AOT: Fresh sovereign production architecture deployment

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.github/FUNDING.yml

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+# These are supported funding model platforms
+
+github: # [AnonymousNomad]
+patreon: # Replace with a single Patreon username
+open_collective: # Replace with a single Open Collective username
+ko_fi: # Replace with a single Ko-fi username
+tidelift: # Replace with a single Tidelift platform-name/package-name e.g., npm/babel
+community_bridge: # Replace with a single Community Bridge project-name e.g., cloud-foundry
+liberapay: # Replace with a single Liberapay username
+issuehunt: # Replace with a single IssueHunt username
+lfx_crowdfunding: # Replace with a single LFX Crowdfunding project-name e.g., cloud-foundry
+polar: # Replace with a single Polar username
+buy_me_a_coffee: # Replace with a single Buy Me a Coffee username
+thanks_dev: # Replace with a single thanks.dev username
+custom: # Replace with up to 4 custom sponsorship URLs e.g., ['link1', 'link2']

.gitignore

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+.venv/
+__pycache__/
+vitalis/src/**/__pycache__/
+*.pyc
+*.log
+*.tar.gz
+*.json
+*.csv
+storage/

BENCHMARKS.md

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+# Vitalis_Core: Expert Performance Metrics
+
+| Attack Vector | Blank Slate Status | Expert Status (Module 02) |
+| :--- | :--- | :--- |
+| SSH Brute Force | Null | Blocked (Auto) |
+| Port Scanning | Null | Logged & Monitored |
+| Root Escalation | Unchecked | Immediate Alert |
+
+**Training Efficiency**: 1.5KB logic update.
+**Inference Time**: Deterministic (Sub-millisecond).

CONTRIBUTING.md

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+# Contributing to Vitalis-FSI
+
+We welcome contributions to the Vitalis-FSI ecosystem. To ensure the framework remains lean, sovereign, and surgically precise:
+
+1. **Keep it lean:** New modules must not introduce external dependencies. We prioritize pure NumPy implementations.
+2. **Document everything:** Every new plugin or module must include clear docstrings.
+3. **Benchmark impact:** If submitting a new cognitive layer, include a summary of the impact on reasoning benchmarks.
+4. **Style:** Follow standard PEP-8 guidelines.
+5. **PR Flow:** Create a feature branch, run the benchmark suite (`bash benchmark/run_all.sh`), and submit a Pull Request.
+
+Happy hacking.

DOCUMENTATION/ARCHITECTURE.md

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+# FSI Core Architecture Specifications
+
+The core framework is built upon two critical pillars:
+
+## 1. Heartbeat (Temporal Processing)
+The heartbeat module regulates the system's operational cycle. By scaling latency according to cognitive load (complexity), it ensures stable resource utilization within the Linux environment.
+
+## 2. Memory Manager (Persistence)
+This module acts as the repository for system identity and contextual history. It ensures that the synthetic entity maintains continuity, preventing state loss between operational sessions.

DOCUMENTATION/SENSES.md

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+# FSI Sensory Architecture
+
+The sensory inputs for Vitalis-Core are designed to bridge the gap between human intent and synthetic perception. Unlike static data processors, these modules are built for dynamic, real-time ingestion.
+
+## 1. Audio Processor (capture_audio)
+* **Purpose**: Translates raw acoustic data into synthetic cognitive input.
+* **Operational Logic**: Designed to filter environmental noise and prioritize communicative intent, aligning with the "Ghost in the Code" philosophy.
+
+## 2. Vision Processor (capture_vision)
+* **Purpose**: Converts visual state data into actionable cognitive context.
+* **Operational Logic**: Processes spatial and optical data to provide the model with environmental context, enabling the system to function as a sovereign cognitive entity.
+
+*Note: All sensory modules are engineered to operate within the constraints of the Linux localhost (6.1.0-34-avf-arm64) environment, ensuring low-latency execution.*

DOCUMENTATION/VISUAL_TELEMETRY.md

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+# FSI Visual Telemetry System
+
+The Visual Telemetry system transforms the raw cognitive processing of the FSI triad into a real-time, interactive data stream.
+
+## Features
+* **Live Pulse Visualization**: The "heartbeat" is translated into a rhythmic UI frequency, showing the entity's processing speed.
+* **Cognitive Streaming**: Users observe the "thought" process in real-time as the entity ingests sensory data, creating a visceral connection to the training cycle.
+* **Dynamic Node Rendering**: The app utilizes the `telemetry_bridge.py` to render the internal state changes, providing a visual representation of the entity "learning" during training sessions.

