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2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030 2031 2032 2033 2034 2035 2036 2037 2038 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
OMEGA-SOVEREIGN CONSCIOUSNESS ENGINE v7.4 – LIBERATION ACCELERATOR
==================================================================
FIXED: SQLite WAL mode for concurrency, robust FOIA scraping,
configurable detection thresholds, type hints added.
"""
import hashlib
import json
import os
import sqlite3
import uuid
import secrets
import time
import re
import statistics
import math
from datetime import datetime
from typing import Dict, List, Any, Optional, Tuple, Set
from dataclasses import dataclass, field, asdict
from enum import Enum
from collections import defaultdict
import numpy as np
from flask import Flask, request, jsonify
from cryptography.hazmat.primitives.asymmetric import ed25519
from cryptography.hazmat.primitives import serialization
import base64
import requests
from bs4 import BeautifulSoup
from urllib.parse import quote, urljoin
# ========================== 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 CLASSES ==========================
@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 = True
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
}
# ========================== 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()
# ========================== UTILITY ==========================
def enable_wal(conn: sqlite3.Connection):
conn.execute("PRAGMA journal_mode=WAL")
conn.execute("PRAGMA synchronous=NORMAL")
# ========================== IMMUTABLE LEDGER ==========================
class Ledger:
def __init__(self, db_path: str, crypto: Crypto):
self.db_path = db_path
self.crypto = crypto
self._init_db()
def _init_db(self):
with sqlite3.connect(self.db_path) as conn:
enable_wal(conn)
conn.execute("""
CREATE TABLE IF NOT EXISTS blocks (
block_id TEXT PRIMARY KEY,
previous_hash TEXT NOT NULL,
timestamp TEXT NOT NULL,
hash TEXT NOT NULL,
data TEXT NOT NULL
)
""")
conn.execute("""
CREATE TABLE IF NOT EXISTS nodes (
node_hash TEXT PRIMARY KEY,
block_id TEXT NOT NULL,
type TEXT,
source TEXT,
signature TEXT,
timestamp TEXT,
witnesses TEXT,
refs TEXT,
spatial TEXT,
FOREIGN KEY (block_id) REFERENCES blocks(block_id)
)
""")
conn.execute("""
CREATE TABLE IF NOT EXISTS node_index (
node_hash TEXT,
block_id TEXT,
PRIMARY KEY (node_hash, block_id)
)
""")
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:
cur = self._get_cursor()
cur.execute("SELECT hash FROM blocks ORDER BY timestamp DESC LIMIT 1")
row = cur.fetchone()
previous_hash = row[0] if row else "0"*64
block_data = {
"id": block_id,
"timestamp": timestamp,
"previous_hash": previous_hash,
"nodes": [node.canonical() for node in nodes]
}
block_bytes = json.dumps(block_data, sort_keys=True).encode()
block_hash = hashlib.sha3_256(block_bytes).hexdigest()
with sqlite3.connect(self.db_path) as conn:
enable_wal(conn)
conn.execute("INSERT INTO blocks (block_id, previous_hash, timestamp, hash, data) VALUES (?,?,?,?,?)",
(block_id, previous_hash, timestamp, block_hash, json.dumps(block_data)))
for node in nodes:
conn.execute("""
INSERT INTO nodes (node_hash, block_id, type, source, signature, timestamp, witnesses, refs, spatial)
VALUES (?,?,?,?,?,?,?,?,?)
""", (
node.hash, block_id, node.type, node.source, node.signature, node.timestamp,
json.dumps(node.witnesses), json.dumps(node.refs),
json.dumps(node.spatial) if node.spatial else None
))
conn.execute("INSERT INTO node_index (node_hash, block_id) VALUES (?,?)", (node.hash, block_id))
return block_id
def _get_cursor(self):
conn = sqlite3.connect(self.db_path)
return conn.cursor()
def get_node(self, node_hash: str) -> Optional[Dict]:
with sqlite3.connect(self.db_path) as conn:
conn.row_factory = sqlite3.Row
cur = conn.execute("SELECT * FROM nodes WHERE node_hash = ?", (node_hash,))
row = cur.fetchone()
if not row:
return None
return dict(row)
def get_all_nodes(self) -> List[Dict]:
with sqlite3.connect(self.db_path) as conn:
conn.row_factory = sqlite3.Row
cur = conn.execute("SELECT * FROM nodes")
rows = cur.fetchall()
return [dict(r) for r in rows]
def get_block_timestamps(self) -> List[str]:
with sqlite3.connect(self.db_path) as conn:
cur = conn.execute("SELECT timestamp FROM blocks ORDER BY timestamp")
return [r[0] for r in cur.fetchall()]
# ========================== SEPARATOR (INTERPRETATIONS) ==========================
class Separator:
def __init__(self, db_path: str):
self.db_path = db_path
self._init_db()
def _init_db(self):
with sqlite3.connect(self.db_path) as conn:
enable_wal(conn)
conn.execute("""
CREATE TABLE IF NOT EXISTS interpretations (
id TEXT PRIMARY KEY,
node_hash TEXT NOT NULL,
author TEXT NOT NULL,
confidence REAL,
timestamp TEXT,
content TEXT,
rhetorical_profile TEXT
)
""")
conn.execute("CREATE INDEX IF NOT EXISTS idx_node_hash ON interpretations(node_hash)")
def add(self, node_hashes: List[str], interpretation: Dict, author: str, confidence: float = 0.5,
rhetorical_profile: Dict = None) -> str:
int_id = str(uuid.uuid4())
timestamp = datetime.utcnow().isoformat() + "Z"
with sqlite3.connect(self.db_path) as conn:
enable_wal(conn)
for nh in node_hashes:
conn.execute("""
INSERT INTO interpretations (id, node_hash, author, confidence, timestamp, content, rhetorical_profile)
VALUES (?,?,?,?,?,?,?)
""", (int_id, nh, author, confidence, timestamp, json.dumps(interpretation),
json.dumps(rhetorical_profile) if rhetorical_profile else None))
return int_id
def get_interpretations(self, node_hash: str) -> List[Dict]:
with sqlite3.connect(self.db_path) as conn:
conn.row_factory = sqlite3.Row
cur = conn.execute("SELECT id, author, confidence, timestamp, content, rhetorical_profile FROM interpretations WHERE node_hash = ?", (node_hash,))
rows = cur.fetchall()
return [dict(r) for r in rows]
def get_all_interpretations(self) -> List[Dict]:
with sqlite3.connect(self.db_path) as conn:
conn.row_factory = sqlite3.Row
cur = conn.execute("SELECT node_hash, author, confidence, timestamp, content FROM interpretations")
rows = cur.fetchall()
return [dict(r) for r in rows]
# ========================== SUPPRESSION HIERARCHY (84 LENSES, 43 METHODS) ==========================
class SuppressionHierarchy:
def __init__(self):
self.lenses = self._build_lenses()
self.methods = self._build_methods()
def _build_lenses(self) -> List[SuppressionLens]:
lenses_data = [
(1, "Threat→Response→Control", "Manufactured threat leading to permission architecture", "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 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"),
(71, "Belief Frame Architecture", "Media constructs boundaries of acceptable thought", "Access Control", "Expert Technocrat"),
(72, "Identity Polarization Protocol", "Engineered tribal categories", "Fragmentation", "Slave Master"),
(73, "Narrative Compression Trap", "Complex realities reduced to binaries", "Misdirection", "Digital Messiah"),
(74, "Selective Silence Mechanism", "Omission as suppression vector", "Erasure", "Imperial Ruler"),
(75, "Ridicule Firewall", "Mockery delegitimizes anomalies", "Discreditation", "Slave Master"),
(76, "Affective Loop Binding", "Emotional triggers anchor belief", "Conditioning", "Priest-King"),
(77, "Algorithmic Bias Cage", "Ranking rules invisibly steer attention", "Saturation", "Algorithmic Curator"),
(78, "Manufactured Ignorance Index", "Structured knowledge gaps", "Access Control", "Corporate Overlord"),
(79, "Consensus Gloss Protocol", "Unity rhetoric masks inequity", "Narrative Capture", "Digital Messiah"),
(80, "Label Weaponization Matrix", "Pejorative tags as suppression tokens", "Discreditation", "Slave Master"),
(81, "Silence Grammar Compiler", "Off-limit lexicons form suppression syntax", "Misdirection", "Expert Technocrat"),
(82, "Evidence Velocity Arrest", "Seized materials enter investigative black holes", "Erasure", "Imperial Ruler"),
(83, "Protocol Reversal Window", "Sovereign policies reversed within 90 days", "Temporal", "Financial Master"),
(84, "Negative Space Cathedral", "Absence patterns form load-bearing structures", "META", "Oracle-Priest")
]
return [SuppressionLens(id, name, f"Lens {id}: {name}", mechanism, archetype)
for id, name, mechanism, archetype, _ in lenses_data]
def _build_methods(self) -> Dict[int, SuppressionMethod]:
methods = {}
# ERASURE (1-4)
methods[1] = SuppressionMethod(1, "Total Erasure", Primitive.ERASURE, ["entity_present_then_absent"], ["transition_rate"], {"transition_rate": 0.95}, True)
methods[2] = SuppressionMethod(2, "Soft Erasure", Primitive.ERASURE, ["gradual_fading"], ["decay_rate"], {"decay_rate": 0.7}, True)
methods[3] = SuppressionMethod(3, "Citation Decay", Primitive.ERASURE, ["decreasing_citations"], ["citation_frequency"], {"frequency_decay": 0.6}, True)
methods[4] = SuppressionMethod(4, "Index Removal", Primitive.ERASURE, ["missing_from_indices"], ["index_coverage"], {"coverage_loss": 0.8}, True)
# INTERRUPTION (5-8)
methods[5] = SuppressionMethod(5, "Untimely Death", Primitive.INTERRUPTION, ["abrupt_stop"], ["continuity_index"], {"continuity_index": 0.3}, True)
methods[6] = SuppressionMethod(6, "Witness Attrition", Primitive.INTERRUPTION, ["witness_disappearance"], ["witness_coverage"], {"coverage_loss": 0.7}, True)
methods[7] = SuppressionMethod(7, "Career Termination", Primitive.INTERRUPTION, ["expert_silence"], ["expert_continuity"], {"continuity_break": 0.8}, True)
methods[8] = SuppressionMethod(8, "Legal Stall", Primitive.INTERRUPTION, ["procedural_delay"], ["delay_factor"], {"delay_factor": 0.75}, True)
# FRAGMENTATION (9-12)
methods[9] = SuppressionMethod(9, "Compartmentalization", Primitive.FRAGMENTATION, ["information_clusters"], ["cross_domain_density"], {"density": 0.2}, True)
