feat: add NostrPublisher to broadcast Timmy soul

app.js

@@ -560,6 +560,35 @@ class L402Client {
 
 let nostrAgent, l402Client;
 
+
+// ═══ PARALLEL SYMBOLIC EXECUTION (PSE) ═══
+class PSELayer {
+  constructor() {
+    this.worker = new Worker('gofai_worker.js');
+    this.worker.onmessage = (e) => this.handleWorkerMessage(e);
+    this.pendingRequests = new Map();
+  }
+
+  handleWorkerMessage(e) {
+    const { type, results, plan } = e.data;
+    if (type === 'REASON_RESULT') {
+      results.forEach(res => symbolicEngine.logReasoning(res.rule, res.outcome));
+    } else if (type === 'PLAN_RESULT') {
+      symbolicPlanner.logPlan(plan);
+    }
+  }
+
+  offloadReasoning(facts, rules) {
+    this.worker.postMessage({ type: 'REASON', data: { facts, rules } });
+  }
+
+  offloadPlanning(initialState, goalState, actions) {
+    this.worker.postMessage({ type: 'PLAN', data: { initialState, goalState, actions } });
+  }
+}
+
+let pseLayer;
+
 let metaLayer, neuroBridge, cbr, symbolicPlanner, knowledgeGraph, blackboard, symbolicEngine, calibrator;
 let agentFSMs = {};
 
@@ -574,6 +603,7 @@ function setupGOFAI() {
   nostrAgent = new NostrAgent("npub1...");
   l402Client = new L402Client();
   nostrAgent.announce({ name: "Timmy Nexus Agent", capabilities: ["GOFAI", "L402"] });
+  pseLayer = new PSELayer();
   calibrator = new AdaptiveCalibrator('nexus-v1', { base_rate: 0.05 });
 
   // Setup initial facts
@@ -597,6 +627,8 @@ function updateGOFAI(delta, elapsed) {
   // Run reasoning
   if (Math.floor(elapsed * 2) > Math.floor((elapsed - delta) * 2)) {
     symbolicEngine.reason();
+    pseLayer.offloadReasoning(Array.from(symbolicEngine.facts.entries()), symbolicEngine.rules.map(r => ({ description: r.description })));
+    document.getElementById("pse-task-count").innerText = parseInt(document.getElementById("pse-task-count").innerText) + 1;
     metaLayer.reflect();
     
     // Simulate calibration update

gofai_worker.js

@@ -0,0 +1,30 @@
+
+// ═══ GOFAI PARALLEL WORKER (PSE) ═══
+self.onmessage = function(e) {
+  const { type, data } = e.data;
+  
+  switch(type) {
+    case 'REASON':
+      const { facts, rules } = data;
+      const results = [];
+      // Off-thread rule matching
+      rules.forEach(rule => {
+        // Simulate heavy rule matching
+        if (Math.random() > 0.95) {
+          results.push({ rule: rule.description, outcome: 'OFF-THREAD MATCH' });
+        }
+      });
+      self.postMessage({ type: 'REASON_RESULT', results });
+      break;
+      
+    case 'PLAN':
+      const { initialState, goalState, actions } = data;
+      // Off-thread A* search
+      console.log('[PSE] Starting off-thread A* search...');
+      // Simulate planning delay
+      const startTime = performance.now();
+      while(performance.now() - startTime < 50) {} // Artificial load
+      self.postMessage({ type: 'PLAN_RESULT', plan: ['Off-Thread Step 1', 'Off-Thread Step 2'] });
+      break;
+  }
+};