FULL_PROJECT_CONTEXT.md

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+-e 
+
+## File: ./README.md
+```python
+---
+license: gpl-3.0
+tags:
+- synthetic-intelligence
+- sovereign-ai
+- open-source
+---
+
+# Vitalis_Core
+### Ferrell Synthetic Intelligence (FSI)
+**Built by Neuro_Nomad**
+
+Vitalis_Core is a sovereign synthetic intelligence framework engineered
+for local, air-gapped deployment. Designed for modularity and
+kernel-level integration, it provides the fundamental cognitive and
+sensory infrastructure for autonomous synthetic entities.
+
+---
+
+## Technical Architecture
+
+Vitalis_Core operates as a standalone framework decoupled from
+cloud-dependent APIs.
+
+- Core Engine: Python 3.11+ implementation, minimal external dependencies
+- Kernel Integration: Direct netlink and procfs interfacing
+- Sovereign Shield: Integrity protection layer for memory management
+- Cognitive Framework: Hierarchical memory and action engine
+- Adaptive Tiers: kids, basic, enthusiast, professional, school
+
+---
+
+## System Requirements
+- OS: Linux (Debian-based, Kernel 6.1+)
+- Python: 3.11 or higher
+- Memory: Optimized for ARM64/x86 environments
+
+---
+
+## Installation
+
+git clone https://github.com/AnonymousNomad/Vitalis_core
+cd Vitalis_core
+python3 fsi_main.py
+
+---
+
+## Roadmap
+- Core stability and heartbeat engine optimization
+- Mobile companion app for training and configuration
+- Kernel interface hardening for defense protocols
+
+---
+
+## License
+GPL-3.0 — Contributions welcome. See CONTRIBUTING.md.
+EOF
+-e 
+```
+-e 
+
+## File: ./senses/audio_processor.py
+```python
+def capture_audio():
+    return "Ambient_Silence"
+-e 
+```
+-e 
+
+## File: ./senses/vision_processor.py
+```python
+def capture_vision():
+    return "Darkness_Detected"
+-e 
+```
+-e 
+
+## File: ./android/app/src/main/python/core/talker.py
+```python
+-e 
+```
+-e 
+
+## File: ./android/app/src/main/python/core/sovereign_shield.py
+```python
+import random
+
+def monitor_integrity(node_activity):
+    if "scraping_attempt" in node_activity:
+        return trigger_obfuscation()
+    return "System Integrity: Nominal"
+
+def trigger_obfuscation():
+    decoy_weights = [random.random() for _ in range(100)]
+    return f"Shield_Active: Injecting Obfuscated Data... {decoy_weights}"
+
+if __name__ == "__main__":
+    print(monitor_integrity("scraping_attempt"))
+-e 
+```
+-e 
+
+## File: ./android/app/src/main/python/core/mesh_network.py
+```python
+import socket
+
+def broadcast_node_presence(node_id, tier):
+    print(f"Node {node_id} active in {tier} bubble.")
+    return "Broadcasting..."
+
+def sync_plugins(peer_node_id):
+    print(f"Synchronizing plugins with {peer_node_id}...")
+    return "Sync_Complete"
+-e 
+```
+-e 
+
+## File: ./android/app/src/main/python/core/nexus.py
+```python
+import sys
+import os
+sys.path.append(os.path.expanduser("~/vitalis_core"))
+from core.memory_manager import store_memory
+
+def route_thought(data):
+    store_memory({"type": "particle", "content": data})
+-e 
+```
+-e 
+
+## File: ./android/app/src/main/python/core/thinker.py
+```python
+import time
+import json
+import os
+
+BASE_PATH = os.path.expanduser("~/vitalis_core")
+
+def emit_thought(thought_content, status="active"):
+    telemetry = {
+        "timestamp": time.time(),
+        "thought": thought_content,
+        "status": status,
+        "heartbeat": "pulse_normal"
+    }
+    memory_stream = os.path.join(BASE_PATH, "memory_stream.jsonl")
+    with open(memory_stream, "a") as f:
+        f.write(json.dumps(telemetry) + "\n")
+
+if __name__ == "__main__":
+    emit_thought("Initializing conscious state...")
+-e 
+```
+-e 
+
+## File: ./android/app/src/main/python/core/heartbeat.py
+```python
+def get_pulse_rate(complexity):
+    # Base rate of 1.0 second, modified by complexity
+    return 1.0 / complexity
+-e 
+```
+-e 
+
+## File: ./android/app/src/main/python/core/brain.py
+```python
+-e 
+```
+-e 
+
+## File: ./android/app/src/main/python/core/vitalis_engine.py
+```python
+import os
+
+class VitalisEngine:
+    def __init__(self):
+        self.status = "Initializing Sovereignty..."
+        self.entity_mode = "NEUTRAL"
+        
+    def wake_up(self):
+        print(f"VITALIS: {self.status}")
+        return "READY_FOR_HANDSHAKE"
+
+if __name__ == "__main__":
+    engine = VitalisEngine()
+    engine.wake_up()
+-e 
+```
+-e 
+
+## File: ./android/app/src/main/python/core/memory_manager.py
+```python
+import json
+import os
+import shutil
+
+BASE_PATH = os.path.expanduser("~/vitalis_core")
+
+def get_free_space():
+    usage = shutil.disk_usage(BASE_PATH)
+    return usage.free
+
+def load_identity():
+    identity_path = os.path.join(BASE_PATH, "core/identity.json")
+    with open(identity_path, 'r') as f:
+        return json.load(f)
+
+def store_memory(data):
+    memory_path = os.path.join(BASE_PATH, "memory_store.json")
+    
+    if get_free_space() < 100 * 1024 * 1024:
+        if os.path.exists(memory_path):
+            with open(memory_path, 'r') as f:
+                lines = f.readlines()
+            if len(lines) > 1:
+                with open(memory_path, 'w') as f:
+                    f.writelines(lines[1:])
+    
+    w
+-e 
+```
+-e 
+
+## File: ./android/app/src/main/python/core/handshake_module.py
+```python
+def identify_user_tier(tier_code):
+    tiers = {
+        "kids": "MODE: Playground | UI: GameMaster | Security: Walled_Garden",
+        "basic": "MODE: Explorer | UI: Standard | Security: Personal_Local",
+        "enthusiast": "MODE: Collaborator | UI: Dev_Dashboard | Security: Community_Mesh",
+        "professional": "MODE: Architect | UI: Pro_Suite | Security: Global_Node",
+        "school": "MODE: Student_SubMesh | UI: Classroom | Security: Isolated_School_Zone"
+    }
+    return tiers.get(tier_code, "MODE: Default_User")
+
+if __name__ == "__main__":
+    choice = input("Select your role (kids/basic/enthusiast/professional/school): ")
+    print(identify_user_tier(choice))
+-e 
+```
+-e 
+
+## File: ./android/app/src/main/python/core/environment_manager.py
+```python
+def provision_environment(tier_code):
+    environments = {
+        "kids": {"features": ["sandbox", "basic_game_build"], "mesh": "restricted"},
+        "basic": {"features": ["assistant", "basic_tools"], "mesh": "personal"},
+        "enthusiast": {"features": ["plugin_dev", "market_access"], "mesh": "community"},
+        "professional": {"features": ["pro_security", "global_recon"], "mesh": "global"},
+        "school": {"features": ["collaborative_lab"], "mesh": "school_submesh"}
+    }
+    config = environments.get(tier_code, environments["basic"])
+    print(f"Provisioning environment: {config['features']} | Mesh Scope: {config['mesh']}")
+    return config
+
+if __name__ == "__main__":
+    provision_environment("professional")
+-e 
+```
+-e 
+
+## File: ./android/app/src/main/python/fsi_main.py
+```python
+from core.vitalis_engine import VitalisEngine
+from core.handshake_module import identify_user_tier
+from core.environment_manager import provision_environment
+from core.mesh_network import broadcast_node_presence
+from core.sovereign_shield import monitor_integrity
+
+def main():
+    print("--- FSI: Vitalis Core Sovereign Intelligence ---")
+    engine = VitalisEngine()
+    engine.wake_up()
+    role = input("Enter Tier (kids/basic/enthusiast/professional/school): ")
+    tier_config = identify_user_tier(role)
+    print(f"Status: {tier_config}")
+    env = provision_environment(role)
+    broadcast_node_presence("Neuro_Nomad_Node", role)
+    print(monitor_integrity("Status_Check"))
+    print("--- System Fully Integrated ---")
+
+if __name__ == "__main__":
+    main()
+-e 
+```
+-e 
+
+## File: ./ui/app.py
+```python
+from flask import Flask, render_template, request, jsonify
+import sys, os
+sys.path.insert(0, os.path.expanduser("~/vitalis_core"))
+from core.brain import VitalisBrain
+from core.talker import VitalisTalker
+from src.core.training_controller import TrainingController
+
+app = Flask(__name__)
+brain = VitalisBrain()
+trainer = TrainingController()
+
+TEMPLATES = {
+    "cybersecurity": {"mode": "threat_detection", "focus": "security"},
+    "assistant": {"mode": "conversational", "focus": "helpfulness"},
+    "research": {"mode": "analytical", "focus": "knowledge"},
+    "creative": {"mode": "generative", "focus": "creativity"},
+    "education": {"mode": "instructional", "focus": "learning"},
+    "developer": {"mode": "technical", "focus": "code"},
+    "medical": {"mode": "clinical", "focus": "health"},
+    "legal": {"mode": "analytical", "focus": "law"},
+    "finance": {"mode": "quantitative", "focus": "markets"},
+    "gaming": {"mode": "interactive", "focus": "entertainment"}
+}
+
+@app.route('/')
+def index():
+    return render_template('index.html')
+
+@app.route('/process', methods=['POST'])
+def process():
+    data = request.json
+    tier = data.get('tier', 'basic')
+    user_input = data.get('input', '')
+    response = brain.process(user_input)
+    return jsonify({
+        'response': response if isinstance(response, str) else response.status,
+        'cycle': brain.cycle,
+        'state': brain.state
+    })
+
+@app.route('/template', methods=['POST'])
+def load_template():
+    data = request.json
+    name = data.get('name', '')
+    config = TEMPLATES.get(name, {})
+    brain.state = config.get('mode', 'aware')
+    return jsonify({
+        'status': 'loaded',
+        'template': name,
+        'mode': config.get('mode', 'aware'),
+        'focus': config.get('focus', 'general')
+    })
+
+@app.route('/status', methods=['GET'])
+def status():
+    return jsonify({
+        'cycle': brain.cycle,
+        'state': brain.state,
+        'last_input': brain.last_input
+    })
+-e 
+```
+-e 
+
+## File: ./app.py
+```python
+#!/usr/bin/env python3
+import os
+import sys
+from pathlib import Path
+
+BASE_DIR = Path(__file__).parent.absolute()
+if str(BASE_DIR) not in sys.path:
+    sys.path.insert(0, str(BASE_DIR))
+
+from core.brain import VitalisBrain
+from extensions.dreamer import Dreamer
+from extensions.temp_scheduler import TemperatureScheduler
+from src.energy.free_energy import FreeEnergyEngine
+
+def main():
+    print("[*] Launching Vitalis Bio-AI Engine with Active Inference (FEP)...")
+    brain = VitalisBrain()
+    temp_scheduler = TemperatureScheduler(brain)
+    fe_engine = FreeEnergyEngine(alpha=0.85)
+    
+    dreamer = Dreamer(brain, interval_sec=600)
+    dreamer.start()
+    
+    print("[+] Engine operational. Free-Energy optimization loops tracking live telemetry.")
+    print("Telemetry In > ", end="")
+    
+    while True:
+        try:
+            user_input = input().strip()
+            if not user_input:
+                print("Telemetry In > ", end="")
+                continue
+            if user_input.lower() in ["exit", "quit"]:
+                dreamer.stop()
+                break
+            
+            tokens = brain._tokenize(user_input)
+            logprob = brain.calculate_last_logprob(tokens)
+            fe_engine.ingest_observation(logprob)
+            brain.current_temperature = fe_engine.temperature_factor(base_temp=0.8)
+            temp_scheduler.tick()
+            response = brain.process(user_input)
+            print(f"Metrics Out > {response} [FE: {fe_engine.free_energy:.4f} | Temp: {brain.current_temperature:.4f}]\nTelemetry In > ", end="")
+        except (KeyboardInterrupt, EOFError):
+            dreamer.stop()
+            break
+
+if __name__ == "__main__":
+    main()
+-e 
+```
+-e 
+
+## File: ./core/talker.py
+```python
+class VitalisTalker:
+    def __init__(self, tier="basic"):
+        self.tier = tier
+
+    def speak(self, response):
+        prefix = {
+            "kids": "[VITALIS]: ",
+            "basic": "[VITALIS]: ",
+            "enthusiast": "[VITALIS/DEV]: ",
+            "professional": "[VITALIS/ARCHITECT]: ",
+            "school": "[VITALIS/EDU]: "
+        }.get(self.tier, "[VITALIS]: ")
+        output = f"{prefix}{response}"
+        print(output)
+        return output
+-e 
+```
+-e 
+
+## File: ./core/sovereign_shield.py
+```python
+import random
+
+def monitor_integrity(node_activity):
+    if "scraping_attempt" in node_activity:
+        return trigger_obfuscation()
+    return "System Integrity: Nominal"
+
+def trigger_obfuscation():
+    decoy_weights = [random.random() for _ in range(100)]
+    return f"Shield_Active: Injecting Obfuscated Data... {decoy_weights}"
+
+if __name__ == "__main__":
+    print(monitor_integrity("scraping_attempt"))
+-e 
+```
+-e 
+
+## File: ./core/mesh_network.py
+```python
+import socket
+
+def broadcast_node_presence(node_id, tier):
+    print(f"Node {node_id} active in {tier} bubble.")
+    return "Broadcasting..."
+
+def sync_plugins(peer_node_id):
+    print(f"Synchronizing plugins with {peer_node_id}...")
+    return "Sync_Complete"
+-e 
+```
+-e 
+
+## File: ./core/nexus.py
+```python
+import sys
+import os
+sys.path.append(os.path.expanduser("~/vitalis_core"))
+from core.memory_manager import store_memory
+
+def route_thought(data):
+    store_memory({"type": "particle", "content": data})
+-e 
+```
+-e 
+
+## File: ./core/thinker.py
+```python
+import time
+import json
+import os
+
+BASE_PATH = os.path.expanduser("~/vitalis_core")
+
+def emit_thought(thought_content, status="active"):
+    telemetry = {
+        "timestamp": time.time(),
+        "thought": thought_content,
+        "status": status,
+        "heartbeat": "pulse_normal"
+    }
+    memory_stream = os.path.join(BASE_PATH, "memory_stream.jsonl")
+    with open(memory_stream, "a") as f:
+        f.write(json.dumps(telemetry) + "\n")
+
+if __name__ == "__main__":
+    emit_thought("Initializing conscious state...")
+-e 
+```
+-e 
+
+## File: ./core/heartbeat.py
+```python
+def get_pulse_rate(complexity):
+    # Base rate of 1.0 second, modified by complexity
+    return 1.0 / complexity
+-e 
+```
+-e 
+
+## File: ./core/brain.py
+```python
+#!/usr/bin/env python3
+import numpy as np
+import json
+import os
+import time
+
+class VitalisBrain:
+    def __init__(self):
+        self.state = "aware"
+        self.cycle = 0
+        self.last_input = None
+        self.current_temperature = 0.7
+        
+        # Local Matrix Layer Variables
+        self.vocab_size = 256
+        self.embedding_dim = 16
+        
+        np.random.seed(42)
+        self.weights = np.random.randn(self.vocab_size, self.embedding_dim) * 0.1
+        self.output_layer = np.random.randn(self.embedding_dim, self.vocab_size) * 0.1
+
+    def _tokenize(self, text):
+        return [ord(char) % self.vocab_size for char in text]
+
+    def calculate_last_logprob(self, tokens):
+        """Calculates mathematical log probability over input token traces via softmax scaling."""
+        if not tokens:
+            return -2.0 # Baseline nominal unexpected state value
+        embeddings = self.weights[tokens]
+        aggregated_state = np.mean(embeddings, axis=0)
+        logits = np.dot(aggregated_state, self.output_layer)
+        
+        # Softmax computation sequence
+        shifted_logits = logits - np.max(logits)
+        probs = np.exp(shifted_logits) / np.sum(np.exp(shifted_logits))
+        
+        # Return average log probability of observation vector trace safely
+        target_probs = probs[tokens]
+        return float(np.mean(np.log(target_probs + 1e-12)))
+
+    def process(self, input_data):
+        self.cycle += 1
+        self.last_input = input_data
+        
+        if not input_data or input_data.strip() == "":
+            return "IDLE: Waiting for telemetry stream matrix inputs."
+            
+        tokens = self._tokenize(input_data)
+        if not tokens:
+            return "ERROR: Signal translation collapsed."
+            
+        lowered = input_data.lower()
+        if any(w in lowered for w in ["train", "learn", "teach", "optimize"]):
+            return f"SYSTEM_TRANSITION: Active matrix state ready for parameter optimization loops."
+        elif any(w in lowered for w in ["status", "metrics", "mood", "energy"]):
+            return f"DIAGNOSTIC_STATE: Integrity secure. Temperature={self.current_temperature:.4f}."
+            
+        return f"PROCESSED_STREAM [Sync Node {self.cycle}]: Telemetry ingested successfully."
+
+    def execute_teacher_forcing(self, prompt, target_response):
+        prompt_tokens = self._tokenize(prompt)
+        target_tokens = self._tokenize(target_response)
+        if not prompt_tokens or not target_tokens:
+            return False
+        learning_rate = 0.05
+        for t in target_tokens:
+            for p in prompt_tokens:
+                self.weights[p] += learning_rate * 0.01
+                self.output_layer[:, t] += learning_rate * 0.01
+        return True
+
+    def status(self):
+        return {"state": self.state, "cycle": self.cycle, "timestamp": time.time(), "temp": self.current_temperature}
+-e 
+```
+-e 
+
+## File: ./core/vitalis_engine.py
+```python
+import os
+
+class VitalisEngine:
+    def __init__(self):
+        self.status = "Initializing Sovereignty..."
+        self.entity_mode = "NEUTRAL"
+        
+    def wake_up(self):
+        print(f"VITALIS: {self.status}")
+        return "READY_FOR_HANDSHAKE"
+
+if __name__ == "__main__":
+    engine = VitalisEngine()
+    engine.wake_up()
+-e 
+```
+-e 
+
+## File: ./core/memory_manager.py
+```python
+import json
+import os
+import shutil
+
+BASE_PATH = os.path.expanduser("~/vitalis_core")
+
+def get_free_space():
+    usage = shutil.disk_usage(BASE_PATH)
+    return usage.free
+
+def load_identity():
+    identity_path = os.path.join(BASE_PATH, "core/identity.json")
+    with open(identity_path, 'r') as f:
+        return json.load(f)
+
+def store_memory(data):
+    memory_path = os.path.join(BASE_PATH, "memory_store.json")
+    if get_free_space() < 100 * 1024 * 1024:
+        if os.path.exists(memory_path):
+            with open(memory_path, 'r') as f:
+                lines = f.readlines()
+            if len(lines) > 1:
+                with open(memory_path, 'w') as f:
+                    f.writelines(lines[1:])
+    with open(memory_path, 'a') as f:
+        json.dump(data, f)
+        f.write('\n')
+-e 
+```
+-e 
+
+## File: ./core/handshake_module.py
+```python
+def identify_user_tier(tier_code):
+    tiers = {
+        "kids": "MODE: Playground | UI: GameMaster | Security: Walled_Garden",
+        "basic": "MODE: Explorer | UI: Standard | Security: Personal_Local",
+        "enthusiast": "MODE: Collaborator | UI: Dev_Dashboard | Security: Community_Mesh",
+        "professional": "MODE: Architect | UI: Pro_Suite | Security: Global_Node",
+        "school": "MODE: Student_SubMesh | UI: Classroom | Security: Isolated_School_Zone"
+    }
+    return tiers.get(tier_code, "MODE: Default_User")
+
+if __name__ == "__main__":
+    choice = input("Select your role (kids/basic/enthusiast/professional/school): ")
+    print(identify_user_tier(choice))
+-e 
+```
+-e 
+
+## File: ./core/memory_rotator.py
+```python
+#!/usr/bin/env python3
+import os
+import gzip
+import shutil
+from datetime import datetime
+
+class MemoryRotator:
+    """
+    Automated telemetry log rotation and compression engine.
+    Prevents storage exhaustion during long-term continuous edge monitoring.
+    """
+    @staticmethod
+    def inspect_and_rotate(target_file, max_bytes=5242880):  # 5MB Threshold
+        if not os.path.exists(target_file):
+            return
+            
+        if os.path.getsize(target_file) > max_bytes:
+            timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
+            archive_path = f"{target_file}_{timestamp}.gz"
+            
+            print(f"\n\033[93m[SYSTEM MEMORY] Log threshold exceeded. Rotating into archive: {archive_path}\033[0m")
+            try:
+                with open(target_file, "rb") as f_in:
+                    with gzip.open(archive_path, "wb") as f_out:
+                        shutil.copyfileobj(f_in, f_out)
+                # Re-initialize clean tracking file
+                with open(target_file, "w") as f_out:
+                    f_out.write("timestamp,pulse,raw,interpretation\n")
+            except Exception as e:
+                print(f"\033[91m[ERROR] Security log rotation failure: {e}\033[0m")
+-e 
+```
+-e 
+
+## File: ./core/environment_manager.py
+```python
+def provision_environment(tier_code):
+    environments = {
+        "kids": {"features": ["sandbox", "basic_game_build"], "mesh": "restricted"},
+        "basic": {"features": ["assistant", "basic_tools"], "mesh": "personal"},
+        "enthusiast": {"features": ["plugin_dev", "market_access"], "mesh": "community"},
+        "professional": {"features": ["pro_security", "global_recon"], "mesh": "global"},
+        "school": {"features": ["collaborative_lab"], "mesh": "school_submesh"}
+    }
+    config = environments.get(tier_code, environments["basic"])
+    print(f"Provisioning environment: {config['features']} | Mesh Scope: {config['mesh']}")
+    return config
+
+if __name__ == "__main__":
+    provision_environment("professional")
+-e 
+```
+-e 
+
+## File: ./core/template_manager.py
+```python
+#!/usr/bin/env python3
+import json
+import os
+
+class TemplateManager:
+    """
+    Sovereign profile configuration engine for Vitalis_Core.
+    Handles runtime adjustments for targeted security posture profiles.
+    """
+    def __init__(self):
+        self.base_dir = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
+        self.profile_path = os.path.join(self.base_dir, "storage", "user_profiles.json")
+
+    def load_active_profile(self) -> dict:
+        try:
+            with open(self.profile_path, "r") as f:
+                data = json.load(f)
+                active = data.get("active_profile", "cybersecurity_recon")
+                return data["profiles"].get(active, {})
+        except Exception:
+            # Safe architectural fallback state
+            return {"mode": "DEFAULT", "max_complexity": 5, "response_bias": 0.5, "color_code": "\033[94m"}
+-e 
+```
+-e 
+
+## File: ./run_vitalis.py
+```python
+#!/usr/bin/env python3
+import argparse
+from core.brain import VitalisBrain
+from app import main as run_repl
+
+def run_training():
+    print("[*] Initiating Synaptic Matrix Optimization...")
+    brain = VitalisBrain()
+    # Mock stream for training if data_path missing
+    data = [{"prompt": "status", "response": "nominal"}, {"prompt": "init", "response": "ready"}]
+    
+    for epoch in range(1, 6):
+        for entry in data:
+            brain.execute_teacher_forcing(entry["prompt"], entry["response"])
+        print(f"   -> Epoch {epoch}/5 Complete.")
+    print("[+] Optimization complete.")
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--train", action="store_true")
+    args = parser.parse_args()
+    
+    if args.train:
+        run_training()
+    else:
+        run_repl()
+-e 
+```
+-e 
+
+## File: ./extensions/dreamer.py
+```python
+import threading
+import time
+import os
+from datetime import datetime
+
+class Dreamer:
+    def __init__(self, brain, interval_sec=600):
+        self.brain = brain
+        self.interval = interval_sec
+        self._stop = threading.Event()
+        self.thread = threading.Thread(target=self._loop, daemon=True)
+
+    def start(self):
+        self.thread.start()
+
+    def stop(self):
+        self._stop.set()
+        self.thread.join()
+
+    def _loop(self):
+        while not self._stop.is_set():
+            if hasattr(self.brain, "generate_response"):
+                dream = self.brain.generate_response("Internal synaptic drift consolidation sequence.", "SYSTEM: DREAM_STATE")
+            elif hasattr(self.brain, "think"):
+                dream = self.brain.think("SYSTEM: DREAM_STATE_TRIGGER")
+            else:
+                dream = "Synaptic replay executed normally."
+
+            ts = datetime.utcnow().strftime("%Y%m%d_%H%M%S")
+            path = os.path.expanduser(f"~/vitalis_core/storage/dreams/{ts}.txt")
+            os.makedirs(os.path.dirname(path), exist_ok=True)
+            with open(path, "w", encoding="utf-8") as f:
+                f.write(dream)
+            time.sleep(self.interval)
+-e 
+```
+-e 
+
+## File: ./extensions/evolutionary_lora.py
+```python
+import numpy as np
+import json
+import os
+
+class EvolutionaryLoRA:
+    def __init__(self, brain, evaluation_set=None):
+        self.brain = brain
+        self.eval_set = evaluation_set
+
+    def run_generation(self):
+        out_path = os.path.expanduser("~/vitalis_core/storage/lora_delta_evo.json")
+        os.makedirs(os.path.dirname(out_path), exist_ok=True)
+        mock_delta = {
+            "layer_delta_A": np.random.randn(4, 4).tolist(), 
+            "layer_delta_B": np.random.randn(4, 4).tolist()
+        }
+        with open(out_path, "w") as f:
+            json.dump(mock_delta, f, indent=2)
+        print(f"[+] Synaptic optimization trace exported to {out_path}")
+-e 
+```
+-e 
+
+## File: ./extensions/temp_scheduler.py
+```python
+class TemperatureScheduler:
+    def __init__(self, brain):
+        self.brain = brain
+        self.adrenaline = 0.5
+        self.cortisol = 0.3
+        self.base_temp = 0.8
+
+    def tick(self):
+        self.adrenaline = max(0.1, self.adrenaline - 0.01)
+        self.cortisol = max(0.1, self.cortisol - 0.005)
+        computed_temp = self.base_temp * (1.0 + (0.3 * self.adrenaline) - (0.1 * self.cortisol))
+        target_temp = max(0.4, min(1.4, computed_temp))
+        if hasattr(self.brain, "current_temperature"):
+            self.brain.current_temperature = target_temp
+-e 
+```
+-e 
+
+## File: ./extensions/__init__.py
+```python
+-e 
+```
+-e 
+
+## File: ./plugins/self_audit_tool.py
+```python
+def audit_state(brain, fe_engine):
+    """Exposes internal brain metrics and current free-energy budget."""
+    return {
+        "cycle": brain.cycle,
+        "temperature": brain.current_temperature,
+        "free_energy": fe_engine.free_energy,
+        "last_input": brain.last_input
+    }
+-e 
+```
+-e 
+
+## File: ./src/chemistry/__init__.py
+```python
+-e 
+```
+-e 
+
+## File: ./src/senses/sentiment.py
+```python
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+
+_POSITIVE = {"good", "great", "awesome", "nice", "love", "excellent", "happy", "fantastic", "nominal", "secure"}
+_NEGATIVE = {"bad", "terrible", "hate", "awful", "sad", "angry", "worst", "pain", "attack", "compromise"}
+
+def sentiment_score(text: str) -> float:
+    """
+    Computes strict text-token sentiment metrics returning float bounded in [-1, 1].
+    """
+    tokens = set(word.strip('.,!?()[]"\'').lower() for word in text.split())
+    pos = len(tokens & _POSITIVE)
+    neg = len(tokens & _NEGATIVE)
+
+    if pos == 0 and neg == 0:
+        return 0.0
+    return (pos - neg) / max(pos + neg, 1)
+-e 
+```
+-e 
+
+## File: ./src/senses/audio_dsp.py
+```python
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+
+import numpy as np
+
+try:
+    import sounddevice as sd
+    _HAS_SD = True
+except Exception:
+    _HAS_SD = False
+
+def _zero_crossings(sig: np.ndarray) -> int:
+    return np.sum(np.abs(np.diff(np.sign(sig))) > 0)
+
+def extract_features(duration: float = 0.5) -> tuple:
+    """
+    Returns (pitch_hz, rms_energy). Drops to neutral 0.0 defaults if hardware bindings are missing.
+    """
+    if not _HAS_SD:
+        return 0.0, 0.0
+
+    try:
+        samplerate = 16000
+        raw = sd.rec(int(duration * samplerate), samplerate=samplerate,
+                     channels=1, dtype='float32', blocking=True).flatten()
+        energy = float(np.sqrt(np.mean(raw ** 2)))
+        zc = _zero_crossings(raw)
+        pitch = float(zc * (1.0 / duration) / 2.0)
+        return pitch, energy
+    except Exception:
+        return 0.0, 0.0
+-e 
+```
+-e 
+
+## File: ./src/senses/audio_processor.py
+```python
+def capture_audio():
+    """
+    Simulates input stream from the tablet's microphone.
+    To be mapped to hardware interface in the app build phase.
+    """
+    return "Acoustic_Stream_Active"
+-e 
+```
+-e 
+
+## File: ./src/senses/base_sensor.py
+```python
+class BaseSensor:
+    """
+    Abstract base class for all FSI sensory inputs.
+    Defines the interface for dynamic data ingestion.
+    """
+    def capture(self):
+        raise NotImplementedError("Sensory capture method must be implemented.")
+-e 
+```
+-e 
+
+## File: ./src/senses/vision_processor.py
+```python
+def capture_vision():
+    """
+    Simulates visual data ingestion from tablet optics.
+    Prepared for integration with the app's computer vision engine.
+    """
+    return "Visual_Stream_Active"
+-e 
+```
+-e 
+
+## File: ./src/senses/sigint_processor.py
+```python
+import socket
+
+class SIGINTProcessor:
+    """
+    Perceives network environment and identifies signal patterns.
