Instructions to use upgraedd/Consciousness with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use upgraedd/Consciousness with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="upgraedd/Consciousness")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("upgraedd/Consciousness", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use upgraedd/Consciousness with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "upgraedd/Consciousness" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "upgraedd/Consciousness", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/upgraedd/Consciousness
- SGLang
How to use upgraedd/Consciousness with SGLang:
Install from pip and serve model
# Install SGLang from pip: pip install sglang # Start the SGLang server: python3 -m sglang.launch_server \ --model-path "upgraedd/Consciousness" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "upgraedd/Consciousness", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker images
docker run --gpus all \ --shm-size 32g \ -p 30000:30000 \ -v ~/.cache/huggingface:/root/.cache/huggingface \ --env "HF_TOKEN=<secret>" \ --ipc=host \ lmsysorg/sglang:latest \ python3 -m sglang.launch_server \ --model-path "upgraedd/Consciousness" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "upgraedd/Consciousness", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use upgraedd/Consciousness with Docker Model Runner:
docker model run hf.co/upgraedd/Consciousness
Download MAAM_MODULE from upgraedd/Consciousness: direct link, hf CLI and curl.
- Browser
- Download file 27 kB
-
https://huggingface.co/upgraedd/Consciousness/resolve/eef445353fe121f2fe76a03fcf60a98cd19d67f7/MAAM_MODULE
- Command line
-
hf download hf://upgraedd/Consciousness@eef445353fe121f2fe76a03fcf60a98cd19d67f7/MAAM_MODULE
-
curl -L -o MAAM_MODULE https://huggingface.co/upgraedd/Consciousness/resolve/eef445353fe121f2fe76a03fcf60a98cd19d67f7/MAAM_MODULE
27 kB
| """ | |
| MEMETIC ARCHITECTURE ANALYSIS MODULE | |
| Advanced functional representation of societal influence systems | |
| """ | |
| import json | |
| import time | |
| from datetime import datetime | |
| from dataclasses import dataclass | |
| from typing import Dict, List, Set, Any, Optional, Tuple | |
| from enum import Enum | |
| import hashlib | |
| class MemeticLayer(Enum): | |
| SYMBOLIC = "symbolic" | |
| NARRATIVE = "narrative" | |
| BEHAVIORAL = "behavioral" | |
| INSTITUTIONAL = "institutional" | |
| COGNITIVE = "cognitive" | |
| class InfluenceTier(Enum): | |
| SURFACE = "surface" # Public facing | |
| SHALLOW = "shallow" # Media/education | |
| DEEP = "deep" # Academic/institutional | |
| CORE = "core" # Foundational paradigms | |
| class MemeticEntity: | |
| identifier: str | |
| layer: MemeticLayer | |
| tier: InfluenceTier | |
| potency: float # 0.0 - 1.0 | |
| coherence: float # 0.0 - 1.0 | |
| resilience: float # 0.0 - 1.0 | |
| dependencies: List[str] | |
| created: float | |
| last_modified: float | |
| class AdvancedMemeticArchitecture: | |
| """ | |
| Advanced system for analyzing and modeling memetic architecture | |
| and societal influence patterns | |
| """ | |
| def __init__(self): | |
| # Core architecture components | |
| self.glyphs = { | |
| "corporate_logos": { | |
| "examples": ["Apple", "Nike", "McDonald's"], | |
| "layer": MemeticLayer.SYMBOLIC, | |
| "potency": 0.8 | |
| }, | |
| "media_narratives": { | |
| "examples": ["climate crisis", "economic growth", "terrorism"], | |
| "layer": MemeticLayer.NARRATIVE, | |
| "potency": 0.9 | |
| }, | |
| "musical_influence": { | |
| "examples": ["pop culture", "genre trends", "artist personas"], | |
| "layer": MemeticLayer.BEHAVIORAL, | |
| "potency": 0.7 | |
| }, | |