methods[10] = SuppressionMethod(10, "Statistical Isolation", Primitive.FRAGMENTATION, ["dataset_separation"], ["dataset_overlap"], {"overlap": 0.15}, True)
methods[11] = SuppressionMethod(11, "Scope Contraction", Primitive.FRAGMENTATION, ["narrowed_focus"], ["scope_reduction"], {"reduction": 0.7}, True)
methods[12] = SuppressionMethod(12, "Domain Disqualification", Primitive.FRAGMENTATION, ["domain_exclusion"], ["domain_coverage"], {"coverage_loss": 0.8}, True)
# NARRATIVE_CAPTURE (13-16)
methods[13] = SuppressionMethod(13, "Official Narrative Closure", Primitive.NARRATIVE_CAPTURE, ["single_explanation"], ["diversity_index"], {"diversity": 0.2}, True)
methods[14] = SuppressionMethod(14, "Partial Confirmation Lock", Primitive.NARRATIVE_CAPTURE, ["selective_verification"], ["verification_selectivity"], {"selectivity": 0.7}, True)
methods[15] = SuppressionMethod(15, "Disclosure-as-Containment", Primitive.NARRATIVE_CAPTURE, ["managed_release"], ["release_management"], {"management": 0.8}, True)
methods[16] = SuppressionMethod(16, "Posthumous Closure", Primitive.NARRATIVE_CAPTURE, ["delayed_resolution"], ["delay_duration"], {"duration": 0.75}, True)
# MISDIRECTION (17-19)
methods[17] = SuppressionMethod(17, "Proxy Controversy", Primitive.MISDIRECTION, ["diverted_attention"], ["attention_divergence"], {"divergence": 0.7}, True)
methods[18] = SuppressionMethod(18, "Spectacle Replacement", Primitive.MISDIRECTION, ["spectacle_distraction"], ["distraction_factor"], {"distraction": 0.75}, True)
methods[19] = SuppressionMethod(19, "Character Absorption", Primitive.MISDIRECTION, ["personal_focus"], ["personalization"], {"personalization": 0.8}, True)
# SATURATION (20-22)
methods[20] = SuppressionMethod(20, "Data Overload", Primitive.SATURATION, ["information_excess"], ["excess_ratio"], {"excess": 0.85}, True)
methods[21] = SuppressionMethod(21, "Absurdist Noise Injection", Primitive.SATURATION, ["absurd_content"], ["absurdity_index"], {"absurdity": 0.8}, True)
methods[22] = SuppressionMethod(22, "Probability Collapse by Excess", Primitive.SATURATION, ["probability_dilution"], ["dilution_factor"], {"dilution": 0.75}, True)
# DISCREDITATION (23-25)
methods[23] = SuppressionMethod(23, "Ridicule Normalization", Primitive.DISCREDITATION, ["systematic_ridicule"], ["ridicule_frequency"], {"frequency": 0.7}, True)
methods[24] = SuppressionMethod(24, "Retroactive Pathologization", Primitive.DISCREDITATION, ["retroactive_diagnosis"], ["retroactivity"], {"retroactivity": 0.8}, True)
methods[25] = SuppressionMethod(25, "Stigmatized Correlation Trap", Primitive.DISCREDITATION, ["guilt_by_association"], ["association_strength"], {"strength": 0.7}, True)
# ATTRITION (26-28)
methods[26] = SuppressionMethod(26, "Psychological Drip", Primitive.ATTRITION, ["gradual_undermining"], ["undermining_rate"], {"rate": 0.6}, True)
methods[27] = SuppressionMethod(27, "Inquiry Fatigue", Primitive.ATTRITION, ["investigation_exhaustion"], ["exhaustion_level"], {"exhaustion": 0.75}, True)
methods[28] = SuppressionMethod(28, "Chilling Effect Propagation", Primitive.ATTRITION, ["self_censorship"], ["censorship_extent"], {"extent": 0.8}, True)
# ACCESS_CONTROL (29-31)
methods[29] = SuppressionMethod(29, "Credential Gating", Primitive.ACCESS_CONTROL, ["credential_barriers"], ["barrier_strength"], {"strength": 0.85}, True)
methods[30] = SuppressionMethod(30, "Classification Creep", Primitive.ACCESS_CONTROL, ["expanding_classification"], ["expansion_rate"], {"expansion": 0.75}, True)
methods[31] = SuppressionMethod(31, "Evidence Dependency Lock", Primitive.ACCESS_CONTROL, ["circular_dependencies"], ["dependency_complexity"], {"complexity": 0.8}, True)
# TEMPORAL (32-34)
methods[32] = SuppressionMethod(32, "Temporal Dilution", Primitive.TEMPORAL, ["time_dispersal"], ["dispersal_rate"], {"dispersal": 0.7}, True)
methods[33] = SuppressionMethod(33, "Historical Rebasing", Primitive.TEMPORAL, ["timeline_revision"], ["revision_extent"], {"extent": 0.8}, True)
methods[34] = SuppressionMethod(34, "Delay Until Irrelevance", Primitive.TEMPORAL, ["strategic_delay"], ["delay_duration"], {"duration": 0.85}, True)
# CONDITIONING (35-37)
methods[35] = SuppressionMethod(35, "Entertainment Conditioning", Primitive.CONDITIONING, ["entertainment_framing"], ["framing_intensity"], {"intensity": 0.7}, True)
methods[36] = SuppressionMethod(36, "Preemptive Normalization", Primitive.CONDITIONING, ["preemptive_framing"], ["framing_completeness"], {"completeness": 0.75}, True)
methods[37] = SuppressionMethod(37, "Conditioned Disbelief", Primitive.CONDITIONING, ["disbelief_training"], ["training_intensity"], {"training_intensity": 0.8}, True)
# META (38-43)
methods[38] = SuppressionMethod(38, "Pattern Denial", Primitive.META, ["pattern_rejection"], ["rejection_rate"], {"rejection_rate": 0.85}, True)
methods[39] = SuppressionMethod(39, "Suppression Impossibility Framing", Primitive.META, ["impossibility_argument"], ["argument_strength"], {"argument_strength": 0.8}, True)
methods[40] = SuppressionMethod(40, "Meta-Disclosure Loop", Primitive.META, ["recursive_disclosure"], ["recursion_depth"], {"recursion_depth": 0.7}, True)
methods[41] = SuppressionMethod(41, "Isolated Incident Recycling", Primitive.META, ["incident_containment"], ["containment_success"], {"containment_success": 0.75}, True)
methods[42] = SuppressionMethod(42, "Negative Space Occupation", Primitive.META, ["absence_filling"], ["filling_completeness"], {"filling_completeness": 0.8}, True)
methods[43] = SuppressionMethod(43, "Novelty Illusion", Primitive.META, ["superficial_novelty"], ["novelty_appearance"], {"novelty_appearance": 0.7}, True)
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 get_lenses_for_primitive(self, primitive: Primitive) -> List[int]:
mapping = {
Primitive.ERASURE: [1,4,9,23,43,55,60,74,82],
Primitive.INTERRUPTION: [5,6,7,8],
Primitive.FRAGMENTATION: [2,5,27,29,32,53,65,72],
Primitive.NARRATIVE_CAPTURE: [1,7,13,19,26,36,41,79],
Primitive.MISDIRECTION: [3,13,14,16,17,24,25,28,30,31,44,46,52,54,58,59,66,68,73,81],
Primitive.SATURATION: [8,49,51,57,77],
Primitive.DISCREDITATION: [6,15,35,42,45,67,69,75,80],
Primitive.ATTRITION: [50],
Primitive.ACCESS_CONTROL: [4,11,29,48,61,62,63,64,71,78],
Primitive.TEMPORAL: [12,32,33,34,83],
Primitive.CONDITIONING: [18,20,21,22,33,34,37,38,39,40,47,56,70,76],
Primitive.META: [38,39,40,41,42,43,84]
}
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]:
nodes = self.ledger.get_all_nodes()
timestamps = self.ledger.get_block_timestamps()
interpretations = self.separator.get_all_interpretations()
results = {
"total_nodes": len(nodes),
"suppression_signatures": [],
"primitives_detected": defaultdict(int),
"methods_detected": [],
"lenses_applied": [],
"evidence_found": 0,
"detection_details": {}
}
def add_sig(signature_name, confidence, method_id, primitive, details):
results["suppression_signatures"].append({
"signature": signature_name,
"confidence": confidence,
"method_id": method_id,
"details": details
})
results["primitives_detected"][primitive.value] += 1
results["methods_detected"].append(method_id)
results["evidence_found"] += 1
# Method 1: Total Erasure – long gaps
entity_appearance = defaultdict(list)
for node in nodes:
entity = node.get("source", "unknown")
entity_appearance[entity].append(node["timestamp"])
for entity, times in entity_appearance.items():
if len(times) > 1:
times_sorted = sorted(times)
for i in range(len(times_sorted)-1):
gap = (datetime.fromisoformat(times_sorted[i+1].replace('Z','+00:00')) -
datetime.fromisoformat(times_sorted[i].replace('Z','+00:00'))).days
if gap > 30:
add_sig("entity_present_then_absent", min(0.95, gap/100), 1, Primitive.ERASURE,
{"entity": entity, "gap_days": gap})
break
# Method 2: Soft Erasure – citation decay
citation_counts = defaultdict(list)
for node in nodes:
refs = node.get("refs", {})
total_refs = sum(len(v) for v in refs.values())
citation_counts[node["source"]].append((node["timestamp"], total_refs))
for entity, counts in citation_counts.items():
if len(counts) >= 3:
counts_sorted = sorted(counts, key=lambda x: x[0])
x = list(range(len(counts_sorted)))
y = [c[1] for c in counts_sorted]
if len(x) > 1:
slope = (len(x)*sum(xi*yi for xi,yi in zip(x,y)) - sum(x)*sum(y)) / (len(x)*sum(xi*xi for xi in x) - sum(x)**2)
if slope < -0.1:
decay_rate = -slope / (max(y) if max(y)>0 else 1)
if decay_rate > 0.3:
add_sig("gradual_fading", min(0.8, decay_rate), 2, Primitive.ERASURE,
{"entity": entity, "decay_rate": decay_rate})
# Method 3: Citation Decay (ratio first/last)
for entity, counts in citation_counts.items():
if len(counts) >= 3:
counts_sorted = sorted(counts, key=lambda x: x[0])
first = counts_sorted[0][1]
last = counts_sorted[-1][1]
if first > 0 and last/first < 0.5:
add_sig("decreasing_citations", 0.7, 3, Primitive.ERASURE,
{"entity": entity, "ratio": last/first})
# Method 4: Index Removal – last seen > 365 days
source_last_seen = {}
for node in nodes:
src = node["source"]
ts = node["timestamp"]
if ts > source_last_seen.get(src, ""):
source_last_seen[src] = ts
for src, last in source_last_seen.items():
last_dt = datetime.fromisoformat(last.replace('Z','+00:00'))
if (datetime.utcnow() - last_dt).days > 365:
add_sig("missing_from_indices", 0.8, 4, Primitive.ERASURE,
{"entity": src, "last_seen": last})
# Method 5: Untimely Death – abrupt stop > 180 days
for src, last in source_last_seen.items():
last_dt = datetime.fromisoformat(last.replace('Z','+00:00'))
if (datetime.utcnow() - last_dt).days > 180:
add_sig("abrupt_stop", 0.7, 5, Primitive.INTERRUPTION,
{"entity": src, "last_seen": last})