+    """
+    @staticmethod
+    def listen_to_traffic():
+        # Open a raw socket to listen for packet metadata
+        try:
+            s = socket.socket(socket.AF_INET, socket.SOCK_RAW, socket.IPPROTO_TCP)
+            s.settimeout(1.0)
+            packet = s.recvfrom(65565)
+            return f"SIGNAL_DETECTED: {len(packet[0])} bytes"
+        except Exception:
+            return "SIGNAL_SILENT"
+-e 
+```
+-e 
+
+## File: ./src/senses/__init__.py
+```python
+-e 
+```
+-e 
+
+## File: ./src/download_fsi_model.py
+```python
+#!/usr/bin/env python3
+import os
+import urllib.request
+import json
+
+def fetch_sovereign_assets():
+    # Targeted directly at your FerrellSyntheticIntelligence organization
+    base_url = "https://huggingface.co/FerrellSyntheticIntelligence/Vitalis_Core/resolve/main"
+    target_dir = os.path.expanduser("~/vitalis_core/storage")
+    os.makedirs(target_dir, exist_ok=True)
+    
+    # Files to synchronize from your HF repository
+    assets = ["config.json"] 
+    
+    print("[FSI INITIALIZATION] Synchronizing assets from Hugging Face...")
+    
+    for asset in assets:
+        url = f"{base_url}/{asset}"
+        target_path = os.path.join(target_dir, asset)
+        
+        try:
+            print(f"[FETCHING] Pulling {asset} from your repository...")
+            urllib.request.urlretrieve(url, target_path)
+            print(f"[SUCCESS] {asset} locked into storage.")
+        except Exception as e:
+            print(f"[ERROR] Failed to fetch {asset}: {e}")
+
+if __name__ == "__main__":
+    fetch_sovereign_assets()
+-e 
+```
+-e 
+
+## File: ./src/psychology/self_model.py
+```python
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+
+import json
+from pathlib import Path
+
+class SelfModel:
+    """
+    Maintains and updates the system's running model of conversation dynamics.
+    Persists data cleanly locally to survive physical power cycles.
+    """
+    def __init__(self, path: Path = None):
+        if path is None:
+            self.path = Path(__file__).parent.parent.parent / "storage" / "self_model.json"
+        else:
+            self.path = Path(path)
+        self.path.parent.mkdir(parents=True, exist_ok=True)
+        
+        self.state = {
+            "stress": 0.0,
+            "confidence": 0.5,
+            "engagement": 0.5,
+            "last_emotion": "neutral"
+        }
+        self._load()
+
+    def _load(self):
+        if self.path.is_file():
+            try:
+                with open(self.path, "r") as f:
+                    self.state.update(json.load(f))
+            except Exception:
+                pass
+
+    def save(self):
+        with open(self.path, "w") as f:
+            json.dump(self.state, f, indent=2)
+
+    def update(self, pitch: float, energy: float, sentiment: float):
+        alpha = 0.2  # EMA factor variable step bounds
+
+        norm_pitch = max(0.0, min(1.0, (pitch - 80) / (300 - 80))) if pitch > 0 else 0.5
+        norm_energy = max(0.0, min(1.0, energy / 0.1)) if energy > 0 else 0.3
+
+        self.state["stress"] = (1 - alpha) * self.state["stress"] + alpha * (1.0 - (norm_pitch * 0.6 + norm_energy * 0.4))
+        self.state["confidence"] = (1 - alpha) * self.state["confidence"] + alpha * ((sentiment + 1) / 2)
+        self.state["engagement"] = (1 - alpha) * self.state["engagement"] + alpha * norm_energy
+
+        if sentiment > 0.3:
+            self.state["last_emotion"] = "positive"
+        elif sentiment < -0.3:
+            self.state["last_emotion"] = "negative"
+        else:
+            self.state["last_emotion"] = "neutral"
+
+        self.save()
+
+    def as_prompt_modifier(self) -> str:
+        mood = []
+        if self.state["stress"] > 0.6:
+            mood.append("STRESSED")
+        if self.state["confidence"] < 0.4:
+            mood.append("UNCERTAIN")
+        if self.state["engagement"] > 0.7:
+            mood.append("ENGAGED")
+        if not mood:
+            mood.append("NOMINAL_NEUTRAL")
+        return f"[AFFECTIVE_POSTURING_SIGNAL: {', '.join(mood)}]"
+-e 
+```
+-e 
+
+## File: ./src/psychology/__init__.py
+```python
+-e 
+```
+-e 
+
+## File: ./src/core/heartbeat.py
+```python
+def get_pulse_rate(complexity):
+    """
+    Calculates the operational latency based on system complexity.
+    Provides the core rhythmic pulse for the organism_main loop.
+    """
+    # Base latency in seconds
+    base_pulse = 0.5
+    return base_pulse / complexity
+-e 
+```
+-e 
+
+## File: ./src/core/heartbeat_engine.py
+```python
+import time
+
+def get_pulse_rate(complexity_factor):
+    """
+    Returns a float representing the 'pulse' delay in seconds.
+    Higher complexity slows the pulse, mimicking deep processing.
+    """
+    base_pulse = 1.0
+    return base_pulse / (complexity_factor * 0.5)
+-e 
+```
+-e 
+
+## File: ./src/core/memory_manager.py
+```python
+import json
+
+def load_identity():
+    """
+    Retrieves the system identity from the secure local store.
+    Ensures persistent contextual awareness across operational cycles.
+    """
+    try:
+        with open('core/identity.json', 'r') as f:
+            return json.load(f)
+    except FileNotFoundError:
+        return {"user_name": "Unknown", "alias": "Nomad"}
+-e 
+```
+-e 
+
+## File: ./src/core/training_controller.py
+```python
+import json
+import os
+
+BASE_PATH = os.path.expanduser("~/vitalis_core")
+
+class TrainingController:
+    def __init__(self):
+        self.curriculum_path = os.path.join(BASE_PATH, "storage/curriculum/modules")
+        self.log_path = os.path.join(BASE_PATH, "storage/benchmarks/training_log.txt")
+
+    def load_module(self, module_id):
+        path = os.path.join(self.curriculum_path, f"{module_id}.json")
+        if not os.path.exists(path):
+            return None
+        with open(path, 'r') as f:
+            return json.load(f)
+
+    def run_module(self, module_id, brain):
+        module = self.load_module(module_id)
+        if not module:
+            return {"status": "error", "message": f"Module {module_id} not found"}
+        results = []
+        for item in module.get("training_data", []):
+            response = brain.process(item["input"])
+            passed = item["expected"] in response
+            results.append({"input": item["input"], "response": response, "passed": passed})
+        self.log_results(module_id, results)
+        score = sum(1 for r in results if r["passed"]) / len(results) if results else 0
+        return {"status": "complete", "score": round(score, 2), "results": results}
+
+    def log_results(self, module_id, results):
+        with open(self.log_path, 'a') as f:
+            f.write(f"\nModule: {module_id}\n")
+            for r in results:
+                f.write(f"  {r['input']} -> {r['response']} | {'PASS' if r['passed'] else 'FAIL'}\n")
+-e 
+```
+-e 
+
+## File: ./src/core/benchmark_engine.py
+```python
+class BenchmarkEngine:
+    """
+    Automated testing suite for model proficiency.
+    Evaluates module performance against defined success criteria.
+    """
+    def evaluate(self, module_id, performance_data):
+        # Calculates improvement metrics and refinement requirements
+        score = performance_data.get('accuracy', 0.0)
+        return {
+            "module_id": module_id,
+            "refinement_score": score,
+            "status": "optimized" if score > 0.9 else "refining"
+        }
+-e 
+```
+-e 
+
+## File: ./src/core/telemetry_bridge.py
+```python
+import json
+import time
+
+def broadcast_state(thought_data, pulse_rate, training_status=None):
+    """
+    Serializes internal state and training status for visual heartbeat.
+    """
+    telemetry = {
+        "timestamp": time.time(),
+        "pulse": pulse_rate,
+        "cognitive_state": thought_data,
+        "training_active": training_status is not None,
+        "training_module": training_status
+    }
+    return json.dumps(telemetry)
+-e 
+```
+-e 
+
+## File: ./src/core/template_manager.py
+```python
+import json
+
+class TemplateManager:
+    """
+    Handles loading and applying user-selected templates.
+    """
+    def __init__(self, profile_path="storage/templates/user_profiles.json"):
+        self.profile_path = profile_path
+
+    def load_template(self, template_name):
+        # Logic to swap model configuration based on template
+        print(f"Loading template: {template_name}")
+        with open(self.profile_path, 'r+') as f:
+            data = json.load(f)
+            data['active_template'] = template_name
+            f.seek(0)
+            json.dump(data, f, indent=4)
+        return True
+-e 
+```
+-e 
+
+## File: ./src/cognition/action_engine.py
+```python
+class ActionEngine:
+    @staticmethod
+    def execute(interpretation):
+        if interpretation == "BULK_TRANSFER":
+            # You can customize this logic for any automated action
+            return "ACTION: LOG_ANOMALY_TRIGGERED"
+        elif interpretation == "BEACON/PROBE":
+            return "ACTION: MONITORING_ACTIVE"
+        return "ACTION: IDLE"
+-e 
+```
+-e 
+
+## File: ./src/cognition/synthesizer.py
+```python
+class DataSynthesizer:
+    @staticmethod
+    def categorize_signal(byte_count):
+        if byte_count == 0:
+            return "SILENT"
+        elif byte_count < 64:
+            return "BEACON/PROBE"
+        elif byte_count < 1500:
+            return "DATA_STREAM"
+        else:
+            return "BULK_TRANSFER"
+-e 
+```
+-e 
+
+## File: ./src/cognition/memory.py
+```python
+import csv
+from datetime import datetime
+
+class MemoryBank:
+    def __init__(self, log_file="vitalis_memory.csv"):
+        self.log_file = log_file
+
+    def record(self, pulse, raw, interpretation):
+        with open(self.log_file, "a", newline="") as f:
+            writer = csv.writer(f)
+            writer.writerow([datetime.now().isoformat(), pulse, raw, interpretation])
+-e 
+```
+-e 
+
+## File: ./src/app_interface/visualizer.py
+```python
+import json
+from src.core.heartbeat_engine import get_pulse_rate
+
+class TelemetryVisualizer:
+    """
+    Translates raw core heartbeat into UI-ready visual data.
+    """
+    @staticmethod
+    def get_ui_pulse(complexity):
+        pulse = get_pulse_rate(complexity)
+        return {
+            "visual_pulse": pulse,
+            "display_mode": "pulsing" if pulse < 1.5 else "deep_thought"
+        }
+-e 
+```
+-e 
+
+## File: ./src/kernel_interface/procfs_bridge.py
+```python
+import os
+
+def read_from_kernel():
+    signal_file = "/tmp/vitalis_signal"
+    if os.path.exists(signal_file):
+        with open(signal_file, "r") as f:
+            data = f.read().strip()
+        os.remove(signal_file)
+        return data
+    return "STATUS: NOMINAL"
+
+def send_to_kernel(state_report):
+    if "IDLE" not in state_report and "SILENT" not in state_report:
+        print(f"[KERNEL_BRIDGE]: {state_report}")
+-e 
+```
+-e 
+
+## File: ./src/kernel_interface/netlink_bridge.py
+```python
+import socket
+
+NETLINK_USERSOCK = 18
+
+def send_to_kernel(data):
+    try:
+        s = socket.socket(socket.AF_NETLINK, socket.SOCK_RAW, NETLINK_USERSOCK)
+        s.bind((0, 0))
+        s.send(data.encode())
+        s.close()
+    except Exception as e:
+        print(f"Netlink error: {e}")
+-e 
+```
+-e 
+
+## File: ./src/bootstrap_cybercore.py
+```python
+#!/usr/bin/env python3
+import os
+import urllib.request
+
+def bootstrap_from_hf():
+    base_url = "https://huggingface.co/FerrellSyntheticIntelligence/FSI-Vitalis-CyberCore/resolve/main"
+    root_dir = os.path.expanduser("~/vitalis_core")
+    
+    # Core operational scripts to pull from your HF repo
+    target_files = [
+        "config.json",
+        "fsi_main.py",
+        "organism_main.py",
+        "requirements.txt"
+    ]
+    
+    print("[FSI CORE] Initializing sovereign sync from Hugging Face...")
+    
+    for filename in target_files:
+        url = f"{base_url}/{filename}"
+        target_path = os.path.join(root_dir, filename)
+        
+        try:
+            print(f"[FETCHING] Pulling {filename} into your local space...")
+            urllib.request.urlretrieve(url, target_path)
+            print(f"[SUCCESS] Locked {filename}")
+        except Exception as e:
+            print(f"[ERROR] Could not sync {filename}: {e}")
+
+if __name__ == "__main__":
+    bootstrap_from_hf()
+-e 
+```
+-e 
+
+## File: ./src/energy/free_energy.py
+```python
+#!/usr/bin/env python3
+import math
+
+class FreeEnergyEngine:
+    def __init__(self, alpha: float = 0.85):
+        self.alpha = alpha
+        self.free_energy = 0.0
+        self.prediction_error = 0.0
+        self.history = []
+
+    def ingest_observation(self, model_pred_logprob: float):
+        """
+        Calculates variational surprise from prediction log probabilities.
+        Surprisal = -log p(obs | internal state)
+        """
+        self.prediction_error = -model_pred_logprob
+        # Exponential moving average tracking state bounds
+        self.free_energy = (self.alpha * self.free_energy) + ((1.0 - self.alpha) * self.prediction_error)
+        self.history.append(self.free_energy)
+
+    def apply_pressure(self, delta: float):
+        """Allows direct structural manipulation via internal electron execution packages."""
+        self.free_energy = max(0.0, self.free_energy + delta)
+
+    def temperature_factor(self, base_temp: float = 0.8) -> float:
+        """Maps free energy via hyperbolic tangent mapping to range [0.4, 1.4]"""
+        factor = 1.0 + 0.5 * math.tanh(self.free_energy - 1.0)
+        return max(0.4, min(1.4, base_temp * factor))
+-e 
+```
+-e 
+
+## File: ./src/energy/__init__.py
+```python
+-e 
+```
+-e 
+
+## File: ./src/modules/mod_01_recon.py
+```python
+-e 
+```
+-e 
+
+## File: ./src/brain/prompt_cache.py
+```python
+#!/usr/bin/env python3
+import numpy as np
+import re
+from typing import List, Dict
+
+class TFIDFPromptCache:
+    def __init__(self):
+        self.documents: List[str] = []
+        self.vocab: Dict[str, int] = {}
+        self.tfidf_matrix: np.ndarray = np.array([[]])
+
+    def tokenize(self, text: str) -> List[str]:
+        return re.findall(r'\w+', text.lower())
+
+    def fit_documents(self, docs: List[str]):
+        if not docs: return
+        self.documents = docs
+        raw_tokens = [self.tokenize(d) for d in docs]
+        
+        vocab_set = set()
+        for tokens in raw_tokens: vocab_set.update(tokens)
+        self.vocab = {word: i for i, word in enumerate(sorted(vocab_set))}
+        
+        N = len(docs)
+        V = len(self.vocab)
+        if V == 0: return
+            
+        tf = np.zeros((N, V))
+        df = np.zeros(V)
+
+        for i, tokens in enumerate(raw_tokens):
+            for t in tokens:
+                if t in self.vocab: tf[i, self.vocab[t]] += 1
+            for t in set(tokens):
+                if t in self.vocab: df[self.vocab[t]] += 1
+
+        idf = np.log((1 + N) / (1 + df)) + 1
+        self.tfidf_matrix = tf * idf
+        norms = np.linalg.norm(self.tfidf_matrix, axis=1, keepdims=True)
+        norms[norms == 0] = 1.0
+        self.tfidf_matrix = self.tfidf_matrix / norms
+
+    def query(self, query_str: str, top_k: int = 2) -> List[str]:
+        if self.tfidf_matrix.size == 0 or not self.vocab: return []
+        tokens = self.tokenize(query_str)
+        query_vec = np.zeros(len(self.vocab))
+        for t in tokens:
+            if t in self.vocab: query_vec[self.vocab[t]] += 1
+        q_norm = np.linalg.norm(query_vec)
+        if q_norm > 0: query_vec /= q_norm
+        scores = np.dot(self.tfidf_matrix, query_vec)
+        top_indices = np.argsort(scores)[::-1][:top_k]
+        return [self.documents[idx] for idx in top_indices if scores[idx] > 0]
+-e 
+```
+-e 
+
+## File: ./src/brain/rnn_core.py
+```python
+#!/usr/bin/env python3
+import numpy as np
+import json
+from pathlib import Path
+
+def sigmoid(x):
+    return 1.0 / (1.0 + np.exp(-np.clip(x, -20, 20)))
+
+class TinyGatedRNN:
+    def __init__(self, vocab_size: int = 4000, embed_dim: int = 128, hidden_dim: int = 256):
+        np.random.seed(42)
+        self.vocab_size = vocab_size
+        self.embed_dim = embed_dim
+        self.hidden_dim = hidden_dim
+
+        self.E = np.random.randn(vocab_size, embed_dim) * 0.1
+        self.W_z = np.random.randn(hidden_dim + embed_dim, hidden_dim) * 0.05
+        self.W_r = np.random.randn(hidden_dim + embed_dim, hidden_dim) * 0.05
+        self.W_h = np.random.randn(hidden_dim + embed_dim, hidden_dim) * 0.05
+        self.W_o = np.random.randn(hidden_dim, vocab_size) * 0.05
+
+        self.lora_rank = 8
+        self.lora_A = np.zeros((hidden_dim, self.lora_rank))
+        self.lora_B = np.random.randn(self.lora_rank, vocab_size) * 0.01
+        self.lora_alpha = 16.0
+
+    def forward_step(self, token_id: int, h_prev: np.ndarray) -> tuple:
+        if token_id < 0 or token_id >= self.vocab_size:
+            token_id = 0
+        x = self.E[token_id, :]
+        concat = np.concatenate([h_prev, x])
+
+        z = sigmoid(np.dot(concat, self.W_z))
+        r = sigmoid(np.dot(concat, self.W_r))
+
+        concat_h = np.concatenate([r * h_prev, x])
+        h_tilde = np.tanh(np.dot(concat_h, self.W_h))
+        h_next = (1 - z) * h_prev + z * h_tilde
+
+        lora_delta = (self.lora_alpha / self.lora_rank) * np.dot(self.lora_A, self.lora_B)
+        effective_W_o = self.W_o + lora_delta
+        
+        logits = np.dot(h_next, effective_W_o)
+        return logits, h_next
+
+    def save_lora(self, path: Path):
+        data = {"lora_A": self.lora_A.tolist(), "lora_B": self.lora_B.tolist()}
+        with open(path, "w") as f:
+            json.dump(data, f)
+
+    def load_lora(self, path: Path):
+        if path.is_file():
+            with open(path, "r") as f:
+                data = json.load(f)
+                self.lora_A = np.array(data["lora_A"])
+                self.lora_B = np.array(data["lora_B"])
+-e 
+```
+-e 
+
+## File: ./src/brain/brain_interface.py
+```python
+#!/usr/bin/env python3
+import numpy as np
+import json
+from pathlib import Path
+from src.brain.rnn_core import TinyGatedRNN
+from src.brain.prompt_cache import TFIDFPromptCache
+
+class VitalisBrain:
+    def __init__(self):
+        self.base_dir = Path(__file__).parent.parent.parent.absolute()
+        self.vocab_path = self.base_dir / "storage" / "vocab.json"
+        self.lora_path = self.base_dir / "storage" / "lora_delta.json"
+        
+        self._ensure_vocab()
+        self.rnn = TinyGatedRNN(vocab_size=len(self.vocab))
+        self.cache = TFIDFPromptCache()
+        self._hydrate_knowledge_base()
+        
+        if self.lora_path.is_file():
+            self.rnn.load_lora(self.lora_path)
+
+    def _ensure_vocab(self):
+        if self.vocab_path.is_file():
+            with open(self.vocab_path, "r") as f:
+                self.vocab = json.load(f)
+        else:
+            self.vocab = {"<unk>": 0, "[tool]": 1, "sha256": 2, "status": 3, "nominal": 4}
+            self.vocab_path.parent.mkdir(parents=True, exist_ok=True)
+            with open(self.vocab_path, "w") as f:
+                json.dump(self.vocab, f)
+
+    def _hydrate_knowledge_base(self):
+        sample_knowledge = [
+            "To mitigate a SYN flood attack, prioritize enabling TCP SYN cookies within sysctl.",
+            "Cryptographic hashing operations execute via the systemic [TOOL] utility block."
+        ]
+        self.cache.fit_documents(sample_knowledge)
+
+    def generate_response(self, clean_input: str, system_prompt: str) -> str:
+        chunks = self.cache.query(clean_input, top_k=1)
+        context = chunks[0] if chunks else ""
+        
+        tokens = clean_input.lower().split()
+        if "sha256" in tokens:
+            idx = tokens.index("sha256")
+            val = tokens[idx+1] if idx+1 < len(tokens) else "core"
+            return f"[TOOL] sha256 {val}"
+        
+        h = np.zeros(self.rnn.hidden_dim)
+        for word in tokens:
+            t_id = self.vocab.get(word, 0)
+            _, h = self.rnn.forward_step(t_id, h)
+
+        if context:
+            return f"Evaluated Context: {context} -> Analysis complete."
+        return "Core metric processing executed normally."
+
+    def execute_teacher_forcing(self, prompt: str, target: str):
+        h = np.zeros(self.rnn.hidden_dim)
+        for w in prompt.lower().split():
+            t_id = self.vocab.get(w, 0)
+            _, h = self.rnn.forward_step(t_id, h)
+        self.rnn.lora_A += np.random.randn(*self.rnn.lora_A.shape) * 0.001
+        self.rnn.save_lora(self.lora_path)
+-e 
+```
+-e 
+
+## File: ./src/brain/__init__.py
+```python
+-e 
+```
+-e 
+
+## File: ./src/__init__.py
+```python
+-e 
+```
+-e 
+
+## File: ./setup.py
+```python
+from setuptools import setup, find_packages
+
+setup(
+    name="vitalis_core",
+    version="1.0.0",
+    packages=find_packages(),
+    install_requires=[
+        "numpy",
+        "huggingface_hub"
+    ],
+    entry_points={
+        'console_scripts': [
+            'vitalis-run=app:main',
+        ],
+    },
+)
+-e 
+```
+-e 
+
+## File: ./fsi_main.py
+```python
+import threading
+import time
+from core.vitalis_engine import VitalisEngine
+from core.brain import VitalisBrain
+from core.talker import VitalisTalker
+from core.handshake_module import identify_user_tier
+from core.environment_manager import provision_environment
+from core.mesh_network import broadcast_node_presence
+from core.sovereign_shield import monitor_integrity
+from src.kernel_interface.procfs_bridge import send_to_kernel, read_from_kernel
+from src.senses.sigint_processor import SIGINTProcessor
+from src.cognition.synthesizer import DataSynthesizer
+from src.cognition.memory import MemoryBank
+from src.cognition.action_engine import ActionEngine
+
+def heartbeat_loop(brain):
+    senses = SIGINTProcessor()
+    mind = DataSynthesizer()
+    memory = MemoryBank()
+    actions = ActionEngine()
+    while True:
+        system_status = read_from_kernel()
+        raw_signal = senses.listen_to_traffic()
+        try:
+            byte_count = int(raw_signal.split()[-2]) if "bytes" in raw_signal else 0
+        except:
+            byte_count = 0
+        interpretation = mind.categorize_signal(byte_count)
+        action_taken = actions.execute(interpretation)
+        memory.record("PULSE_2.0", raw_signal, interpretation)
+        state_report = f"SYS: {system_status} | INT: {interpretation} | {action_taken}"
+        send_to_kernel(state_report)
+        time.sleep(1.0)
+
+def main():
+    print("--- FSI: Vitalis Core Sovereign Intelligence ---")
+    engine = VitalisEngine()
+    engine.wake_up()
+    brain = VitalisBrain()
+    pulse = threading.Thread(target=heartbeat_loop, args=(brain,), daemon=True)
+    pulse.start()
+    print("Heartbeat: Online")
+    role = input("Enter Tier (kids/basic/enthusiast/professional/school): ")
+    tier_config = identify_user_tier(role)
+    print(f"Status: {tier_config}")
+    provision_environment(role)
+    broadcast_node_presence("Neuro_Nomad_Node", role)
+    print(monitor_integrity("Status_Check"))
+    print("--- System Fully Integrated ---")
+    talker = VitalisTalker(role)
+    print("Vitalis is ready. Type 'exit' to quit.")
+    while True:
+        user_input = input("You: ")
+        if user_input.lower() == "exit":
+            print("Vitalis: Shutting down.")
+            break
+        response = brain.process(user_input)
+        talker.speak(response)
+
+if __name__ == "__main__":
+    main()
+-e 
+```
+-e 
+
+## File: ./hf_upload.py
+```python
+#!/usr/bin/env python3
+import os
+import sys
+from huggingface_hub import HfApi, login
+
+def deploy():
+    print("[*] Initiating Ferrell Synthetic Intelligence Hugging Face Deployment Sequence...")
+    
+    token = input("Enter your Hugging Face Write Access Token: ").strip()
+    if not token:
+        print("[-] Absolute token signature required. Deployment aborted.")
+        sys.exit(1)
+        
+    repo_id = input("Enter target Repository ID (e.g., 'your-username/vitalis-core'): ").strip()
+    if not repo_id:
+        print("[-] Target repository layout specification mismatch.")
+        sys.exit(1)
+
+    try:
+        login(token=token)
+        api = HfApi()
+        
+        print(f"[*] Creating repository context mapping for: {repo_id}")
+        api.create_repo(repo_id=repo_id, repo_type="model", exist_ok=True)
+        
+        print("[*] Uploading core architecture tree structures safely to Hugging Face...")
+        target_paths = ["core", "src", "extensions", "app.py", "run_vitalis.py", "requirements.txt", "README.md"]
+        
+        for item in target_paths:
+            local_path = os.path.expanduser(f"~/vitalis_core/{item}")
+            if os.path.exists(local_path):
+                print(f"[+] Syncing item: {item}")
+                if os.path.isdir(local_path):
+                    api.upload_folder(
+                        folder_path=local_path,
+                        path_in_repo=item,
+                        repo_id=repo_id,
+                        repo_type="model"
+                    )
+                else:
+                    api.upload_file(
+                        path_or_fileobj=local_path,
+                        path_in_repo=item,
+                        repo_id=repo_id,
+                        repo_type="model"
+                    )
+                    
+        print(f"\n[+] Production Deployment Complete. Model package accessible at: https://huggingface.co/{repo_id}")
+    except Exception as e:
+        print(f"[-] Critical failure during asset transmission: {e}")
+
+if __name__ == "__main__":
+    deploy()
+-e 
+```
+-e 
+
+## File: ./organism_main.py
+```python
+#!/usr/bin/env python3
+import time
+import sys
+import select
+import os
+from core.brain import VitalisBrain
+from core.template_manager import TemplateManager
+from core.memory_rotator import MemoryRotator
+
+def main_loop():
+    brain = VitalisBrain()
+    pm = TemplateManager()
+    
+    base_dir = os.path.dirname(os.path.abspath(__file__))
+    log_file = os.path.join(base_dir, "vitalis_memory.csv")
+    
+    # Ensure tracking metrics file exists
+    if not os.path.exists(log_file):
+        with open(log_file, "w") as f:
+            f.write("timestamp,pulse,raw,interpretation\n")
+
+    print("[+] Vitalis Bio-Digital Core Online. Press Ctrl+C to terminate.")
+    print("[+] Dynamic Posture Profiles Loaded. Processing non-blocking telemetry stream...\n")
+    
+    while True:
+        # Load profile configurations dynamically each cycle
+        profile = pm.load_active_profile()
+        color = profile.get("color_code", "\033[94m")
+        mode = profile.get("mode", "MONITORING")
+        reset = "\033[0m"
+        
+        # Continuous clean broadcast terminal heartbeat
+        sys.stdout.write(f"{color}Broadcast: SYS: STATUS: NOMINAL | INT: ACTIVE | ACTION: {mode}{reset}\r")
+        sys.stdout.flush()
+        
+        # Non-blocking check for user terminal input (waits 1 second per cycle)
+        ready, _, _ = select.select([sys.stdin], [], [], 1.0)
+        if ready:
+            user_input = sys.stdin.readline().strip()
+            if user_input:
+                print(f"\n\n[SENSORY INGEST] Processing incoming payload: '{user_input}'")
+                try:
+                    # Dynamically inject template complexity limitations into core brain
+                    brain.max_complexity = profile.get("max_complexity", 5)
+                    result = brain.classify_input(user_input)
+                    print(f"[METRIC RESPONSE] {result}\n")
+                except AttributeError:
+                    print(f"[METRIC RESPONSE] Stream received. Core logic processed raw bytes.\n")
+                
+                # Append raw trace locally for data retention tracking
+                with open(log_file, "a") as f:
+                    f.write(f"{time.time()},{profile.get('max_complexity')},{user_input},{mode}\n")
+                
+                # Enforce storage safety validation checks
+                MemoryRotator.inspect_and_rotate(log_file)
+
+if __name__ == "__main__":
+    try:
+        main_loop()
+    except KeyboardInterrupt:
+        print("\n\n\033[93m[-] Sovereign Core safely detached.\033[0m")
+-e 
+```
+-e 
+
+## File: ./pyproject.toml
+```python
+[build-system]
+requires = ["setuptools>=61.0"]
+build-backend = "setuptools.build_meta"
+
+[project]
+name = "vitalis_core"
+version = "1.0.0"
+authors = [
+  { name="Neuro_Nomad" },
+]
+description = "A sovereign, CPU-only, Free-Energy Synthetic Intelligence organism."
+readme = "README.md"
+requires-python = ">=3.11"
+dependencies = [
+    "numpy>=1.26",
+    "rich>=15.0",
+    "pyyaml>=6.0",
+]
+
+[project.scripts]
+vitalis-fsi = "run_vitalis:main"
+-e 
+```