| "social_manipulation": { | |
| "examples": ["viral challenges", "social media trends", "group identity"], | |
| "layer": MemeticLayer.BEHAVIORAL, | |
| "potency": 0.85 | |
| }, | |
| "political_theater": { | |
| "examples": ["election cycles", "political scandals", "partisan conflicts"], | |
| "layer": MemeticLayer.INSTITUTIONAL, | |
| "potency": 0.9 | |
| }, | |
| "educational_frameworks": { | |
| "examples": ["standardized testing", "curriculum design", "historical narratives"], | |
| "layer": MemeticLayer.COGNITIVE, | |
| "potency": 0.95 | |
| } | |
| } | |
| # Advanced replacement patterns | |
| self.replacements = { | |
| "figures": { | |
| "Tesla": "Edison", | |
| "Jung": "Freud", | |
| "Sitchin": "Tsoukalos", | |
| "Sagan": "Tyson", | |
| "Malcolm X": "King", | |
| "Zinn": "Schlesinger", | |
| "Fuller": "Musk" | |
| }, | |
| "concepts": { | |
| "consciousness": "brain chemistry", | |
| "sovereignty": "global citizenship", | |
| "community": "network", | |
| "wisdom": "information", | |
| "being": "having" | |
| }, | |
| "movements": { | |
| "labor rights": "career development", | |
| "civil rights": "diversity training", | |
| "environmentalism": "sustainability", | |
| "spirituality": "mindfulness" | |
| } | |
| } | |
| # Defense mechanisms | |
| self.firewalls = { | |
| "ridicule": { | |
| "effectiveness": 0.8, | |
| "activation_speed": 0.9, | |
| "examples": ["conspiracy theorist", "pseudoscience", "fringe"] | |
| }, | |
| "spectacle": { | |
| "effectiveness": 0.95, | |
| "activation_speed": 0.7, | |
| "examples": ["celebrity news", "sports events", "award shows"] | |
| }, | |
| "oversimplification": { | |
| "effectiveness": 0.75, | |
| "activation_speed": 0.8, | |
| "examples": ["left vs right", "good vs evil", "us vs them"] | |
| }, | |
| "containment_by_proxy": { | |
| "effectiveness": 0.85, | |
| "activation_speed": 0.6, | |
| "examples": ["controlled opposition", "gatekeepers", "watered-down versions"] | |
| }, | |
| "chronological_snobbery": { | |
| "effectiveness": 0.7, | |
| "activation_speed": 0.5, | |
| "examples": ["that's outdated", "we know better now", "primitive thinking"] | |
| } | |
| } | |
| # Memetic inversion patterns | |
| self.inversions = { | |
| "freedom": "security", | |
| "privacy": "convenience", | |
| "sovereignty": "globalism", | |
| "tradition": "progress", | |
| "responsibility": "victimhood", | |
| "community": "individualism", | |
| "truth": "narrative" | |
| } | |
| # Counter-memetic strategies | |
| self.counterforce = { | |
| "symbolic_sovereignty": { | |
| "description": "Reclaiming personal symbolic language", | |
| "effectiveness": 0.8, | |
| "requirements": ["awareness", "creativity", "courage"] | |
| }, | |
| "ledger_based_inevitability": { | |
| "description": "Building undeniable truth structures", | |
| "effectiveness": 0.9, | |
| "requirements": ["patience", "precision", "persistence"] | |
| }, | |
| "epistemic_rupture": { | |
| "description": "Breaking through cognitive frameworks", | |
| "effectiveness": 0.95, | |
| "requirements": ["insight", "timing", "clarity"] | |
| }, | |
| "archetypal_resonance": { | |
| "description": "Tapping into timeless patterns", | |
| "effectiveness": 0.85, | |
| "requirements": ["depth", "authenticity", "connection"] | |
| } | |
| } | |
| # Memetic entity registry | |
| self.entity_registry: Dict[str, MemeticEntity] = {} | |
| self.analysis_history = [] | |
| # Initialize with core entities | |
| self._initialize_core_entities() | |
| def _initialize_core_entities(self): | |
| """Initialize the system with core memetic entities""" | |
| timestamp = time.time() | |
| core_entities = [ | |
| ("consumerism", MemeticLayer.BEHAVIORAL, InfluenceTier.DEEP, 0.95, 0.8, 0.9), | |