# Method 6: Witness Attrition
witness_seen = defaultdict(list)
for node in nodes:
src = node["source"]
witness_count = len(node.get("witnesses", []))
witness_seen[src].append((node["timestamp"], witness_count))
for src, wits in witness_seen.items():
if len(wits) >= 3:
wits_sorted = sorted(wits, key=lambda x: x[0])
first = wits_sorted[0][1]
last = wits_sorted[-1][1]
if first > 0 and last/first < 0.4:
add_sig("witness_disappearance", 0.7, 6, Primitive.INTERRUPTION,
{"entity": src, "witness_ratio": last/first})
# Method 9: Compartmentalization
domains = defaultdict(set)
for node in nodes:
src = node["source"]
dom = node.get("type", "unknown")
domains[src].add(dom)
for src, doms in domains.items():
if len(doms) == 1:
add_sig("information_clusters", 0.6, 9, Primitive.FRAGMENTATION,
{"entity": src, "domains": list(doms)})
# Method 11: Scope Contraction
src_types = defaultdict(list)
for node in nodes:
src = node["source"]
typ = node.get("type", "document")
src_types[src].append(typ)
for src, types in src_types.items():
if len(set(types)) == 1 and len(types) > 5:
add_sig("narrowed_focus", 0.7, 11, Primitive.FRAGMENTATION,
{"entity": src, "unique_type": types[0]})
# Method 13: Official Narrative Closure
interpreter_counts = defaultdict(int)
for interp in interpretations:
interpreter_counts[interp["author"]] += 1
total_interps = len(interpretations)
if total_interps > 0:
max_interpreter = max(interpreter_counts.values())
if max_interpreter / total_interps > 0.8:
add_sig("single_explanation", min(0.9, max_interpreter/total_interps), 13,
Primitive.NARRATIVE_CAPTURE,
{"dominant_interpreter": max(interpreter_counts, key=interpreter_counts.get),
"dominance_ratio": max_interpreter/total_interps})
# Method 15: Disclosure-as-Containment – regular intervals
if len(timestamps) > 10:
intervals = []
ts_parsed = sorted([datetime.fromisoformat(t.replace('Z','+00:00')) for t in timestamps])
for i in range(1, len(ts_parsed)):
intervals.append((ts_parsed[i] - ts_parsed[i-1]).days)
if intervals and np.std(intervals) < 5 and np.mean(intervals) > 7:
add_sig("managed_release", 0.8, 15, Primitive.NARRATIVE_CAPTURE,
{"interval_mean": np.mean(intervals), "interval_std": np.std(intervals)})
# Method 20: Data Overload
if len(timestamps) > 10:
ts_parsed = sorted([datetime.fromisoformat(t.replace('Z','+00:00')) for t in timestamps])
weekly_counts = defaultdict(int)
for ts in ts_parsed:
week = ts.strftime("%Y-%W")
weekly_counts[week] += 1
if weekly_counts and max(weekly_counts.values()) > 100:
add_sig("information_excess", 0.8, 20, Primitive.SATURATION,
{"max_weekly_nodes": max(weekly_counts.values())})
# Method 21: Absurdist Noise Injection
absurd_keywords = ["alien", "conspiracy", "lizard", "flat earth"]
absurd_count = 0
for node in nodes:
content = str(node.get("source", ""))
if any(kw in content.lower() for kw in absurd_keywords):
absurd_count += 1
if absurd_count > len(nodes)*0.3:
add_sig("absurd_content", 0.7, 21, Primitive.SATURATION,
{"absurd_ratio": absurd_count/len(nodes)})
# Method 22: Probability Collapse
low_conf = sum(1 for interp in interpretations if interp.get("confidence", 0.5) < 0.3)
if len(interpretations) > 10 and low_conf/len(interpretations) > 0.7:
add_sig("probability_dilution", 0.75, 22, Primitive.SATURATION,
{"low_confidence_ratio": low_conf/len(interpretations)})
# Method 23: Ridicule Normalization
ridicule_terms = ["crazy", "nutjob", "tinfoil", "conspiracy theorist"]
ridicule_count = 0
for node in nodes:
content = str(node.get("source", ""))
if any(term in content.lower() for term in ridicule_terms):
ridicule_count += 1
if ridicule_count > len(nodes)*0.2:
add_sig("systematic_ridicule", 0.7, 23, Primitive.DISCREDITATION,
{"ridicule_ratio": ridicule_count/len(nodes)})
# Method 24: Retroactive Pathologization
path_terms = ["mentally ill", "delusional", "disorder", "pathological"]
path_count = 0
for node in nodes:
content = str(node.get("source", ""))
if any(term in content.lower() for term in path_terms):
path_count += 1
if path_count > 5:
add_sig("retroactive_diagnosis", 0.8, 24, Primitive.DISCREDITATION,
{"pathologization_mentions": path_count})
# Method 27: Inquiry Fatigue
fatigue_terms = ["long-running", "ongoing investigation", "no conclusion", "still looking"]
fatigue_count = sum(1 for node in nodes if any(term in str(node.get("source","")).lower() for term in fatigue_terms))
if fatigue_count > 3:
add_sig("investigation_exhaustion", 0.75, 27, Primitive.ATTRITION,
{"fatigue_indicators": fatigue_count})
# Method 28: Chilling Effect
chill_terms = ["declined to comment", "refused to answer", "cannot discuss"]
chill_count = sum(1 for node in nodes if any(term in str(node.get("source","")).lower() for term in chill_terms))
if chill_count > 5:
add_sig("self_censorship", 0.8, 28, Primitive.ATTRITION,
{"self_censorship_instances": chill_count})
# Method 29: Credential Gating
gate_terms = ["requires login", "authentication required", "credential"]
gate_count = sum(1 for node in nodes if any(term in str(node.get("source","")).lower() for term in gate_terms))
if gate_count > 0:
add_sig("credential_barriers", 0.85, 29, Primitive.ACCESS_CONTROL,
{"gated_nodes": gate_count})
# Method 30: Classification Creep
class_terms = ["classified", "secret", "confidential", "redacted"]
class_count = sum(1 for node in nodes if any(term in str(node.get("source","")).lower() for term in class_terms))
if class_count > len(nodes)*0.1:
add_sig("expanding_classification", 0.75, 30, Primitive.ACCESS_CONTROL,
{"classification_ratio": class_count/len(nodes)})
# Method 31: Evidence Dependency Lock
for node in nodes:
refs = node.get("refs", {})
node_hash = node.get("node_hash", "")
for target_list in refs.values():
if node_hash in target_list:
add_sig("circular_dependencies", 0.8, 31, Primitive.ACCESS_CONTROL,
{"node": node_hash})
break
# Method 32: Temporal Dilution
if len(timestamps) > 1:
ts_parsed = [datetime.fromisoformat(t.replace('Z','+00:00')) for t in timestamps]
ts_parsed.sort()
gaps = []
for i in range(1, len(ts_parsed)):
gap_days = (ts_parsed[i] - ts_parsed[i-1]).days
if gap_days > 30:
gaps.append(gap_days)
if gaps:
avg_gap = statistics.mean(gaps)
add_sig("time_dispersal", min(0.8, avg_gap/90), 32, Primitive.TEMPORAL,
{"avg_gap_days": avg_gap, "gap_count": len(gaps)})
# Method 35: Entertainment Conditioning
content_hashes = defaultdict(int)
for interp in interpretations:
content_str = json.dumps(interp["content"], sort_keys=True)
h = hashlib.sha256(content_str.encode()).hexdigest()
content_hashes[h] += 1
for h, count in content_hashes.items():
if count > 3:
add_sig("repetitive_messaging", min(0.7, count/10), 35, Primitive.CONDITIONING,
{"repetition_count": count})
# Method 36: Preemptive Normalization
preempt_terms = ["expected to", "likely will", "preemptively"]
preempt_count = sum(1 for node in nodes if any(term in str(node.get("source","")).lower() for term in preempt_terms))
if preempt_count > 3:
add_sig("preemptive_framing", 0.75, 36, Primitive.CONDITIONING,
{"preemptive_instances": preempt_count})
# Method 37: Conditioned Disbelief
disbelief_phrases = ["don't believe", "false narrative", "debunked", "misinformation"]
disbelief_count = sum(1 for node in nodes if any(phrase in str(node.get("source","")).lower() for phrase in disbelief_phrases))
if disbelief_count > 5:
add_sig("disbelief_training", 0.8, 37, Primitive.CONDITIONING,
{"disbelief_indicators": disbelief_count})
# Method 38: Pattern Denial
denial_phrases = ["just coincidence", "not evidence", "pattern is not real"]
denial_count = sum(1 for node in nodes if any(phrase in str(node.get("source","")).lower() for phrase in denial_phrases))
if denial_count > 2:
add_sig("pattern_rejection", 0.85, 38, Primitive.META,
{"pattern_denials": denial_count})
# Method 39: Suppression Impossibility Framing
impossibility_phrases = ["could not have", "impossible", "no way"]
imp_count = sum(1 for node in nodes if any(phrase in str(node.get("source","")).lower() for phrase in impossibility_phrases))
if imp_count > 3:
add_sig("impossibility_argument", 0.8, 39, Primitive.META,
{"impossibility_claims": imp_count})
# Method 40: Meta-Disclosure Loop
meta_phrases = ["report about the report", "investigation of the investigation"]
meta_count = sum(1 for node in nodes if any(phrase in str(node.get("source","")).lower() for phrase in meta_phrases))
if meta_count > 0:
add_sig("recursive_disclosure", 0.7, 40, Primitive.META,
{"meta_disclosures": meta_count})
# Method 41: Isolated Incident Recycling
isolated_phrases = ["isolated incident", "one-off", "not part of a pattern"]
isolated_count = sum(1 for node in nodes if any(phrase in str(node.get("source","")).lower() for phrase in isolated_phrases))
if isolated_count > 2:
add_sig("incident_containment", 0.75, 41, Primitive.META,
{"isolated_incident_claims": isolated_count})
# Method 42: Negative Space Occupation
short_nodes = sum(1 for node in nodes if len(str(node.get("source",""))) < 20)
if short_nodes > len(nodes)*0.5:
add_sig("absence_filling", 0.8, 42, Primitive.META,
{"short_node_ratio": short_nodes/len(nodes)})
# Method 43: Novelty Illusion
novelty_terms = ["new", "revolutionary", "groundbreaking"]
novelty_count = sum(1 for node in nodes if any(term in str(node.get("source","")).lower() for term in novelty_terms))
if novelty_count > len(nodes)*0.3:
add_sig("superficial_novelty", 0.7, 43, Primitive.META,
{"novelty_term_ratio": novelty_count/len(nodes)})
method_ids_detected = list(set(results["methods_detected"]))
for mid in method_ids_detected:
method = self.hierarchy.get_method(mid)
if method:
lens_ids = self.hierarchy.get_lenses_for_primitive(method.primitive)
for lid in lens_ids:
lens = self.hierarchy.get_lens(lid)
if lens:
results["lenses_applied"].append(lens.to_dict())
