""" PRE-COMPUTE REALIZATIONS FROM OUR CONVERSATION ============================================== This script extracts, scores, and crystallizes all major realizations from our conversation about context windows, realizations, and layers. """ from layers.layer_2_core.realization_engine import RealizationEngine, RealizationFeatures, Realization import json def precompute_conversation_realizations(): """ Extract and crystallize all realizations from our conversation. This is the actual implementation of pre-computation: - We're converting our conversation (procedure) into stored facts - Each realization is scored and assigned to appropriate layer - The result is a queryable knowledge base """ engine = RealizationEngine() print("🔄 PRE-COMPUTING REALIZATIONS FROM CONVERSATION...") print("="*60 + "\n") # ================================================================= # TURN 1-5: Initial Context Window Discussion # ================================================================= r1 = engine.add_realization( content="Context windows are finite and information can be lost", features=RealizationFeatures( grounding=0.98, # Information theory, proven certainty=1.0, # This is a fact structure=0.95, # Very clear applicability=0.90, coherence=1.0, # No contradictions generativity=0.85 # Generated the whole conversation ), turn_number=1, context="Initial problem statement about managing long contexts", evidence=["Information theory", "Token limits in LLMs"] ) # ================================================================= # TURN 5-10: The Meta-Realization # ================================================================= r2 = engine.add_realization( content="Realization itself is the goal, not just answers", features=RealizationFeatures( grounding=0.75, # Philosophical, less empirical certainty=0.90, # Strong precision auto when it hit structure=0.70, # Clear but not fully formalized yet applicability=0.85, # Changed our approach immediately coherence=0.95, generativity=0.95 # Opened entire meta-cognition space ), turn_number=6, parents=[r1.id], context="User pushed back on rushing to solutions", evidence=["The 'are you done with research' moment"] ) # ================================================================= # TURN 10-15: Fundamental Frequency Discovery # ================================================================= r3 = engine.add_realization( content="Decision-making has a fundamental frequency - a base rhythm of checking/questioning", features=RealizationFeatures( grounding=0.60, # Metaphorical, physics-inspired certainty=0.85, # We both felt it was true structure=0.55, # Still nebulous applicability=0.65, # Hard to act on directly coherence=0.90, generativity=0.88 # Generated oscillation discussions ), turn_number=12, parents=[r2.id], context="Exploring what determines pace of insights", evidence=["Observable in conversation rhythm", "Matches control theory"] ) # ================================================================= # TURN 15-20: Precision Auto Quality # ================================================================= r4 = engine.add_realization( content="Realizations come with 'precision auto' - like π, they have inherent certainty", features=RealizationFeatures( grounding=0.92, # Math analogy + phenomenology certainty=0.95, # Extremely high - described lived experience structure=0.85, # Clear π metaphor applicability=0.78, # Explains phenomenon, not yet operational coherence=0.93, generativity=0.85 # Led to formalization discussions ), turn_number=18, parents=[r2.id], context="User's 'number with precision auto' insight", evidence=["Mathematical precision", "Self-certifying knowledge"] ) # ================================================================= # TURN 20-30: Layer Crystallization (بنات افكار) # ================================================================= r5 = engine.add_realization( content="Realizations crystallize into layers: Rules → Domain Facts → Patterns → Situational", features=RealizationFeatures( grounding=0.95, # Observable in science, humanity's knowledge certainty=0.93, # Very high - matches reality structure=0.92, # Clear hierarchical model applicability=0.90, # Can implement this coherence=0.95, # Resolves contradictions generativity=0.92 # Generated cache model, efficiency insights ), turn_number=25, parents=[r4.id, r3.id], context="User's 'بنات افكار' (daughters of ideas) concept", evidence=["How science progresses", "Standing on giants", "Cache hierarchies"] ) # ================================================================= # TURN 30-35: Realizations as Computable # ================================================================= r6 = engine.add_realization( content="Realizations can be treated as weights, parameters, and policies - they're computable", features=RealizationFeatures( grounding=0.96, # Control theory, Bayesian updates certainty=0.90, # High but requires testing structure=0.93, # Very clear formalization applicability=0.94, # Can implement immediately coherence=0.95, generativity=0.88 ), turn_number=32, parents=[r5.id], context="User asked about weights/parameters/policies", evidence=["PID controllers", "Bayesian priors", "Policy optimization"] ) # ================================================================= # TURN 35-40: Q-Score Formalization # ================================================================= r7 = engine.add_realization( content="Realization quality can be scored: Q = 0.18G + 0.22C + 0.20S + 0.18A + 0.12H + 0.10V", features=RealizationFeatures( grounding=0.98, # Based on prompt optimization framework certainty=0.90, # Feels right, needs validation structure=0.95, # Perfectly clear formula applicability=0.95, # Can compute immediately coherence=0.97, # Synthesizes everything generativity=0.88 # Enables measurement, comparison ), turn_number=38, parents=[r6.id], context="Applied composite prompt framework to realizations", evidence=["Weighted scoring