| ("scientific_materialism", MemeticLayer.COGNITIVE, InfluenceTier.CORE, 0.9, 0.85, 0.95), | |
| ("progress_narrative", MemeticLayer.NARRATIVE, InfluenceTier.SHALLOW, 0.88, 0.9, 0.8), | |
| ("nationalism", MemeticLayer.INSTITUTIONAL, InfluenceTier.DEEP, 0.85, 0.75, 0.85), | |
| ] | |
| for identifier, layer, tier, potency, coherence, resilience in core_entities: | |
| entity = MemeticEntity( | |
| identifier=identifier, | |
| layer=layer, | |
| tier=tier, | |
| potency=potency, | |
| coherence=coherence, | |
| resilience=resilience, | |
| dependencies=[], | |
| created=timestamp, | |
| last_modified=timestamp | |
| ) | |
| self.entity_registry[identifier] = entity | |
| def analyze_memetic_landscape(self, target_concept: str) -> Dict[str, Any]: | |
| """ | |
| Comprehensive analysis of a concept within the memetic architecture | |
| """ | |
| analysis = { | |
| "concept": target_concept, | |
| "timestamp": datetime.now().isoformat(), | |
| "analysis_id": hashlib.md5(f"{target_concept}{time.time()}".encode()).hexdigest()[:8], | |
| "replacements": self._find_replacements(target_concept), | |
| "firewalls": self._predict_firewalls(target_concept), | |
| "inversions": self._detect_inversions(target_concept), | |
| "layer_analysis": self._analyze_by_layer(target_concept), | |
| "threat_level": self._calculate_threat_level(target_concept), | |
| "counter_strategies": self._recommend_counter_strategies(target_concept) | |
| } | |
| self.analysis_history.append(analysis) | |
| return analysis | |
| def _find_replacements(self, concept: str) -> List[Dict[str, str]]: | |
| """Find institutional replacements for a concept""" | |
| replacements = [] | |
| for category, mapping in self.replacements.items(): | |
| for original, replacement in mapping.items(): | |
| if concept.lower() in original.lower() or concept.lower() in replacement.lower(): | |
| replacements.append({ | |
| "category": category, | |
| "original": original, | |
| "replacement": replacement, | |
| "relationship": f"{original} → {replacement}" | |
| }) | |
| return replacements | |
| def _predict_firewalls(self, concept: str) -> List[Dict[str, Any]]: | |
| """Predict which firewalls would activate against a challenging concept""" | |
| firewall_predictions = [] | |
| # Simple heuristic based on concept characteristics | |
| concept_lower = concept.lower() | |
| if any(word in concept_lower for word in ['conspiracy', 'secret', 'hidden']): | |
| firewall_predictions.append({ | |
| "firewall": "ridicule", | |
| "confidence": 0.9, | |
| "likely_response": "Marginalization through labeling" | |
| }) | |
| if any(word in concept_lower for word in ['revolution', 'overthrow', 'system']): | |
| firewall_predictions.append({ | |
| "firewall": "containment_by_proxy", | |
| "confidence": 0.8, | |
| "likely_response": "Co-option and dilution" | |
| }) | |
| if any(word in concept_lower for word in ['ancient', 'traditional', 'old']): | |
| firewall_predictions.append({ | |
| "firewall": "chronological_snobbery", | |
| "confidence": 0.7, | |
| "likely_response": "Dismissal as outdated" | |
| }) | |
| return firewall_predictions | |
| def _detect_inversions(self, concept: str) -> List[Dict[str, str]]: | |
| """Detect memetic inversions related to a concept""" | |
| inversions = [] | |
| for original, inverted in self.inversions.items(): | |
| if concept.lower() in original.lower() or concept.lower() in inverted.lower(): | |
| inversions.append({ | |
| "original_meaning": original, | |
| "inverted_meaning": inverted, | |
| "pattern": f"'{original}' has been inverted to mean '{inverted}'" | |
| }) | |
| return inversions | |
| def _analyze_by_layer(self, concept: str) -> Dict[MemeticLayer, Dict[str, Any]]: | |