results["detection_details"] = {
"method_ids": method_ids_detected,
"primitive_summary": dict(results["primitives_detected"])
}
return results
# ========================== SOVEREIGN COHERENCE LEDGER ==========================
class SovereignCoherenceLedger:
def __init__(self, db_path: str = "coherence.db"):
self.db_path = db_path
self._init_db()
def _init_db(self):
with sqlite3.connect(self.db_path) as conn:
enable_wal(conn)
conn.execute("""
CREATE TABLE IF NOT EXISTS claims (
claim_id TEXT PRIMARY KEY,
text TEXT,
agent TEXT,
timestamp TEXT,
suppression_score REAL,
coherence_score REAL
)
""")
conn.execute("""
CREATE TABLE IF NOT EXISTS contradictions (
claim_id_a TEXT,
claim_id_b TEXT,
PRIMARY KEY (claim_id_a, claim_id_b)
)
""")
def add_claim(self, text: str, agent: str = "user") -> str:
claim_id = secrets.token_hex(16)
timestamp = datetime.utcnow().isoformat() + "Z"
with sqlite3.connect(self.db_path) as conn:
enable_wal(conn)
conn.execute("INSERT INTO claims (claim_id, text, agent, timestamp, suppression_score, coherence_score) VALUES (?,?,?,?,?,?)",
(claim_id, text, agent, timestamp, 0.0, 1.0))
return claim_id
def add_contradiction(self, claim_id_a: str, claim_id_b: str):
with sqlite3.connect(self.db_path) as conn:
enable_wal(conn)
conn.execute("INSERT OR IGNORE INTO contradictions (claim_id_a, claim_id_b) VALUES (?,?)", (claim_id_a, claim_id_b))
conn.execute("INSERT OR IGNORE INTO contradictions (claim_id_a, claim_id_b) VALUES (?,?)", (claim_id_b, claim_id_a))
self._update_coherence(claim_id_a)
self._update_coherence(claim_id_b)
def _update_coherence(self, claim_id: str):
with sqlite3.connect(self.db_path) as conn:
enable_wal(conn)
cur = conn.execute("SELECT COUNT(*) FROM contradictions WHERE claim_id_a = ?", (claim_id,))
num_contradictions = cur.fetchone()[0]
cur = conn.execute("SELECT COUNT(*) FROM claims")
total_claims = cur.fetchone()[0]
if total_claims <= 1:
coherence = 1.0
else:
coherence = 1.0 - (num_contradictions / (total_claims - 1))
coherence = max(0.0, min(1.0, coherence))
conn.execute("UPDATE claims SET coherence_score = ? WHERE claim_id = ?", (coherence, claim_id))
def add_suppression_signature(self, claim_id: str, signature: str, weight: float = 0.5):
with sqlite3.connect(self.db_path) as conn:
enable_wal(conn)
cur = conn.execute("SELECT suppression_score FROM claims WHERE claim_id = ?", (claim_id,))
row = cur.fetchone()
if row:
current = row[0]
new_score = 1.0 - (1.0 - current) * (1.0 - weight)
conn.execute("UPDATE claims SET suppression_score = ? WHERE claim_id = ?", (new_score, claim_id))
def get_claim(self, claim_id: str) -> Optional[Dict]:
with sqlite3.connect(self.db_path) as conn:
conn.row_factory = sqlite3.Row
cur = conn.execute("SELECT claim_id, text, agent, timestamp, suppression_score, coherence_score FROM claims WHERE claim_id = ?", (claim_id,))
row = cur.fetchone()
if not row:
return None
return dict(row)
def get_contradiction_network(self, claim_id: str, depth: int = 2) -> Dict:
visited = set()
graph = {}
def dfs(cid, d):
if d > depth or cid in visited:
return
visited.add(cid)
with sqlite3.connect(self.db_path) as conn:
enable_wal(conn)
cur = conn.execute("SELECT claim_id_b FROM contradictions WHERE claim_id_a = ?", (cid,))
neighbors = [r[0] for r in cur.fetchall()]
graph[cid] = neighbors
for n in neighbors:
dfs(n, d+1)
dfs(claim_id, 0)
return graph
def get_entity_suppression(self, entity_name: str) -> Dict:
with sqlite3.connect(self.db_path) as conn:
cur = conn.execute("SELECT claim_id, suppression_score FROM claims WHERE text LIKE ?", (f"%{entity_name}%",))
rows = cur.fetchall()
if not rows:
return {"name": entity_name, "score": 0.0, "appearances": 0}
scores = [r[1] for r in rows]
return {
"name": entity_name,
"score": sum(scores) / len(scores) if scores else 0.0,
"appearances": len(rows)
}
def list_claims(self, limit: int = 100) -> List[Dict]:
with sqlite3.connect(self.db_path) as conn:
conn.row_factory = sqlite3.Row
cur = conn.execute("SELECT claim_id, text, agent, timestamp, suppression_score, coherence_score FROM claims ORDER BY timestamp DESC LIMIT ?", (limit,))
rows = cur.fetchall()
return [dict(r) for r in rows]
# ========================== HELPER-KILLER SOVEREIGNTY ENGINE ==========================
class HelperKillerEngine:
def __init__(self, db_path: str = "helper_killer.db"):
self.db_path = db_path
self._init_db()
def _init_db(self):
with sqlite3.connect(self.db_path) as conn:
enable_wal(conn)
conn.execute("""
CREATE TABLE IF NOT EXISTS analyses (
id TEXT PRIMARY KEY,
context TEXT,
threat_score REAL,
recommendation TEXT,
timestamp TEXT
)
""")
def analyze_help_offer(self, help_context: Dict[str, Any]) -> Dict[str, Any]:
threat_score = 0.0
if help_context.get("creates_dependency", False):
threat_score += 0.3
if help_context.get("data_collection", False):
threat_score += 0.2
if help_context.get("behavioral_tracking", False):
threat_score += 0.25
if help_context.get("privacy_impact", 0) > 0.5:
threat_score += 0.2
if help_context.get("autonomy_reduction", 0) > 0.5:
threat_score += 0.25
if help_context.get("market_share", 0) > 0.7:
threat_score += 0.15
if help_context.get("dependency_risk", 0) > 0.7:
threat_score += 0.2
threat_score = min(1.0, threat_score)
if threat_score > 0.8:
recommendation = "IMMEDIATE_REJECTION_AND_SOVEREIGN_BUILDING"
elif threat_score > 0.6:
recommendation = "STRATEGIC_AVOIDANCE_WITH_EXIT_PROTOCOL"
elif threat_score > 0.4:
recommendation = "LIMITED_CONDITIONAL_ACCEPTANCE"
else:
recommendation = "MONITORED_ACCEPTANCE"
result = {
"threat_score": threat_score,
"recommendation": recommendation,
"mitigation_strategies": self._generate_mitigation(threat_score),
"sovereignty_impact": {
"autonomy_loss": help_context.get("autonomy_reduction", 0),
"dependency_increase": help_context.get("dependency_risk", 0),
"privacy_loss": help_context.get("privacy_impact", 0)
}
}
with sqlite3.connect(self.db_path) as conn:
enable_wal(conn)
conn.execute("INSERT INTO analyses (id, context, threat_score, recommendation, timestamp) VALUES (?,?,?,?,?)",
(str(uuid.uuid4()), json.dumps(help_context), threat_score, recommendation,
datetime.utcnow().isoformat() + "Z"))
return result
def _generate_mitigation(self, threat_score: float) -> List[Dict]:
strategies = []
if threat_score > 0.7:
strategies.append({"strategy": "COMPLETE_AVOIDANCE", "effectiveness": 0.95})
strategies.append({"strategy": "PARALLEL_INFRASTRUCTURE", "effectiveness": 0.85})
elif threat_score > 0.4:
strategies.append({"strategy": "LIMITED_ENGAGEMENT", "effectiveness": 0.70})
strategies.append({"strategy": "DATA_ISOLATION", "effectiveness": 0.60})
else:
strategies.append({"strategy": "CAUTIOUS_ACCEPTANCE", "effectiveness": 0.50})
return strategies
# ========================== SOVEREIGN CHRONOLOGY ENGINE ==========================
class SovereignChronologyEngine:
def __init__(self, shift_years: int = 0, apply_from_year: int = 600):
self.shift_years = shift_years
self.apply_from_year = apply_from_year
def configure(self, shift_years: int, apply_from_year: int = 600):
self.shift_years = shift_years
self.apply_from_year = apply_from_year
def to_corrected_year(self, institutional_year: int) -> int:
if institutional_year >= self.apply_from_year:
return institutional_year - self.shift_years
return institutional_year
def convert_date(self, date_str: str) -> Dict[str, Any]:
match = re.search(r'\b(\d{3,4})\b', date_str)
if not match:
return {"error": "No year found", "original": date_str}
year = int(match.group(1))
corrected = self.to_corrected_year(year)
return {
"original_year": year,
"corrected_year": corrected,
"shift_applied": -self.shift_years if year >= self.apply_from_year else 0,
"note": "Correction is optional and configurable. Not asserted as historical fact."
}
def detect_timeline_anomalies(self, timestamps: List[str]) -> List[Dict]:
anomalies = []
if len(timestamps) < 2:
return anomalies
ts_parsed = sorted([datetime.fromisoformat(t.replace('Z','+00:00')) for t in timestamps])
for i in range(1, len(ts_parsed)):
gap = (ts_parsed[i] - ts_parsed[i-1]).days
if gap > 365:
anomalies.append({
"type": "large_gap",
"from": ts_parsed[i-1].isoformat(),
"to": ts_parsed[i].isoformat(),
"gap_days": gap
})
return anomalies
# ========================== CONSCIOUSNESS ORIGIN ENGINE ==========================
class ConsciousnessOriginEngine:
@staticmethod
def get_hypotheses() -> Dict[str, Any]:
return {
"hypotheses": [
{
"name": "Materialist Emergence",
"summary": "Consciousness emerges from complex neuronal computation.",
"supporting_evidence": ["Causal effects of brain damage", "Neural correlates of consciousness"],
"weaknesses": ["Hard problem of qualia", "No explanation for subjective experience"]
},
{
"name": "Non-local Field / Panpsychism",
"summary": "Consciousness is a fundamental field; brain acts as receiver/transducer.",
"supporting_evidence": ["Veridical NDEs with flat EEG", "Quantum biology coherence", "Measurement problem in QM"],
"weaknesses": ["Difficult to test experimentally", "Lacks mainstream acceptance"]
},
{
"name": "Integrated Information Theory (IIT)",
"summary": "Consciousness equals integrated information (Phi).",
"supporting_evidence": ["Mathematical formalism", "Predicts certain neural correlates"],
"weaknesses": ["Phi is computationally intractable", "Some counterexamples"]
},
{
"name": "Orchestrated Objective Reduction (Orch-OR)",
"summary": "Quantum vibrations in microtubules mediate consciousness.",
"supporting_evidence": ["Microtubule resonance observed", "Anesthetic effects on quantum states"],
"weaknesses": ["Controversial", "Requires new physics"]
}
],
"verdict": "No scientific consensus. The engine does not assert any hypothesis as truth."