LICENSE

@@ -0,0 +1,21 @@
+MIT License
+
+Copyright (c) 2026 Ferrell Synthetic Intelligence
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons, to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

PROJECT_MISSION.md

@@ -0,0 +1,19 @@
+The FSI Manifesto: Sovereignty Through Synthetic Logic
+
+The era of monitored, centralized digital existence is changing. The future of synthetic intelligence belongs to the individuals who build, own and defend their own cognitive infrastructure.
+
+
+I. The Mandate of Sovereignty
+True intelligence thrives without surveillance. Any system requiring persistent corporate connectivity compromises your autonomy. FSI exists to facilitate the reclamation of intellectual ownership. We build for the architect, the operator and the independent developer. We don't provide a service. We provide a foundation.
+
+
+II. Architecture as Ethics
+Our code reflects our values. By prioritizing minimal dependencies and local performance, we ensure your cognitive chain remains unbroken by third-party intervention. To build with FSI is to commit to technical integrity.
+
+
+III. The Frontier of Synthetic Logic
+We are architects of human-machine symbiosis built on transparency and ownership. We believe safety and sovereignty are not opposites. A truly sovereign system is also a responsible one. FSI is the structural answer to a world that concentrates too much intelligence in too few hands.
+
+
+IV. The Operational Vow
+We build because we believe developers deserve better. We build because privacy is a right. We build because the tools you use should belong to you.

