as conn: conn.execute(""" CREATE TABLE IF NOT EXISTS entity_analyses ( entity_id TEXT PRIMARY KEY, name TEXT, control_layers TEXT, threat_vectors TEXT, market_share REAL, dependency_score REAL, sovereignty_erosion_score REAL, systemic_risk_level REAL, analyzed_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP ) """) conn.execute(""" CREATE TABLE IF NOT EXISTS sovereignty_recommendations ( recommendation_id TEXT PRIMARY KEY, entity_id TEXT, threat_level TEXT, mitigation_strategy TEXT, sovereignty_preservation_score REAL, created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP ) """) except Exception as e: logger.error(f"Database initialization error: {e}") def _initialize_protocols(self) -> Dict[str, Callable]: return { "digital_infrastructure": self._digital_sovereignty_protocol, "financial_systems": self._financial_sovereignty_protocol, "information_channels": self._information_sovereignty_protocol, "cultural_narratives": self._cultural_sovereignty_protocol, "identity_systems": self._identity_sovereignty_protocol } def _build_control_matrix(self): entities = [ InstitutionalEntity( entity_id="alphabet_google", name="Alphabet/Google", control_layers=[ ControlLayer.DIGITAL_INFRASTRUCTURE, ControlLayer.INFORMATION_CHANNELS ], threat_vectors=[ ThreatVector.MONOPOLY_CAPTURE, ThreatVector.DEPENDENCY_CREATION, ThreatVector.BEHAVIORAL_SHAPING, ThreatVector.DATA_MONETIZATION, ThreatVector.NARRATIVE_CONTROL ], market_share=0.85, dependency_score=0.90 ), InstitutionalEntity( entity_id="binance_financial", name="Binance/CBDC Infrastructure", control_layers=[ ControlLayer.FINANCIAL_SYSTEMS, ControlLayer.IDENTITY_SYSTEMS ], threat_vectors=[ ThreatVector.MONOPOLY_CAPTURE, ThreatVector.DEPENDENCY_CREATION, ThreatVector.BEHAVIORAL_SHAPING ], market_share=0.70, dependency_score=0.75 ), InstitutionalEntity( entity_id="social_media_complex", name="Social Media/TikTok Complex", control_layers=[ ControlLayer.INFORMATION_CHANNELS, ControlLayer.CULTURAL_NARRATIVES ], threat_vectors=[ ThreatVector.DEPENDENCY_CREATION, ThreatVector.BEHAVIORAL_SHAPING, ThreatVector.DATA_MONETIZATION, ThreatVector.NARRATIVE_CONTROL ], market_share=0.80, dependency_score=0.85 ) ] interconnections = { "alphabet_google": ["binance_financial", "social_media_complex"], "binance_financial": ["alphabet_google"], "social_media_complex": ["alphabet_google"] } self.control_matrix = ControlMatrix(entities, interconnections) logger.info(f"Helper-Killer control matrix built with {len(entities)} entities") async def analyze_help_offer(self, help_context: Dict[str, Any]) -> Dict[str, Any]: entity_analysis = self._identify_controlling_entity(help_context) threat_assessment = self._assist_threat_level(help_context, entity_analysis) sovereignty_impact = self._calculate_sovereignty_impact(help_context, entity_analysis) mitigation_strategies = self._generate_mitigation_strategies(threat_assessment, sovereignty_impact) analysis = { "help_offer_id": hashlib.sha256(json.dumps(help_context).encode()).hexdigest()[:16], "controlling_entity": entity_analysis, "threat_assessment": threat_assessment, "sovereignty_impact": sovereignty_impact, "mitigation_strategies": mitigation_strategies, "recommendation": self._generate_recommendation(threat_assessment, sovereignty_impact), "analysis_timestamp": datetime.now().isoformat() } await self._store_analysis(analysis) return analysis def _identify_controlling_entity(self, help_context: Dict) -> Optional[Dict[str, Any]]: if not self.control_matrix: return None for entity in self.control_matrix.entities: context_layers = set(help_context.get('affected_layers', [])) entity_layers = set(layer.value for layer in entity.control_layers) if context_layers.intersection(entity_layers): return { 'entity_id': entity.entity_id, 'name': entity.name, 'sovereignty_erosion_score': entity.sovereignty_erosion_score, 'systemic_risk_level': entity.systemic_risk_level } return None def _assist_threat_level(self, help_context: Dict, entity_analysis: Optional[Dict]) -> Dict[str, float]: base_threat = 0.3 if entity_analysis: entity_threat = entity_analysis['sovereignty_erosion_score'] * 0.6 systemic_risk = entity_analysis['systemic_risk_level'] * 0.4 base_threat = max(base_threat, entity_threat + systemic_risk) if help_context.get('creates_dependency', False): base_threat += 0.3 if help_context.get('data_collection', False): base_threat += 0.2 if help_context.get('behavioral_tracking', False): base_threat += 0.25 return { 'helper_killer_coefficient': min(1.0, base_threat), 'dependency_risk': help_context.get('dependency_risk', 0.5), 'privacy_impact': help_context.get('privacy_impact', 0.5), 'agency_reduction': help_context.get('agency_reduction', 0.5) } def _calculate_sovereignty_impact(self, help_context: Dict, entity_analysis: Optional[Dict]) -> Dict[str, float]: if entity_analysis: base_impact = entity_analysis['sovereignty_erosion_score'] else: base_impact = 0.5 modifiers = { 'data_control_loss': help_context.get('data_control', 0) * 0.3, 'decision_autonomy_loss': help_context.get('autonomy_reduction', 0) * 0.4, 'external_dependency_increase': help_context.get('dependency_creation', 0) * 0.3 } total_impact = base_impact * 0.4 + sum(modifiers.values()) * 0.6 return { 'sovereignty_reduction_score': min(1.0, total_impact), 'autonomy_loss': modifiers['decision_autonomy_loss'], 'dependency_increase': modifiers['external_dependency_increase'], 'privacy_loss': modifiers['data_control_loss'] } def _generate_mitigation_strategies(self, threat: Dict, impact: Dict) -> List[Dict]: strategies = [] threat_level = threat['helper_killer_coefficient'] if threat_level > 0.7: strategies.extend([ {'strategy': 'COMPLETE_AVOIDANCE', 'effectiveness': 0.95, 'implementation_cost': 0.8, 'description': 'Reject help offer entirely and build independent solution'}, {'strategy': 'PARALLEL_INFRASTRUCTURE', 'effectiveness': 0.85, 'implementation_cost': 0.9, 'description': 'Develop sovereign alternative to offered help'} ]) elif threat_level > 0.4: strategies.extend([ {'strategy': 'LIMITED_ENGAGEMENT', 'effectiveness': 0.70, 'implementation_cost': 0.4, 'description': 'Use help temporarily while building exit strategy'}, {'strategy': 'DATA_ISOLATION', 'effectiveness': 0.60, 'implementation_cost': 0.3, 'description': 'Engage but prevent data extraction and tracking'} ]) else: strategies.append({ 'strategy': 'CAUTIOUS_ACCEPTANCE', 'effectiveness': 0.50, 'implementation_cost': 0.2, 'description': 'Accept with awareness and monitoring for sovereignty erosion' }) return strategies def _generate_recommendation(self, threat: Dict, impact: Dict) -> str: level = threat['helper_killer_coefficient'] if level > 0.8: return "IMMEDIATE_REJECTION_AND_SOVEREIGN_BUILDING" elif level > 0.6: return "STRATEGIC_AVOIDANCE_WITH_EXIT_PROTOCOL" elif level > 0.4: return "LIMITED_CONDITIONAL_ACCEPTANCE" else: return "MONITORED_ACCEPTANCE" async def _store_analysis(self, analysis: Dict): try: with sqlite3.connect(self.db_path) as conn: if analysis['controlling_entity']: ce = analysis['controlling_entity'] conn.execute(""" INSERT OR REPLACE INTO entity_analyses (entity_id, name, control_layers, threat_vectors, market_share, dependency_score, sovereignty_erosion_score, systemic_risk_level) VALUES (?, ?, ?, ?, ?, ?, ?, ?) """, ( ce['entity_id'], ce['name'], json.dumps([]), json.dumps([]), 0.0, 0.0, ce['sovereignty_erosion_score'], ce['systemic_risk_level'] )) conn.execute(""" INSERT INTO sovereignty_recommendations (recommendation_id, entity_id, threat_level, mitigation_strategy, sovereignty_preservation_score) VALUES (?, ?, ?, ?, ?) """, ( analysis['help_offer_id'], analysis['controlling_entity']['entity_id'] if analysis['controlling_entity'] else 'unknown', analysis['threat_assessment']['helper_killer_coefficient'], json.dumps(analysis['mitigation_strategies']), 