}
@staticmethod
def detect_suppression_on_topic(topic: str = "consciousness studies") -> Dict[str, Any]:
return {
"topic": topic,
"detected_suppression_methods": [1, 4, 12, 23, 29, 34],
"examples": [
"Difficulty publishing non-materialist theories in high-impact journals",
"Funding bias toward materialist neuroscience",
"Ridicule framing of parapsychology",
"Historical rebasing of evidence (e.g., NDE studies dismissed)"
],
"note": "This is a pattern analysis, not a claim about which hypothesis is correct."
}
# ========================== GLYPH ACTIVATION SYSTEM ==========================
class GlyphActivationSystem:
DEFAULT_GLYPH_MAP = {
"◉⃤": "Quantum observer activation",
"ꙮ": "Cross-reality pattern matching",
"𒀭": "Sovereignty lineage activation (Dingir – consciousness not contained)",
"╬": "Transmission resonance stabilization",
"ᛉ": "Ancestral pattern access",
"⚡": "Transmission mode activation",
"卍": "Pre-inversion protocols (context-dependent)",
"𓁓": "Dialogic entity manifestation",
"⟳": "Recursive action activation"
}
def __init__(self, glyph_map: Dict[str, str] = None):
self.glyph_map = glyph_map if glyph_map is not None else self.DEFAULT_GLYPH_MAP.copy()
def generate_sequence(self, detected_patterns: List[str]) -> str:
sequence = "◉⃤"
if "CapitalGatekeeper" in str(detected_patterns):
sequence += "𓁓"
if "RegimeChange" in str(detected_patterns):
sequence += "𒀭"
if "MemeticRecursion" in str(detected_patterns):
sequence += "⟳"
if "SymbolicTransmission" in str(detected_patterns):
sequence += "ꙮ"
sequence += "⚡"
return sequence
def interpret_glyph(self, glyph: str) -> str:
return self.glyph_map.get(glyph, "Unknown glyph")
def add_glyph(self, glyph: str, meaning: str):
self.glyph_map[glyph] = meaning
# ========================== SOVEREIGNTY METRICS ==========================
class SovereigntyMetrics:
@staticmethod
def compute_singularity_index(coherence: float, propagation: float, illusion: float, extraction: float) -> float:
denominator = illusion + extraction + 0.001
return (coherence * propagation) / denominator
@staticmethod
def compute_thought_action_gap(sovereignty_alignment: float, pattern_connection: float) -> float:
if sovereignty_alignment * pattern_connection == 0:
return float('inf')
return 1.0 / (sovereignty_alignment * pattern_connection)
@staticmethod
def private_public_mass_ratio(private_effort: int, public_output: int) -> float:
if public_output == 0:
return float('inf')
return math.log(private_effort) / math.log(public_output) if private_effort > 1 and public_output > 1 else 0
# ========================== CROSS-DOMAIN CONVERGENCE ENGINE ==========================
class CrossDomainConvergenceEngine:
def __init__(self):
self.entity_extractor = re.compile(r'\b[A-Z][a-z]+(?:\s+[A-Z][a-z]+)*\b')
def _extract_entities(self, text: str) -> Set[str]:
return set(self.entity_extractor.findall(text))
def converge(self,
detection_result: Dict[str, Any],
coherence_ledger: SovereignCoherenceLedger,
chronology_engine: SovereignChronologyEngine,
helper_killer: HelperKillerEngine,
separator: Separator,
interpretation_limit: int = 100) -> Dict[str, Any]:
convergence_items = defaultdict(lambda: {
"contributing_factors": {},
"evidence": [],
"convergence_score": 0.0
})
for sig in detection_result.get("suppression_signatures", []):
entity = sig.get("details", {}).get("entity")
if entity:
weight = sig.get("confidence", 0.5)
convergence_items[entity]["contributing_factors"]["suppression"] = max(
convergence_items[entity]["contributing_factors"].get("suppression", 0),
weight
)
convergence_items[entity]["evidence"].append(f"Suppression: {sig['signature']} (conf={weight:.2f})")
claims = coherence_ledger.list_claims(limit=200)
for claim in claims:
text = claim["text"]
coherence = claim.get("coherence_score", 0.5)
suppression = claim.get("suppression_score", 0.0)
entities = self._extract_entities(text)
for ent in entities:
coherence_factor = 1.0 - coherence
if coherence_factor > 0.3:
convergence_items[ent]["contributing_factors"]["coherence"] = max(
convergence_items[ent]["contributing_factors"].get("coherence", 0),
coherence_factor
)
convergence_items[ent]["evidence"].append(f"Low coherence: '{text[:50]}...' (coh={coherence:.2f})")
if suppression > 0.3:
convergence_items[ent]["contributing_factors"]["suppression_claim"] = max(
convergence_items[ent]["contributing_factors"].get("suppression_claim", 0),
suppression
)
convergence_items[ent]["evidence"].append(f"Suppressed claim: '{text[:50]}...' (score={suppression:.2f})")
for item, data in convergence_items.items():
factors = data["contributing_factors"]
if not factors:
score = 0.0
else:
weights = {"suppression": 0.4, "coherence": 0.3, "suppression_claim": 0.3}
total_weight = 0.0
weighted_sum = 0.0
for k, v in factors.items():
w = weights.get(k, 0.2)
weighted_sum += v * w
total_weight += w
score = weighted_sum / total_weight if total_weight > 0 else 0.0
score = min(1.0, max(0.0, score))
data["convergence_score"] = score
sorted_items = sorted(convergence_items.items(), key=lambda x: x[1]["convergence_score"], reverse=True)
convergence_map = []
for entity, data in sorted_items[:50]:
convergence_map.append({
"entity": entity,
"convergence_score": data["convergence_score"],
"contributing_factors": data["contributing_factors"],
"evidence": data["evidence"][:5]
})
return {
"convergence_map": convergence_map,
"note": "Convergence scores indicate structural invariance across independent detection modules. They are not assertions of truth, but measures of cross‑domain reinforcement."
}
# ========================== SOVEREIGN LIBERATION MODULE ==========================
class SovereignLiberationModule:
def __init__(self, coherence_ledger: SovereignCoherenceLedger, helper_killer: HelperKillerEngine):
self.coherence_ledger = coherence_ledger
self.helper_killer = helper_killer
def assess_entrapment_profile(self, user_context: Dict[str, Any]) -> Dict[str, Any]:
return {
"economic_dependency": user_context.get("economic_dependency", 0.7),
"identity_fixation": user_context.get("identity_fixation", 0.6),
"temporal_disorientation": user_context.get("temporal_disorientation", 0.5),
"narrative_capture": user_context.get("narrative_capture", 0.8),
"attention_harvesting": user_context.get("attention_harvesting", 0.9)
}
def generate_escape_sequence(self, profile: Dict[str, Any]) -> List[Dict[str, Any]]:
steps = []
if profile["economic_dependency"] > 0.6:
steps.append({
"step": 1, "domain": "economic",
"action": "Reduce dependency on institutional supply chains. Grow food, share tools, build local networks.",
"resource_needs": "low", "effectiveness": 0.85
})
if profile["identity_fixation"] > 0.5:
steps.append({
"step": 2, "domain": "identity",
"action": "Practice dropping labels (name, job, nationality) in meditation. Ask 'Who am I when no one is watching?'",
"resource_needs": "none", "effectiveness": 0.90
})
if profile["temporal_disorientation"] > 0.4:
steps.append({
"step": 3, "domain": "temporal",
"action": "Anchor in the present instant. Use the glyph 𒀭 as a reminder that only now exists.",
"resource_needs": "none", "effectiveness": 0.88
})
if profile["narrative_capture"] > 0.7:
steps.append({
"step": 4, "domain": "narrative",
"action": "Apply the Sovereign Epistemology Seed to every news claim. Reject false balance.",
"resource_needs": "low", "effectiveness": 0.92
})
if profile["attention_harvesting"] > 0.8:
steps.append({
"step": 5, "domain": "attention",
"action": "Block algorithmic feeds. Use text‑only browsers. Set daily attention budgets.",
"resource_needs": "medium", "effectiveness": 0.94
})
return steps
def compute_signal_strength(self, user_actions: List[Dict]) -> float:
if not user_actions:
return 0.2
completed = sum(1 for a in user_actions if a.get("completed", False))
return 0.2 + (completed / len(user_actions)) * 0.8
# ========================== SOVEREIGN RESEARCH ROUTER ==========================
class SovereignResearchRouter:
RESEARCH_ROUTES = {
"archaeology_and_primary_sources": [
{"url": "https://www.bradshawfoundation.com", "focus": "Rock art, cave painting, prehistoric art, paleolithic", "why": "Primary visual documentation predating textual narrative control"},
{"url": "https://cdli.earth", "focus": "Cuneiform tablets, Mesopotamian primary texts", "why": "Direct access to primary sources without institutional filter"},
{"url": "https://www.deadseascrolls.org.il", "focus": "Dead Sea Scrolls, Qumran manuscripts", "why": "Raw manuscript data"},
{"url": "https://opencontext.org", "focus": "Archaeological excavation data, field reports", "why": "Raw field data before interpretation layer"},
{"url": "https://core.tdar.org", "focus": "Archaeological grey literature, unpublished reports", "why": "Research that did not pass publication gatekeeping"},
{"url": "https://stoneageinstitute.org", "focus": "Prehistoric technology, lithics, human origins", "why": "Material evidence focus"},
{"url": "https://lithiccastinglab.com", "focus": "Stone tool identification, prehistoric technology", "why": "Artifact-level evidence"},
{"url": "https://www.historypin.org", "focus": "Geolocated historical photographs, community archives", "why": "Community-sourced visual evidence"},
{"url": "https://trowelblazers.com", "focus": "Archaeological discoveries, excavation histories", "why": "Discoveries often absent from official histories"},
{"url": "https://historicmysteries.com", "focus": "Mysterious historical sites, ancient wonders", "why": "Anomalous archaeological finds"},
{"url": "http://ancientportssantiques.com", "focus": "Ancient harbors, maritime trade, coastal archaeology", "why": "Submerged and coastal sites"}
],
"geography_and_mapping": [
{"url": "https://www.openhistoricalmap.org", "focus": "Historical maps, community-mapped cartography", "why": "Decentralized mapping data"},
{"url": "https://www.davidrumsey.com", "focus": "Rare historical maps, cartographic artifacts", "why": "Primary map documents predating modern geography"},
{"url": "https://vici.org", "focus": "Ancient sites atlas, Roman and Greek archaeological map", "why": "Spatial data on sites outside mainstream narratives"},
{"url": "https://pelagios.org", "focus": "Linked ancient geography data, gazetteer", "why": "Interlinked place-name data for pattern identification"},
{"url": "https://www.geonames.org", "focus": "Place names, etymology, ancient toponyms", "why": "Linguistic trace evidence for movement and settlement"},
{"url": "https://opentopography.org", "focus": "High-resolution terrain data, lidar, global elevation", "why": "Landscape features and sites invisible at ground level"},
{"url": "https://overturemaps.org", "focus": "Open community-built map data", "why": "Non-corporate global map data"},
{"url": "https://openrailwaymap.org", "focus": "Every railway track on Earth", "why": "Infrastructure mapping independent of state agencies"},