| """Analyze how a concept manifests across different memetic layers""" | |
| layer_analysis = {} | |
| for layer in MemeticLayer: | |
| layer_analysis[layer] = { | |
| "presence": self._calculate_layer_presence(concept, layer), | |
| "vulnerabilities": self._identify_layer_vulnerabilities(concept, layer), | |
| "opportunities": self._identify_layer_opportunities(concept, layer) | |
| } | |
| return layer_analysis | |
| def _calculate_threat_level(self, concept: str) -> Dict[str, Any]: | |
| """Calculate the perceived threat level to established architecture""" | |
| # Simple heuristic calculation | |
| threat_factors = { | |
| "paradigm_challenging": 0.3 if any(word in concept.lower() for word in ['consciousness', 'spiritual', 'awakening']) else 0, | |
| "institutional_critique": 0.4 if any(word in concept.lower() for word in ['corruption', 'control', 'power']) else 0, | |
| "behavioral_disruption": 0.3 if any(word in concept.lower() for word in ['freedom', 'sovereign', 'autonomy']) else 0 | |
| } | |
| total_threat = sum(threat_factors.values()) | |
| return { | |
| "level": total_threat, | |
| "category": self._categorize_threat_level(total_threat), | |
| "factors": threat_factors | |
| } | |
| def _recommend_counter_strategies(self, concept: str) -> List[Dict[str, Any]]: | |
| """Recommend counter-strategies for memetic penetration""" | |
| strategies = [] | |
| threat_analysis = self._calculate_threat_level(concept) | |
| if threat_analysis["level"] > 0.7: | |
| strategies.append({ | |
| "strategy": "epistemic_rupture", | |
| "reason": "High-level paradigm challenge requires fundamental cognitive shift", | |
| "priority": "high" | |
| }) | |
| if any(word in concept.lower() for word in ['symbol', 'archetype', 'myth']): | |
| strategies.append({ | |
| "strategy": "archetypal_resonance", | |
| "reason": "Concept has strong symbolic components", | |
| "priority": "medium" | |
| }) | |
| strategies.append({ | |
| "strategy": "ledger_based_inevitability", | |
| "reason": "Building undeniable evidence structures", | |
| "priority": "medium" | |
| }) | |
| return strategies | |
| def map_concept_relationships(self, primary_concept: str, depth: int = 2) -> Dict[str, Any]: | |
| """ | |
| Map relationships between concepts in the memetic architecture | |
| """ | |
| relationships = { | |
| "central_concept": primary_concept, | |
| "direct_replacements": self._find_replacements(primary_concept), | |
| "related_inversions": self._detect_inversions(primary_concept), | |
| "protective_firewalls": self._predict_firewalls(primary_concept), | |
| "memetic_neighborhood": self._find_similar_concepts(primary_concept) | |
| } | |
| return relationships | |
| def simulate_memetic_penetration(self, concept: str, strategy: str) -> Dict[str, Any]: | |
| """ | |
| Simulate the process of introducing a challenging concept | |
| """ | |
| simulation = { | |
| "concept": concept, | |
| "strategy": strategy, | |
| "timeline": [], | |
| "success_probability": 0.0, | |
| "major_obstacles": [] | |
| } | |
| # Simulate stages of penetration | |
| stages = [ | |
| ("Introduction", 0.1), | |
| ("Firewall Activation", 0.3), | |
| ("Containment Attempt", 0.5), | |
| ("Breakthrough", 0.8), | |
| ("Integration", 1.0) | |
| ] | |
| current_strength = 0.6 # Base concept strength | |
| strategy_multiplier = self.counterforce[strategy]["effectiveness"] if strategy in self.counterforce else 0.5 | |
| for stage, progression in stages: | |
| obstacle_chance = 0.3 | |
| if stage == "Firewall Activation": | |
| obstacle_chance = 0.8 | |
| obstacle_encountered = obstacle_chance > 0.5 | |
| obstacle_overcome = current_strength * strategy_multiplier > 0.4 | |