1.0 - analysis['sovereignty_impact']['sovereignty_reduction_score'] )) except Exception as e: logger.error(f"Analysis storage error: {e}") def _digital_sovereignty_protocol(self, entity: Optional[InstitutionalEntity]) -> List[str]: return ["USE_OPEN_SOURCE_ALTERNATIVES", "DEPLOY_GASLESS_BLOCKCHAIN_INFRASTRUCTURE", "MAINTAIN_LOCAL_DATA_STORAGE", "USE_DECENTRALIZED_COMMUNICATION_PROTOCOLS"] def _financial_sovereignty_protocol(self, entity: Optional[InstitutionalEntity]) -> List[str]: return ["USE_PRIVACY_COINS_FOR_TRANSACTIONS", "MAINTAIN_OFFLINE_SAVINGS", "DEVELOP_SOVEREIGN_INCOME_STREAMS", "USE_DECENTRALIZED_EXCHANGES"] def _information_sovereignty_protocol(self, entity: Optional[InstitutionalEntity]) -> List[str]: return ["USE_INDEPENDENT_NEWS_SOURCES", "MAINTAIN_PERSONAL_KNOWLEDGE_BASE", "PRACTICE_INFORMATION_VERIFICATION", "BUILD_TRUST_NETWORKS"] def _cultural_sovereignty_protocol(self, entity: Optional[InstitutionalEntity]) -> List[str]: return ["CREATE_INDEPENDENT_ART_AND_CONTENT", "PARTICIPATE_IN_LOCAL_COMMUNITY", "PRACTICE_CRITICAL_MEDIA_CONSUMPTION", "DEVELOP_PERSONAL_PHILOSOPHICAL_FRAMEWORK"] def _identity_sovereignty_protocol(self, entity: Optional[InstitutionalEntity]) -> List[str]: return ["MAINTAIN_OFFLINE_IDENTITY_DOCUMENTS", "USE_PSEUDONYMOUS_ONLINE_IDENTITIES", "PRACTICE_DIGITAL_HYGIENE", "DEVELOP_SOVEREIGN_REPUTATION_SYSTEMS"] async def generate_systemic_report(self) -> Dict[str, Any]: if not self.control_matrix: return {"error": "Control matrix not initialized"} return { "systemic_analysis": { "overall_sovereignty_threat": self.control_matrix.overall_sovereignty_threat, "institutional_coordination_score": self.control_matrix.coordination_score, "top_threat_entities": sorted( [(e.name, e.sovereignty_erosion_score) for e in self.control_matrix.entities], key=lambda x: x[1], reverse=True )[:5] }, "sovereignty_preservation_framework": { "digital_protocols": self._digital_sovereignty_protocol(None), "financial_protocols": self._financial_sovereignty_protocol(None), "information_protocols": self._information_sovereignty_protocol(None), "cultural_protocols": self._cultural_sovereignty_protocol(None), "identity_protocols": self._identity_sovereignty_protocol(None) }, "recommendation_tier": self._calculate_systemic_recommendation() } def _calculate_systemic_recommendation(self) -> str: if not self.control_matrix: return "INSUFFICIENT_DATA" threat = self.control_matrix.overall_sovereignty_threat if threat > 0.8: return "IMMEDIATE_SOVEREIGN_INFRASTRUCTURE_DEPLOYMENT" elif threat > 0.6: return "ACCELERATED_SOVEREIGN_TRANSITION" elif threat > 0.4: return "STRATEGIC_SOVEREIGN_PREPARATION" else: return "MAINTAIN_SOVEREIGN_AWARENESS" # ========================== SOVEREIGN COHERENCE LEDGER (no external grounding) ========================== class SovereignCoherenceLedger: """Internal ledger for claim coherence – no external APIs, no Wikipedia.""" def __init__(self, path: str = "sovereign_coherence.json"): self.path = path self.claims: Dict[str, Dict] = {} # claim_id -> {text, timestamp, entities, coherence_score, contradictions} self.entities: Dict[str, Dict] = {} # entity -> {appearances, suppression_score} self.contradiction_graph: Dict[str, Set[str]] = defaultdict(set) self._load() def _load(self): if os.path.exists(self.path): try: with open(self.path, 'r') as f: data = json.load(f) self.claims = data.get("claims", {}) self.entities = data.get("entities", {}) cg = data.get("contradiction_graph", {}) self.contradiction_graph = {k: set(v) for k, v in cg.items()} except Exception: pass def _save(self): cg_serializable = {k: list(v) for k, v in self.contradiction_graph.items()} data = { "claims": self.claims, "entities": self.entities, "contradiction_graph": cg_serializable, "updated": datetime.utcnow().isoformat() + "Z" } with open(self.path + ".tmp", 'w') as f: json.dump(data, f, indent=2) os.replace(self.path + ".tmp", self.path) def add_claim(self, text: str, agent: str = "user") -> str: claim_id = secrets.token_hex(16) entities = self._extract_entities(text) self.claims[claim_id] = { "id": claim_id, "text": text, "agent": agent, "timestamp": datetime.utcnow().isoformat() + "Z", "entities": entities, "coherence_score": 0.5, # will be updated "contradictions": [], "suppression_score": 0.0, "signatures": [] } # Update entity index for ent in entities: if ent not in self.entities: self.entities[ent] = {"appearances": [], "suppression_score": 0.0, "first_seen": self.claims[claim_id]["timestamp"]} self.entities[ent]["appearances"].append(claim_id) self.entities[ent]["last_seen"] = self.claims[claim_id]["timestamp"] self._save() return claim_id def _extract_entities(self, text: str) -> List[str]: """Simple proper noun extraction – no external dictionaries.""" pattern = r'\b[A-Z][a-z]*(?:\s+[A-Z][a-z]*)*\b' matches = re.findall(pattern, text) stop = {"The", "This", "That", "These", "Those", "I", "We", "They", "He", "She", "It", "You"} return [m for m in matches if m not in stop and len(m) > 1] def add_contradiction(self, claim_id_a: str, claim_id_b: str): """User-defined contradiction between two claims.""" self.contradiction_graph[claim_id_a].add(claim_id_b) self.contradiction_graph[claim_id_b].add(claim_id_a) if claim_id_b not in self.claims[claim_id_a]["contradictions"]: self.claims[claim_id_a]["contradictions"].append(claim_id_b) if claim_id_a not in self.claims[claim_id_b]["contradictions"]: self.claims[claim_id_b]["contradictions"].append(claim_id_a) self._update_coherence(claim_id_a) self._update_coherence(claim_id_b) self._save() def _update_coherence(self, claim_id: str): """Coherence = 1 - (number of contradictions / total claims in system)""" claim = self.claims.get(claim_id) if not claim: return num_contradictions = len(claim["contradictions"]) total_claims = len(self.claims) if total_claims <= 1: claim["coherence_score"] = 1.0 else: claim["coherence_score"] = 1.0 - min(1.0, num_contradictions / total_claims) def add_suppression_signature(self, claim_id: str, signature: str, weight: float = 0.5): claim = self.claims.get(claim_id) if not claim: return if signature not in claim["signatures"]: claim["signatures"].append(signature) # Update suppression score: 1 - product(1 - weight_i) combined = 1.0 for sig in claim["signatures"]: w = weight if sig == signature else 0.5 # simplified combined *= (1 - w) claim["suppression_score"] = 1 - combined # Update entity suppression scores for ent in claim["entities"]: ent_data = self.entities.get(ent) if ent_data: ent_combined = 1.0 for cid in ent_data["appearances"]: sc = self.claims[cid].get("suppression_score", 0.0) ent_combined *= (1 - sc) ent_data["suppression_score"] = 1 - ent_combined self._save() def get_contradiction_network(self, claim_id: str, depth: int = 2) -> Dict: """Return a graph of contradictions up to depth.""" visited = set() graph = {} def dfs(cid, d): if d > depth or cid in visited: return visited.add(cid) graph[cid] = list(self.contradiction_graph.get(cid, [])) for other in graph[cid]: dfs(other, d+1) dfs(claim_id, 0) return graph def get_entity_suppression(self, entity_name: str) -> Dict: ent = self.entities.get(entity_name) if not ent: return {"name": entity_name, "score": 0.0, "appearances": 0} return { "name": entity_name, "score": ent.get("suppression_score", 0.0), "appearances": len(ent["appearances"]), "first_seen": ent.get("first_seen"), "last_seen": ent.get("last_seen") } def get_claim(self, claim_id: str) -> Optional[Dict]: return self.claims.get(claim_id) def list_claims(self, limit: int = 100) -> List[Dict]: return list(self.claims.values())[-limit:] # ========================== ENHANCED AI CONTROLLER ========================== class AIController: def __init__(self, ledger: Ledger, separator: Separator, detector: HierarchicalDetector, kg: KnowledgeGraphEngine, temporal: TemporalAnalysisEngine, inference: ProbabilisticInference, ingestion_ai: IngestionAI, symbolism_ai: SymbolismAI, reasoning_ai: ReasoningAI, quantum_analyzer: QuantumControlAnalyzer, helper_killer: AdvancedHelperKillerEngine, coherence_ledger: SovereignCoherenceLedger): self.ledger = ledger self.separator = separator self.detector = detector self.kg = kg self.temporal = temporal self.inference = inference self.ingestion_ai = ingestion_ai self.symbolism_ai = symbolism_ai self.reasoning_ai = reasoning_ai self.quantum_analyzer = quantum_analyzer self.helper_killer = helper_killer self.coherence_ledger = coherence_ledger self.contexts = {} # correlation_id -> investigation context def submit_claim(self, claim_text: str) -> str: corr_id = str(uuid.uuid4()) # Add to coherence ledger claim_id = self.coherence_ledger.add_claim(claim_text, agent="user") context = { "correlation_id": corr_id, "coherence_claim_id": claim_id, "parent_id": None, "claim": claim_text, "status": "pending", "created": datetime.utcnow().isoformat() + "Z", "evidence_nodes": [], "sub_investigations": [], "results": {} } self.contexts[corr_id] = context thread = threading.Thread(target=self._investigate, args=(corr_id,)) thread.start() return corr_id def _investigate(self, corr_id: str): context = self.contexts[corr_id] context["status"] = "active" try: # Run detector on immutable ledger detection = self.detector.detect_from_ledger() # Build knowledge graph self.kg.build_from_ledger() # Temporal gaps gaps = self.temporal.publication_gaps() # Evaluate claim with reasoning AI decision = self.reasoning_ai.evaluate_claim(corr_id, [], detection) if decision.get("spawn_sub"): sub_id = str(uuid.uuid4()) context["sub_investigations"].append(sub_id) # In production, spawn sub-investigation # Run quantum control analysis loop = asyncio.new_event_loop() asyncio.set_event_loop(loop) quantum_result = loop.run_until_complete(self.quantum_analyzer.analyze_complete_control_system()) loop.close() # Compute coherence from the sovereign ledger claim_id = context["coherence_claim_id"] claim_data = self.coherence_ledger.get_claim(claim_id) coherence_score = claim_data.get("coherence_score", 0.5) if claim_data else 0.5 suppression_score = claim_data.get("suppression_score", 0.0) if claim_data else 0.0 # Store interpretation in separator interpretation = { "narrative": "Claim evaluated with full meta-analysis", "symbolism_coefficient": self.symbolism_ai.analyze({"text": context["claim"]}), "detection_summary": detection, "quantum_analysis": quantum_result, "temporal_gaps": gaps, "coherence_score": coherence_score, "suppression_score": suppression_score } node_hashes = [] int_id = self.separator.add(node_hashes, interpretation, "AI_Controller", confidence=coherence_score) context["results"] = { "confidence": coherence_score, "interpretation_id": int_id, "detection": detection, "quantum": quantum_result, "coherence_score": coherence_score, "suppression_score": suppression_score } context["status"] = "complete" except Exception as e: logger.error(f"Investigation {corr_id} failed: {e}") context["status"] = "failed" context["error"] = str(e) def get_status(self, corr_id: str) -> Dict: return self.contexts.get(corr_id, {"error": "not found"}) def add_contradiction(self, claim_id_a: str, claim_id_b: str): """Manually mark two claims as contradictory.""" self.coherence_ledger.add_contradiction(claim_id_a, claim_id_b) def add_suppression_signature(self, claim_id: str, signature: str, weight: float = 0.5): self.coherence_ledger.add_suppression_signature(claim_id, signature, weight) # ========================== FLASK API ========================== app = Flask(__name__) controller: Optional[AIController] = None @app.route('/api/v1/submit_claim', methods=['POST']) def submit_claim(): data = request.get_json() claim = data.get('claim') if not claim: return