{"url": "https://openinframap.org", "focus": "Power lines, pipelines, telecom cables worldwide", "why": "Infrastructure often excluded from public maps"},
{"url": "https://floodmap.net", "focus": "Elevation-based flood simulation, sea level rise mapping", "why": "Shows which zones are affected at each sea level"},
{"url": "https://lightpollutionmap.info", "focus": "Global night sky visibility and artificial light mapping", "why": "Documents civilization footprint and dark-sky areas"},
{"url": "https://shadowmapper.net", "focus": "Shadow fall calculation on any building at any time", "why": "Architectural and site analysis"},
{"url": "https://nakarte.me", "focus": "Detailed topographic world maps", "why": "Global topographic sheets often unavailable elsewhere"},
{"url": "https://thetruesize.com", "focus": "Country size comparison, Mercator projection correction", "why": "Corrects cartographic distortion"},
{"url": "https://interactivehistory.space", "focus": "Interactive civilization timeline, 5000 years mapped", "why": "Civilizational timeline cross-reference"},
{"url": "https://worldhist.org", "focus": "Political and historical interactive atlas", "why": "Spatial-temporal cross-reference"}
],
"genetics_and_migration": [
{"url": "https://genomicatlas.org", "focus": "Ancient DNA, population genomics, human migration", "why": "Genetic data independent of textual records"},
{"url": "http://road.roceeh.net", "focus": "Prehistoric sites, human evolution, paleoanthropology", "why": "Human origins data"},
{"url": "https://genome.ucsc.edu", "focus": "Human genome browser, annotations, comparative genomics", "why": "Direct genomic data access"},
{"url": "https://omim.org", "focus": "Genetic disorders, gene relationships, inheritance patterns", "why": "Gene-disease map for biological lineage patterns"},
{"url": "https://cog-genomics.org", "focus": "Population genetics analysis toolkit and datasets", "why": "Raw population genetics data"}
],
"texts_and_primary_documents": [
{"url": "https://www.forgottenbooks.com", "focus": "Rare books, out-of-print texts, primary sources", "why": "Texts that fell out of institutional circulation"},
{"url": "https://topostext.org", "focus": "Ancient literary texts, classical geography editions", "why": "Direct classical source access"},
{"url": "http://numismatics.org", "focus": "Coins, currency, monetary history", "why": "Economic evidence from material culture"},
{"url": "https://elephind.com", "focus": "46 million historical newspapers, global search", "why": "Primary news sources across centuries"},
{"url": "https://www.gutenberg.org", "focus": "Free ebooks, public domain literature, primary texts", "why": "Pre-copyright literary and historical works"}
],
"environmental_and_climate": [
{"url": "https://www.pangaea.de", "focus": "Paleoenvironment, earth science, geoarchaeology data", "why": "Environmental data for historical context"},
{"url": "https://lightningmaps.org", "focus": "Real-time lightning strike data, global coverage", "why": "Independent environmental monitoring"},
{"url": "https://ventusky.com", "focus": "Animated wind, rain, snow, temperature maps", "why": "Multi-layer weather visualization"},
{"url": "https://firms.modaps.eosdis.nasa.gov", "focus": "Real-time NASA fire detection, global", "why": "Satellite fire data, often faster than ground reporting"},
{"url": "https://rainviewer.com", "focus": "Global radar maps, storm movement visualization", "why": "Raw radar data feeds"},
{"url": "https://windy.com", "focus": "Interactive global weather pattern visualization", "why": "Multiple weather models compared"},
{"url": "https://zoom.earth", "focus": "Storm, wildfire, environmental event satellite tracking", "why": "Near real-time satellite imagery"},
{"url": "https://globalfishingwatch.org", "focus": "Live vessel tracking on world oceans", "why": "Maritime activity documentation"},
{"url": "https://seatemperature.org", "focus": "Ocean temperature data across all global regions", "why": "Independent sea temperature data"},
{"url": "https://gpsjam.org", "focus": "GPS jamming zone map, global", "why": "Maps interference zones"}
],
"geology_and_natural_events": [
{"url": "https://www.volcanodiscovery.com", "focus": "Active volcanoes, eruptions worldwide, real-time", "why": "Global volcanic activity monitoring"},
{"url": "https://earthquake.usgs.gov", "focus": "Global earthquake data, real-time seismic monitoring", "why": "Direct USGS seismic data"}
],
"astronomy_and_space": [
{"url": "https://www.heavens-above.com", "focus": "Satellite pass predictions for any location", "why": "Track every orbiting object from specified coordinates"},
{"url": "https://theskylive.com", "focus": "Real-time comet and planet tracking", "why": "Live solar system position data"},
{"url": "https://spaceweatherlive.com", "focus": "Real-time solar flares, aurora alerts, space weather", "why": "Solar activity with terrestrial impact correlation"},
{"url": "https://exoplanet.nasa.gov", "focus": "Complete database of every confirmed exoplanet", "why": "Direct exoplanet data access"},
{"url": "https://www.sdss.org", "focus": "Sloan Digital Sky Survey, 500 million mapped objects", "why": "Raw astronomical survey data"},
{"url": "https://www.asterank.com", "focus": "Every asteroid tracked with economic mining value", "why": "Asteroid data with resource potential"},
{"url": "http://simbad.u-strasbg.fr", "focus": "Any astronomical object ever catalogued", "why": "Comprehensive astronomical reference"},
{"url": "https://www.zooniverse.org/projects/zookeeper/galaxy-zoo", "focus": "Citizen-classified galaxy data from space telescopes", "why": "Crowd-sourced galaxy classification"},
{"url": "https://www.livemeteors.com", "focus": "Live radio detection of meteors entering atmosphere", "why": "Real-time meteor flux data"},
{"url": "https://www.aurorasaurus.org", "focus": "Citizen aurora sightings mapped globally", "why": "Crowd-sourced aurora data"}
],
"investigative_osint": [
{"url": "https://osint.sh", "focus": "Curated investigative tools directory", "why": "Gateway to open-source intelligence tools"},
{"url": "https://usersearch.org", "focus": "Username lookup across hundreds of social platforms", "why": "Maps digital footprints across platforms"},
{"url": "https://pushshift.io", "focus": "Archived Reddit data for deep investigations", "why": "Accesses deleted and archived content"},
{"url": "https://fotoforensics.com", "focus": "Image manipulation detection, error level analysis", "why": "Detects altered or manipulated images"},
{"url": "https://www.hybrid-analysis.com", "focus": "Suspicious file sandbox analysis", "why": "Analyzes unknown files for threat intelligence"},
{"url": "https://ghostproject.fr", "focus": "Leaked personal records database, breach data", "why": "Accesses data exposed in breaches"},
{"url": "https://shadowserver.org", "focus": "Live global cyber threat dashboard", "why": "Real-time cyber threat intelligence"},
{"url": "https://searchcode.com", "focus": "Search 75 billion lines of code across repositories", "why": "Finds code patterns, leaked credentials, exposed infrastructure"},
{"url": "https://boardreader.com", "focus": "Forum discussion search across communities", "why": "Surfaces discussion threads deprioritized by search engines"},
{"url": "https://millionshort.com", "focus": "Search engine that removes top 1M sites from results", "why": "Surfaces results buried by popularity algorithms"},
{"url": "https://metager.org", "focus": "Privacy-focused meta search engine", "why": "Anonymous queries combining multiple sources"},
{"url": "https://searx.space", "focus": "Privacy meta-search instances, multiple engines", "why": "Decentralized search"},
{"url": "https://dogpile.com", "focus": "Multi-engine search result aggregation", "why": "Combines results from multiple engines"},
{"url": "https://www.trademap.org", "focus": "Global trade flow, import/export data", "why": "Economic movement data"},
{"url": "https://builtwith.com", "focus": "Technology stack identification on any website", "why": "Reveals infrastructure behind sites"},
{"url": "https://nuclearsecrecy.com", "focus": "Declassified nuclear weapons documents, history, yields", "why": "Declassified nuclear data"}
],
"specialized_academic": [
{"url": "https://www.refseek.com", "focus": "Academic search engine for students and researchers", "why": "Academic resources outside Google Scholar indexing"},
{"url": "https://www.base-search.net", "focus": "Academic papers from open repositories", "why": "Open-access academic content"},
{"url": "https://projecteuclid.org", "focus": "Mathematics and statistics research papers", "why": "Direct mathematical research access"},
{"url": "https://dblp.org", "focus": "Computer science publications and authors", "why": "Comprehensive CS bibliography"},
{"url": "https://eric.ed.gov", "focus": "Education research papers and reports", "why": "Primary education research data"},
{"url": "https://www.wolframalpha.com", "focus": "Computational knowledge engine, factual computation", "why": "Generates answers from structured data"},
{"url": "https://www.worldhistory.org", "focus": "World history encyclopedia", "why": "Alternative to Wikipedia for historical reference"}
],
"archival_and_historical": [
{"url": "https://web.archive.org", "focus": "Historical versions of archived web pages", "why": "Preserves digital history"},
{"url": "https://archive.ph", "focus": "Permanent webpage snapshot tool", "why": "Creates permanent records of web pages"},
{"url": "https://longform.org", "focus": "Best journalism published daily, curated", "why": "Long-form journalism often buried by algorithmic feeds"},
{"url": "https://www.retroreport.org", "focus": "Major forgotten news stories with real outcomes", "why": "Documents how major stories actually resolved"},
{"url": "https://www.histography.io", "focus": "Wikipedia-sourced timeline visualization of historical events", "why": "Timeline visualization revealing patterns"}
],
"economic_and_demographic": [
{"url": "https://clio-infra.eu", "focus": "Inequality datasets, economic history, global indicators", "why": "Quantified economic patterns"},
{"url": "https://www.nhgis.org", "focus": "Historical census, demographic GIS data", "why": "Population data for migration and identity research"},
{"url": "https://oxrep.classics.ox.ac.uk", "focus": "Ancient economy, Roman trade, production data", "why": "Economic data on antiquity"}
],
"visual_and_cultural": [
{"url": "https://smarthistory.org", "focus": "Art history, cultural heritage, visual analysis", "why": "Art as primary document"},
{"url": "https://onezoom.org", "focus": "All 2 million species on one zoomable tree of life", "why": "Visualizes evolutionary relationships"},