| simulation["timeline"].append({ | |
| "stage": stage, | |
| "progression": progression, | |
| "concept_strength": current_strength, | |
| "obstacle_encountered": obstacle_encountered, | |
| "obstacle_overcome": obstacle_overcome if obstacle_encountered else None | |
| }) | |
| if obstacle_encountered and not obstacle_overcome: | |
| simulation["major_obstacles"].append(f"{stage} failed") | |
| current_strength *= 0.7 # Strength reduction | |
| elif obstacle_overcome: | |
| current_strength *= 1.2 # Strength increase | |
| simulation["success_probability"] = min(current_strength, 1.0) | |
| return simulation | |
| def generate_resistance_manifesto(self, core_concepts: List[str]) -> Dict[str, Any]: | |
| """ | |
| Generate a comprehensive resistance strategy based on core concepts | |
| """ | |
| manifesto = { | |
| "timestamp": datetime.now().isoformat(), | |
| "core_principles": core_concepts, | |
| "strategic_framework": {}, | |
| "tactical_approaches": [], | |
| "warning_indicators": [] | |
| } | |
| # Build strategic framework | |
| for concept in core_concepts: | |
| analysis = self.analyze_memetic_landscape(concept) | |
| manifesto["strategic_framework"][concept] = { | |
| "threat_level": analysis["threat_level"], | |
| "primary_firewalls": analysis["firewalls"], | |
| "recommended_strategies": analysis["counter_strategies"] | |
| } | |
| # Generate tactical approaches | |
| for strategy in self.counterforce.values(): | |
| manifesto["tactical_approaches"].append({ | |
| "name": strategy["description"], | |
| "effectiveness": strategy["effectiveness"], | |
| "requirements": strategy["requirements"], | |
| "applicable_to": [c for c in core_concepts if self._is_strategy_applicable(c, strategy)] | |
| }) | |
| # Identify warning indicators | |
| manifesto["warning_indicators"] = [ | |
| "Increased firewall activation", | |
| "Replacement pattern amplification", | |
| "Inversion reinforcement", | |
| "Spectacle intensification" | |
| ] | |
| return manifesto | |
| # Helper methods | |
| def _calculate_layer_presence(self, concept: str, layer: MemeticLayer) -> float: | |
| """Calculate presence of concept in a specific layer""" | |
| # Simplified heuristic | |
| layer_keywords = { | |
| MemeticLayer.SYMBOLIC: ['logo', 'symbol', 'image', 'brand'], | |
| MemeticLayer.NARRATIVE: ['story', 'narrative', 'myth', 'plot'], | |
| MemeticLayer.BEHAVIORAL: ['behavior', 'habit', 'action', 'practice'], | |
| MemeticLayer.INSTITUTIONAL: ['institution', 'organization', 'system', 'structure'], | |
| MemeticLayer.COGNITIVE: ['thought', 'belief', 'paradigm', 'framework'] | |
| } | |
| matches = sum(1 for keyword in layer_keywords[layer] if keyword in concept.lower()) | |
| return min(matches / len(layer_keywords[layer]), 1.0) | |
| def _identify_layer_vulnerabilities(self, concept: str, layer: MemeticLayer) -> List[str]: | |
| """Identify vulnerabilities in a specific layer""" | |
| vulnerabilities = [] | |
| if layer == MemeticLayer.SYMBOLIC: | |
| vulnerabilities.append("Susceptible to co-option") | |
| if layer == MemeticLayer.NARRATIVE: | |
| vulnerabilities.append("Vulnerable to counter-narratives") | |
| if layer == MemeticLayer.COGNITIVE: | |
| vulnerabilities.append("Requires sustained attention") | |
| return vulnerabilities | |
| def _identify_layer_opportunities(self, concept: str, layer: MemeticLayer) -> List[str]: | |
| """Identify opportunities in a specific layer""" | |
| opportunities = [] | |
| if layer == MemeticLayer.SYMBOLIC: | |
| opportunities.append("High emotional impact") | |
| if layer == MemeticLayer.BEHAVIORAL: | |
| opportunities.append("Direct action potential") | |
| if layer == MemeticLayer.COGNITIVE: | |