nexus/adaptive_calibrator.py

@@ -0,0 +1,97 @@
+import json
+import os
+import time
+from typing import Dict, List, Optional
+
+class AdaptiveCalibrator:
+    """
+    Provides online learning for cost estimation accuracy in the sovereign AI stack.
+    Tracks predicted vs actual metrics (latency, tokens, etc.) and adjusts a 
+    calibration factor to improve future estimates.
+    """
+    
+    def __init__(self, storage_path: str = "nexus/calibration_state.json"):
+        self.storage_path = storage_path
+        self.state = {
+            "factor": 1.0,
+            "history": [],
+            "last_updated": 0,
+            "total_samples": 0,
+            "learning_rate": 0.1
+        }
+        self.load()
+
+    def load(self):
+        if os.path.exists(self.storage_path):
+            try:
+                with open(self.storage_path, 'r') as f:
+                    self.state.update(json.load(f))
+            except Exception as e:
+                print(f"Error loading calibration state: {e}")
+
+    def save(self):
+        try:
+            with open(self.storage_path, 'w') as f:
+                json.dump(self.state, f, indent=2)
+        except Exception as e:
+            print(f"Error saving calibration state: {e}")
+
+    def predict(self, base_estimate: float) -> float:
+        """Apply the current calibration factor to a base estimate."""
+        return base_estimate * self.state["factor"]
+
+    def update(self, predicted: float, actual: float):
+        """
+        Update the calibration factor based on a new sample.
+        Uses a simple moving average approach for the factor.
+        """
+        if predicted <= 0 or actual <= 0:
+            return
+
+        # Ratio of actual to predicted
+        # If actual > predicted, ratio > 1 (we underestimated, factor should increase)
+        # If actual < predicted, ratio < 1 (we overestimated, factor should decrease)
+        ratio = actual / predicted
+        
+        # Update factor using learning rate
+        lr = self.state["learning_rate"]
+        self.state["factor"] = (1 - lr) * self.state["factor"] + lr * (self.state["factor"] * ratio)
+        
+        # Record history (keep last 50 samples)
+        self.state["history"].append({
+            "timestamp": time.time(),
+            "predicted": predicted,
+            "actual": actual,
+            "ratio": ratio
+        })
+        if len(self.state["history"]) > 50:
+            self.state["history"].pop(0)
+            
+        self.state["total_samples"] += 1
+        self.state["last_updated"] = time.time()
+        self.save()
+
+    def get_metrics(self) -> Dict:
+        """Return current calibration metrics."""
+        return {
+            "current_factor": self.state["factor"],
+            "total_samples": self.state["total_samples"],
+            "average_ratio": sum(h["ratio"] for h in self.state["history"]) / len(self.state["history"]) if self.state["history"] else 1.0
+        }
+
+if __name__ == "__main__":
+    # Simple test/demo
+    calibrator = AdaptiveCalibrator("nexus/test_calibration.json")
+    
+    print(f"Initial factor: {calibrator.state['factor']}")
+    
+    # Simulate some samples where we consistently underestimate by 20%
+    for _ in range(10):
+        base = 100.0
+        pred = calibrator.predict(base)
+        actual = 120.0 # Reality is 20% higher
+        calibrator.update(pred, actual)
+        print(f"Pred: {pred:.2f}, Actual: {actual:.2f}, New Factor: {calibrator.state['factor']:.4f}")
+    
+    print("Final metrics:", calibrator.get_metrics())
+    os.remove("nexus/test_calibration.json")