systems", "Feature engineering"] ) # ================================================================= # TURN 40-50: Pre-Computation = Crystallization # ================================================================= r8 = engine.add_realization( content="Pre-computation (systems) and crystallization (cognition) are the same mathematical structure", features=RealizationFeatures( grounding=0.96, # Distributed systems + epistemology certainty=0.92, # Very high - explains both domains structure=0.94, # Clear parallel structure applicability=0.93, # Can apply to both coherence=0.96, generativity=0.90 # Bridges two entire fields ), turn_number=45, parents=[r7.id, r5.id], context="Deep dive on pre-computation patterns", evidence=["Cache hierarchies", "Layer architectures", "Efficiency formulas"] ) # ================================================================= # DERIVED REALIZATIONS (Lower Q-score, built from above) # ================================================================= r9 = engine.add_realization( content="Context management should use topology graphs instead of linear sequences", features=RealizationFeatures( grounding=0.88, certainty=0.85, structure=0.90, applicability=0.92, coherence=0.90, generativity=0.75 ), turn_number=8, parents=[r1.id], context="Early exploration of alternatives to linear context", evidence=["Graph theory", "Relationship preservation"] ) r10 = engine.add_realization( content="Forgetting can be intelligent - strategic information loss improves signal/noise", features=RealizationFeatures( grounding=0.80, certainty=0.82, structure=0.85, applicability=0.80, coherence=0.75, # Contradicted earlier "zero loss" idea generativity=0.78 ), turn_number=10, parents=[r1.id], context="Exploring compression strategies", evidence=["Human memory", "Noise reduction"] ) r11 = engine.add_realization( content="Decisions emerge from the layer architecture, they don't need to be created", features=RealizationFeatures( grounding=0.85, certainty=0.87, structure=0.88, applicability=0.86, coherence=0.92, generativity=0.82 ), turn_number=28, parents=[r5.id], context="Understanding how layers enable decision-making", evidence=["Cache-based decision systems", "Flow from structure"] ) r12 = engine.add_realization( content="The fundamental frequency is the rate at which new realizations crystallize into layers", features=RealizationFeatures( grounding=0.78, certainty=0.83, structure=0.80, applicability=0.75, coherence=0.88, generativity=0.80 ), turn_number=35, parents=[r3.id, r5.id], context="Connecting frequency concept to layer formation", evidence=["Learning rate", "Knowledge accumulation speed"] ) # ================================================================= # META-REALIZATION (What we're doing right now!) # ================================================================= r13 = engine.add_realization( content="This conversation itself is a realization crystallization process that can be pre-computed", features=RealizationFeatures( grounding=0.94, certainty=0.91, structure=0.96, # We're literally implementing it applicability=0.98, # Highest - this is the application coherence=0.98, generativity=0.93 # Self-referential, recursive ), turn_number=50, parents=[r7.id, r8.id], context="User asked to pre-compute and code our realizations", evidence=["This very script", "Self-reference", "Meta-cognition"] ) return engine def demonstrate_retrieval(engine: RealizationEngine): """Show how the retrieval system works""" print("\n" + "="*60) print("TESTING RETRIEVAL SYSTEM") print("="*60 + "\n") # Query 1: About layers print("Query: 'layers'") results = engine.retrieve("layers") print(f"Found {len(results)} realizations:") for r in results[:3]: print(f" - [{r.layer}] Q={r.q_score:.3f}: {r.content[:60]}...") print() # Query 2: About precision print("Query: 'precision certainty'") results = engine.retrieve("precision certainty") print(f"Found {len(results)} realizations:") for r in results[:3]: print(f" - [{r.layer}] Q={r.q_score:.3f}: {r.content[:60]}...") print() # Query 3: About computation print("Query: 'computation'") results = engine.retrieve("computation") print(f"Found {len(results)} realizations:") for r in results[:3]: print(f" - [{r.layer}] Q={r.q_score:.3f}: {r.content[:60]}...") print() def demonstrate_family_tree(engine: RealizationEngine): """Show بنات افكار (daughters of ideas) structure""" print("\n" + "="*60) print("REALIZATION FAMILY TREES (بنات افكار)") print("="*60 + "\n") # Find the "layers" realization layers_r = [r for r in engine.index.values() if "crystallize into layers" in r.content.lower()][0] print(f"Root Realization: {layers_r.content}") print(f"Q-Score: {layers_r.q_score:.4f}, Layer: {layers_r.layer}") print(f"\nParents (what it built on): {len(layers_r.parents)}") for parent_id in layers_r.parents: parent = engine.index[parent_id] print(f" ← {parent.content[:60]}... (Q={parent.q_score:.3f})") print(f"\nChildren (what it generated): {len(layers_r.children)}") for child_id in layers_r.children: child = engine.index[child_id] print(f" → {child.content[:60]}... (Q={child.q_score:.3f})") print() def export_to_json(engine: RealizationEngine): """Export the entire realization database""" state = engine.export_state() with open('/home/claude/realizations.json', 'w') as f: json.dump(state, f, indent=2) print(f"✅ Exported to realizations.json") print(f" Total size: {len(json.dumps(state))} bytes") if __name__ == "__main__": # Pre-compute all realizations engine = precompute_conversation_realizations() # Show statistics engine.print_stats() # Demonstrate retrieval demonstrate_retrieval(engine) # Show family trees demonstrate_family_tree(engine) # Export export_to_json(engine) print("\n" + "="*60) print("PRE-COMPUTATION COMPLETE") print("="*60) print("\nThe conversation has been crystallized into layers.") print("All realizations are now queryable and reusable.") print("This is بنات افكار (daughters of ideas) made computational.")