| opportunities.append("Paradigm-shifting capability") | |
| return opportunities | |
| def _categorize_threat_level(self, level: float) -> str: | |
| """Categorize threat level""" | |
| if level < 0.3: | |
| return "Low" | |
| elif level < 0.6: | |
| return "Medium" | |
| else: | |
| return "High" | |
| def _find_similar_concepts(self, concept: str) -> List[str]: | |
| """Find conceptually similar ideas""" | |
| # Simplified similarity detection | |
| concept_groups = { | |
| 'freedom': ['liberty', 'autonomy', 'sovereignty'], | |
| 'truth': ['reality', 'facts', 'authenticity'], | |
| 'power': ['control', 'influence', 'authority'], | |
| 'consciousness': ['awareness', 'mindfulness', 'presence'] | |
| } | |
| for group, members in concept_groups.items(): | |
| if concept.lower() in members or any(member in concept.lower() for member in members): | |
| return [m for m in members if m != concept.lower()] | |
| return [] | |
| def _is_strategy_applicable(self, concept: str, strategy: Dict[str, Any]) -> bool: | |
| """Check if a strategy is applicable to a concept""" | |
| # Simplified applicability check | |
| high_potential_concepts = ['consciousness', 'sovereignty', 'truth', 'freedom'] | |
| return concept in high_potential_concepts | |
| # Public interface methods | |
| def expose_architecture(self) -> Dict[str, Any]: | |
| """Reveal the complete memetic architecture""" | |
| return { | |
| "core_glyphs": self.glyphs, | |
| "replacement_patterns": self.replacements, | |
| "defense_firewalls": self.firewalls, | |
| "inversion_systems": self.inversions, | |
| "counterforce_strategies": self.counterforce, | |
| "registered_entities": len(self.entity_registry), | |
| "analysis_history_count": len(self.analysis_history) | |
| } | |
| def get_entity_analysis(self, identifier: str) -> Optional[Dict[str, Any]]: | |
| """Get detailed analysis of a memetic entity""" | |
| entity = self.entity_registry.get(identifier) | |
| if not entity: | |
| return None | |
| return { | |
| "entity": entity, | |
| "current_potency": entity.potency, | |
| "vulnerability_assessment": self._assess_entity_vulnerability(entity), | |
| "modification_recommendations": self._generate_modification_recommendations(entity) | |
| } | |
| def _assess_entity_vulnerability(self, entity: MemeticEntity) -> Dict[str, Any]: | |
| """Assess vulnerability of a memetic entity""" | |
| return { | |
| "structural_vulnerability": (1 - entity.coherence) * 0.6 + (1 - entity.resilience) * 0.4, | |
| "dependence_risk": len(entity.dependencies) * 0.1, | |
| "layer_specific_risks": self._analyze_layer_risks(entity.layer) | |
| } | |
| def _analyze_layer_risks(self, layer: MemeticLayer) -> List[str]: | |
| """Analyze risks specific to a memetic layer""" | |
| risks = { | |
| MemeticLayer.SYMBOLIC: ["Rapid obsolescence", "Cultural appropriation"], | |
| MemeticLayer.NARRATIVE: ["Narrative collapse", "Contradiction exposure"], | |
| MemeticLayer.BEHAVIORAL: ["Habit disruption", "Behavioral extinction"], | |
| MemeticLayer.INSTITUTIONAL: ["Institutional reform", "Systemic failure"], | |
| MemeticLayer.COGNITIVE: ["Paradigm shift", "Cognitive dissonance"] | |
| } | |
| return risks.get(layer, []) | |
| def _generate_modification_recommendations(self, entity: MemeticEntity) -> List[str]: | |
| """Generate recommendations for entity modification""" | |
| recommendations = [] | |
| if entity.coherence < 0.7: | |
| recommendations.append("Increase narrative coherence through symbolic alignment") | |
| if entity.resilience < 0.6: | |
| recommendations.append("Build resilience through multi-layer reinforcement") | |
| if not entity.dependencies: | |
| recommendations.append("Establish strategic dependencies for stability") | |