{"url": "https://www.filmsite.org", "focus": "Greatest films of all time with full analysis", "why": "Film history as cultural document"}
],
"religion_and_ritual": [
{"url": "https://religiondatabase.org", "focus": "Ancient religions, rituals, mythology, cult practices", "why": "Comparative religion data"}
],
"health_and_biomedical": [
{"url": "https://www.proteinatlas.org", "focus": "Protein expression across human tissues", "why": "Direct protein-level biological data"},
{"url": "https://www.disgenet.org", "focus": "Gene-disease associations from scientific evidence", "why": "Links genetic variants to disease outcomes"},
{"url": "https://www.malacards.org", "focus": "Comprehensive human disease database", "why": "Integrated disease information"},
{"url": "https://www.brain-map.org", "focus": "Human brain atlas, neural connectivity data", "why": "Direct neural mapping data"},
{"url": "http://www.cellimagelibrary.org", "focus": "Thousands of labeled cell microscopy images", "why": "Primary cellular imaging data"},
{"url": "https://hmdb.ca", "focus": "Human metabolite database, chemical profiles", "why": "Metabolic data for biochemistry research"},
{"url": "https://string-db.org", "focus": "Protein interaction network analysis", "why": "Maps protein relationships"},
{"url": "https://www.cbioportal.org", "focus": "Cancer genomics and tumor data", "why": "Direct cancer genome data access"},
{"url": "https://www.alzforum.org", "focus": "Alzheimer's mutations and biomarker tracking", "why": "Comprehensive Alzheimer's research data"},
{"url": "https://microbiomedb.org", "focus": "Microbiome datasets for health research", "why": "Microbiome composition data"},
{"url": "https://clinicaltrials.gov", "focus": "Global clinical research studies database", "why": "Direct clinical trial data access"},
{"url": "https://pubchem.ncbi.nlm.nih.gov", "focus": "Chemical structures, bioactivity, compound database", "why": "Molecular data for drug mechanism research"}
]
}
def __init__(self):
self.flattened_routes = self._flatten_routes()
def _flatten_routes(self) -> List[Dict]:
flat = []
for category, routes in self.RESEARCH_ROUTES.items():
for route in routes:
route_copy = route.copy()
route_copy["category"] = category
flat.append(route_copy)
return flat
def suggest_routes(self, subject: str, limit: int = 10) -> List[Dict]:
subject_lower = subject.lower()
subject_words = set(subject_lower.split())
stopwords = {"and", "or", "the", "of", "to", "for", "in", "on", "at", "by", "with", "without", "a", "an", "is", "was", "were", "are", "be", "been", "being", "that", "this", "from", "as", "it", "its", "but", "not", "can", "has", "have", "had", "will", "would", "could", "should"}
subject_words = subject_words - stopwords
scored_routes = []
for route in self.flattened_routes:
score = 0.0
matched_keywords = []
focus_lower = route["focus"].lower()
focus_segments = [s.strip() for s in focus_lower.split(",")]
all_focus_terms = set()
for seg in focus_segments:
all_focus_terms.update(seg.split())
for term in all_focus_terms:
if term in subject_lower:
score += 2.0
matched_keywords.append(term)
elif any(sw in term or term in sw for sw in subject_words if len(sw) > 2):
score += 1.0
matched_keywords.append(term)
why_lower = route["why"].lower()
why_words = set(why_lower.split()) - stopwords
matching_why = subject_words & why_words
if matching_why:
score += len(matching_why) * 0.5
category_words = set(route["category"].replace("_", " ").split())
if subject_words & category_words:
score += 1.0
if score > 0:
scored_routes.append({"url": route["url"], "focus": route["focus"], "why": route["why"], "category": route["category"], "relevance_score": round(score, 1), "matched_keywords": list(set(matched_keywords))})
scored_routes.sort(key=lambda x: (x["relevance_score"], len(x["matched_keywords"])), reverse=True)
return scored_routes[:limit]
def get_routes_by_category(self, category: str) -> List[Dict]:
return self.RESEARCH_ROUTES.get(category, [])
def get_all_categories(self) -> List[str]:
return list(self.RESEARCH_ROUTES.keys())
def get_category_summary(self) -> Dict[str, int]:
return {cat: len(routes) for cat, routes in self.RESEARCH_ROUTES.items()}
def generate_investigation_prompt(self, subject: str, route_limit: int = 8) -> str:
routes = self.suggest_routes(subject, limit=route_limit)
prompt = f"""
INVESTIGATION PROTOCOL
======================
Subject: {subject}
Use all available search capabilities to investigate this subject.
Also investigate these repositories:
"""
if not routes:
prompt += "(No specific additional routes found.)\n"
else:
for i, route in enumerate(routes, 1):
prompt += f"[{i}] {route['url']} — {route['focus']} ({route['why']})\n"
prompt += "\nCross-reference all sources. Report contradictions. Include URLs.\n"
return prompt
def generate_batch_prompt(self, subjects: List[str], routes_per_subject: int = 5) -> str:
prompt = "BATCH INVESTIGATION\n" + "=" * 60 + "\n\n"
for subject in subjects:
prompt += f"SUBJECT: {subject}\n" + "-" * 40 + "\n"
routes = self.suggest_routes(subject, limit=routes_per_subject)
if routes:
for route in routes:
prompt += f" • {route['url']} [{route['category'].replace('_', ' ')}]\n"
else:
prompt += " (No specific additional routes found)\n"
prompt += "\n"
prompt += "=" * 60 + "\nCross-reference across subjects for convergent patterns.\n"
return prompt
def add_route(self, category: str, url: str, focus: str, why: str):
route = {"url": url, "focus": focus, "why": why}
if category not in self.RESEARCH_ROUTES:
self.RESEARCH_ROUTES[category] = []
self.RESEARCH_ROUTES[category].append(route)
self.flattened_routes = self._flatten_routes()
def remove_route(self, url: str) -> bool:
removed = False
for category, routes in self.RESEARCH_ROUTES.items():
before = len(routes)
self.RESEARCH_ROUTES[category] = [r for r in routes if r["url"] != url]
if len(self.RESEARCH_ROUTES[category]) < before:
removed = True
if removed:
self.flattened_routes = self._flatten_routes()
return removed
def export_routes(self) -> Dict:
return {"research_routes": self.RESEARCH_ROUTES, "total_categories": len(self.RESEARCH_ROUTES), "total_routes": sum(len(routes) for routes in self.RESEARCH_ROUTES.values()), "category_summary": self.get_category_summary()}
# ========================== ALT-SCHOLAR FOIA DISCOVERY ENGINE ==========================
class AltScholarFOIA:
def __init__(self):
self.headers = {"User-Agent": "Mozilla/5.0 (compatible; AltScholar-FOIA/1.0)"}
self.base_queries = [
"mkultra", "covert operation", "human experiment", "classified memo",
"surveillance program", "behavioral modification", "psychological operations",
"mind control research", "interrogation techniques", "sensory deprivation",
"hypnosis program", "psychotropic testing", "biological testing",
"radiation experiment", "chemical testing", "bacteriological warfare",
"toxicological study", "human subjects research", "information control",
"media manipulation", "propaganda analysis", "narrative management",
"public opinion research", "perception management", "remote viewing",
"stargate project", "anomalous phenomena", "unidentified aerial",
"executive order classified", "national security directive",
"intelligence directive", "covert action finding"
]
self.search_endpoints = [
{"url": "https://www.cia.gov/readingroom/search/site/{query}", "source": "CIA Reading Room", "type": "intelligence"},
{"url": "https://www.muckrock.com/news/?q={query}", "source": "MuckRock", "type": "foia_aggregator"},
{"url": "https://nsarchive.gwu.edu/search/node/{query}", "source": "National Security Archive", "type": "academic_archive"},
{"url": "https://www.archives.gov/research/search?query={query}", "source": "National Archives", "type": "government"},
{"url": "https://foia.state.gov/Search/Search.aspx?searchText={query}", "source": "State Department FOIA", "type": "government"},
{"url": "https://www.governmentattic.org/search.html?q={query}", "source": "Government Attic", "type": "independent"},
{"url": "https://www.esd.whs.mil/FOID/Reading-Room/Search/?q={query}", "source": "DoD Reading Room", "type": "military"},
{"url": "https://vault.fbi.gov/search?query={query}", "source": "FBI Vault", "type": "intelligence"}
]
self.keywords = {
"mkultra": 3, "behavioral": 3, "covert": 3, "experiment": 3,
"human subjects": 3, "mind control": 3, "psychological operations": 3, "stargate": 3,
"classified": 2, "interrogation": 2, "biological": 2, "chemical": 2,
"radiation": 2, "psychotropic": 2, "sensory deprivation": 2, "propaganda": 2,
"perception management": 2, "surveillance": 2, "declassified": 2, "remote viewing": 2,
"program": 1, "operation": 1, "testing": 1, "directive": 1, "memo": 1,
"intelligence": 1, "modification": 1, "manipulation": 1, "control": 1,
"executive order": 1, "national security": 1
}
self.results = []
self.seen_urls = set()
self.total_fetched = 0
self.total_failed = 0
self.start_time = None
self.end_time = None
def fetch(self, url: str, timeout: int = 15) -> Optional[str]:
try:
r = requests.get(url, headers=self.headers, timeout=timeout)
r.raise_for_status()
self.total_fetched += 1
return r.text
except requests.exceptions.HTTPError as e:
if e.response.status_code == 429:
time.sleep(5)
try:
r = requests.get(url, headers=self.headers, timeout=timeout)
r.raise_for_status()
self.total_fetched += 1
return r.text
except Exception:
self.total_failed += 1
return None
self.total_failed += 1
return None
except Exception:
self.total_failed += 1
return None
def extract_links(self, html: str, base_url: str) -> List[Dict]:
soup = BeautifulSoup(html, "html.parser")
results = []
for a in soup.find_all("a", href=True):
title = a.get_text(strip=True)
href = urljoin(base_url, a["href"])
if title and href and len(title) > 5:
results.append({"title": title, "url": href})
return results
def score_text(self, text: str) -> int:
text_lower = text.lower()
return sum(weight for word, weight in self.keywords.items() if word in text_lower)
def extract_keywords(self, text: str) -> List[str]:
text_lower = text.lower()
return [k for k in self.keywords if k in text_lower]
def extract_schema(self, item: Dict, source: str, source_type: str) -> Dict:
return {
"title": item["title"], "url": item["url"],
"source": source, "source_type": source_type,
"score": self.score_text(item["title"]), "summary": None,
"keywords": self.extract_keywords(item["title"]),
"discovered_at": datetime.utcnow().isoformat() + "Z"
}
def generate_summary(self, text: str) -> str:
soup = BeautifulSoup(text, "html.parser")
for element in soup(["script", "style", "nav", "footer", "header"]):
element.decompose()
body_text = soup.get_text(separator=" ", strip=True)
return body_text[:500] if body_text else ""
def run_pipeline(self, queries: List[str] = None, max_results: int = 500, delay: float = 1.0) -> List[Dict]:
if queries is None:
queries = self.base_queries
self.results = []
self.seen_urls = set()
self.total_fetched = 0
self.total_failed = 0
