#!/usr/bin/env python3 # ============================================================================== # COPYRIGHT NO ONE EVERYWHERE LLC (WYOMING HOLDING COMPANY) # PROJECT: SOVEREIGN STACK # This artifact is entirely proprietary and cryptographically proven. # Open-Source usage requires explicit permission from Brandon Scott Schneider. # ============================================================================== """Cognitive Mirror — predict the next concept your brain would go to, using the same sym_idx + feature_byte mechanics as the Hutter v3 oracle, but run over substrate_index.db's semantic vectors. The hits = your natural cognitive flow. The misses = connections you can't see on your own.""" import sqlite3 import json import math from collections import defaultdict import sys DB = "substrate_index.db" # ── Cognitive feature byte: same 8-bit concept but for ideas, not bytes ── # Each concept in the database is classified by: # bit 0: ACTIVE (forming_load > 1.0 = still hot) # bit 1: CRYSTALLIZED (tier in SETTLED/COMPRESSED = crystallized) # bit 2: CONNECTED (has deps on other packages) # bit 3: DEEP (load > 2.0 = high cognitive pressure) # bit 4: HYPOTHESIS (concept_anchor says seed/forming) # bit 5: BRIDGE (spans multiple domains via concept_vector similarity) # bit 6: RECENT (indexed in last N commits/packages) # bit 7: CORE (domain is COMPUTE or substrate) def concept_feature(pkg): """Compute 8-bit cognitive feature byte for a package.""" fb = 0 fl = pkg.get('forming_load') or 0 tier = (pkg.get('tier') or '').upper() if fl > 1.0: fb |= 0b00000001 # ACTIVE if tier in ('FOAM', 'CRYSTALLINE', 'SINGULARITY'): fb |= 0b00000010 # CRYSTALLIZED if pkg.get('dependencies'): fb |= 0b00000100 # CONNECTED if fl > 2.0: fb |= 0b00001000 # DEEP anchor = pkg.get('concept_anchor') or '' if 'SEED' in anchor or 'FORMING' in anchor: fb |= 0b00010000 # HYPOTHESIS if pkg.get('concept_vector'): fb |= 0b00100000 # BRIDGE idx = pkg.get('_index', 0) if idx > len(_all) - 50: fb |= 0b01000000 # RECENT dom = pkg.get('domain', '').upper() if dom in ('COMPUTE', 'SUBSTRATE'): fb |= 0b10000000 # CORE return fb # ── Triangular pairing (same as mirror.rs) ── def sym_idx(p, q): lo = min(p, q) hi = max(p, q) return hi * (hi + 1) // 2 + lo # ── Build the LUT from the corpus ── def build_lut(packages): """Build cognitive mirror LUT: for each (prev_fb, curr_fb) pair, store the most frequent next_fb.""" counts = defaultdict(lambda: defaultdict(int)) for i in range(2, len(packages)): prev = concept_feature(packages[i-2]) curr = concept_feature(packages[i-1]) nxt = concept_feature(packages[i]) addr = sym_idx(prev, curr) counts[addr][nxt] += 1 # Freeze: pick most common next lut = {} for addr, next_counts in counts.items(): best = max(next_counts.items(), key=lambda x: x[1])[0] total = sum(next_counts.values()) confidence = next_counts[best] / total # Also get second best for "what you're NOT thinking" sorted_nexts = sorted(next_counts.items(), key=lambda x: -x[1]) second = sorted_nexts[1][0] if len(sorted_nexts) > 1 else None second_prob = sorted_nexts[1][1] / total if len(sorted_nexts) > 1 else 0 lut[addr] = (best, second, confidence, second_prob) return lut def predict_next(prev_pkg, curr_pkg, _all): """Predict what concept comes next after the current trajectory.""" prev_fb = concept_feature(prev_pkg) curr_fb = concept_feature(curr_pkg) addr = sym_idx(prev_fb, curr_fb) return addr # ── Decode: find packages that match a predicted feature byte ── def decode_prediction(target_fb, packages, current_pkgs): """Find packages whose feature byte matches the prediction, but that aren't in the current cognitive trajectory.""" matches = [] for p in packages: if p['pkg'] in current_pkgs: continue pfb = concept_feature(p) if