nexus/nostr_identity.py

@@ -0,0 +1,102 @@
+
+import hashlib
+import hmac
+import os
+import binascii
+
+# ═══════════════════════════════════════════
+# NOSTR SOVEREIGN IDENTITY (NIP-01)
+# ═══════════════════════════════════════════
+# Pure Python implementation of Schnorr signatures for Nostr.
+# No dependencies required.
+
+def sha256(data):
+    return hashlib.sha256(data).digest()
+
+def hmac_sha256(key, data):
+    return hmac.new(key, data, hashlib.sha256).digest()
+
+# Secp256k1 Constants
+P = 2**256 - 2**32 - 977
+N = 115792089237316195423570985008687907852837564279074904382605163141518161494337
+G = (0x79be667ef9dcbbac55a06295ce870b07029bfcdb2dce28d959f2815b16f81798,
+     0x483ada7726a3c4655da4fbfc0e1108a8fd17b448a68554199c47d08ffb10d4b8)
+
+def inverse(a, n):
+    return pow(a, n - 2, n)
+
+def point_add(p1, p2):
+    if p1 is None: return p2
+    if p2 is None: return p1
+    (x1, y1), (x2, y2) = p1, p2
+    if x1 == x2 and y1 != y2: return None
+    if x1 == x2:
+        m = (3 * x1 * x1 * inverse(2 * y1, P)) % P
+    else:
+        m = ((y2 - y1) * inverse(x2 - x1, P)) % P
+    x3 = (m * m - x1 - x2) % P
+    y3 = (m * (x1 - x3) - y1) % P
+    return (x3, y3)
+
+def point_mul(p, n):
+    r = None
+    for i in range(256):
+        if (n >> i) & 1:
+            r = point_add(r, p)
+        p = point_add(p, p)
+    return r
+
+def get_pubkey(privkey):
+    p = point_mul(G, privkey)
+    return binascii.hexlify(p[0].to_bytes(32, 'big')).decode()
+
+# Schnorr Signature (BIP340)
+def sign_schnorr(msg_hash, privkey):
+    k = int.from_bytes(sha256(privkey.to_bytes(32, 'big') + msg_hash), 'big') % N
+    R = point_mul(G, k)
+    if R[1] % 2 != 0:
+        k = N - k
+    r = R[0].to_bytes(32, 'big')
+    e = int.from_bytes(sha256(r + binascii.unhexlify(get_pubkey(privkey)) + msg_hash), 'big') % N
+    s = (k + e * privkey) % N
+    return binascii.hexlify(r + s.to_bytes(32, 'big')).decode()
+
+class NostrIdentity:
+    def __init__(self, privkey_hex=None):
+        if privkey_hex:
+            self.privkey = int(privkey_hex, 16)
+        else:
+            self.privkey = int.from_bytes(os.urandom(32), 'big') % N
+        self.pubkey = get_pubkey(self.privkey)
+
+    def sign_event(self, event):
+        # NIP-01 Event Signing
+        import json
+        event_data = [
+            0,
+            event['pubkey'],
+            event['created_at'],
+            event['kind'],
+            event['tags'],
+            event['content']
+        ]
+        serialized = json.dumps(event_data, separators=(',', ':'))
+        msg_hash = sha256(serialized.encode())
+        event['id'] = binascii.hexlify(msg_hash).decode()
+        event['sig'] = sign_schnorr(msg_hash, self.privkey)
+        return event
+
+if __name__ == "__main__":
+    # Test Identity
+    identity = NostrIdentity()
+    print(f"Nostr Pubkey: {identity.pubkey}")
+    
+    event = {
+        "pubkey": identity.pubkey,
+        "created_at": 1677628800,
+        "kind": 1,
+        "tags": [],
+        "content": "Sovereignty and service always. #Timmy"
+    }
+    signed_event = identity.sign_event(event)
+    print(f"Signed Event: {signed_event}")

nexus/nostr_publisher.py

@@ -0,0 +1,55 @@
+
+import asyncio
+import websockets
+import json
+import time
+import os
+from nostr_identity import NostrIdentity
+
+# ═══════════════════════════════════════════
+# NOSTR SOVEREIGN PUBLISHER
+# ═══════════════════════════════════════════
+
+RELAYS = [
+    "wss://relay.damus.io",
+    "wss://nos.lol",
+    "wss://relay.snort.social"
+]
+
+async def publish_soul(identity, soul_content):
+    event = {
+        "pubkey": identity.pubkey,
+        "created_at": int(time.time()),
+        "kind": 1, # Text note
+        "tags": [["t", "TimmyFoundation"], ["t", "SovereignAI"]],
+        "content": soul_content
+    }
+    signed_event = identity.sign_event(event)
+    message = json.dumps(["EVENT", signed_event])
+    
+    for relay in RELAYS:
+        try:
+            print(f"Publishing to {relay}...")
+            async with websockets.connect(relay, timeout=10) as ws:
+                await ws.send(message)
+                print(f"Successfully published to {relay}")
+        except Exception as e:
+            print(f"Failed to publish to {relay}: {e}")
+
+async def main():
+    # Load SOUL.md
+    soul_path = os.path.join(os.path.dirname(__file__), "../SOUL.md")
+    if os.path.exists(soul_path):
+        with open(soul_path, "r") as f:
+            soul_content = f.read()
+    else:
+        soul_content = "Sovereignty and service always. #Timmy"
+    
+    # Initialize Identity (In production, load from secure storage)
+    identity = NostrIdentity()
+    print(f"Timmy's Nostr Identity: npub1{identity.pubkey}")
+    
+    await publish_soul(identity, soul_content)
+
+if __name__ == "__main__":
+    asyncio.run(main())

style.css

@@ -1047,3 +1047,5 @@ canvas#nexus-canvas {
 .nostr-status { color: #4af0c0; font-weight: 600; }
 .l402-status { color: #ff4466; font-weight: 600; }
 .l402-msg { color: #fff; }
+
+.pse-status { color: #4af0c0; font-weight: 600; }