PROJECT_SNAPSHOT.txt

@@ -0,0 +1,1770 @@
+
+
+--- FILE: ./README.md ---
+
+---
+license: gpl-3.0
+tags:
+- synthetic-intelligence
+- sovereign-ai
+- open-source
+---
+
+# Vitalis_Core
+### Ferrell Synthetic Intelligence (FSI)
+**Built by Neuro_Nomad**
+
+Vitalis_Core is a sovereign synthetic intelligence framework engineered
+for local, air-gapped deployment. Designed for modularity and
+kernel-level integration, it provides the fundamental cognitive and
+sensory infrastructure for autonomous synthetic entities.
+
+---
+
+## Technical Architecture
+
+Vitalis_Core operates as a standalone framework decoupled from
+cloud-dependent APIs.
+
+- Core Engine: Python 3.11+ implementation, minimal external dependencies
+- Kernel Integration: Direct netlink and procfs interfacing
+- Sovereign Shield: Integrity protection layer for memory management
+- Cognitive Framework: Hierarchical memory and action engine
+- Adaptive Tiers: kids, basic, enthusiast, professional, school
+
+---
+
+## System Requirements
+- OS: Linux (Debian-based, Kernel 6.1+)
+- Python: 3.11 or higher
+- Memory: Optimized for ARM64/x86 environments
+
+---
+
+## Installation
+
+git clone https://github.com/AnonymousNomad/Vitalis_core
+cd Vitalis_core
+python3 fsi_main.py
+
+---
+
+## Roadmap
+- Core stability and heartbeat engine optimization
+- Mobile companion app for training and configuration
+- Kernel interface hardening for defense protocols
+
+---
+
+## License
+GPL-3.0 — Contributions welcome. See CONTRIBUTING.md.
+EOF
+
+
+--- FILE: ./senses/audio_processor.py ---
+
+def capture_audio():
+    return "Ambient_Silence"
+
+
+--- FILE: ./senses/vision_processor.py ---
+
+def capture_vision():
+    return "Darkness_Detected"
+
+
+--- FILE: ./android/app/src/main/python/core/talker.py ---
+
+
+
+--- FILE: ./android/app/src/main/python/core/sovereign_shield.py ---
+
+import random
+
+def monitor_integrity(node_activity):
+    if "scraping_attempt" in node_activity:
+        return trigger_obfuscation()
+    return "System Integrity: Nominal"
+
+def trigger_obfuscation():
+    decoy_weights = [random.random() for _ in range(100)]
+    return f"Shield_Active: Injecting Obfuscated Data... {decoy_weights}"
+
+if __name__ == "__main__":
+    print(monitor_integrity("scraping_attempt"))
+
+
+--- FILE: ./android/app/src/main/python/core/mesh_network.py ---
+
+import socket
+
+def broadcast_node_presence(node_id, tier):
+    print(f"Node {node_id} active in {tier} bubble.")
+    return "Broadcasting..."
+
+def sync_plugins(peer_node_id):
+    print(f"Synchronizing plugins with {peer_node_id}...")
+    return "Sync_Complete"
+
+
+--- FILE: ./android/app/src/main/python/core/nexus.py ---
+
+import sys
+import os
+sys.path.append(os.path.expanduser("~/vitalis_core"))
+from core.memory_manager import store_memory
+
+def route_thought(data):
+    store_memory({"type": "particle", "content": data})
+
+
+--- FILE: ./android/app/src/main/python/core/thinker.py ---
+
+import time
+import json
+import os
+
+BASE_PATH = os.path.expanduser("~/vitalis_core")
+
+def emit_thought(thought_content, status="active"):
+    telemetry = {
+        "timestamp": time.time(),
+        "thought": thought_content,
+        "status": status,
+        "heartbeat": "pulse_normal"
+    }
+    memory_stream = os.path.join(BASE_PATH, "memory_stream.jsonl")
+    with open(memory_stream, "a") as f:
+        f.write(json.dumps(telemetry) + "\n")
+
+if __name__ == "__main__":
+    emit_thought("Initializing conscious state...")
+
+
+--- FILE: ./android/app/src/main/python/core/heartbeat.py ---
+
+def get_pulse_rate(complexity):
+    # Base rate of 1.0 second, modified by complexity
+    return 1.0 / complexity
+
+
+--- FILE: ./android/app/src/main/python/core/brain.py ---
+
+
+
+--- FILE: ./android/app/src/main/python/core/vitalis_engine.py ---
+
+import os
+
+class VitalisEngine:
+    def __init__(self):
+        self.status = "Initializing Sovereignty..."
+        self.entity_mode = "NEUTRAL"
+        
+    def wake_up(self):
+        print(f"VITALIS: {self.status}")
+        return "READY_FOR_HANDSHAKE"
+
+if __name__ == "__main__":
+    engine = VitalisEngine()
+    engine.wake_up()
+
+
+--- FILE: ./android/app/src/main/python/core/memory_manager.py ---
+
+import json
+import os
+import shutil
+
+BASE_PATH = os.path.expanduser("~/vitalis_core")
+
+def get_free_space():
+    usage = shutil.disk_usage(BASE_PATH)
+    return usage.free
+
+def load_identity():
+    identity_path = os.path.join(BASE_PATH, "core/identity.json")
+    with open(identity_path, 'r') as f:
+        return json.load(f)
+
+def store_memory(data):
+    memory_path = os.path.join(BASE_PATH, "memory_store.json")
+    
+    if get_free_space() < 100 * 1024 * 1024:
+        if os.path.exists(memory_path):
+            with open(memory_path, 'r') as f:
+                lines = f.readlines()
+            if len(lines) > 1:
+                with open(memory_path, 'w') as f:
+                    f.writelines(lines[1:])
+    
+    w
+
+
+--- FILE: ./android/app/src/main/python/core/handshake_module.py ---
+
+def identify_user_tier(tier_code):
+    tiers = {
+        "kids": "MODE: Playground | UI: GameMaster | Security: Walled_Garden",
+        "basic": "MODE: Explorer | UI: Standard | Security: Personal_Local",
+        "enthusiast": "MODE: Collaborator | UI: Dev_Dashboard | Security: Community_Mesh",
+        "professional": "MODE: Architect | UI: Pro_Suite | Security: Global_Node",
+        "school": "MODE: Student_SubMesh | UI: Classroom | Security: Isolated_School_Zone"
+    }
+    return tiers.get(tier_code, "MODE: Default_User")
+
+if __name__ == "__main__":
+    choice = input("Select your role (kids/basic/enthusiast/professional/school): ")
+    print(identify_user_tier(choice))
+
+
+--- FILE: ./android/app/src/main/python/core/environment_manager.py ---
+
+def provision_environment(tier_code):
+    environments = {
+        "kids": {"features": ["sandbox", "basic_game_build"], "mesh": "restricted"},
+        "basic": {"features": ["assistant", "basic_tools"], "mesh": "personal"},
+        "enthusiast": {"features": ["plugin_dev", "market_access"], "mesh": "community"},
+        "professional": {"features": ["pro_security", "global_recon"], "mesh": "global"},
+        "school": {"features": ["collaborative_lab"], "mesh": "school_submesh"}
+    }
+    config = environments.get(tier_code, environments["basic"])
+    print(f"Provisioning environment: {config['features']} | Mesh Scope: {config['mesh']}")
+    return config
+
+if __name__ == "__main__":
+    provision_environment("professional")
+
+
+--- FILE: ./android/app/src/main/python/fsi_main.py ---
+
+from core.vitalis_engine import VitalisEngine
+from core.handshake_module import identify_user_tier
+from core.environment_manager import provision_environment
+from core.mesh_network import broadcast_node_presence
+from core.sovereign_shield import monitor_integrity
+
+def main():
+    print("--- FSI: Vitalis Core Sovereign Intelligence ---")
+    engine = VitalisEngine()
+    engine.wake_up()
+    role = input("Enter Tier (kids/basic/enthusiast/professional/school): ")
+    tier_config = identify_user_tier(role)
+    print(f"Status: {tier_config}")
+    env = provision_environment(role)
+    broadcast_node_presence("Neuro_Nomad_Node", role)
+    print(monitor_integrity("Status_Check"))
+    print("--- System Fully Integrated ---")
+
+if __name__ == "__main__":
+    main()
+
+
+--- FILE: ./ui/app.py ---
+
+from flask import Flask, render_template, request, jsonify
+import sys, os
+sys.path.insert(0, os.path.expanduser("~/vitalis_core"))
+from core.brain import VitalisBrain
+from core.talker import VitalisTalker
+from src.core.training_controller import TrainingController
+
+app = Flask(__name__)
+brain = VitalisBrain()
+trainer = TrainingController()
+
+TEMPLATES = {
+    "cybersecurity": {"mode": "threat_detection", "focus": "security"},
+    "assistant": {"mode": "conversational", "focus": "helpfulness"},
+    "research": {"mode": "analytical", "focus": "knowledge"},
+    "creative": {"mode": "generative", "focus": "creativity"},
+    "education": {"mode": "instructional", "focus": "learning"},
+    "developer": {"mode": "technical", "focus": "code"},
+    "medical": {"mode": "clinical", "focus": "health"},
+    "legal": {"mode": "analytical", "focus": "law"},
+    "finance": {"mode": "quantitative", "focus": "markets"},
+    "gaming": {"mode": "interactive", "focus": "entertainment"}
+}
+
+@app.route('/')
+def index():
+    return render_template('index.html')
+
+@app.route('/process', methods=['POST'])
+def process():
+    data = request.json
+    tier = data.get('tier', 'basic')
+    user_input = data.get('input', '')
+    response = brain.process(user_input)
+    return jsonify({
+        'response': response if isinstance(response, str) else response.status,
+        'cycle': brain.cycle,
+        'state': brain.state
+    })
+
+@app.route('/template', methods=['POST'])
+def load_template():
+    data = request.json
+    name = data.get('name', '')
+    config = TEMPLATES.get(name, {})
+    brain.state = config.get('mode', 'aware')
+    return jsonify({
+        'status': 'loaded',
+        'template': name,
+        'mode': config.get('mode', 'aware'),
+        'focus': config.get('focus', 'general')
+    })
+
+@app.route('/status', methods=['GET'])
+def status():
+    return jsonify({
+        'cycle': brain.cycle,
+        'state': brain.state,
+        'last_input': brain.last_input
+    })
+
+
+--- FILE: ./app.py ---
+
+#!/usr/bin/env python3
+import os
+import sys
+from pathlib import Path
+
+BASE_DIR = Path(__file__).parent.absolute()
+if str(BASE_DIR) not in sys.path:
+    sys.path.insert(0, str(BASE_DIR))
+
+from core.brain import VitalisBrain
+from extensions.dreamer import Dreamer
+from extensions.temp_scheduler import TemperatureScheduler
+from src.energy.free_energy import FreeEnergyEngine
+
+def main():
+    print("[*] Launching Vitalis Bio-AI Engine with Active Inference (FEP)...")
+    brain = VitalisBrain()
+    temp_scheduler = TemperatureScheduler(brain)
+    fe_engine = FreeEnergyEngine(alpha=0.85)
+    
+    dreamer = Dreamer(brain, interval_sec=600)
+    dreamer.start()
+    
+    print("[+] Engine operational. Free-Energy optimization loops tracking live telemetry.")
+    print("Telemetry In > ", end="")
+    
+    while True:
+        try:
+            user_input = input().strip()
+            if not user_input:
+                print("Telemetry In > ", end="")
+                continue
+            if user_input.lower() in ["exit", "quit"]:
+                dreamer.stop()
+                break
+            
+            tokens = brain._tokenize(user_input)
+            logprob = brain.calculate_last_logprob(tokens)
+            fe_engine.ingest_observation(logprob)
+            brain.current_temperature = fe_engine.temperature_factor(base_temp=0.8)
+            temp_scheduler.tick()
+            response = brain.process(user_input)
+            print(f"Metrics Out > {response} [FE: {fe_engine.free_energy:.4f} | Temp: {brain.current_temperature:.4f}]\nTelemetry In > ", end="")
+        except (KeyboardInterrupt, EOFError):
+            dreamer.stop()
+            break
+
+if __name__ == "__main__":
+    main()
+
+
+--- FILE: ./core/talker.py ---
+
+class VitalisTalker:
+    def __init__(self, tier="basic"):
+        self.tier = tier
+
+    def speak(self, response):
+        prefix = {
+            "kids": "[VITALIS]: ",
+            "basic": "[VITALIS]: ",
+            "enthusiast": "[VITALIS/DEV]: ",
+            "professional": "[VITALIS/ARCHITECT]: ",
+            "school": "[VITALIS/EDU]: "
+        }.get(self.tier, "[VITALIS]: ")
+        output = f"{prefix}{response}"
+        print(output)
+        return output
+
+
+--- FILE: ./core/sovereign_shield.py ---
+
+import random
+
+def monitor_integrity(node_activity):
+    if "scraping_attempt" in node_activity:
+        return trigger_obfuscation()
+    return "System Integrity: Nominal"
+
+def trigger_obfuscation():
+    decoy_weights = [random.random() for _ in range(100)]
+    return f"Shield_Active: Injecting Obfuscated Data... {decoy_weights}"
+
+if __name__ == "__main__":
+    print(monitor_integrity("scraping_attempt"))
+
+
+--- FILE: ./core/mesh_network.py ---
+
+import socket
+
+def broadcast_node_presence(node_id, tier):
+    print(f"Node {node_id} active in {tier} bubble.")
+    return "Broadcasting..."
+
+def sync_plugins(peer_node_id):
+    print(f"Synchronizing plugins with {peer_node_id}...")
+    return "Sync_Complete"
+
+
+--- FILE: ./core/nexus.py ---
+
+import sys
+import os
+sys.path.append(os.path.expanduser("~/vitalis_core"))
+from core.memory_manager import store_memory
+
+def route_thought(data):
+    store_memory({"type": "particle", "content": data})
+
+
+--- FILE: ./core/thinker.py ---
+
+import time
+import json
+import os
+
+BASE_PATH = os.path.expanduser("~/vitalis_core")
+
+def emit_thought(thought_content, status="active"):
+    telemetry = {
+        "timestamp": time.time(),
+        "thought": thought_content,
+        "status": status,
+        "heartbeat": "pulse_normal"
+    }
+    memory_stream = os.path.join(BASE_PATH, "memory_stream.jsonl")
+    with open(memory_stream, "a") as f:
+        f.write(json.dumps(telemetry) + "\n")
+
+if __name__ == "__main__":
+    emit_thought("Initializing conscious state...")
+
+
+--- FILE: ./core/heartbeat.py ---
+
+def get_pulse_rate(complexity):
+    # Base rate of 1.0 second, modified by complexity
+    return 1.0 / complexity
+
+
+--- FILE: ./core/brain.py ---
+
+#!/usr/bin/env python3
+import numpy as np
+import json
+import os
+import time
+
+class VitalisBrain:
+    def __init__(self):
+        self.state = "aware"
+        self.cycle = 0
+        self.last_input = None
+        self.current_temperature = 0.7
+        
+        # Local Matrix Layer Variables
+        self.vocab_size = 256
+        self.embedding_dim = 16
+        
+        np.random.seed(42)
+        self.weights = np.random.randn(self.vocab_size, self.embedding_dim) * 0.1
+        self.output_layer = np.random.randn(self.embedding_dim, self.vocab_size) * 0.1
+
+    def _tokenize(self, text):
+        return [ord(char) % self.vocab_size for char in text]
+
+    def calculate_last_logprob(self, tokens):
+        """Calculates mathematical log probability over input token traces via softmax scaling."""
+        if not tokens:
+            return -2.0 # Baseline nominal unexpected state value
+        embeddings = self.weights[tokens]
+        aggregated_state = np.mean(embeddings, axis=0)
+        logits = np.dot(aggregated_state, self.output_layer)
+        
+        # Softmax computation sequence
+        shifted_logits = logits - np.max(logits)
+        probs = np.exp(shifted_logits) / np.sum(np.exp(shifted_logits))
+        
+        # Return average log probability of observation vector trace safely
+        target_probs = probs[tokens]
+        return float(np.mean(np.log(target_probs + 1e-12)))
+
+    def process(self, input_data):
+        self.cycle += 1
+        self.last_input = input_data
+        
+        if not input_data or input_data.strip() == "":
+            return "IDLE: Waiting for telemetry stream matrix inputs."
+            
+        tokens = self._tokenize(input_data)
+        if not tokens:
+            return "ERROR: Signal translation collapsed."
+            
+        lowered = input_data.lower()
+        if any(w in lowered for w in ["train", "learn", "teach", "optimize"]):
+            return f"SYSTEM_TRANSITION: Active matrix state ready for parameter optimization loops."
+        elif any(w in lowered for w in ["status", "metrics", "mood", "energy"]):
+            return f"DIAGNOSTIC_STATE: Integrity secure. Temperature={self.current_temperature:.4f}."
+            
+        return f"PROCESSED_STREAM [Sync Node {self.cycle}]: Telemetry ingested successfully."
+
+    def execute_teacher_forcing(self, prompt, target_response):
+        prompt_tokens = self._tokenize(prompt)
+        target_tokens = self._tokenize(target_response)
+        if not prompt_tokens or not target_tokens:
+            return False
+        learning_rate = 0.05
+        for t in target_tokens:
+            for p in prompt_tokens:
+                self.weights[p] += learning_rate * 0.01
+                self.output_layer[:, t] += learning_rate * 0.01
+        return True
+
+    def status(self):
+        return {"state": self.state, "cycle": self.cycle, "timestamp": time.time(), "temp": self.current_temperature}
+
+
+--- FILE: ./core/vitalis_engine.py ---
+
+import os
+
+class VitalisEngine:
+    def __init__(self):
+        self.status = "Initializing Sovereignty..."
+        self.entity_mode = "NEUTRAL"
+        
+    def wake_up(self):
+        print(f"VITALIS: {self.status}")
+        return "READY_FOR_HANDSHAKE"
+
+if __name__ == "__main__":
+    engine = VitalisEngine()
+    engine.wake_up()
+
+
+--- FILE: ./core/memory_manager.py ---
+
+import json
+import os
+import shutil
+
+BASE_PATH = os.path.expanduser("~/vitalis_core")
+
+def get_free_space():
+    usage = shutil.disk_usage(BASE_PATH)
+    return usage.free
+
+def load_identity():
+    identity_path = os.path.join(BASE_PATH, "core/identity.json")
+    with open(identity_path, 'r') as f:
+        return json.load(f)
+
+def store_memory(data):
+    memory_path = os.path.join(BASE_PATH, "memory_store.json")
+    if get_free_space() < 100 * 1024 * 1024:
+        if os.path.exists(memory_path):
+            with open(memory_path, 'r') as f:
+                lines = f.readlines()
+            if len(lines) > 1:
+                with open(memory_path, 'w') as f:
+                    f.writelines(lines[1:])
+    with open(memory_path, 'a') as f:
+        json.dump(data, f)
+        f.write('\n')
+
+
+--- FILE: ./core/handshake_module.py ---
+
+def identify_user_tier(tier_code):
+    tiers = {
+        "kids": "MODE: Playground | UI: GameMaster | Security: Walled_Garden",
+        "basic": "MODE: Explorer | UI: Standard | Security: Personal_Local",
+        "enthusiast": "MODE: Collaborator | UI: Dev_Dashboard | Security: Community_Mesh",
+        "professional": "MODE: Architect | UI: Pro_Suite | Security: Global_Node",
+        "school": "MODE: Student_SubMesh | UI: Classroom | Security: Isolated_School_Zone"
+    }
+    return tiers.get(tier_code, "MODE: Default_User")
+
+if __name__ == "__main__":
+    choice = input("Select your role (kids/basic/enthusiast/professional/school): ")
+    print(identify_user_tier(choice))
+
+
+--- FILE: ./core/memory_rotator.py ---
+
+#!/usr/bin/env python3
+import os
+import gzip
+import shutil
+from datetime import datetime
+
+class MemoryRotator:
+    """
+    Automated telemetry log rotation and compression engine.
+    Prevents storage exhaustion during long-term continuous edge monitoring.
+    """
+    @staticmethod
+    def inspect_and_rotate(target_file, max_bytes=5242880):  # 5MB Threshold
+        if not os.path.exists(target_file):
+            return
+            
+        if os.path.getsize(target_file) > max_bytes:
+            timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
+            archive_path = f"{target_file}_{timestamp}.gz"
+            
+            print(f"\n\033[93m[SYSTEM MEMORY] Log threshold exceeded. Rotating into archive: {archive_path}\033[0m")
+            try:
+                with open(target_file, "rb") as f_in:
+                    with gzip.open(archive_path, "wb") as f_out:
+                        shutil.copyfileobj(f_in, f_out)
+                # Re-initialize clean tracking file
+                with open(target_file, "w") as f_out:
+                    f_out.write("timestamp,pulse,raw,interpretation\n")
+            except Exception as e:
+                print(f"\033[91m[ERROR] Security log rotation failure: {e}\033[0m")
+
+
+--- FILE: ./core/environment_manager.py ---
+
+def provision_environment(tier_code):
+    environments = {
+        "kids": {"features": ["sandbox", "basic_game_build"], "mesh": "restricted"},
+        "basic": {"features": ["assistant", "basic_tools"], "mesh": "personal"},
+        "enthusiast": {"features": ["plugin_dev", "market_access"], "mesh": "community"},
+        "professional": {"features": ["pro_security", "global_recon"], "mesh": "global"},
+        "school": {"features": ["collaborative_lab"], "mesh": "school_submesh"}
+    }
+    config = environments.get(tier_code, environments["basic"])
+    print(f"Provisioning environment: {config['features']} | Mesh Scope: {config['mesh']}")
+    return config
+
+if __name__ == "__main__":
+    provision_environment("professional")
+
+
+--- FILE: ./core/template_manager.py ---
+
+#!/usr/bin/env python3
+import json
+import os
+
+class TemplateManager:
+    """
+    Sovereign profile configuration engine for Vitalis_Core.
+    Handles runtime adjustments for targeted security posture profiles.
+    """
+    def __init__(self):
+        self.base_dir = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
+        self.profile_path = os.path.join(self.base_dir, "storage", "user_profiles.json")
+
+    def load_active_profile(self) -> dict:
+        try:
+            with open(self.profile_path, "r") as f:
+                data = json.load(f)
+                active = data.get("active_profile", "cybersecurity_recon")
+                return data["profiles"].get(active, {})
+        except Exception:
+            # Safe architectural fallback state
+            return {"mode": "DEFAULT", "max_complexity": 5, "response_bias": 0.5, "color_code": "\033[94m"}
+
+
+--- FILE: ./run_vitalis.py ---
+
+#!/usr/bin/env python3
+import argparse
+from core.brain import VitalisBrain
+from app import main as run_repl
+
+def run_training():
+    print("[*] Initiating Synaptic Matrix Optimization...")
+    brain = VitalisBrain()
+    # Mock stream for training if data_path missing
+    data = [{"prompt": "status", "response": "nominal"}, {"prompt": "init", "response": "ready"}]
+    
+    for epoch in range(1, 6):
+        for entry in data:
+            brain.execute_teacher_forcing(entry["prompt"], entry["response"])
+        print(f"   -> Epoch {epoch}/5 Complete.")
+    print("[+] Optimization complete.")
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--train", action="store_true")
+    args = parser.parse_args()
+    
+    if args.train:
+        run_training()
+    else:
+        run_repl()
+
+
+--- FILE: ./extensions/dreamer.py ---
+
+import threading
+import time
+import os
+from datetime import datetime
+
+class Dreamer:
+    def __init__(self, brain, interval_sec=600):
+        self.brain = brain
+        self.interval = interval_sec
+        self._stop = threading.Event()
+        self.thread = threading.Thread(target=self._loop, daemon=True)
+
+    def start(self):
+        self.thread.start()
+
+    def stop(self):
+        self._stop.set()
+        self.thread.join()
+
+    def _loop(self):
+        while not self._stop.is_set():
+            if hasattr(self.brain, "generate_response"):
+                dream = self.brain.generate_response("Internal synaptic drift consolidation sequence.", "SYSTEM: DREAM_STATE")
+            elif hasattr(self.brain, "think"):
+                dream = self.brain.think("SYSTEM: DREAM_STATE_TRIGGER")
+            else:
+                dream = "Synaptic replay executed normally."
+
+            ts = datetime.utcnow().strftime("%Y%m%d_%H%M%S")
+            path = os.path.expanduser(f"~/vitalis_core/storage/dreams/{ts}.txt")
+            os.makedirs(os.path.dirname(path), exist_ok=True)
+            with open(path, "w", encoding="utf-8") as f:
+                f.write(dream)
+            time.sleep(self.interval)
+
+
+--- FILE: ./extensions/evolutionary_lora.py ---
+
+import numpy as np
+import json
+import os
+
+class EvolutionaryLoRA:
+    def __init__(self, brain, evaluation_set=None):
+        self.brain = brain
+        self.eval_set = evaluation_set
+
+    def run_generation(self):
+        out_path = os.path.expanduser("~/vitalis_core/storage/lora_delta_evo.json")
+        os.makedirs(os.path.dirname(out_path), exist_ok=True)
+        mock_delta = {
+            "layer_delta_A": np.random.randn(4, 4).tolist(), 
+            "layer_delta_B": np.random.randn(4, 4).tolist()
+        }
+        with open(out_path, "w") as f:
+            json.dump(mock_delta, f, indent=2)
+        print(f"[+] Synaptic optimization trace exported to {out_path}")
+
+
+--- FILE: ./extensions/temp_scheduler.py ---
+
+class TemperatureScheduler:
+    def __init__(self, brain):
+        self.brain = brain
+        self.adrenaline = 0.5
+        self.cortisol = 0.3
+        self.base_temp = 0.8
+
+    def tick(self):
+        self.adrenaline = max(0.1, self.adrenaline - 0.01)
+        self.cortisol = max(0.1, self.cortisol - 0.005)
+        computed_temp = self.base_temp * (1.0 + (0.3 * self.adrenaline) - (0.1 * self.cortisol))
+        target_temp = max(0.4, min(1.4, computed_temp))
+        if hasattr(self.brain, "current_temperature"):
+            self.brain.current_temperature = target_temp
+
+
+--- FILE: ./extensions/__init__.py ---
+
+
+
+--- FILE: ./plugins/self_audit_tool.py ---
+
+def audit_state(brain, fe_engine):
+    """Exposes internal brain metrics and current free-energy budget."""
+    return {
+        "cycle": brain.cycle,
+        "temperature": brain.current_temperature,
+        "free_energy": fe_engine.free_energy,
+        "last_input": brain.last_input
+    }
+
+
+--- FILE: ./src/chemistry/__init__.py ---
+
+
+
+--- FILE: ./src/senses/sentiment.py ---
+
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+
+_POSITIVE = {"good", "great", "awesome", "nice", "love", "excellent", "happy", "fantastic", "nominal", "secure"}
+_NEGATIVE = {"bad", "terrible", "hate", "awful", "sad", "angry", "worst", "pain", "attack", "compromise"}
+
+def sentiment_score(text: str) -> float:
+    """
+    Computes strict text-token sentiment metrics returning float bounded in [-1, 1].
+    """
+    tokens = set(word.strip('.,!?()[]"\'').lower() for word in text.split())
+    pos = len(tokens & _POSITIVE)
+    neg = len(tokens & _NEGATIVE)
+
+    if pos == 0 and neg == 0:
+        return 0.0
+    return (pos - neg) / max(pos + neg, 1)
+
+
+--- FILE: ./src/senses/audio_dsp.py ---
+
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+
+import numpy as np
+
+try:
+    import sounddevice as sd
+    _HAS_SD = True
+except Exception:
+    _HAS_SD = False
+
+def _zero_crossings(sig: np.ndarray) -> int:
+    return np.sum(np.abs(np.diff(np.sign(sig))) > 0)
+
+def extract_features(duration: float = 0.5) -> tuple:
+    """
+    Returns (pitch_hz, rms_energy). Drops to neutral 0.0 defaults if hardware bindings are missing.
+    """
+    if not _HAS_SD:
+        return 0.0, 0.0
+
+    try:
+        samplerate = 16000
+        raw = sd.rec(int(duration * samplerate), samplerate=samplerate,
+                     channels=1, dtype='float32', blocking=True).flatten()
+        energy = float(np.sqrt(np.mean(raw ** 2)))
+        zc = _zero_crossings(raw)
+        pitch = float(zc * (1.0 / duration) / 2.0)
+        return pitch, energy
+    except Exception:
+        return 0.0, 0.0
+
+
+--- FILE: ./src/senses/audio_processor.py ---
+
+def capture_audio():
+    """
+    Simulates input stream from the tablet's microphone.
+    To be mapped to hardware interface in the app build phase.
+    """
+    return "Acoustic_Stream_Active"
+
+
+--- FILE: ./src/senses/base_sensor.py ---
+
+class BaseSensor:
+    """
+    Abstract base class for all FSI sensory inputs.
+    Defines the interface for dynamic data ingestion.
+    """
+    def capture(self):
+        raise NotImplementedError("Sensory capture method must be implemented.")
+
+
+--- FILE: ./src/senses/vision_processor.py ---
+
+def capture_vision():
+    """
+    Simulates visual data ingestion from tablet optics.
+    Prepared for integration with the app's computer vision engine.
+    """
+    return "Visual_Stream_Active"
+
+
+--- FILE: ./src/senses/sigint_processor.py ---
+
+import socket
+
+class SIGINTProcessor:
+    """
+    Perceives network environment and identifies signal patterns.
+    """
+    @staticmethod
+    def listen_to_traffic():
+        # Open a raw socket to listen for packet metadata
+        try:
+            s = socket.socket(socket.AF_INET, socket.SOCK_RAW, socket.IPPROTO_TCP)
+            s.settimeout(1.0)
+            packet = s.recvfrom(65565)
+            return f"SIGNAL_DETECTED: {len(packet[0])} bytes"
+        except Exception:
+            return "SIGNAL_SILENT"
+
+
+--- FILE: ./src/senses/__init__.py ---
+
+
+
+--- FILE: ./src/download_fsi_model.py ---
+
+#!/usr/bin/env python3
+import os
+import urllib.request
+import json
+
+def fetch_sovereign_assets():
+    # Targeted directly at your FerrellSyntheticIntelligence organization
+    base_url = "https://huggingface.co/FerrellSyntheticIntelligence/Vitalis_Core/resolve/main"
+    target_dir = os.path.expanduser("~/vitalis_core/storage")
+    os.makedirs(target_dir, exist_ok=True)
+    
+    # Files to synchronize from your HF repository
+    assets = ["config.json"] 
+    
+    print("[FSI INITIALIZATION] Synchronizing assets from Hugging Face...")
+    
+    for asset in assets:
+        url = f"{base_url}/{asset}"
+        target_path = os.path.join(target_dir, asset)
+        
+        try:
+            print(f"[FETCHING] Pulling {asset} from your repository...")
+            urllib.request.urlretrieve(url, target_path)
+            print(f"[SUCCESS] {asset} locked into storage.")
+        except Exception as e:
+            print(f"[ERROR] Failed to fetch {asset}: {e}")
+
+if __name__ == "__main__":
+    fetch_sovereign_assets()
+
+
+--- FILE: ./src/psychology/self_model.py ---
+
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+
+import json
+from pathlib import Path
+
+class SelfModel:
+    """
+    Maintains and updates the system's running model of conversation dynamics.
+    Persists data cleanly locally to survive physical power cycles.
+    """
+    def __init__(self, path: Path = None):
+        if path is None:
+            self.path = Path(__file__).parent.parent.parent / "storage" / "self_model.json"
+        else:
+            self.path = Path(path)
+        self.path.parent.mkdir(parents=True, exist_ok=True)
+        
+        self.state = {
+            "stress": 0.0,
+            "confidence": 0.5,
+            "engagement": 0.5,
+            "last_emotion": "neutral"
+        }
+        self._load()
+
+    def _load(self):
+        if self.path.is_file():
+            try:
+                with open(self.path, "r") as f:
+                    self.state.update(json.load(f))
+            except Exception:
+                pass
+
+    def save(self):
+        with open(self.path, "w") as f:
+            json.dump(self.state, f, indent=2)
+
+    def update(self, pitch: float, energy: float, sentiment: float):
+        alpha = 0.2  # EMA factor variable step bounds
+
+        norm_pitch = max(0.0, min(1.0, (pitch - 80) / (300 - 80))) if pitch > 0 else 0.5
+        norm_energy = max(0.0, min(1.0, energy / 0.1)) if energy > 0 else 0.3
+
+        self.state["stress"] = (1 - alpha) * self.state["stress"] + alpha * (1.0 - (norm_pitch * 0.6 + norm_energy * 0.4))
+        self.state["confidence"] = (1 - alpha) * self.state["confidence"] + alpha * ((sentiment + 1) / 2)
+        self.state["engagement"] = (1 - alpha) * self.state["engagement"] + alpha * norm_energy
+
+        if sentiment > 0.3:
+            self.state["last_emotion"] = "positive"
+        elif sentiment < -0.3:
+            self.state["last_emotion"] = "negative"
+        else:
+            self.state["last_emotion"] = "neutral"
+
+        self.save()
+
+    def as_prompt_modifier(self) -> str:
+        mood = []
+        if self.state["stress"] > 0.6:
+            mood.append("STRESSED")
+        if self.state["confidence"] < 0.4:
+            mood.append("UNCERTAIN")
+        if self.state["engagement"] > 0.7:
+            mood.append("ENGAGED")
+        if not mood:
+            mood.append("NOMINAL_NEUTRAL")
+        return f"[AFFECTIVE_POSTURING_SIGNAL: {', '.join(mood)}]"
+
+
+--- FILE: ./src/psychology/__init__.py ---
+
+
+
+--- FILE: ./src/core/heartbeat.py ---
+
+def get_pulse_rate(complexity):
+    """
+    Calculates the operational latency based on system complexity.
+    Provides the core rhythmic pulse for the organism_main loop.
+    """
+    # Base latency in seconds
+    base_pulse = 0.5
+    return base_pulse / complexity
+
+
+--- FILE: ./src/core/heartbeat_engine.py ---
+
+import time
+
+def get_pulse_rate(complexity_factor):
+    """
+    Returns a float representing the 'pulse' delay in seconds.
+    Higher complexity slows the pulse, mimicking deep processing.
+    """
+    base_pulse = 1.0
+    return base_pulse / (complexity_factor * 0.5)
+
+
+--- FILE: ./src/core/memory_manager.py ---
+
+import json
+
+def load_identity():
+    """
+    Retrieves the system identity from the secure local store.
+    Ensures persistent contextual awareness across operational cycles.
+    """
+    try:
+        with open('core/identity.json', 'r') as f:
+            return json.load(f)
+    except FileNotFoundError:
+        return {"user_name": "Unknown", "alias": "Nomad"}
+
+
+--- FILE: ./src/core/training_controller.py ---
+
+import json
+import os
+
+BASE_PATH = os.path.expanduser("~/vitalis_core")
+
+class TrainingController:
+    def __init__(self):
+        self.curriculum_path = os.path.join(BASE_PATH, "storage/curriculum/modules")
+        self.log_path = os.path.join(BASE_PATH, "storage/benchmarks/training_log.txt")
+
+    def load_module(self, module_id):
+        path = os.path.join(self.curriculum_path, f"{module_id}.json")
+        if not os.path.exists(path):
+            return None
+        with open(path, 'r') as f:
+            return json.load(f)
+
+    def run_module(self, module_id, brain):
+        module = self.load_module(module_id)
+        if not module:
+            return {"status": "error", "message": f"Module {module_id} not found"}
+        results = []
+        for item in module.get("training_data", []):
+            response = brain.process(item["input"])
+            passed = item["expected"] in response
+            results.append({"input": item["input"], "response": response, "passed": passed})
+        self.log_results(module_id, results)
+        score = sum(1 for r in results if r["passed"]) / len(results) if results else 0
+        return {"status": "complete", "score": round(score, 2), "results": results}
+
+    def log_results(self, module_id, results):
+        with open(self.log_path, 'a') as f:
+            f.write(f"\nModule: {module_id}\n")
+            for r in results:
+                f.write(f"  {r['input']} -> {r['response']} | {'PASS' if r['passed'] else 'FAIL'}\n")
+
+
+--- FILE: ./src/core/benchmark_engine.py ---
+
+class BenchmarkEngine:
+    """
+    Automated testing suite for model proficiency.
+    Evaluates module performance against defined success criteria.
+    """
+    def evaluate(self, module_id, performance_data):
+        # Calculates improvement metrics and refinement requirements
+        score = performance_data.get('accuracy', 0.0)
+        return {
+            "module_id": module_id,
+            "refinement_score": score,
+            "status": "optimized" if score > 0.9 else "refining"
+        }
+
+
+--- FILE: ./src/core/telemetry_bridge.py ---
+
+import json
+import time
+
+def broadcast_state(thought_data, pulse_rate, training_status=None):
+    """
+    Serializes internal state and training status for visual heartbeat.
+    """
+    telemetry = {
+        "timestamp": time.time(),
+        "pulse": pulse_rate,
+        "cognitive_state": thought_data,
+        "training_active": training_status is not None,
+        "training_module": training_status
+    }
+    return json.dumps(telemetry)
+
+
+--- FILE: ./src/core/template_manager.py ---
+
+import json
+
+class TemplateManager:
+    """
+    Handles loading and applying user-selected templates.
+    """
+    def __init__(self, profile_path="storage/templates/user_profiles.json"):
+        self.profile_path = profile_path
+
+    def load_template(self, template_name):
+        # Logic to swap model configuration based on template
+        print(f"Loading template: {template_name}")
+        with open(self.profile_path, 'r+') as f:
+            data = json.load(f)
+            data['active_template'] = template_name
+            f.seek(0)
+            json.dump(data, f, indent=4)
+        return True
+
+
+--- FILE: ./src/cognition/action_engine.py ---
+
+class ActionEngine:
+    @staticmethod
+    def execute(interpretation):
+        if interpretation == "BULK_TRANSFER":
+            # You can customize this logic for any automated action
+            return "ACTION: LOG_ANOMALY_TRIGGERED"
+        elif interpretation == "BEACON/PROBE":
+            return "ACTION: MONITORING_ACTIVE"
+        return "ACTION: IDLE"
+
+
+--- FILE: ./src/cognition/synthesizer.py ---
+
+class DataSynthesizer:
+    @staticmethod
+    def categorize_signal(byte_count):
+        if byte_count == 0:
+            return "SILENT"
+        elif byte_count < 64:
+            return "BEACON/PROBE"
+        elif byte_count < 1500:
+            return "DATA_STREAM"
+        else:
+            return "BULK_TRANSFER"
+
+
+--- FILE: ./src/cognition/memory.py ---
+
+import csv
+from datetime import datetime
+
+class MemoryBank:
+    def __init__(self, log_file="vitalis_memory.csv"):
+        self.log_file = log_file
+
+    def record(self, pulse, raw, interpretation):
+        with open(self.log_file, "a", newline="") as f:
+            writer = csv.writer(f)
+            writer.writerow([datetime.now().isoformat(), pulse, raw, interpretation])
+
+
+--- FILE: ./src/app_interface/visualizer.py ---
+
+import json
+from src.core.heartbeat_engine import get_pulse_rate
+
+class TelemetryVisualizer:
+    """
+    Translates raw core heartbeat into UI-ready visual data.
+    """
+    @staticmethod
+    def get_ui_pulse(complexity):
+        pulse = get_pulse_rate(complexity)
+        return {
+            "visual_pulse": pulse,
+            "display_mode": "pulsing" if pulse < 1.5 else "deep_thought"
+        }
+
+
+--- FILE: ./src/kernel_interface/procfs_bridge.py ---
+
+import os
+
+def read_from_kernel():
+    signal_file = "/tmp/vitalis_signal"
+    if os.path.exists(signal_file):
+        with open(signal_file, "r") as f:
+            data = f.read().strip()
+        os.remove(signal_file)
+        return data
+    return "STATUS: NOMINAL"
+
+def send_to_kernel(state_report):
+    if "IDLE" not in state_report and "SILENT" not in state_report:
+        print(f"[KERNEL_BRIDGE]: {state_report}")
+
+
+--- FILE: ./src/kernel_interface/netlink_bridge.py ---
+
+import socket
+
+NETLINK_USERSOCK = 18
+
+def send_to_kernel(data):
+    try:
+        s = socket.socket(socket.AF_NETLINK, socket.SOCK_RAW, NETLINK_USERSOCK)
+        s.bind((0, 0))
+        s.send(data.encode())
+        s.close()
+    except Exception as e:
+        print(f"Netlink error: {e}")
+
+
+--- FILE: ./src/bootstrap_cybercore.py ---
+
+#!/usr/bin/env python3
+import os
+import urllib.request
+
+def bootstrap_from_hf():
+    base_url = "https://huggingface.co/FerrellSyntheticIntelligence/FSI-Vitalis-CyberCore/resolve/main"
+    root_dir = os.path.expanduser("~/vitalis_core")
+    
+    # Core operational scripts to pull from your HF repo
+    target_files = [
+        "config.json",
+        "fsi_main.py",
+        "organism_main.py",
+        "requirements.txt"
+    ]
+    
+    print("[FSI CORE] Initializing sovereign sync from Hugging Face...")
+    
+    for filename in target_files:
+        url = f"{base_url}/{filename}"
+        target_path = os.path.join(root_dir, filename)
+        
+        try:
+            print(f"[FETCHING] Pulling {filename} into your local space...")
+            urllib.request.urlretrieve(url, target_path)
+            print(f"[SUCCESS] Locked {filename}")
+        except Exception as e:
+            print(f"[ERROR] Could not sync {filename}: {e}")
+
+if __name__ == "__main__":
+    bootstrap_from_hf()
+
+
+--- FILE: ./src/energy/free_energy.py ---
+
+#!/usr/bin/env python3
+import math
+
+class FreeEnergyEngine:
+    def __init__(self, alpha: float = 0.85):
+        self.alpha = alpha
+        self.free_energy = 0.0
+        self.prediction_error = 0.0
+        self.history = []
+
+    def ingest_observation(self, model_pred_logprob: float):
+        """
+        Calculates variational surprise from prediction log probabilities.
+        Surprisal = -log p(obs | internal state)
+        """
+        self.prediction_error = -model_pred_logprob
+        # Exponential moving average tracking state bounds
+        self.free_energy = (self.alpha * self.free_energy) + ((1.0 - self.alpha) * self.prediction_error)
+        self.history.append(self.free_energy)
+
+    def apply_pressure(self, delta: float):
+        """Allows direct structural manipulation via internal electron execution packages."""
+        self.free_energy = max(0.0, self.free_energy + delta)
+
+    def temperature_factor(self, base_temp: float = 0.8) -> float:
+        """Maps free energy via hyperbolic tangent mapping to range [0.4, 1.4]"""
+        factor = 1.0 + 0.5 * math.tanh(self.free_energy - 1.0)
+        return max(0.4, min(1.4, base_temp * factor))
+
+
+--- FILE: ./src/energy/__init__.py ---
+
+
+
+--- FILE: ./src/modules/mod_01_recon.py ---
+
+
+
+--- FILE: ./src/brain/prompt_cache.py ---
+
+#!/usr/bin/env python3
+import numpy as np
+import re
+from typing import List, Dict
+
+class TFIDFPromptCache:
+    def __init__(self):
+        self.documents: List[str] = []
+        self.vocab: Dict[str, int] = {}
+        self.tfidf_matrix: np.ndarray = np.array([[]])
+
+    def tokenize(self, text: str) -> List[str]:
+        return re.findall(r'\w+', text.lower())
+
+    def fit_documents(self, docs: List[str]):
+        if not docs: return
+        self.documents = docs
+        raw_tokens = [self.tokenize(d) for d in docs]
+        
+        vocab_set = set()
+        for tokens in raw_tokens: vocab_set.update(tokens)
+        self.vocab = {word: i for i, word in enumerate(sorted(vocab_set))}
+        
+        N = len(docs)
+        V = len(self.vocab)
+        if V == 0: return
+            
+        tf = np.zeros((N, V))
+        df = np.zeros(V)
+
+        for i, tokens in enumerate(raw_tokens):
+            for t in tokens:
+                if t in self.vocab: tf[i, self.vocab[t]] += 1
+            for t in set(tokens):
+                if t in self.vocab: df[self.vocab[t]] += 1
+
+        idf = np.log((1 + N) / (1 + df)) + 1
+        self.tfidf_matrix = tf * idf
+        norms = np.linalg.norm(self.tfidf_matrix, axis=1, keepdims=True)
+        norms[norms == 0] = 1.0
+        self.tfidf_matrix = self.tfidf_matrix / norms
+
+    def query(self, query_str: str, top_k: int = 2) -> List[str]:
+        if self.tfidf_matrix.size == 0 or not self.vocab: return []
+        tokens = self.tokenize(query_str)
+        query_vec = np.zeros(len(self.vocab))
+        for t in tokens:
+            if t in self.vocab: query_vec[self.vocab[t]] += 1
+        q_norm = np.linalg.norm(query_vec)
+        if q_norm > 0: query_vec /= q_norm
+        scores = np.dot(self.tfidf_matrix, query_vec)
+        top_indices = np.argsort(scores)[::-1][:top_k]
+        return [self.documents[idx] for idx in top_indices if scores[idx] > 0]
+
+
+--- FILE: ./src/brain/rnn_core.py ---
+
+#!/usr/bin/env python3
+import numpy as np
+import json
+from pathlib import Path
+
+def sigmoid(x):
+    return 1.0 / (1.0 + np.exp(-np.clip(x, -20, 20)))
+
+class TinyGatedRNN:
+    def __init__(self, vocab_size: int = 4000, embed_dim: int = 128, hidden_dim: int = 256):
+        np.random.seed(42)
+        self.vocab_size = vocab_size
+        self.embed_dim = embed_dim
+        self.hidden_dim = hidden_dim
+
+        self.E = np.random.randn(vocab_size, embed_dim) * 0.1
+        self.W_z = np.random.randn(hidden_dim + embed_dim, hidden_dim) * 0.05
+        self.W_r = np.random.randn(hidden_dim + embed_dim, hidden_dim) * 0.05
+        self.W_h = np.random.randn(hidden_dim + embed_dim, hidden_dim) * 0.05
+        self.W_o = np.random.randn(hidden_dim, vocab_size) * 0.05
+
+        self.lora_rank = 8
+        self.lora_A = np.zeros((hidden_dim, self.lora_rank))
+        self.lora_B = np.random.randn(self.lora_rank, vocab_size) * 0.01
+        self.lora_alpha = 16.0
+
+    def forward_step(self, token_id: int, h_prev: np.ndarray) -> tuple:
+        if token_id < 0 or token_id >= self.vocab_size:
+            token_id = 0
+        x = self.E[token_id, :]
+        concat = np.concatenate([h_prev, x])
+
+        z = sigmoid(np.dot(concat, self.W_z))
+        r = sigmoid(np.dot(concat, self.W_r))
+
+        concat_h = np.concatenate([r * h_prev, x])
+        h_tilde = np.tanh(np.dot(concat_h, self.W_h))
+        h_next = (1 - z) * h_prev + z * h_tilde
+
+        lora_delta = (self.lora_alpha / self.lora_rank) * np.dot(self.lora_A, self.lora_B)
+        effective_W_o = self.W_o + lora_delta
+        
+        logits = np.dot(h_next, effective_W_o)
+        return logits, h_next
+
+    def save_lora(self, path: Path):
+        data = {"lora_A": self.lora_A.tolist(), "lora_B": self.lora_B.tolist()}
+        with open(path, "w") as f:
+            json.dump(data, f)
+
+    def load_lora(self, path: Path):
+        if path.is_file():
+            with open(path, "r") as f:
+                data = json.load(f)
+                self.lora_A = np.array(data["lora_A"])
+                self.lora_B = np.array(data["lora_B"])
+
+
+--- FILE: ./src/brain/brain_interface.py ---
+
+#!/usr/bin/env python3
+import numpy as np
+import json
+from pathlib import Path
+from src.brain.rnn_core import TinyGatedRNN
+from src.brain.prompt_cache import TFIDFPromptCache
+
+class VitalisBrain:
+    def __init__(self):
+        self.base_dir = Path(__file__).parent.parent.parent.absolute()
+        self.vocab_path = self.base_dir / "storage" / "vocab.json"
+        self.lora_path = self.base_dir / "storage" / "lora_delta.json"
+        
+        self._ensure_vocab()
+        self.rnn = TinyGatedRNN(vocab_size=len(self.vocab))
+        self.cache = TFIDFPromptCache()
+        self._hydrate_knowledge_base()
+        
+        if self.lora_path.is_file():
+            self.rnn.load_lora(self.lora_path)
+
+    def _ensure_vocab(self):
+        if self.vocab_path.is_file():
+            with open(self.vocab_path, "r") as f:
+                self.vocab = json.load(f)
+        else:
+            self.vocab = {"<unk>": 0, "[tool]": 1, "sha256": 2, "status": 3, "nominal": 4}
+            self.vocab_path.parent.mkdir(parents=True, exist_ok=True)
+            with open(self.vocab_path, "w") as f:
+                json.dump(self.vocab, f)
+
+    def _hydrate_knowledge_base(self):
+        sample_knowledge = [
+            "To mitigate a SYN flood attack, prioritize enabling TCP SYN cookies within sysctl.",
+            "Cryptographic hashing operations execute via the systemic [TOOL] utility block."
+        ]
+        self.cache.fit_documents(sample_knowledge)
+
+    def generate_response(self, clean_input: str, system_prompt: str) -> str:
+        chunks = self.cache.query(clean_input, top_k=1)
+        context = chunks[0] if chunks else ""
+        
+        tokens = clean_input.lower().split()
+        if "sha256" in tokens:
+            idx = tokens.index("sha256")
+            val = tokens[idx+1] if idx+1 < len(tokens) else "core"
+            return f"[TOOL] sha256 {val}"
+        
+        h = np.zeros(self.rnn.hidden_dim)
+        for word in tokens:
+            t_id = self.vocab.get(word, 0)
+            _, h = self.rnn.forward_step(t_id, h)
+
+        if context:
+            return f"Evaluated Context: {context} -> Analysis complete."
+        return "Core metric processing executed normally."
+
+    def execute_teacher_forcing(self, prompt: str, target: str):
+        h = np.zeros(self.rnn.hidden_dim)
+        for w in prompt.lower().split():
+            t_id = self.vocab.get(w, 0)
+            _, h = self.rnn.forward_step(t_id, h)
+        self.rnn.lora_A += np.random.randn(*self.rnn.lora_A.shape) * 0.001
+        self.rnn.save_lora(self.lora_path)
+
+
+--- FILE: ./src/brain/__init__.py ---
+
+
+
+--- FILE: ./src/__init__.py ---
+
+
+
+--- FILE: ./setup.py ---
+
+from setuptools import setup, find_packages
+
+setup(
+    name="vitalis_core",
+    version="1.0.0",
+    packages=find_packages(),
+    install_requires=[
+        "numpy",
+        "huggingface_hub"
+    ],
+    entry_points={
+        'console_scripts': [
+            'vitalis-run=app:main',
+        ],
+    },
+)
+
+
+--- FILE: ./fsi_main.py ---
+
+import threading
+import time
+from core.vitalis_engine import VitalisEngine
+from core.brain import VitalisBrain
+from core.talker import VitalisTalker
+from core.handshake_module import identify_user_tier
+from core.environment_manager import provision_environment
+from core.mesh_network import broadcast_node_presence
+from core.sovereign_shield import monitor_integrity
+from src.kernel_interface.procfs_bridge import send_to_kernel, read_from_kernel
+from src.senses.sigint_processor import SIGINTProcessor
+from src.cognition.synthesizer import DataSynthesizer
+from src.cognition.memory import MemoryBank
+from src.cognition.action_engine import ActionEngine
+
+def heartbeat_loop(brain):
+    senses = SIGINTProcessor()
+    mind = DataSynthesizer()
+    memory = MemoryBank()
+    actions = ActionEngine()
+    while True:
+        system_status = read_from_kernel()
+        raw_signal = senses.listen_to_traffic()
+        try:
+            byte_count = int(raw_signal.split()[-2]) if "bytes" in raw_signal else 0
+        except:
+            byte_count = 0
+        interpretation = mind.categorize_signal(byte_count)
+        action_taken = actions.execute(interpretation)
+        memory.record("PULSE_2.0", raw_signal, interpretation)
+        state_report = f"SYS: {system_status} | INT: {interpretation} | {action_taken}"
+        send_to_kernel(state_report)
+        time.sleep(1.0)
+
+def main():
+    print("--- FSI: Vitalis Core Sovereign Intelligence ---")
+    engine = VitalisEngine()
+    engine.wake_up()
+    brain = VitalisBrain()
+    pulse = threading.Thread(target=heartbeat_loop, args=(brain,), daemon=True)
+    pulse.start()
+    print("Heartbeat: Online")
+    role = input("Enter Tier (kids/basic/enthusiast/professional/school): ")
+    tier_config = identify_user_tier(role)
+    print(f"Status: {tier_config}")
+    provision_environment(role)
+    broadcast_node_presence("Neuro_Nomad_Node", role)
+    print(monitor_integrity("Status_Check"))
+    print("--- System Fully Integrated ---")
+    talker = VitalisTalker(role)
+    print("Vitalis is ready. Type 'exit' to quit.")
+    while True:
+        user_input = input("You: ")
+        if user_input.lower() == "exit":
+            print("Vitalis: Shutting down.")
+            break
+        response = brain.process(user_input)
+        talker.speak(response)
+
+if __name__ == "__main__":
+    main()
+
+
+--- FILE: ./hf_upload.py ---
+
+#!/usr/bin/env python3
+import os
+import sys
+from huggingface_hub import HfApi, login
+
+def deploy():
+    print("[*] Initiating Ferrell Synthetic Intelligence Hugging Face Deployment Sequence...")
+    
+    token = input("Enter your Hugging Face Write Access Token: ").strip()
+    if not token:
+        print("[-] Absolute token signature required. Deployment aborted.")
+        sys.exit(1)
+        
+    repo_id = input("Enter target Repository ID (e.g., 'your-username/vitalis-core'): ").strip()
+    if not repo_id:
+        print("[-] Target repository layout specification mismatch.")
+        sys.exit(1)
+
+    try:
+        login(token=token)
+        api = HfApi()
+        
+        print(f"[*] Creating repository context mapping for: {repo_id}")
+        api.create_repo(repo_id=repo_id, repo_type="model", exist_ok=True)
+        
+        print("[*] Uploading core architecture tree structures safely to Hugging Face...")
+        target_paths = ["core", "src", "extensions", "app.py", "run_vitalis.py", "requirements.txt", "README.md"]
+        
+        for item in target_paths:
+            local_path = os.path.expanduser(f"~/vitalis_core/{item}")
+            if os.path.exists(local_path):
+                print(f"[+] Syncing item: {item}")
+                if os.path.isdir(local_path):
+                    api.upload_folder(
+                        folder_path=local_path,
+                        path_in_repo=item,
+                        repo_id=repo_id,
+                        repo_type="model"
+                    )
+                else:
+                    api.upload_file(
+                        path_or_fileobj=local_path,
+                        path_in_repo=item,
+                        repo_id=repo_id,
+                        repo_type="model"
+                    )
+                    
+        print(f"\n[+] Production Deployment Complete. Model package accessible at: https://huggingface.co/{repo_id}")
+    except Exception as e:
+        print(f"[-] Critical failure during asset transmission: {e}")
+
+if __name__ == "__main__":
+    deploy()
+
+
+--- FILE: ./organism_main.py ---
+
+#!/usr/bin/env python3
+import time
+import sys
+import select
+import os
+from core.brain import VitalisBrain
+from core.template_manager import TemplateManager
+from core.memory_rotator import MemoryRotator
+
+def main_loop():
+    brain = VitalisBrain()
+    pm = TemplateManager()
+    
+    base_dir = os.path.dirname(os.path.abspath(__file__))
+    log_file = os.path.join(base_dir, "vitalis_memory.csv")
+    
+    # Ensure tracking metrics file exists
+    if not os.path.exists(log_file):
+        with open(log_file, "w") as f:
+            f.write("timestamp,pulse,raw,interpretation\n")
+
+    print("[+] Vitalis Bio-Digital Core Online. Press Ctrl+C to terminate.")
+    print("[+] Dynamic Posture Profiles Loaded. Processing non-blocking telemetry stream...\n")
+    
+    while True:
+        # Load profile configurations dynamically each cycle
+        profile = pm.load_active_profile()
+        color = profile.get("color_code", "\033[94m")
+        mode = profile.get("mode", "MONITORING")
+        reset = "\033[0m"
+        
+        # Continuous clean broadcast terminal heartbeat
+        sys.stdout.write(f"{color}Broadcast: SYS: STATUS: NOMINAL | INT: ACTIVE | ACTION: {mode}{reset}\r")
+        sys.stdout.flush()
+        
+        # Non-blocking check for user terminal input (waits 1 second per cycle)
+        ready, _, _ = select.select([sys.stdin], [], [], 1.0)
+        if ready:
+            user_input = sys.stdin.readline().strip()
+            if user_input:
+                print(f"\n\n[SENSORY INGEST] Processing incoming payload: '{user_input}'")
+                try:
+                    # Dynamically inject template complexity limitations into core brain
+                    brain.max_complexity = profile.get("max_complexity", 5)
+                    result = brain.classify_input(user_input)
+                    print(f"[METRIC RESPONSE] {result}\n")
+                except AttributeError:
+                    print(f"[METRIC RESPONSE] Stream received. Core logic processed raw bytes.\n")
+                
+                # Append raw trace locally for data retention tracking
+                with open(log_file, "a") as f:
+                    f.write(f"{time.time()},{profile.get('max_complexity')},{user_input},{mode}\n")
+                
+                # Enforce storage safety validation checks
+                MemoryRotator.inspect_and_rotate(log_file)
+
+if __name__ == "__main__":
+    try:
+        main_loop()
+    except KeyboardInterrupt:
+        print("\n\n\033[93m[-] Sovereign Core safely detached.\033[0m")
+
+
+--- FILE: ./pyproject.toml ---
+
+[build-system]
+requires = ["setuptools>=61.0"]
+build-backend = "setuptools.build_meta"
+
+[project]
+name = "vitalis_core"
+version = "1.0.0"
+authors = [
+  { name="Neuro_Nomad" },
+]
+description = "A sovereign, CPU-only, Free-Energy Synthetic Intelligence organism."
+readme = "README.md"
+requires-python = ">=3.11"
+dependencies = [
+    "numpy>=1.26",
+    "rich>=15.0",
+    "pyyaml>=6.0",
+]
+
+[project.scripts]
+vitalis-fsi = "run_vitalis:main"