pfb == target_fb: matches.append(p) return sorted(matches, key=lambda x: -(x.get('forming_load') or 0)) # ── Main frack ── _all = [] def main(): global _all conn = sqlite3.connect(DB) cur = conn.cursor() # Get all packages with semantic data, ordered by forming_load (hot first) cur.execute(""" SELECT pkg, version, domain, concept_anchor, concept_vector, idea_weights, forming_load, confirmed_load, layer, tier, tags, description FROM packages WHERE concept_vector IS NOT NULL OR concept_anchor IS NOT NULL ORDER BY forming_load DESC """) rows = cur.fetchall() _all = [{ 'pkg': r[0], 'version': r[1], 'domain': r[2], 'concept_anchor': r[3], 'concept_vector': json.loads(r[4]) if r[4] else [], 'idea_weights': json.loads(r[5]) if r[5] else {}, 'forming_load': r[6], 'confirmed_load': r[7], 'layer': r[8], 'tier': r[9], 'tags': json.loads(r[10]) if r[10] else [], 'description': (r[11] or '')[:200], } for r in rows] # Index them for i, p in enumerate(_all): p['_index'] = i print("=" * 70) print("COGNITIVE MIRROR — predicting your next concept") print("=" * 70) # Build LUT lut = build_lut(_all) print(f"\nLUT size: {len(lut)} unique transitions learned") # Run predictions: for each pair of consecutive hot concepts, # predict what comes next predictions = [] for i in range(2, len(_all)): prev = _all[i-2] curr = _all[i-1] actual = _all[i] addr = predict_next(prev, curr, _all) if addr in lut: predicted_fb, second_fb, confidence, second_prob = lut[addr] actual_fb = concept_feature(actual) hit = (predicted_fb == actual_fb) predictions.append({ 'prev': prev['pkg'], 'prev_fb': feature_name(concept_feature(prev)), 'curr': curr['pkg'], 'curr_fb': feature_name(concept_feature(curr)), 'predicted_fb': predicted_fb, 'second_fb': second_fb, 'actual': actual['pkg'], 'actual_fb': feature_name(actual_fb), 'hit': hit, 'confidence': confidence, }) hits = [p for p in predictions if p['hit']] misses = [p for p in predictions if not p['hit']] print(f"\n{'─' * 70}") print(f"PREDICTIONS: {len(predictions)} total") print(f" HITS: {len(hits):4d} ({len(hits)/max(len(predictions),1)*100:.1f}%) — natural cognitive flow") print(f" MISSES: {len(misses):4d} ({len(misses)/max(len(predictions),1)*100:.1f}%) — connections you can't see") # The misses are the interesting part if misses: print(f"\n{'─' * 70}") print(f"BLIND SPOTS (misses) — connections you're NOT seeing") print(f"{'─' * 70}") for m in misses[:15]: print(f"\n After: {m['prev']} → {m['curr']}") print(f" Your brain went: {m['actual']}") print(f" Mirror predicted: {feature_name(m['predicted_fb'])} " f"(confidence {m['confidence']:.1%})") # Find what packages have that predicted feature predicted_pkgs = decode_prediction( m['predicted_fb'], _all, {m['prev'], m['curr'], m['actual']} ) if predicted_pkgs: print(f" You should also look at:") for pkg in predicted_pkgs[:3]: print(f" → {pkg['pkg']} (load={pkg.get('forming_load', 0):.3f}, " f"domain={pkg['domain']})") # The hits show your natural flow if hits[:5]: print(f"\n{'─' * 70}") print(f"NATURAL FLOW (hits) — where your brain naturally goes") print(f"{'─' * 70}") for h in hits[:10]: print(f" {h['prev']} → {h['curr']} → {h['actual']} " f"(predicted {h['actual_fb']}, confidence {h['confidence']:.1%})") conn.close() def feature_name(fb): """Decode an 8-bit cognitive feature byte to readable name.""" parts = [] if fb & 0b10000000: parts.append('CORE') if fb & 0b01000000: parts.append('RECENT') if fb & 0b00100000: parts.append('BRIDGE') if fb & 0b00010000: parts.append('HYPOTHESIS') if fb & 0b00001000: parts.append('DEEP') if fb & 0b00000100: parts.append('CONNECTED') if fb & 0b00000010: parts.append('CRYSTALLIZED') if fb & 0b00000001: parts.append('ACTIVE') return '+'.join(parts) if parts else 'NULL' if __name__ == "__main__": main()