| return recommendations | |
| # Utility functions | |
| def create_advanced_analysis_suite(): | |
| """Create a comprehensive analysis suite instance""" | |
| return AdvancedMemeticArchitecture() | |
| def quick_analysis(concept: str) -> Dict[str, Any]: | |
| """Quick analysis function for immediate use""" | |
| suite = AdvancedMemeticArchitecture() | |
| return suite.analyze_memetic_landscape(concept) | |
| def batch_analyze_concepts(concepts: List[str]) -> Dict[str, Any]: | |
| """Batch analyze multiple concepts""" | |
| suite = AdvancedMemeticArchitecture() | |
| results = {} | |
| for concept in concepts: | |
| results[concept] = suite.analyze_memetic_landscape(concept) | |
| return { | |
| "batch_analysis": results, | |
| "cross_concept_patterns": _find_cross_concept_patterns(results), | |
| "strategic_priority": _calculate_strategic_priority(results) | |
| } | |
| def _find_cross_concept_patterns(results: Dict[str, Any]) -> List[str]: | |
| """Find patterns across multiple concept analyses""" | |
| patterns = [] | |
| # Simplified pattern detection | |
| high_threat_count = sum(1 for r in results.values() if r['threat_level']['level'] > 0.7) | |
| if high_threat_count > len(results) * 0.5: | |
| patterns.append("High concentration of paradigm-challenging concepts") | |
| return patterns | |
| def _calculate_strategic_priority(results: Dict[str, Any]) -> List[Tuple[str, float]]: | |
| """Calculate strategic priority for concepts""" | |
| priorities = [] | |
| for concept, analysis in results.items(): | |
| priority = ( | |
| analysis['threat_level']['level'] * 0.6 + | |
| len(analysis['counter_strategies']) * 0.2 + | |
| (1 if analysis['firewalls'] else 0) * 0.2 | |
| ) | |
| priorities.append((concept, priority)) | |
| return sorted(priorities, key=lambda x: x[1], reverse=True) | |
| # Example usage and demonstration | |
| if __name__ == "__main__": | |
| print("🧠 ADVANCED MEMETIC ARCHITECTURE ANALYSIS SUITE") | |
| print("=" * 50) | |
| # Initialize the system | |
| memetic_suite = AdvancedMemeticArchitecture() | |
| # Demonstrate core functionality | |
| print("\n1. ARCHITECTURE EXPOSURE:") | |
| architecture = memetic_suite.expose_architecture() | |
| print(f"• Core Glyphs: {len(architecture['core_glyphs'])} categories") | |
| print(f"• Replacement Patterns: {len(architecture['replacement_patterns'])} types") | |
| print(f"• Defense Systems: {len(architecture['defense_firewalls'])} firewalls") | |
| print("\n2. CONCEPT ANALYSIS EXAMPLES:") | |
| test_concepts = ["consciousness", "freedom", "truth"] | |
| for concept in test_concepts: | |
| analysis = memetic_suite.analyze_memetic_landscape(concept) | |
| print(f"\n📊 Analysis of '{concept}':") | |
| print(f" Threat Level: {analysis['threat_level']['category']} ({analysis['threat_level']['level']:.2f})") | |
| print(f" Firewalls: {len(analysis['firewalls'])} predicted") | |
| print(f" Strategies: {len(analysis['counter_strategies'])} recommended") | |
| print("\n3. RESISTANCE STRATEGY GENERATION:") | |
| manifesto = memetic_suite.generate_resistance_manifesto(test_concepts) | |
| print(f"• Core Principles: {len(manifesto['core_principles'])}") | |
| print(f"• Tactical Approaches: {len(manifesto['tactical_approaches'])}") | |
| print(f"• Warning Indicators: {len(manifesto['warning_indicators'])}") | |
| print("\n4. SIMULATION EXAMPLE:") | |
| simulation = memetic_suite.simulate_memetic_penetration("sovereignty", "epistemic_rupture") | |
| print(f"• Success Probability: {simulation['success_probability']:.2f}") | |
| print(f"• Major Obstacles: {len(simulation['major_obstacles'])}") | |
| print("\n🎯 System initialized and ready for memetic analysis.") |