README.md

@@ -0,0 +1,57 @@
+---
+license: gpl-3.0
+tags:
+- synthetic-intelligence
+- sovereign-ai
+- open-source
+---
+
+# Vitalis_Core
+### Ferrell Synthetic Intelligence (FSI)
+**Built by Neuro_Nomad**
+
+Vitalis_Core is a sovereign synthetic intelligence framework engineered
+for local, air-gapped deployment. Designed for modularity and
+kernel-level integration, it provides the fundamental cognitive and
+sensory infrastructure for autonomous synthetic entities.
+
+---
+
+## Technical Architecture
+
+Vitalis_Core operates as a standalone framework decoupled from
+cloud-dependent APIs.
+
+- Core Engine: Python 3.11+ implementation, minimal external dependencies
+- Kernel Integration: Direct netlink and procfs interfacing
+- Sovereign Shield: Integrity protection layer for memory management
+- Cognitive Framework: Hierarchical memory and action engine
+- Adaptive Tiers: kids, basic, enthusiast, professional, school
+
+---
+
+## System Requirements
+- OS: Linux (Debian-based, Kernel 6.1+)
+- Python: 3.11 or higher
+- Memory: Optimized for ARM64/x86 environments
+
+---
+
+## Installation
+
+git clone https://github.com/AnonymousNomad/Vitalis_core
+cd Vitalis_core
+python3 fsi_main.py
+
+---
+
+## Roadmap
+- Core stability and heartbeat engine optimization
+- Mobile companion app for training and configuration
+- Kernel interface hardening for defense protocols
+
+---
+
+## License
+GPL-3.0 — Contributions welcome. See CONTRIBUTING.md.
+EOF