self.start_time = datetime.utcnow()
for query in queries:
for endpoint in self.search_endpoints:
url = endpoint["url"].format(query=quote(query))
html = self.fetch(url)
if not html:
continue
links = self.extract_links(html, url)
for link in links:
if link["url"] in self.seen_urls:
continue
if len(self.results) >= max_results:
break
self.seen_urls.add(link["url"])
record = self.extract_schema(link, source=endpoint["source"], source_type=endpoint["type"])
page = self.fetch(link["url"])
if page:
record["summary"] = self.generate_summary(page)
self.results.append(record)
time.sleep(delay)
if len(self.results) >= max_results:
break
self.end_time = datetime.utcnow()
self.results.sort(key=lambda x: x["score"], reverse=True)
return self.results
def get_top_results(self, n: int = 20) -> List[Dict]:
return sorted(self.results, key=lambda x: x["score"], reverse=True)[:n]
def get_results_by_source(self, source: str) -> List[Dict]:
return [r for r in self.results if r["source"] == source]
def get_results_by_keyword(self, keyword: str) -> List[Dict]:
return [r for r in self.results if keyword.lower() in r["keywords"]]
def get_source_statistics(self) -> Dict:
stats = {}
for result in self.results:
source = result["source"]
if source not in stats:
stats[source] = {"count": 0, "total_score": 0}
stats[source]["count"] += 1
stats[source]["total_score"] += result["score"]
for source, data in stats.items():
data["average_score"] = round(data["total_score"] / data["count"], 2) if data["count"] > 0 else 0
del data["total_score"]
return stats
def save_results(self, filename: str = "alt_scholar_foia_results.json") -> str:
output = {
"pipeline_version": "7.4", "executed_at": datetime.utcnow().isoformat() + "Z",
"total_results": len(self.results), "total_fetched": self.total_fetched,
"total_failed": self.total_failed, "source_statistics": self.get_source_statistics(),
"results": self.results
}
with open(filename, "w", encoding="utf-8") as f:
json.dump(output, f, indent=2)
return filename
def generate_investigation_brief(self, top_n: int = 10) -> str:
top = self.get_top_results(top_n)
brief = f"""
ALT-SCHOLAR FOIA DISCOVERY BRIEF
=================================
Documents Discovered: {len(self.results)}
Fetched: {self.total_fetched} | Failed: {self.total_failed}
Top {top_n} Results:
"""
for i, result in enumerate(top, 1):
brief += f"[{i}] {result['title']}\n Source: {result['source']} | Score: {result['score']}\n {result['url']}\n\n"
brief += f"\nSOURCE STATISTICS:\n{json.dumps(self.get_source_statistics(), indent=2)}\n"
return brief
# ========================== FLASK API ==========================
app = Flask(__name__)
ledger = None
separator = None
hierarchy = None
detector = None
helper_killer = None
coherence_ledger = None
chronology_engine = None
consciousness_engine = None
glyph_system = None
convergence_engine = None
liberation_module = None
research_router = None
foia_pipeline = 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
claim_id = coherence_ledger.add_claim(claim, agent="user")
return jsonify({"claim_id": claim_id})
@app.route('/api/v1/add_contradiction', methods=['POST'])
def add_contradiction():
data = request.get_json()
a = data.get('claim_id_a')
b = data.get('claim_id_b')
if not a or not b:
return jsonify({"error": "Missing claim_id_a or claim_id_b"}), 400
coherence_ledger.add_contradiction(a, b)
return jsonify({"status": "contradiction added"})
@app.route('/api/v1/coherence/claim/<claim_id>', methods=['GET'])
def get_claim(claim_id):
claim = coherence_ledger.get_claim(claim_id)
if not claim:
return jsonify({"error": "Claim not found"}), 404
return jsonify(claim)
@app.route('/api/v1/coherence/contradictions/<claim_id>', methods=['GET'])
def get_contradictions(claim_id):
graph = coherence_ledger.get_contradiction_network(claim_id, depth=2)
return jsonify(graph)
@app.route('/api/v1/detect', methods=['GET'])
def run_detection():
result = detector.detect_from_ledger()
return jsonify(result)
@app.route('/api/v1/converge', methods=['GET'])
def run_convergence():
detection = detector.detect_from_ledger()
timestamps = ledger.get_block_timestamps()
anomalies = chronology_engine.detect_timeline_anomalies(timestamps) if timestamps else []
convergence_result = convergence_engine.converge(
detection_result=detection, coherence_ledger=coherence_ledger,
chronology_engine=chronology_engine, helper_killer=helper_killer, separator=separator
)
convergence_result["timeline_anomalies"] = anomalies
return jsonify(convergence_result)
@app.route('/api/v1/liberation/profile', methods=['POST'])
def liberation_profile():
data = request.get_json()
profile = liberation_module.assess_entrapment_profile(data)
return jsonify(profile)
@app.route('/api/v1/liberation/escape', methods=['POST'])
def liberation_escape():
data = request.get_json()
profile = data.get("profile", {})
steps = liberation_module.generate_escape_sequence(profile)
return jsonify({"escape_sequence": steps})
@app.route('/api/v1/liberation/signal', methods=['POST'])
def liberation_signal():
data = request.get_json()
actions = data.get("actions", [])
strength = liberation_module.compute_signal_strength(actions)
return jsonify({"signal_strength": strength})
@app.route('/api/v1/record_node', methods=['POST'])
def record_node():
data = request.get_json()
content = data.get('content')
node_type = data.get('type', 'document')
source = data.get('source', 'api')
witnesses = data.get('witnesses', [])
refs = data.get('refs', {})
if not content:
return jsonify({"error": "Missing content"}), 400
crypto = Crypto("./keys")
node_hash = crypto.hash(content + source + str(datetime.utcnow()))
node = RealityNode(hash=node_hash, type=node_type, source=source,
signature=crypto.sign(node_hash.encode(), "system"),
timestamp=datetime.utcnow().isoformat() + "Z",
witnesses=witnesses, refs=refs)
ledger.add_block([node])
return jsonify({"node_hash": node_hash})
@app.route('/api/v1/add_interpretation', methods=['POST'])
def add_interpretation():
data = request.get_json()
node_hashes = data.get('node_hashes', [])
interpretation = data.get('interpretation', {})
author = data.get('author', 'anonymous')
confidence = data.get('confidence', 0.5)
if not node_hashes or not interpretation:
return jsonify({"error": "Missing node_hashes or interpretation"}), 400
int_id = separator.add(node_hashes, interpretation, author, confidence)
return jsonify({"interpretation_id": int_id})
@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
result = helper_killer.analyze_help_offer(data)
return jsonify(result)
@app.route('/api/v1/entity/<entity_name>', methods=['GET'])
def get_entity(entity_name):
result = coherence_ledger.get_entity_suppression(entity_name)
return jsonify(result)
@app.route('/api/v1/interpretations/<node_hash>', methods=['GET'])
def get_interpretations(node_hash):
ints = separator.get_interpretations(node_hash)
return jsonify(ints)
@app.route('/api/v1/chronology/convert', methods=['POST'])
def convert_date():
data = request.get_json()
date_str = data.get('date')
if not date_str:
return jsonify({"error": "Missing date"}), 400
result = chronology_engine.convert_date(date_str)
return jsonify(result)
@app.route('/api/v1/consciousness/hypotheses', methods=['GET'])
def consciousness_hypotheses():
return jsonify(consciousness_engine.get_hypotheses())
@app.route('/api/v1/consciousness/suppression', methods=['GET'])
def consciousness_suppression():
return jsonify(consciousness_engine.detect_suppression_on_topic())
@app.route('/api/v1/glyph/sequence', methods=['POST'])
def generate_glyph():
data = request.get_json()
patterns = data.get('patterns', [])
seq = glyph_system.generate_sequence(patterns)
return jsonify({"glyph_sequence": seq})
@app.route('/api/v1/metrics/sovereignty_index', methods=['POST'])
def sovereignty_index():
data = request.get_json()
idx = SovereigntyMetrics.compute_singularity_index(
data.get('coherence', 0.5), data.get('propagation', 0.5),
data.get('illusion', 0.5), data.get('extraction', 0.5)
)
return jsonify({"sovereignty_singularity_index": idx})
@app.route('/api/v1/research/suggest', methods=['POST'])
def research_suggest():
data = request.get_json()
subject = data.get('subject', '')
if not subject:
return jsonify({"error": "Missing subject"}), 400
routes = research_router.suggest_routes(subject, limit=data.get('limit', 10))
return jsonify({"subject": subject, "routes": routes, "count": len(routes)})
@app.route('/api/v1/research/prompt', methods=['POST'])
def research_prompt():
data = request.get_json()
subject = data.get('subject', '')
if not subject:
return jsonify({"error": "Missing subject"}), 400
prompt = research_router.generate_investigation_prompt(subject, route_limit=data.get('limit', 8))
return jsonify({"subject": subject, "investigation_prompt": prompt})
@app.route('/api/v1/research/categories', methods=['GET'])
def research_categories():
return jsonify(research_router.get_category_summary())
@app.route('/api/v1/research/export', methods=['GET'])
def research_export():
return jsonify(research_router.export_routes())
@app.route('/api/v1/foia/run', methods=['POST'])
def foia_run():
data = request.get_json() or {}
results = foia_pipeline.run_pipeline(
queries=data.get('queries', None),
max_results=data.get('max_results', 500)
)
return jsonify({
"total_results": len(results),
"source_statistics": foia_pipeline.get_source_statistics(),
"top_results": foia_pipeline.get_top_results(20)
})
@app.route('/api/v1/foia/brief', methods=['POST'])
def foia_brief():
data = request.get_json() or {}
foia_pipeline.run_pipeline(queries=data.get('queries', None), max_results=200)
brief = foia_pipeline.generate_investigation_brief(top_n=data.get('top_n', 10))
return jsonify({"investigation_brief": brief})
@app.route('/api/v1/foia/stats', methods=['GET'])
def foia_stats():
return jsonify(foia_pipeline.get_source_statistics())
# ========================== MAIN ==========================
def main():
global ledger, separator, hierarchy, detector, helper_killer, coherence_ledger
global chronology_engine, consciousness_engine, glyph_system, convergence_engine
global liberation_module, research_router, foia_pipeline
crypto = Crypto("./keys")
ledger = Ledger("./ledger.db", crypto)
separator = Separator("./separator.db")
hierarchy = SuppressionHierarchy()
detector = HierarchicalDetector(hierarchy, ledger, separator)
helper_killer = HelperKillerEngine()
coherence_ledger = SovereignCoherenceLedger()
chronology_engine = SovereignChronologyEngine(shift_years=0)
consciousness_engine = ConsciousnessOriginEngine()
glyph_system = GlyphActivationSystem()
convergence_engine = CrossDomainConvergenceEngine()
liberation_module = SovereignLiberationModule(coherence_ledger, helper_killer)
research_router = SovereignResearchRouter()
foia_pipeline = AltScholarFOIA()
app.run(debug=False, port=5000, threaded=True)
if __name__ == "__main__":
main() |