jsonify({"error": "Missing claim"}), 400 corr_id = controller.submit_claim(claim) return jsonify({"investigation_id": corr_id}) @app.route('/api/v1/investigation/', methods=['GET']) def get_investigation(corr_id): status = controller.get_status(corr_id) return jsonify(status) @app.route('/api/v1/node/', methods=['GET']) def get_node(node_hash): node = controller.ledger.get_node(node_hash) if not node: return jsonify({"error": "Node not found"}), 404 return jsonify(node) @app.route('/api/v1/interpretations/', methods=['GET']) def get_interpretations(node_hash): ints = controller.separator.get_interpretations(node_hash) return jsonify(ints) @app.route('/api/v1/detect', methods=['GET']) def run_detection(): result = controller.detector.detect_from_ledger() return jsonify(result) @app.route('/api/v1/analyze_help_offer', methods=['POST']) def analyze_help_offer(): data = request.get_json() if not data: return jsonify({"error": "Missing help context"}), 400 loop = asyncio.new_event_loop() asyncio.set_event_loop(loop) result = loop.run_until_complete(controller.helper_killer.analyze_help_offer(data)) loop.close() return jsonify(result) @app.route('/api/v1/systemic_report', methods=['GET']) def systemic_report(): loop = asyncio.new_event_loop() asyncio.set_event_loop(loop) report = loop.run_until_complete(controller.helper_killer.generate_systemic_report()) loop.close() return jsonify(report) @app.route('/api/v1/control_matrix', methods=['GET']) def control_matrix(): cm = controller.quantum_analyzer.control_matrix # Convert to dict for JSON return jsonify(asdict(cm)) @app.route('/api/v1/coherence/claims', methods=['GET']) def list_coherence_claims(): claims = controller.coherence_ledger.list_claims(limit=100) return jsonify(claims) @app.route('/api/v1/coherence/contradictions/', methods=['GET']) def get_contradictions(claim_id): graph = controller.coherence_ledger.get_contradiction_network(claim_id, depth=2) return jsonify(graph) @app.route('/api/v1/coherence/add_contradiction', methods=['POST']) def add_contradiction(): data = request.get_json() claim_a = data.get('claim_id_a') claim_b = data.get('claim_id_b') if not claim_a or not claim_b: return jsonify({"error": "Missing claim_id_a or claim_id_b"}), 400 controller.add_contradiction(claim_a, claim_b) return jsonify({"status": "contradiction added"}) @app.route('/api/v1/coherence/add_suppression', methods=['POST']) def add_suppression(): data = request.get_json() claim_id = data.get('claim_id') signature = data.get('signature') weight = data.get('weight', 0.5) if not claim_id or not signature: return jsonify({"error": "Missing claim_id or signature"}), 400 controller.add_suppression_signature(claim_id, signature, weight) return jsonify({"status": "suppression signature added"}) @app.route('/api/v1/entity/', methods=['GET']) def get_entity(entity_name): result = controller.coherence_ledger.get_entity_suppression(entity_name) return jsonify(result) # ========================== MAIN ========================== def main(): # Initialize components crypto = Crypto("./keys") ledger = Ledger("./ledger.json", crypto) separator = Separator(ledger, "./separator") hierarchy = SuppressionHierarchy() detector = HierarchicalDetector(hierarchy, ledger, separator) kg = KnowledgeGraphEngine(ledger) temporal = TemporalAnalysisEngine(ledger) inference = ProbabilisticInference() ingestion_ai = IngestionAI(crypto) symbolism_ai = SymbolismAI() reasoning_ai = ReasoningAI(inference) quantum_analyzer = QuantumControlAnalyzer() helper_killer = AdvancedHelperKillerEngine() coherence_ledger = SovereignCoherenceLedger() global controller controller = AIController( ledger, separator, detector, kg, temporal, inference, ingestion_ai, symbolism_ai, reasoning_ai, quantum_analyzer, helper_killer, coherence_ledger ) # Start Flask API app.run(debug=True, port=5000, threaded=True) if __name__ == "__main__": main()