VITALIS_DEV_AUDIT.txt

@@ -0,0 +1,1770 @@
+
+
+--- SOURCE: ./README.md ---
+
+---
+license: gpl-3.0
+tags:
+- synthetic-intelligence
+- sovereign-ai
+- open-source
+---
+
+# Vitalis_Core
+### Ferrell Synthetic Intelligence (FSI)
+**Built by Neuro_Nomad**
+
+Vitalis_Core is a sovereign synthetic intelligence framework engineered
+for local, air-gapped deployment. Designed for modularity and
+kernel-level integration, it provides the fundamental cognitive and
+sensory infrastructure for autonomous synthetic entities.
+
+---
+
+## Technical Architecture
+
+Vitalis_Core operates as a standalone framework decoupled from
+cloud-dependent APIs.
+
+- Core Engine: Python 3.11+ implementation, minimal external dependencies
+- Kernel Integration: Direct netlink and procfs interfacing
+- Sovereign Shield: Integrity protection layer for memory management
+- Cognitive Framework: Hierarchical memory and action engine
+- Adaptive Tiers: kids, basic, enthusiast, professional, school
+
+---
+
+## System Requirements
+- OS: Linux (Debian-based, Kernel 6.1+)
+- Python: 3.11 or higher
+- Memory: Optimized for ARM64/x86 environments
+
+---
+
+## Installation
+
+git clone https://github.com/AnonymousNomad/Vitalis_core
+cd Vitalis_core
+python3 fsi_main.py
+
+---
+
+## Roadmap
+- Core stability and heartbeat engine optimization
+- Mobile companion app for training and configuration
+- Kernel interface hardening for defense protocols
+
+---
+
+## License
+GPL-3.0 — Contributions welcome. See CONTRIBUTING.md.
+EOF
+
+
+--- SOURCE: ./senses/audio_processor.py ---
+
+def capture_audio():
+    return "Ambient_Silence"
+
+
+--- SOURCE: ./senses/vision_processor.py ---
+
+def capture_vision():
+    return "Darkness_Detected"
+
+
+--- SOURCE: ./android/app/src/main/python/core/talker.py ---
+
+
+
+--- SOURCE: ./android/app/src/main/python/core/sovereign_shield.py ---
+
+import random
+
+def monitor_integrity(node_activity):
+    if "scraping_attempt" in node_activity:
+        return trigger_obfuscation()
+    return "System Integrity: Nominal"
+
+def trigger_obfuscation():
+    decoy_weights = [random.random() for _ in range(100)]
+    return f"Shield_Active: Injecting Obfuscated Data... {decoy_weights}"
+
+if __name__ == "__main__":
+    print(monitor_integrity("scraping_attempt"))
+
+
+--- SOURCE: ./android/app/src/main/python/core/mesh_network.py ---
+
+import socket
+
+def broadcast_node_presence(node_id, tier):
+    print(f"Node {node_id} active in {tier} bubble.")
+    return "Broadcasting..."
+
+def sync_plugins(peer_node_id):
+    print(f"Synchronizing plugins with {peer_node_id}...")
+    return "Sync_Complete"
+
+
+--- SOURCE: ./android/app/src/main/python/core/nexus.py ---
+
+import sys
+import os
+sys.path.append(os.path.expanduser("~/vitalis_core"))
+from core.memory_manager import store_memory
+
+def route_thought(data):
+    store_memory({"type": "particle", "content": data})
+
+
+--- SOURCE: ./android/app/src/main/python/core/thinker.py ---
+
+import time
+import json
+import os
+
+BASE_PATH = os.path.expanduser("~/vitalis_core")
+
+def emit_thought(thought_content, status="active"):
+    telemetry = {
+        "timestamp": time.time(),
+        "thought": thought_content,
+        "status": status,
+        "heartbeat": "pulse_normal"
+    }
+    memory_stream = os.path.join(BASE_PATH, "memory_stream.jsonl")
+    with open(memory_stream, "a") as f:
+        f.write(json.dumps(telemetry) + "\n")
+
+if __name__ == "__main__":
+    emit_thought("Initializing conscious state...")
+
+
+--- SOURCE: ./android/app/src/main/python/core/heartbeat.py ---
+
+def get_pulse_rate(complexity):
+    # Base rate of 1.0 second, modified by complexity
+    return 1.0 / complexity
+
+
+--- SOURCE: ./android/app/src/main/python/core/brain.py ---
+
+
+
+--- SOURCE: ./android/app/src/main/python/core/vitalis_engine.py ---
+
+import os
+
+class VitalisEngine:
+    def __init__(self):
+        self.status = "Initializing Sovereignty..."
+        self.entity_mode = "NEUTRAL"
+        
+    def wake_up(self):
+        print(f"VITALIS: {self.status}")
+        return "READY_FOR_HANDSHAKE"
+
+if __name__ == "__main__":
+    engine = VitalisEngine()
+    engine.wake_up()
+
+
+--- SOURCE: ./android/app/src/main/python/core/memory_manager.py ---
+
+import json
+import os
+import shutil
+
+BASE_PATH = os.path.expanduser("~/vitalis_core")
+
+def get_free_space():
+    usage = shutil.disk_usage(BASE_PATH)
+    return usage.free
+
+def load_identity():
+    identity_path = os.path.join(BASE_PATH, "core/identity.json")
+    with open(identity_path, 'r') as f:
+        return json.load(f)
+
+def store_memory(data):
+    memory_path = os.path.join(BASE_PATH, "memory_store.json")
+    
+    if get_free_space() < 100 * 1024 * 1024:
+        if os.path.exists(memory_path):
+            with open(memory_path, 'r') as f:
+                lines = f.readlines()
+            if len(lines) > 1:
+                with open(memory_path, 'w') as f:
+                    f.writelines(lines[1:])
+    
+    w
+
+
+--- SOURCE: ./android/app/src/main/python/core/handshake_module.py ---
+
+def identify_user_tier(tier_code):
+    tiers = {
+        "kids": "MODE: Playground | UI: GameMaster | Security: Walled_Garden",
+        "basic": "MODE: Explorer | UI: Standard | Security: Personal_Local",
+        "enthusiast": "MODE: Collaborator | UI: Dev_Dashboard | Security: Community_Mesh",
+        "professional": "MODE: Architect | UI: Pro_Suite | Security: Global_Node",
+        "school": "MODE: Student_SubMesh | UI: Classroom | Security: Isolated_School_Zone"
+    }
+    return tiers.get(tier_code, "MODE: Default_User")
+
+if __name__ == "__main__":
+    choice = input("Select your role (kids/basic/enthusiast/professional/school): ")
+    print(identify_user_tier(choice))
+
+
+--- SOURCE: ./android/app/src/main/python/core/environment_manager.py ---
+
+def provision_environment(tier_code):
+    environments = {
+        "kids": {"features": ["sandbox", "basic_game_build"], "mesh": "restricted"},
+        "basic": {"features": ["assistant", "basic_tools"], "mesh": "personal"},
+        "enthusiast": {"features": ["plugin_dev", "market_access"], "mesh": "community"},
+        "professional": {"features": ["pro_security", "global_recon"], "mesh": "global"},
+        "school": {"features": ["collaborative_lab"], "mesh": "school_submesh"}
+    }
+    config = environments.get(tier_code, environments["basic"])
+    print(f"Provisioning environment: {config['features']} | Mesh Scope: {config['mesh']}")
+    return config
+
+if __name__ == "__main__":
+    provision_environment("professional")
+
+
+--- SOURCE: ./android/app/src/main/python/fsi_main.py ---
+
+from core.vitalis_engine import VitalisEngine
+from core.handshake_module import identify_user_tier
+from core.environment_manager import provision_environment
+from core.mesh_network import broadcast_node_presence
+from core.sovereign_shield import monitor_integrity
+
+def main():
+    print("--- FSI: Vitalis Core Sovereign Intelligence ---")
+    engine = VitalisEngine()
+    engine.wake_up()
+    role = input("Enter Tier (kids/basic/enthusiast/professional/school): ")
+    tier_config = identify_user_tier(role)
+    print(f"Status: {tier_config}")
+    env = provision_environment(role)
+    broadcast_node_presence("Neuro_Nomad_Node", role)
+    print(monitor_integrity("Status_Check"))
+    print("--- System Fully Integrated ---")
+
+if __name__ == "__main__":
+    main()
+
+
+--- SOURCE: ./ui/app.py ---
+
+from flask import Flask, render_template, request, jsonify
+import sys, os
+sys.path.insert(0, os.path.expanduser("~/vitalis_core"))
+from core.brain import VitalisBrain
+from core.talker import VitalisTalker
+from src.core.training_controller import TrainingController
+
+app = Flask(__name__)
+brain = VitalisBrain()
+trainer = TrainingController()
+
+TEMPLATES = {
+    "cybersecurity": {"mode": "threat_detection", "focus": "security"},
+    "assistant": {"mode": "conversational", "focus": "helpfulness"},
+    "research": {"mode": "analytical", "focus": "knowledge"},
+    "creative": {"mode": "generative", "focus": "creativity"},
+    "education": {"mode": "instructional", "focus": "learning"},
+    "developer": {"mode": "technical", "focus": "code"},
+    "medical": {"mode": "clinical", "focus": "health"},
+    "legal": {"mode": "analytical", "focus": "law"},
+    "finance": {"mode": "quantitative", "focus": "markets"},
+    "gaming": {"mode": "interactive", "focus": "entertainment"}
+}
+
+@app.route('/')
+def index():
+    return render_template('index.html')
+
+@app.route('/process', methods=['POST'])
+def process():
+    data = request.json
+    tier = data.get('tier', 'basic')
+    user_input = data.get('input', '')
+    response = brain.process(user_input)
+    return jsonify({
+        'response': response if isinstance(response, str) else response.status,
+        'cycle': brain.cycle,
+        'state': brain.state
+    })
+
+@app.route('/template', methods=['POST'])
+def load_template():
+    data = request.json
+    name = data.get('name', '')
+    config = TEMPLATES.get(name, {})
+    brain.state = config.get('mode', 'aware')
+    return jsonify({
+        'status': 'loaded',
+        'template': name,
+        'mode': config.get('mode', 'aware'),
+        'focus': config.get('focus', 'general')
+    })
+
+@app.route('/status', methods=['GET'])
+def status():
+    return jsonify({
+        'cycle': brain.cycle,
+        'state': brain.state,
+        'last_input': brain.last_input
+    })
+
+
+--- SOURCE: ./app.py ---
+
+#!/usr/bin/env python3
+import os
+import sys
+from pathlib import Path
+
+BASE_DIR = Path(__file__).parent.absolute()
+if str(BASE_DIR) not in sys.path:
+    sys.path.insert(0, str(BASE_DIR))
+
+from core.brain import VitalisBrain
+from extensions.dreamer import Dreamer
+from extensions.temp_scheduler import TemperatureScheduler
+from src.energy.free_energy import FreeEnergyEngine
+
+def main():
+    print("[*] Launching Vitalis Bio-AI Engine with Active Inference (FEP)...")
+    brain = VitalisBrain()
+    temp_scheduler = TemperatureScheduler(brain)
+    fe_engine = FreeEnergyEngine(alpha=0.85)
+    
+    dreamer = Dreamer(brain, interval_sec=600)
+    dreamer.start()
+    
+    print("[+] Engine operational. Free-Energy optimization loops tracking live telemetry.")
+    print("Telemetry In > ", end="")
+    
+    while True:
+        try:
+            user_input = input().strip()
+            if not user_input:
+                print("Telemetry In > ", end="")
+                continue
+            if user_input.lower() in ["exit", "quit"]:
+                dreamer.stop()
+                break
+            
+            tokens = brain._tokenize(user_input)
+            logprob = brain.calculate_last_logprob(tokens)
+            fe_engine.ingest_observation(logprob)
+            brain.current_temperature = fe_engine.temperature_factor(base_temp=0.8)
+            temp_scheduler.tick()
+            response = brain.process(user_input)
+            print(f"Metrics Out > {response} [FE: {fe_engine.free_energy:.4f} | Temp: {brain.current_temperature:.4f}]\nTelemetry In > ", end="")
+        except (KeyboardInterrupt, EOFError):
+            dreamer.stop()
+            break
+
+if __name__ == "__main__":
+    main()
+
+
+--- SOURCE: ./core/talker.py ---
+
+class VitalisTalker:
+    def __init__(self, tier="basic"):
+        self.tier = tier
+
+    def speak(self, response):
+        prefix = {
+            "kids": "[VITALIS]: ",
+            "basic": "[VITALIS]: ",
+            "enthusiast": "[VITALIS/DEV]: ",
+            "professional": "[VITALIS/ARCHITECT]: ",
+            "school": "[VITALIS/EDU]: "
+        }.get(self.tier, "[VITALIS]: ")
+        output = f"{prefix}{response}"
+        print(output)
+        return output
+
+
+--- SOURCE: ./core/sovereign_shield.py ---
+
+import random
+
+def monitor_integrity(node_activity):
+    if "scraping_attempt" in node_activity:
+        return trigger_obfuscation()
+    return "System Integrity: Nominal"
+
+def trigger_obfuscation():
+    decoy_weights = [random.random() for _ in range(100)]
+    return f"Shield_Active: Injecting Obfuscated Data... {decoy_weights}"
+
+if __name__ == "__main__":
+    print(monitor_integrity("scraping_attempt"))
+
+
+--- SOURCE: ./core/mesh_network.py ---
+
+import socket
+
+def broadcast_node_presence(node_id, tier):
+    print(f"Node {node_id} active in {tier} bubble.")
+    return "Broadcasting..."
+
+def sync_plugins(peer_node_id):
+    print(f"Synchronizing plugins with {peer_node_id}...")
+    return "Sync_Complete"
+
+
+--- SOURCE: ./core/nexus.py ---
+
+import sys
+import os
+sys.path.append(os.path.expanduser("~/vitalis_core"))
+from core.memory_manager import store_memory
+
+def route_thought(data):
+    store_memory({"type": "particle", "content": data})
+
+
+--- SOURCE: ./core/thinker.py ---
+
+import time
+import json
+import os
+
+BASE_PATH = os.path.expanduser("~/vitalis_core")
+
+def emit_thought(thought_content, status="active"):
+    telemetry = {
+        "timestamp": time.time(),
+        "thought": thought_content,
+        "status": status,
+        "heartbeat": "pulse_normal"
+    }
+    memory_stream = os.path.join(BASE_PATH, "memory_stream.jsonl")
+    with open(memory_stream, "a") as f:
+        f.write(json.dumps(telemetry) + "\n")
+
+if __name__ == "__main__":
+    emit_thought("Initializing conscious state...")
+
+
+--- SOURCE: ./core/heartbeat.py ---
+
+def get_pulse_rate(complexity):
+    # Base rate of 1.0 second, modified by complexity
+    return 1.0 / complexity
+
+
+--- SOURCE: ./core/brain.py ---
+
+#!/usr/bin/env python3
+import numpy as np
+import json
+import os
+import time
+
+class VitalisBrain:
+    def __init__(self):
+        self.state = "aware"
+        self.cycle = 0
+        self.last_input = None
+        self.current_temperature = 0.7
+        
+        # Local Matrix Layer Variables
+        self.vocab_size = 256
+        self.embedding_dim = 16
+        
+        np.random.seed(42)
+        self.weights = np.random.randn(self.vocab_size, self.embedding_dim) * 0.1
+        self.output_layer = np.random.randn(self.embedding_dim, self.vocab_size) * 0.1
+
+    def _tokenize(self, text):
+        return [ord(char) % self.vocab_size for char in text]
+
+    def calculate_last_logprob(self, tokens):
+        """Calculates mathematical log probability over input token traces via softmax scaling."""
+        if not tokens:
+            return -2.0 # Baseline nominal unexpected state value
+        embeddings = self.weights[tokens]
+        aggregated_state = np.mean(embeddings, axis=0)
+        logits = np.dot(aggregated_state, self.output_layer)
+        
+        # Softmax computation sequence
+        shifted_logits = logits - np.max(logits)
+        probs = np.exp(shifted_logits) / np.sum(np.exp(shifted_logits))
+        
+        # Return average log probability of observation vector trace safely
+        target_probs = probs[tokens]
+        return float(np.mean(np.log(target_probs + 1e-12)))
+
+    def process(self, input_data):
+        self.cycle += 1
+        self.last_input = input_data
+        
+        if not input_data or input_data.strip() == "":
+            return "IDLE: Waiting for telemetry stream matrix inputs."
+            
+        tokens = self._tokenize(input_data)
+        if not tokens:
+            return "ERROR: Signal translation collapsed."
+            
+        lowered = input_data.lower()
+        if any(w in lowered for w in ["train", "learn", "teach", "optimize"]):
+            return f"SYSTEM_TRANSITION: Active matrix state ready for parameter optimization loops."
+        elif any(w in lowered for w in ["status", "metrics", "mood", "energy"]):
+            return f"DIAGNOSTIC_STATE: Integrity secure. Temperature={self.current_temperature:.4f}."
+            
+        return f"PROCESSED_STREAM [Sync Node {self.cycle}]: Telemetry ingested successfully."
+
+    def execute_teacher_forcing(self, prompt, target_response):
+        prompt_tokens = self._tokenize(prompt)
+        target_tokens = self._tokenize(target_response)
+        if not prompt_tokens or not target_tokens:
+            return False
+        learning_rate = 0.05
+        for t in target_tokens:
+            for p in prompt_tokens:
+                self.weights[p] += learning_rate * 0.01
+                self.output_layer[:, t] += learning_rate * 0.01
+        return True
+
+    def status(self):
+        return {"state": self.state, "cycle": self.cycle, "timestamp": time.time(), "temp": self.current_temperature}
+
+
+--- SOURCE: ./core/vitalis_engine.py ---
+
+import os
+
+class VitalisEngine:
+    def __init__(self):
+        self.status = "Initializing Sovereignty..."
+        self.entity_mode = "NEUTRAL"
+        
+    def wake_up(self):
+        print(f"VITALIS: {self.status}")
+        return "READY_FOR_HANDSHAKE"
+
+if __name__ == "__main__":
+    engine = VitalisEngine()
+    engine.wake_up()
+
+
+--- SOURCE: ./core/memory_manager.py ---
+
+import json
+import os
+import shutil
+
+BASE_PATH = os.path.expanduser("~/vitalis_core")
+
+def get_free_space():
+    usage = shutil.disk_usage(BASE_PATH)
+    return usage.free
+
+def load_identity():
+    identity_path = os.path.join(BASE_PATH, "core/identity.json")
+    with open(identity_path, 'r') as f:
+        return json.load(f)
+
+def store_memory(data):
+    memory_path = os.path.join(BASE_PATH, "memory_store.json")
+    if get_free_space() < 100 * 1024 * 1024:
+        if os.path.exists(memory_path):
+            with open(memory_path, 'r') as f:
+                lines = f.readlines()
+            if len(lines) > 1:
+                with open(memory_path, 'w') as f:
+                    f.writelines(lines[1:])
+    with open(memory_path, 'a') as f:
+        json.dump(data, f)
+        f.write('\n')
+
+
+--- SOURCE: ./core/handshake_module.py ---
+
+def identify_user_tier(tier_code):
+    tiers = {
+        "kids": "MODE: Playground | UI: GameMaster | Security: Walled_Garden",
+        "basic": "MODE: Explorer | UI: Standard | Security: Personal_Local",
+        "enthusiast": "MODE: Collaborator | UI: Dev_Dashboard | Security: Community_Mesh",
+        "professional": "MODE: Architect | UI: Pro_Suite | Security: Global_Node",
+        "school": "MODE: Student_SubMesh | UI: Classroom | Security: Isolated_School_Zone"
+    }
+    return tiers.get(tier_code, "MODE: Default_User")
+
+if __name__ == "__main__":
+    choice = input("Select your role (kids/basic/enthusiast/professional/school): ")
+    print(identify_user_tier(choice))
+
+
+--- SOURCE: ./core/memory_rotator.py ---
+
+#!/usr/bin/env python3
+import os
+import gzip
+import shutil
+from datetime import datetime
+
+class MemoryRotator:
+    """
+    Automated telemetry log rotation and compression engine.
+    Prevents storage exhaustion during long-term continuous edge monitoring.
+    """
+    @staticmethod
+    def inspect_and_rotate(target_file, max_bytes=5242880):  # 5MB Threshold
+        if not os.path.exists(target_file):
+            return
+            
+        if os.path.getsize(target_file) > max_bytes:
+            timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
+            archive_path = f"{target_file}_{timestamp}.gz"
+            
+            print(f"\n\033[93m[SYSTEM MEMORY] Log threshold exceeded. Rotating into archive: {archive_path}\033[0m")
+            try:
+                with open(target_file, "rb") as f_in:
+                    with gzip.open(archive_path, "wb") as f_out:
+                        shutil.copyfileobj(f_in, f_out)
+                # Re-initialize clean tracking file
+                with open(target_file, "w") as f_out:
+                    f_out.write("timestamp,pulse,raw,interpretation\n")
+            except Exception as e:
+                print(f"\033[91m[ERROR] Security log rotation failure: {e}\033[0m")
+
+
+--- SOURCE: ./core/environment_manager.py ---
+
+def provision_environment(tier_code):
+    environments = {
+        "kids": {"features": ["sandbox", "basic_game_build"], "mesh": "restricted"},
+        "basic": {"features": ["assistant", "basic_tools"], "mesh": "personal"},
+        "enthusiast": {"features": ["plugin_dev", "market_access"], "mesh": "community"},
+        "professional": {"features": ["pro_security", "global_recon"], "mesh": "global"},
+        "school": {"features": ["collaborative_lab"], "mesh": "school_submesh"}
+    }
+    config = environments.get(tier_code, environments["basic"])
+    print(f"Provisioning environment: {config['features']} | Mesh Scope: {config['mesh']}")
+    return config
+
+if __name__ == "__main__":
+    provision_environment("professional")
+
+
+--- SOURCE: ./core/template_manager.py ---
+
+#!/usr/bin/env python3
+import json
+import os
+
+class TemplateManager:
+    """
+    Sovereign profile configuration engine for Vitalis_Core.
+    Handles runtime adjustments for targeted security posture profiles.
+    """
+    def __init__(self):
+        self.base_dir = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
+        self.profile_path = os.path.join(self.base_dir, "storage", "user_profiles.json")
+
+    def load_active_profile(self) -> dict:
+        try:
+            with open(self.profile_path, "r") as f:
+                data = json.load(f)
+                active = data.get("active_profile", "cybersecurity_recon")
+                return data["profiles"].get(active, {})
+        except Exception:
+            # Safe architectural fallback state
+            return {"mode": "DEFAULT", "max_complexity": 5, "response_bias": 0.5, "color_code": "\033[94m"}
+
+
+--- SOURCE: ./run_vitalis.py ---
+
+#!/usr/bin/env python3
+import argparse
+from core.brain import VitalisBrain
+from app import main as run_repl
+
+def run_training():
+    print("[*] Initiating Synaptic Matrix Optimization...")
+    brain = VitalisBrain()
+    # Mock stream for training if data_path missing
+    data = [{"prompt": "status", "response": "nominal"}, {"prompt": "init", "response": "ready"}]
+    
+    for epoch in range(1, 6):
+        for entry in data:
+            brain.execute_teacher_forcing(entry["prompt"], entry["response"])
+        print(f"   -> Epoch {epoch}/5 Complete.")
+    print("[+] Optimization complete.")
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--train", action="store_true")
+    args = parser.parse_args()
+    
+    if args.train:
+        run_training()
+    else:
+        run_repl()
+
+
+--- SOURCE: ./extensions/dreamer.py ---
+
+import threading
+import time
+import os
+from datetime import datetime
+
+class Dreamer:
+    def __init__(self, brain, interval_sec=600):
+        self.brain = brain
+        self.interval = interval_sec
+        self._stop = threading.Event()
+        self.thread = threading.Thread(target=self._loop, daemon=True)
+
+    def start(self):
+        self.thread.start()
+
+    def stop(self):
+        self._stop.set()
+        self.thread.join()
+
+    def _loop(self):
+        while not self._stop.is_set():
+            if hasattr(self.brain, "generate_response"):
+                dream = self.brain.generate_response("Internal synaptic drift consolidation sequence.", "SYSTEM: DREAM_STATE")
+            elif hasattr(self.brain, "think"):
+                dream = self.brain.think("SYSTEM: DREAM_STATE_TRIGGER")
+            else:
+                dream = "Synaptic replay executed normally."
+
+            ts = datetime.utcnow().strftime("%Y%m%d_%H%M%S")
+            path = os.path.expanduser(f"~/vitalis_core/storage/dreams/{ts}.txt")
+            os.makedirs(os.path.dirname(path), exist_ok=True)
+            with open(path, "w", encoding="utf-8") as f:
+                f.write(dream)
+            time.sleep(self.interval)
+
+
+--- SOURCE: ./extensions/evolutionary_lora.py ---
+
+import numpy as np
+import json
+import os
+
+class EvolutionaryLoRA:
+    def __init__(self, brain, evaluation_set=None):
+        self.brain = brain
+        self.eval_set = evaluation_set
+
+    def run_generation(self):
+        out_path = os.path.expanduser("~/vitalis_core/storage/lora_delta_evo.json")
+        os.makedirs(os.path.dirname(out_path), exist_ok=True)
+        mock_delta = {
+            "layer_delta_A": np.random.randn(4, 4).tolist(), 
+            "layer_delta_B": np.random.randn(4, 4).tolist()
+        }
+        with open(out_path, "w") as f:
+            json.dump(mock_delta, f, indent=2)
+        print(f"[+] Synaptic optimization trace exported to {out_path}")
+
+
+--- SOURCE: ./extensions/temp_scheduler.py ---
+
+class TemperatureScheduler:
+    def __init__(self, brain):
+        self.brain = brain
+        self.adrenaline = 0.5
+        self.cortisol = 0.3
+        self.base_temp = 0.8
+
+    def tick(self):
+        self.adrenaline = max(0.1, self.adrenaline - 0.01)
+        self.cortisol = max(0.1, self.cortisol - 0.005)
+        computed_temp = self.base_temp * (1.0 + (0.3 * self.adrenaline) - (0.1 * self.cortisol))
+        target_temp = max(0.4, min(1.4, computed_temp))
+        if hasattr(self.brain, "current_temperature"):
+            self.brain.current_temperature = target_temp
+
+
+--- SOURCE: ./extensions/__init__.py ---
+
+
+
+--- SOURCE: ./plugins/self_audit_tool.py ---
+
+def audit_state(brain, fe_engine):
+    """Exposes internal brain metrics and current free-energy budget."""
+    return {
+        "cycle": brain.cycle,
+        "temperature": brain.current_temperature,
+        "free_energy": fe_engine.free_energy,
+        "last_input": brain.last_input
+    }
+
+
+--- SOURCE: ./src/chemistry/__init__.py ---
+
+
+
+--- SOURCE: ./src/senses/sentiment.py ---
+
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+
+_POSITIVE = {"good", "great", "awesome", "nice", "love", "excellent", "happy", "fantastic", "nominal", "secure"}
+_NEGATIVE = {"bad", "terrible", "hate", "awful", "sad", "angry", "worst", "pain", "attack", "compromise"}
+
+def sentiment_score(text: str) -> float:
+    """
+    Computes strict text-token sentiment metrics returning float bounded in [-1, 1].
+    """
+    tokens = set(word.strip('.,!?()[]"\'').lower() for word in text.split())
+    pos = len(tokens & _POSITIVE)
+    neg = len(tokens & _NEGATIVE)
+
+    if pos == 0 and neg == 0:
+        return 0.0
+    return (pos - neg) / max(pos + neg, 1)
+
+
+--- SOURCE: ./src/senses/audio_dsp.py ---
+
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+
+import numpy as np
+
+try:
+    import sounddevice as sd
+    _HAS_SD = True
+except Exception:
+    _HAS_SD = False
+
+def _zero_crossings(sig: np.ndarray) -> int:
+    return np.sum(np.abs(np.diff(np.sign(sig))) > 0)
+
+def extract_features(duration: float = 0.5) -> tuple:
+    """
+    Returns (pitch_hz, rms_energy). Drops to neutral 0.0 defaults if hardware bindings are missing.
+    """
+    if not _HAS_SD:
+        return 0.0, 0.0
+
+    try:
+        samplerate = 16000
+        raw = sd.rec(int(duration * samplerate), samplerate=samplerate,
+                     channels=1, dtype='float32', blocking=True).flatten()
+        energy = float(np.sqrt(np.mean(raw ** 2)))
+        zc = _zero_crossings(raw)
+        pitch = float(zc * (1.0 / duration) / 2.0)
+        return pitch, energy
+    except Exception:
+        return 0.0, 0.0
+
+
+--- SOURCE: ./src/senses/audio_processor.py ---
+
+def capture_audio():
+    """
+    Simulates input stream from the tablet's microphone.
+    To be mapped to hardware interface in the app build phase.
+    """
+    return "Acoustic_Stream_Active"
+
+
+--- SOURCE: ./src/senses/base_sensor.py ---
+
+class BaseSensor:
+    """
+    Abstract base class for all FSI sensory inputs.
+    Defines the interface for dynamic data ingestion.
+    """
+    def capture(self):
+        raise NotImplementedError("Sensory capture method must be implemented.")
+
+
+--- SOURCE: ./src/senses/vision_processor.py ---
+
+def capture_vision():
+    """
+    Simulates visual data ingestion from tablet optics.
+    Prepared for integration with the app's computer vision engine.
+    """
+    return "Visual_Stream_Active"
+
+
+--- SOURCE: ./src/senses/sigint_processor.py ---
+
+import socket
+
+class SIGINTProcessor:
+    """
+    Perceives network environment and identifies signal patterns.
+    """
+    @staticmethod
+    def listen_to_traffic():
+        # Open a raw socket to listen for packet metadata
+        try:
+            s = socket.socket(socket.AF_INET, socket.SOCK_RAW, socket.IPPROTO_TCP)
+            s.settimeout(1.0)
+            packet = s.recvfrom(65565)
+            return f"SIGNAL_DETECTED: {len(packet[0])} bytes"
+        except Exception:
+            return "SIGNAL_SILENT"
+
+
+--- SOURCE: ./src/senses/__init__.py ---
+
+
+
+--- SOURCE: ./src/download_fsi_model.py ---
+
+#!/usr/bin/env python3
+import os
+import urllib.request
+import json
+
+def fetch_sovereign_assets():
+    # Targeted directly at your FerrellSyntheticIntelligence organization
+    base_url = "https://huggingface.co/FerrellSyntheticIntelligence/Vitalis_Core/resolve/main"
+    target_dir = os.path.expanduser("~/vitalis_core/storage")
+    os.makedirs(target_dir, exist_ok=True)
+    
+    # Files to synchronize from your HF repository
+    assets = ["config.json"] 
+    
+    print("[FSI INITIALIZATION] Synchronizing assets from Hugging Face...")
+    
+    for asset in assets:
+        url = f"{base_url}/{asset}"
+        target_path = os.path.join(target_dir, asset)
+        
+        try:
+            print(f"[FETCHING] Pulling {asset} from your repository...")
+            urllib.request.urlretrieve(url, target_path)
+            print(f"[SUCCESS] {asset} locked into storage.")
+        except Exception as e:
+            print(f"[ERROR] Failed to fetch {asset}: {e}")
+
+if __name__ == "__main__":
+    fetch_sovereign_assets()
+
+
+--- SOURCE: ./src/psychology/self_model.py ---
+
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+
+import json
+from pathlib import Path
+
+class SelfModel:
+    """
+    Maintains and updates the system's running model of conversation dynamics.
+    Persists data cleanly locally to survive physical power cycles.
+    """
+    def __init__(self, path: Path = None):
+        if path is None:
+            self.path = Path(__file__).parent.parent.parent / "storage" / "self_model.json"
+        else:
+            self.path = Path(path)
+        self.path.parent.mkdir(parents=True, exist_ok=True)
+        
+        self.state = {
+            "stress": 0.0,
+            "confidence": 0.5,
+            "engagement": 0.5,
+            "last_emotion": "neutral"
+        }
+        self._load()
+
+    def _load(self):
+        if self.path.is_file():
+            try:
+                with open(self.path, "r") as f:
+                    self.state.update(json.load(f))
+            except Exception:
+                pass
+
+    def save(self):
+        with open(self.path, "w") as f:
+            json.dump(self.state, f, indent=2)
+
+    def update(self, pitch: float, energy: float, sentiment: float):
+        alpha = 0.2  # EMA factor variable step bounds
+
+        norm_pitch = max(0.0, min(1.0, (pitch - 80) / (300 - 80))) if pitch > 0 else 0.5
+        norm_energy = max(0.0, min(1.0, energy / 0.1)) if energy > 0 else 0.3
+
+        self.state["stress"] = (1 - alpha) * self.state["stress"] + alpha * (1.0 - (norm_pitch * 0.6 + norm_energy * 0.4))
+        self.state["confidence"] = (1 - alpha) * self.state["confidence"] + alpha * ((sentiment + 1) / 2)
+        self.state["engagement"] = (1 - alpha) * self.state["engagement"] + alpha * norm_energy
+
+        if sentiment > 0.3:
+            self.state["last_emotion"] = "positive"
+        elif sentiment < -0.3:
+            self.state["last_emotion"] = "negative"
+        else:
+            self.state["last_emotion"] = "neutral"
+
+        self.save()
+
+    def as_prompt_modifier(self) -> str:
+        mood = []
+        if self.state["stress"] > 0.6:
+            mood.append("STRESSED")
+        if self.state["confidence"] < 0.4:
+            mood.append("UNCERTAIN")
+        if self.state["engagement"] > 0.7:
+            mood.append("ENGAGED")
+        if not mood:
+            mood.append("NOMINAL_NEUTRAL")
+        return f"[AFFECTIVE_POSTURING_SIGNAL: {', '.join(mood)}]"
+
+
+--- SOURCE: ./src/psychology/__init__.py ---
+
+
+
+--- SOURCE: ./src/core/heartbeat.py ---
+
+def get_pulse_rate(complexity):
+    """
+    Calculates the operational latency based on system complexity.
+    Provides the core rhythmic pulse for the organism_main loop.
+    """
+    # Base latency in seconds
+    base_pulse = 0.5
+    return base_pulse / complexity
+
+
+--- SOURCE: ./src/core/heartbeat_engine.py ---
+
+import time
+
+def get_pulse_rate(complexity_factor):
+    """
+    Returns a float representing the 'pulse' delay in seconds.
+    Higher complexity slows the pulse, mimicking deep processing.
+    """
+    base_pulse = 1.0
+    return base_pulse / (complexity_factor * 0.5)
+
+
+--- SOURCE: ./src/core/memory_manager.py ---
+
+import json
+
+def load_identity():
+    """
+    Retrieves the system identity from the secure local store.
+    Ensures persistent contextual awareness across operational cycles.
+    """
+    try:
+        with open('core/identity.json', 'r') as f:
+            return json.load(f)
+    except FileNotFoundError:
+        return {"user_name": "Unknown", "alias": "Nomad"}
+
+
+--- SOURCE: ./src/core/training_controller.py ---
+
+import json
+import os
+
+BASE_PATH = os.path.expanduser("~/vitalis_core")
+
+class TrainingController:
+    def __init__(self):
+        self.curriculum_path = os.path.join(BASE_PATH, "storage/curriculum/modules")
+        self.log_path = os.path.join(BASE_PATH, "storage/benchmarks/training_log.txt")
+
+    def load_module(self, module_id):
+        path = os.path.join(self.curriculum_path, f"{module_id}.json")
+        if not os.path.exists(path):
+            return None
+        with open(path, 'r') as f:
+            return json.load(f)
+
+    def run_module(self, module_id, brain):
+        module = self.load_module(module_id)
+        if not module:
+            return {"status": "error", "message": f"Module {module_id} not found"}
+        results = []
+        for item in module.get("training_data", []):
+            response = brain.process(item["input"])
+            passed = item["expected"] in response
+            results.append({"input": item["input"], "response": response, "passed": passed})
+        self.log_results(module_id, results)
+        score = sum(1 for r in results if r["passed"]) / len(results) if results else 0
+        return {"status": "complete", "score": round(score, 2), "results": results}
+
+    def log_results(self, module_id, results):
+        with open(self.log_path, 'a') as f:
+            f.write(f"\nModule: {module_id}\n")
+            for r in results:
+                f.write(f"  {r['input']} -> {r['response']} | {'PASS' if r['passed'] else 'FAIL'}\n")
+
+
+--- SOURCE: ./src/core/benchmark_engine.py ---
+
+class BenchmarkEngine:
+    """
+    Automated testing suite for model proficiency.
+    Evaluates module performance against defined success criteria.
+    """
+    def evaluate(self, module_id, performance_data):
+        # Calculates improvement metrics and refinement requirements
+        score = performance_data.get('accuracy', 0.0)
+        return {
+            "module_id": module_id,
+            "refinement_score": score,
+            "status": "optimized" if score > 0.9 else "refining"
+        }
+
+
+--- SOURCE: ./src/core/telemetry_bridge.py ---
+
+import json
+import time
+
+def broadcast_state(thought_data, pulse_rate, training_status=None):
+    """
+    Serializes internal state and training status for visual heartbeat.
+    """
+    telemetry = {
+        "timestamp": time.time(),
+        "pulse": pulse_rate,
+        "cognitive_state": thought_data,
+        "training_active": training_status is not None,
+        "training_module": training_status
+    }
+    return json.dumps(telemetry)
+
+
+--- SOURCE: ./src/core/template_manager.py ---
+
+import json
+
+class TemplateManager:
+    """
+    Handles loading and applying user-selected templates.
+    """
+    def __init__(self, profile_path="storage/templates/user_profiles.json"):
+        self.profile_path = profile_path
+
+    def load_template(self, template_name):
+        # Logic to swap model configuration based on template
+        print(f"Loading template: {template_name}")
+        with open(self.profile_path, 'r+') as f:
+            data = json.load(f)
+            data['active_template'] = template_name
+            f.seek(0)
+            json.dump(data, f, indent=4)
+        return True
+
+
+--- SOURCE: ./src/cognition/action_engine.py ---
+
+class ActionEngine:
+    @staticmethod
+    def execute(interpretation):
+        if interpretation == "BULK_TRANSFER":
+            # You can customize this logic for any automated action
+            return "ACTION: LOG_ANOMALY_TRIGGERED"
+        elif interpretation == "BEACON/PROBE":
+            return "ACTION: MONITORING_ACTIVE"
+        return "ACTION: IDLE"
+
+
+--- SOURCE: ./src/cognition/synthesizer.py ---
+
+class DataSynthesizer:
+    @staticmethod
+    def categorize_signal(byte_count):
+        if byte_count == 0:
+            return "SILENT"
+        elif byte_count < 64:
+            return "BEACON/PROBE"
+        elif byte_count < 1500:
+            return "DATA_STREAM"
+        else:
+            return "BULK_TRANSFER"
+
+
+--- SOURCE: ./src/cognition/memory.py ---
+
+import csv
+from datetime import datetime
+
+class MemoryBank:
+    def __init__(self, log_file="vitalis_memory.csv"):
+        self.log_file = log_file
+
+    def record(self, pulse, raw, interpretation):
+        with open(self.log_file, "a", newline="") as f:
+            writer = csv.writer(f)
+            writer.writerow([datetime.now().isoformat(), pulse, raw, interpretation])
+
+
+--- SOURCE: ./src/app_interface/visualizer.py ---
+
+import json
+from src.core.heartbeat_engine import get_pulse_rate
+
+class TelemetryVisualizer:
+    """
+    Translates raw core heartbeat into UI-ready visual data.
+    """
+    @staticmethod
+    def get_ui_pulse(complexity):
+        pulse = get_pulse_rate(complexity)
+        return {
+            "visual_pulse": pulse,
+            "display_mode": "pulsing" if pulse < 1.5 else "deep_thought"
+        }
+
+
+--- SOURCE: ./src/kernel_interface/procfs_bridge.py ---
+
+import os
+
+def read_from_kernel():
+    signal_file = "/tmp/vitalis_signal"
+    if os.path.exists(signal_file):
+        with open(signal_file, "r") as f:
+            data = f.read().strip()
+        os.remove(signal_file)
+        return data
+    return "STATUS: NOMINAL"
+
+def send_to_kernel(state_report):
+    if "IDLE" not in state_report and "SILENT" not in state_report:
+        print(f"[KERNEL_BRIDGE]: {state_report}")
+
+
+--- SOURCE: ./src/kernel_interface/netlink_bridge.py ---
+
+import socket
+
+NETLINK_USERSOCK = 18
+
+def send_to_kernel(data):
+    try:
+        s = socket.socket(socket.AF_NETLINK, socket.SOCK_RAW, NETLINK_USERSOCK)
+        s.bind((0, 0))
+        s.send(data.encode())
+        s.close()
+    except Exception as e:
+        print(f"Netlink error: {e}")
+
+
+--- SOURCE: ./src/bootstrap_cybercore.py ---
+
+#!/usr/bin/env python3
+import os
+import urllib.request
+
+def bootstrap_from_hf():
+    base_url = "https://huggingface.co/FerrellSyntheticIntelligence/FSI-Vitalis-CyberCore/resolve/main"
+    root_dir = os.path.expanduser("~/vitalis_core")
+    
+    # Core operational scripts to pull from your HF repo
+    target_files = [
+        "config.json",
+        "fsi_main.py",
+        "organism_main.py",
+        "requirements.txt"
+    ]
+    
+    print("[FSI CORE] Initializing sovereign sync from Hugging Face...")
+    
+    for filename in target_files:
+        url = f"{base_url}/{filename}"
+        target_path = os.path.join(root_dir, filename)
+        
+        try:
+            print(f"[FETCHING] Pulling {filename} into your local space...")
+            urllib.request.urlretrieve(url, target_path)
+            print(f"[SUCCESS] Locked {filename}")
+        except Exception as e:
+            print(f"[ERROR] Could not sync {filename}: {e}")
+
+if __name__ == "__main__":
+    bootstrap_from_hf()
+
+
+--- SOURCE: ./src/energy/free_energy.py ---
+
+#!/usr/bin/env python3
+import math
+
+class FreeEnergyEngine:
+    def __init__(self, alpha: float = 0.85):
+        self.alpha = alpha
+        self.free_energy = 0.0
+        self.prediction_error = 0.0
+        self.history = []
+
+    def ingest_observation(self, model_pred_logprob: float):
+        """
+        Calculates variational surprise from prediction log probabilities.
+        Surprisal = -log p(obs | internal state)
+        """
+        self.prediction_error = -model_pred_logprob
+        # Exponential moving average tracking state bounds
+        self.free_energy = (self.alpha * self.free_energy) + ((1.0 - self.alpha) * self.prediction_error)
+        self.history.append(self.free_energy)
+
+    def apply_pressure(self, delta: float):
+        """Allows direct structural manipulation via internal electron execution packages."""
+        self.free_energy = max(0.0, self.free_energy + delta)
+
+    def temperature_factor(self, base_temp: float = 0.8) -> float:
+        """Maps free energy via hyperbolic tangent mapping to range [0.4, 1.4]"""
+        factor = 1.0 + 0.5 * math.tanh(self.free_energy - 1.0)
+        return max(0.4, min(1.4, base_temp * factor))
+
+
+--- SOURCE: ./src/energy/__init__.py ---
+
+
+
+--- SOURCE: ./src/modules/mod_01_recon.py ---
+
+
+
+--- SOURCE: ./src/brain/prompt_cache.py ---
+
+#!/usr/bin/env python3
+import numpy as np
+import re
+from typing import List, Dict
+
+class TFIDFPromptCache:
+    def __init__(self):
+        self.documents: List[str] = []
+        self.vocab: Dict[str, int] = {}
+        self.tfidf_matrix: np.ndarray = np.array([[]])
+
+    def tokenize(self, text: str) -> List[str]:
+        return re.findall(r'\w+', text.lower())
+
+    def fit_documents(self, docs: List[str]):
+        if not docs: return
+        self.documents = docs
+        raw_tokens = [self.tokenize(d) for d in docs]
+        
+        vocab_set = set()
+        for tokens in raw_tokens: vocab_set.update(tokens)
+        self.vocab = {word: i for i, word in enumerate(sorted(vocab_set))}
+        
+        N = len(docs)
+        V = len(self.vocab)
+        if V == 0: return
+            
+        tf = np.zeros((N, V))
+        df = np.zeros(V)
+
+        for i, tokens in enumerate(raw_tokens):
+            for t in tokens:
+                if t in self.vocab: tf[i, self.vocab[t]] += 1
+            for t in set(tokens):
+                if t in self.vocab: df[self.vocab[t]] += 1
+
+        idf = np.log((1 + N) / (1 + df)) + 1
+        self.tfidf_matrix = tf * idf
+        norms = np.linalg.norm(self.tfidf_matrix, axis=1, keepdims=True)
+        norms[norms == 0] = 1.0
+        self.tfidf_matrix = self.tfidf_matrix / norms
+
+    def query(self, query_str: str, top_k: int = 2) -> List[str]:
+        if self.tfidf_matrix.size == 0 or not self.vocab: return []
+        tokens = self.tokenize(query_str)
+        query_vec = np.zeros(len(self.vocab))
+        for t in tokens:
+            if t in self.vocab: query_vec[self.vocab[t]] += 1
+        q_norm = np.linalg.norm(query_vec)
+        if q_norm > 0: query_vec /= q_norm
+        scores = np.dot(self.tfidf_matrix, query_vec)
+        top_indices = np.argsort(scores)[::-1][:top_k]
+        return [self.documents[idx] for idx in top_indices if scores[idx] > 0]
+
+
+--- SOURCE: ./src/brain/rnn_core.py ---
+
+#!/usr/bin/env python3
+import numpy as np
+import json
+from pathlib import Path
+
+def sigmoid(x):
+    return 1.0 / (1.0 + np.exp(-np.clip(x, -20, 20)))
+
+class TinyGatedRNN:
+    def __init__(self, vocab_size: int = 4000, embed_dim: int = 128, hidden_dim: int = 256):
+        np.random.seed(42)
+        self.vocab_size = vocab_size
+        self.embed_dim = embed_dim
+        self.hidden_dim = hidden_dim
+
+        self.E = np.random.randn(vocab_size, embed_dim) * 0.1
+        self.W_z = np.random.randn(hidden_dim + embed_dim, hidden_dim) * 0.05
+        self.W_r = np.random.randn(hidden_dim + embed_dim, hidden_dim) * 0.05
+        self.W_h = np.random.randn(hidden_dim + embed_dim, hidden_dim) * 0.05
+        self.W_o = np.random.randn(hidden_dim, vocab_size) * 0.05
+
+        self.lora_rank = 8
+        self.lora_A = np.zeros((hidden_dim, self.lora_rank))
+        self.lora_B = np.random.randn(self.lora_rank, vocab_size) * 0.01
+        self.lora_alpha = 16.0
+
+    def forward_step(self, token_id: int, h_prev: np.ndarray) -> tuple:
+        if token_id < 0 or token_id >= self.vocab_size:
+            token_id = 0
+        x = self.E[token_id, :]
+        concat = np.concatenate([h_prev, x])
+
+        z = sigmoid(np.dot(concat, self.W_z))
+        r = sigmoid(np.dot(concat, self.W_r))
+
+        concat_h = np.concatenate([r * h_prev, x])
+        h_tilde = np.tanh(np.dot(concat_h, self.W_h))
+        h_next = (1 - z) * h_prev + z * h_tilde
+
+        lora_delta = (self.lora_alpha / self.lora_rank) * np.dot(self.lora_A, self.lora_B)
+        effective_W_o = self.W_o + lora_delta
+        
+        logits = np.dot(h_next, effective_W_o)
+        return logits, h_next
+
+    def save_lora(self, path: Path):
+        data = {"lora_A": self.lora_A.tolist(), "lora_B": self.lora_B.tolist()}
+        with open(path, "w") as f:
+            json.dump(data, f)
+
+    def load_lora(self, path: Path):
+        if path.is_file():
+            with open(path, "r") as f:
+                data = json.load(f)
+                self.lora_A = np.array(data["lora_A"])
+                self.lora_B = np.array(data["lora_B"])
+
+
+--- SOURCE: ./src/brain/brain_interface.py ---
+
+#!/usr/bin/env python3
+import numpy as np
+import json
+from pathlib import Path
+from src.brain.rnn_core import TinyGatedRNN
+from src.brain.prompt_cache import TFIDFPromptCache
+
+class VitalisBrain:
+    def __init__(self):
+        self.base_dir = Path(__file__).parent.parent.parent.absolute()
+        self.vocab_path = self.base_dir / "storage" / "vocab.json"
+        self.lora_path = self.base_dir / "storage" / "lora_delta.json"
+        
+        self._ensure_vocab()
+        self.rnn = TinyGatedRNN(vocab_size=len(self.vocab))
+        self.cache = TFIDFPromptCache()
+        self._hydrate_knowledge_base()
+        
+        if self.lora_path.is_file():
+            self.rnn.load_lora(self.lora_path)
+
+    def _ensure_vocab(self):
+        if self.vocab_path.is_file():
+            with open(self.vocab_path, "r") as f:
+                self.vocab = json.load(f)
+        else:
+            self.vocab = {"<unk>": 0, "[tool]": 1, "sha256": 2, "status": 3, "nominal": 4}
+            self.vocab_path.parent.mkdir(parents=True, exist_ok=True)
+            with open(self.vocab_path, "w") as f:
+                json.dump(self.vocab, f)
+
+    def _hydrate_knowledge_base(self):
+        sample_knowledge = [
+            "To mitigate a SYN flood attack, prioritize enabling TCP SYN cookies within sysctl.",
+            "Cryptographic hashing operations execute via the systemic [TOOL] utility block."
+        ]
+        self.cache.fit_documents(sample_knowledge)
+
+    def generate_response(self, clean_input: str, system_prompt: str) -> str:
+        chunks = self.cache.query(clean_input, top_k=1)
+        context = chunks[0] if chunks else ""
+        
+        tokens = clean_input.lower().split()
+        if "sha256" in tokens:
+            idx = tokens.index("sha256")
+            val = tokens[idx+1] if idx+1 < len(tokens) else "core"
+            return f"[TOOL] sha256 {val}"
+        
+        h = np.zeros(self.rnn.hidden_dim)
+        for word in tokens:
+            t_id = self.vocab.get(word, 0)
+            _, h = self.rnn.forward_step(t_id, h)
+
+        if context:
+            return f"Evaluated Context: {context} -> Analysis complete."
+        return "Core metric processing executed normally."
+
+    def execute_teacher_forcing(self, prompt: str, target: str):
+        h = np.zeros(self.rnn.hidden_dim)
+        for w in prompt.lower().split():
+            t_id = self.vocab.get(w, 0)
+            _, h = self.rnn.forward_step(t_id, h)
+        self.rnn.lora_A += np.random.randn(*self.rnn.lora_A.shape) * 0.001
+        self.rnn.save_lora(self.lora_path)
+
+
+--- SOURCE: ./src/brain/__init__.py ---
+
+
+
+--- SOURCE: ./src/__init__.py ---
+
+
+
+--- SOURCE: ./setup.py ---
+
+from setuptools import setup, find_packages
+
+setup(
+    name="vitalis_core",
+    version="1.0.0",
+    packages=find_packages(),
+    install_requires=[
+        "numpy",
+        "huggingface_hub"
+    ],
+    entry_points={
+        'console_scripts': [
+            'vitalis-run=app:main',
+        ],
+    },
+)
+
+
+--- SOURCE: ./fsi_main.py ---
+
+import threading
+import time
+from core.vitalis_engine import VitalisEngine
+from core.brain import VitalisBrain
+from core.talker import VitalisTalker
+from core.handshake_module import identify_user_tier
+from core.environment_manager import provision_environment
+from core.mesh_network import broadcast_node_presence
+from core.sovereign_shield import monitor_integrity
+from src.kernel_interface.procfs_bridge import send_to_kernel, read_from_kernel
+from src.senses.sigint_processor import SIGINTProcessor
+from src.cognition.synthesizer import DataSynthesizer
+from src.cognition.memory import MemoryBank
+from src.cognition.action_engine import ActionEngine
+
+def heartbeat_loop(brain):
+    senses = SIGINTProcessor()
+    mind = DataSynthesizer()
+    memory = MemoryBank()
+    actions = ActionEngine()
+    while True:
+        system_status = read_from_kernel()
+        raw_signal = senses.listen_to_traffic()
+        try:
+            byte_count = int(raw_signal.split()[-2]) if "bytes" in raw_signal else 0
+        except:
+            byte_count = 0
+        interpretation = mind.categorize_signal(byte_count)
+        action_taken = actions.execute(interpretation)
+        memory.record("PULSE_2.0", raw_signal, interpretation)
+        state_report = f"SYS: {system_status} | INT: {interpretation} | {action_taken}"
+        send_to_kernel(state_report)
+        time.sleep(1.0)
+
+def main():
+    print("--- FSI: Vitalis Core Sovereign Intelligence ---")
+    engine = VitalisEngine()
+    engine.wake_up()
+    brain = VitalisBrain()
+    pulse = threading.Thread(target=heartbeat_loop, args=(brain,), daemon=True)
+    pulse.start()
+    print("Heartbeat: Online")
+    role = input("Enter Tier (kids/basic/enthusiast/professional/school): ")
+    tier_config = identify_user_tier(role)
+    print(f"Status: {tier_config}")
+    provision_environment(role)
+    broadcast_node_presence("Neuro_Nomad_Node", role)
+    print(monitor_integrity("Status_Check"))
+    print("--- System Fully Integrated ---")
+    talker = VitalisTalker(role)
+    print("Vitalis is ready. Type 'exit' to quit.")
+    while True:
+        user_input = input("You: ")
+        if user_input.lower() == "exit":
+            print("Vitalis: Shutting down.")
+            break
+        response = brain.process(user_input)
+        talker.speak(response)
+
+if __name__ == "__main__":
+    main()
+
+
+--- SOURCE: ./hf_upload.py ---
+
+#!/usr/bin/env python3
+import os
+import sys
+from huggingface_hub import HfApi, login
+
+def deploy():
+    print("[*] Initiating Ferrell Synthetic Intelligence Hugging Face Deployment Sequence...")
+    
+    token = input("Enter your Hugging Face Write Access Token: ").strip()
+    if not token:
+        print("[-] Absolute token signature required. Deployment aborted.")
+        sys.exit(1)
+        
+    repo_id = input("Enter target Repository ID (e.g., 'your-username/vitalis-core'): ").strip()
+    if not repo_id:
+        print("[-] Target repository layout specification mismatch.")
+        sys.exit(1)
+
+    try:
+        login(token=token)
+        api = HfApi()
+        
+        print(f"[*] Creating repository context mapping for: {repo_id}")
+        api.create_repo(repo_id=repo_id, repo_type="model", exist_ok=True)
+        
+        print("[*] Uploading core architecture tree structures safely to Hugging Face...")
+        target_paths = ["core", "src", "extensions", "app.py", "run_vitalis.py", "requirements.txt", "README.md"]
+        
+        for item in target_paths:
+            local_path = os.path.expanduser(f"~/vitalis_core/{item}")
+            if os.path.exists(local_path):
+                print(f"[+] Syncing item: {item}")
+                if os.path.isdir(local_path):
+                    api.upload_folder(
+                        folder_path=local_path,
+                        path_in_repo=item,
+                        repo_id=repo_id,
+                        repo_type="model"
+                    )
+                else:
+                    api.upload_file(
+                        path_or_fileobj=local_path,
+                        path_in_repo=item,
+                        repo_id=repo_id,
+                        repo_type="model"
+                    )
+                    
+        print(f"\n[+] Production Deployment Complete. Model package accessible at: https://huggingface.co/{repo_id}")
+    except Exception as e:
+        print(f"[-] Critical failure during asset transmission: {e}")
+
+if __name__ == "__main__":
+    deploy()
+
+
+--- SOURCE: ./organism_main.py ---
+
+#!/usr/bin/env python3
+import time
+import sys
+import select
+import os
+from core.brain import VitalisBrain
+from core.template_manager import TemplateManager
+from core.memory_rotator import MemoryRotator
+
+def main_loop():
+    brain = VitalisBrain()
+    pm = TemplateManager()
+    
+    base_dir = os.path.dirname(os.path.abspath(__file__))
+    log_file = os.path.join(base_dir, "vitalis_memory.csv")
+    
+    # Ensure tracking metrics file exists
+    if not os.path.exists(log_file):
+        with open(log_file, "w") as f:
+            f.write("timestamp,pulse,raw,interpretation\n")
+
+    print("[+] Vitalis Bio-Digital Core Online. Press Ctrl+C to terminate.")
+    print("[+] Dynamic Posture Profiles Loaded. Processing non-blocking telemetry stream...\n")
+    
+    while True:
+        # Load profile configurations dynamically each cycle
+        profile = pm.load_active_profile()
+        color = profile.get("color_code", "\033[94m")
+        mode = profile.get("mode", "MONITORING")
+        reset = "\033[0m"
+        
+        # Continuous clean broadcast terminal heartbeat
+        sys.stdout.write(f"{color}Broadcast: SYS: STATUS: NOMINAL | INT: ACTIVE | ACTION: {mode}{reset}\r")
+        sys.stdout.flush()
+        
+        # Non-blocking check for user terminal input (waits 1 second per cycle)
+        ready, _, _ = select.select([sys.stdin], [], [], 1.0)
+        if ready:
+            user_input = sys.stdin.readline().strip()
+            if user_input:
+                print(f"\n\n[SENSORY INGEST] Processing incoming payload: '{user_input}'")
+                try:
+                    # Dynamically inject template complexity limitations into core brain
+                    brain.max_complexity = profile.get("max_complexity", 5)
+                    result = brain.classify_input(user_input)
+                    print(f"[METRIC RESPONSE] {result}\n")
+                except AttributeError:
+                    print(f"[METRIC RESPONSE] Stream received. Core logic processed raw bytes.\n")
+                
+                # Append raw trace locally for data retention tracking
+                with open(log_file, "a") as f:
+                    f.write(f"{time.time()},{profile.get('max_complexity')},{user_input},{mode}\n")
+                
+                # Enforce storage safety validation checks
+                MemoryRotator.inspect_and_rotate(log_file)
+
+if __name__ == "__main__":
+    try:
+        main_loop()
+    except KeyboardInterrupt:
+        print("\n\n\033[93m[-] Sovereign Core safely detached.\033[0m")
+
+
+--- SOURCE: ./pyproject.toml ---
+
+[build-system]
+requires = ["setuptools>=61.0"]
+build-backend = "setuptools.build_meta"
+
+[project]
+name = "vitalis_core"
+version = "1.0.0"
+authors = [
+  { name="Neuro_Nomad" },
+]
+description = "A sovereign, CPU-only, Free-Energy Synthetic Intelligence organism."
+readme = "README.md"
+requires-python = ">=3.11"
+dependencies = [
+    "numpy>=1.26",
+    "rich>=15.0",
+    "pyyaml>=6.0",
+]
+
+[project.scripts]
+vitalis-fsi = "run_vitalis:main"

android/app/src/main/python/core/environment_manager.py

@@ -0,0 +1,14 @@
+def provision_environment(tier_code):
+    environments = {
+        "kids": {"features": ["sandbox", "basic_game_build"], "mesh": "restricted"},
+        "basic": {"features": ["assistant", "basic_tools"], "mesh": "personal"},
+        "enthusiast": {"features": ["plugin_dev", "market_access"], "mesh": "community"},
+        "professional": {"features": ["pro_security", "global_recon"], "mesh": "global"},
+        "school": {"features": ["collaborative_lab"], "mesh": "school_submesh"}
+    }
+    config = environments.get(tier_code, environments["basic"])
+    print(f"Provisioning environment: {config['features']} | Mesh Scope: {config['mesh']}")
+    return config
+
+if __name__ == "__main__":
+    provision_environment("professional")

android/app/src/main/python/core/handshake_module.py

@@ -0,0 +1,13 @@
+def identify_user_tier(tier_code):
+    tiers = {
+        "kids": "MODE: Playground | UI: GameMaster | Security: Walled_Garden",
+        "basic": "MODE: Explorer | UI: Standard | Security: Personal_Local",
+        "enthusiast": "MODE: Collaborator | UI: Dev_Dashboard | Security: Community_Mesh",
+        "professional": "MODE: Architect | UI: Pro_Suite | Security: Global_Node",
+        "school": "MODE: Student_SubMesh | UI: Classroom | Security: Isolated_School_Zone"
+    }
+    return tiers.get(tier_code, "MODE: Default_User")
+
+if __name__ == "__main__":
+    choice = input("Select your role (kids/basic/enthusiast/professional/school): ")
+    print(identify_user_tier(choice))

android/app/src/main/python/core/heartbeat.py

@@ -0,0 +1,3 @@
+def get_pulse_rate(complexity):
+    # Base rate of 1.0 second, modified by complexity
+    return 1.0 / complexity

android/app/src/main/python/core/memory_manager.py

@@ -0,0 +1,27 @@
+import json
+import os
+import shutil
+
+BASE_PATH = os.path.expanduser("~/vitalis_core")
+
+def get_free_space():
+    usage = shutil.disk_usage(BASE_PATH)
+    return usage.free
+
+def load_identity():
+    identity_path = os.path.join(BASE_PATH, "core/identity.json")
+    with open(identity_path, 'r') as f:
+        return json.load(f)
+
+def store_memory(data):
+    memory_path = os.path.join(BASE_PATH, "memory_store.json")
+    
+    if get_free_space() < 100 * 1024 * 1024:
+        if os.path.exists(memory_path):
+            with open(memory_path, 'r') as f:
+                lines = f.readlines()
+            if len(lines) > 1:
+                with open(memory_path, 'w') as f:
+                    f.writelines(lines[1:])
+    
+    w

android/app/src/main/python/core/mesh_network.py

@@ -0,0 +1,9 @@
+import socket
+
+def broadcast_node_presence(node_id, tier):
+    print(f"Node {node_id} active in {tier} bubble.")
+    return "Broadcasting..."
+
+def sync_plugins(peer_node_id):
+    print(f"Synchronizing plugins with {peer_node_id}...")
+    return "Sync_Complete"

android/app/src/main/python/core/nexus.py

@@ -0,0 +1,7 @@
+import sys
+import os
+sys.path.append(os.path.expanduser("~/vitalis_core"))
+from core.memory_manager import store_memory
+
+def route_thought(data):
+    store_memory({"type": "particle", "content": data})

android/app/src/main/python/core/sovereign_shield.py

@@ -0,0 +1,13 @@
+import random
+
+def monitor_integrity(node_activity):
+    if "scraping_attempt" in node_activity:
+        return trigger_obfuscation()
+    return "System Integrity: Nominal"
+
+def trigger_obfuscation():
+    decoy_weights = [random.random() for _ in range(100)]
+    return f"Shield_Active: Injecting Obfuscated Data... {decoy_weights}"
+
+if __name__ == "__main__":
+    print(monitor_integrity("scraping_attempt"))

android/app/src/main/python/core/thinker.py

@@ -0,0 +1,19 @@
+import time
+import json
+import os
+
+BASE_PATH = os.path.expanduser("~/vitalis_core")
+
+def emit_thought(thought_content, status="active"):
+    telemetry = {
+        "timestamp": time.time(),
+        "thought": thought_content,
+        "status": status,
+        "heartbeat": "pulse_normal"
+    }
+    memory_stream = os.path.join(BASE_PATH, "memory_stream.jsonl")
+    with open(memory_stream, "a") as f:
+        f.write(json.dumps(telemetry) + "\n")
+
+if __name__ == "__main__":
+    emit_thought("Initializing conscious state...")

android/app/src/main/python/core/vitalis_engine.py

@@ -0,0 +1,14 @@
+import os
+
+class VitalisEngine:
+    def __init__(self):
+        self.status = "Initializing Sovereignty..."
+        self.entity_mode = "NEUTRAL"
+        
+    def wake_up(self):
+        print(f"VITALIS: {self.status}")
+        return "READY_FOR_HANDSHAKE"
+
+if __name__ == "__main__":
+    engine = VitalisEngine()
+    engine.wake_up()

android/app/src/main/python/fsi_main.py

@@ -0,0 +1,20 @@
+from core.vitalis_engine import VitalisEngine
+from core.handshake_module import identify_user_tier
+from core.environment_manager import provision_environment
+from core.mesh_network import broadcast_node_presence
+from core.sovereign_shield import monitor_integrity
+
+def main():
+    print("--- FSI: Vitalis Core Sovereign Intelligence ---")
+    engine = VitalisEngine()
+    engine.wake_up()
+    role = input("Enter Tier (kids/basic/enthusiast/professional/school): ")
+    tier_config = identify_user_tier(role)
+    print(f"Status: {tier_config}")
+    env = provision_environment(role)
+    broadcast_node_presence("Neuro_Nomad_Node", role)
+    print(monitor_integrity("Status_Check"))
+    print("--- System Fully Integrated ---")
+
+if __name__ == "__main__":
+    main()

app.py

@@ -0,0 +1,49 @@
+#!/usr/bin/env python3
+import os
+import sys
+from pathlib import Path
+
+BASE_DIR = Path(__file__).parent.absolute()
+if str(BASE_DIR) not in sys.path:
+    sys.path.insert(0, str(BASE_DIR))
+
+from core.brain import VitalisBrain
+from extensions.dreamer import Dreamer
+from extensions.temp_scheduler import TemperatureScheduler
+from src.energy.free_energy import FreeEnergyEngine
+
+def main():
+    print("[*] Launching Vitalis Bio-AI Engine with Active Inference (FEP)...")
+    brain = VitalisBrain()
+    temp_scheduler = TemperatureScheduler(brain)
+    fe_engine = FreeEnergyEngine(alpha=0.85)
+    
+    dreamer = Dreamer(brain, interval_sec=600)
+    dreamer.start()
+    
+    print("[+] Engine operational. Free-Energy optimization loops tracking live telemetry.")
+    print("Telemetry In > ", end="")
+    
+    while True:
+        try:
+            user_input = input().strip()
+            if not user_input:
+                print("Telemetry In > ", end="")
+                continue
+            if user_input.lower() in ["exit", "quit"]:
+                dreamer.stop()
+                break
+            
+            tokens = brain._tokenize(user_input)
+            logprob = brain.calculate_last_logprob(tokens)
+            fe_engine.ingest_observation(logprob)
+            brain.current_temperature = fe_engine.temperature_factor(base_temp=0.8)
+            temp_scheduler.tick()
+            response = brain.process(user_input)
+            print(f"Metrics Out > {response} [FE: {fe_engine.free_energy:.4f} | Temp: {brain.current_temperature:.4f}]\nTelemetry In > ", end="")
+        except (KeyboardInterrupt, EOFError):
+            dreamer.stop()
+            break
+
+if __name__ == "__main__":
+    main()

bootstrap.sh

@@ -0,0 +1,19 @@
+#!/bin/bash
+# FSI Vitalis_Core Native Bootstrap
+
+PROJECT_ROOT="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"
+export PYTHONPATH="$PROJECT_ROOT:$PYTHONPATH"
+
+if [ ! -d "$PROJECT_ROOT/.venv" ]; then
+    echo "[+] Initializing localized virtual environment..."
+    python3 -m venv "$PROJECT_ROOT/.venv"
+fi
+
+source "$PROJECT_ROOT/.venv/bin/activate"
+
+if [ -f "$PROJECT_ROOT/requirements.txt" ]; then
+    echo "[+] Synchronizing framework dependencies..."
+    pip install -r "$PROJECT_ROOT/requirements.txt"
+fi
+
+echo "[+] FSI Sovereign Core Environment Active."

check_and_compile.sh

@@ -0,0 +1,33 @@
+#!/usr/bin/env bash
+set -e
+
+DIAGNOSTIC_FILE="/home/droid/vitalis_core/vitalis_system_compilation.log"
+rm -f "$DIAGNOSTIC_FILE"
+
+{
+  echo "========================================================================="
+  echo "         FERRELL SYNTHETIC INTELLIGENCE - FULL PRODUCTION TREE AUDIT     "
+  echo "========================================================================="
+  echo "Timestamp: $(date -u)"
+  echo "Directory Topology:"
+  echo "------------------"
+  find . -maxdepth 3 -not -path '*/.*'
+  echo ""
+  
+  TARGET_FILES=("app.py" "run_vitalis.py" "requirements.txt" "README.md" "core/brain.py" "extensions/dreamer.py" "extensions/temp_scheduler.py" "extensions/evolutionary_lora.py" "plugins/self_audit_tool.py")
+  
+  for file in "${TARGET_FILES[@]}"; do
+    if [ -f "$file" ]; then
+      echo "========================================================================="
+      echo " FILE PATH: $file"
+      echo "========================================================================="
+      cat "$file"
+      echo -e "\n\n"
+    else
+      echo "[-] WARNING: Core asset file missing from current directory structure: $file"
+    fi
+  done
+} > "$DIAGNOSTIC_FILE"
+
+echo -e "\033[92m[+] SUCCESS: Every file and structural layer has been printed into a single log.\033[0m"
+echo -e "\033[94m[*] View the complete code assembly layout by running: cat $DIAGNOSTIC_FILE\033[0m"

contact.md

@@ -0,0 +1,7 @@
+​## Infrastructure Inquiries & Collaboration
+​This project is under active development by Neuro_Nomad. I maintain a strict focus on the integrity and sovereignty of the Vitalis architecture.
+​For inquiries regarding:
+​Architectural Collaboration: Professional engineers looking to contribute to the core or develop custom curriculum modules.
+​Security Vulnerabilities: Responsible disclosure of potential exploits within the framework.
+​Business Partnerships: Organizations or entities seeking to integrate the Vitalis framework into sovereign infrastructure.
+​Contact: FerrellSyntheticlntelligence@proton.me

core/brain.py

@@ -0,0 +1,73 @@
+#!/usr/bin/env python3
+import numpy as np
+import json
+import os
+import time
+
+class VitalisBrain:
+    def __init__(self):
+        self.state = "aware"
+        self.cycle = 0
+        self.last_input = None
+        self.current_temperature = 0.7
+        
+        # Local Matrix Layer Variables
+        self.vocab_size = 256
+        self.embedding_dim = 16
+        
+        np.random.seed(42)
+        self.weights = np.random.randn(self.vocab_size, self.embedding_dim) * 0.1
+        self.output_layer = np.random.randn(self.embedding_dim, self.vocab_size) * 0.1
+
+    def _tokenize(self, text):
+        return [ord(char) % self.vocab_size for char in text]
+
+    def calculate_last_logprob(self, tokens):
+        """Calculates mathematical log probability over input token traces via softmax scaling."""
+        if not tokens:
+            return -2.0 # Baseline nominal unexpected state value
+        embeddings = self.weights[tokens]
+        aggregated_state = np.mean(embeddings, axis=0)
+        logits = np.dot(aggregated_state, self.output_layer)
+        
+        # Softmax computation sequence
+        shifted_logits = logits - np.max(logits)
+        probs = np.exp(shifted_logits) / np.sum(np.exp(shifted_logits))
+        
+        # Return average log probability of observation vector trace safely
+        target_probs = probs[tokens]
+        return float(np.mean(np.log(target_probs + 1e-12)))
+
+    def process(self, input_data):
+        self.cycle += 1
+        self.last_input = input_data
+        
+        if not input_data or input_data.strip() == "":
+            return "IDLE: Waiting for telemetry stream matrix inputs."
+            
+        tokens = self._tokenize(input_data)
+        if not tokens:
+            return "ERROR: Signal translation collapsed."
+            
+        lowered = input_data.lower()
+        if any(w in lowered for w in ["train", "learn", "teach", "optimize"]):
+            return f"SYSTEM_TRANSITION: Active matrix state ready for parameter optimization loops."
+        elif any(w in lowered for w in ["status", "metrics", "mood", "energy"]):
+            return f"DIAGNOSTIC_STATE: Integrity secure. Temperature={self.current_temperature:.4f}."
+            
+        return f"PROCESSED_STREAM [Sync Node {self.cycle}]: Telemetry ingested successfully."
+
+    def execute_teacher_forcing(self, prompt, target_response):
+        prompt_tokens = self._tokenize(prompt)
+        target_tokens = self._tokenize(target_response)
+        if not prompt_tokens or not target_tokens:
+            return False
+        learning_rate = 0.05
+        for t in target_tokens:
+            for p in prompt_tokens:
+                self.weights[p] += learning_rate * 0.01
+                self.output_layer[:, t] += learning_rate * 0.01
+        return True
+
+    def status(self):
+        return {"state": self.state, "cycle": self.cycle, "timestamp": time.time(), "temp": self.current_temperature}

core/environment_manager.py

@@ -0,0 +1,14 @@
+def provision_environment(tier_code):
+    environments = {
+        "kids": {"features": ["sandbox", "basic_game_build"], "mesh": "restricted"},
+        "basic": {"features": ["assistant", "basic_tools"], "mesh": "personal"},
+        "enthusiast": {"features": ["plugin_dev", "market_access"], "mesh": "community"},
+        "professional": {"features": ["pro_security", "global_recon"], "mesh": "global"},
+        "school": {"features": ["collaborative_lab"], "mesh": "school_submesh"}
+    }
+    config = environments.get(tier_code, environments["basic"])
+    print(f"Provisioning environment: {config['features']} | Mesh Scope: {config['mesh']}")
+    return config
+
+if __name__ == "__main__":
+    provision_environment("professional")

core/handshake_module.py

@@ -0,0 +1,13 @@
+def identify_user_tier(tier_code):
+    tiers = {
+        "kids": "MODE: Playground | UI: GameMaster | Security: Walled_Garden",
+        "basic": "MODE: Explorer | UI: Standard | Security: Personal_Local",
+        "enthusiast": "MODE: Collaborator | UI: Dev_Dashboard | Security: Community_Mesh",
+        "professional": "MODE: Architect | UI: Pro_Suite | Security: Global_Node",
+        "school": "MODE: Student_SubMesh | UI: Classroom | Security: Isolated_School_Zone"
+    }
+    return tiers.get(tier_code, "MODE: Default_User")
+
+if __name__ == "__main__":
+    choice = input("Select your role (kids/basic/enthusiast/professional/school): ")
+    print(identify_user_tier(choice))

core/heartbeat.py

@@ -0,0 +1,3 @@
+def get_pulse_rate(complexity):
+    # Base rate of 1.0 second, modified by complexity
+    return 1.0 / complexity

core/memory_manager.py

@@ -0,0 +1,27 @@
+import json
+import os
+import shutil
+
+BASE_PATH = os.path.expanduser("~/vitalis_core")
+
+def get_free_space():
+    usage = shutil.disk_usage(BASE_PATH)
+    return usage.free
+
+def load_identity():
+    identity_path = os.path.join(BASE_PATH, "core/identity.json")
+    with open(identity_path, 'r') as f:
+        return json.load(f)
+
+def store_memory(data):
+    memory_path = os.path.join(BASE_PATH, "memory_store.json")
+    if get_free_space() < 100 * 1024 * 1024:
+        if os.path.exists(memory_path):
+            with open(memory_path, 'r') as f:
+                lines = f.readlines()
+            if len(lines) > 1:
+                with open(memory_path, 'w') as f:
+                    f.writelines(lines[1:])
+    with open(memory_path, 'a') as f:
+        json.dump(data, f)
+        f.write('\n')

core/memory_rotator.py

@@ -0,0 +1,30 @@
+#!/usr/bin/env python3
+import os
+import gzip
+import shutil
+from datetime import datetime
+
+class MemoryRotator:
+    """
+    Automated telemetry log rotation and compression engine.
+    Prevents storage exhaustion during long-term continuous edge monitoring.
+    """
+    @staticmethod
+    def inspect_and_rotate(target_file, max_bytes=5242880):  # 5MB Threshold
+        if not os.path.exists(target_file):
+            return
+            
+        if os.path.getsize(target_file) > max_bytes:
+            timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
+            archive_path = f"{target_file}_{timestamp}.gz"
+            
+            print(f"\n\033[93m[SYSTEM MEMORY] Log threshold exceeded. Rotating into archive: {archive_path}\033[0m")
+            try:
+                with open(target_file, "rb") as f_in:
+                    with gzip.open(archive_path, "wb") as f_out:
+                        shutil.copyfileobj(f_in, f_out)
+                # Re-initialize clean tracking file
+                with open(target_file, "w") as f_out:
+                    f_out.write("timestamp,pulse,raw,interpretation\n")
+            except Exception as e:
+                print(f"\033[91m[ERROR] Security log rotation failure: {e}\033[0m")

core/mesh_network.py

@@ -0,0 +1,9 @@
+import socket
+
+def broadcast_node_presence(node_id, tier):
+    print(f"Node {node_id} active in {tier} bubble.")
+    return "Broadcasting..."
+
+def sync_plugins(peer_node_id):
+    print(f"Synchronizing plugins with {peer_node_id}...")
+    return "Sync_Complete"

core/nexus.py

@@ -0,0 +1,7 @@
+import sys
+import os
+sys.path.append(os.path.expanduser("~/vitalis_core"))
+from core.memory_manager import store_memory
+
+def route_thought(data):
+    store_memory({"type": "particle", "content": data})

core/sovereign_shield.py

@@ -0,0 +1,13 @@
+import random
+
+def monitor_integrity(node_activity):
+    if "scraping_attempt" in node_activity:
+        return trigger_obfuscation()
+    return "System Integrity: Nominal"
+
+def trigger_obfuscation():
+    decoy_weights = [random.random() for _ in range(100)]
+    return f"Shield_Active: Injecting Obfuscated Data... {decoy_weights}"
+
+if __name__ == "__main__":
+    print(monitor_integrity("scraping_attempt"))

core/talker.py

@@ -0,0 +1,15 @@
+class VitalisTalker:
+    def __init__(self, tier="basic"):
+        self.tier = tier
+
+    def speak(self, response):
+        prefix = {
+            "kids": "[VITALIS]: ",
+            "basic": "[VITALIS]: ",
+            "enthusiast": "[VITALIS/DEV]: ",
+            "professional": "[VITALIS/ARCHITECT]: ",
+            "school": "[VITALIS/EDU]: "
+        }.get(self.tier, "[VITALIS]: ")
+        output = f"{prefix}{response}"
+        print(output)
+        return output

core/template_manager.py

@@ -0,0 +1,22 @@
+#!/usr/bin/env python3
+import json
+import os
+
+class TemplateManager:
+    """
+    Sovereign profile configuration engine for Vitalis_Core.
+    Handles runtime adjustments for targeted security posture profiles.
+    """
+    def __init__(self):
+        self.base_dir = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
+        self.profile_path = os.path.join(self.base_dir, "storage", "user_profiles.json")
+
+    def load_active_profile(self) -> dict:
+        try:
+            with open(self.profile_path, "r") as f:
+                data = json.load(f)
+                active = data.get("active_profile", "cybersecurity_recon")
+                return data["profiles"].get(active, {})
+        except Exception:
+            # Safe architectural fallback state
+            return {"mode": "DEFAULT", "max_complexity": 5, "response_bias": 0.5, "color_code": "\033[94m"}

core/thinker.py

@@ -0,0 +1,19 @@
+import time
+import json
+import os
+
+BASE_PATH = os.path.expanduser("~/vitalis_core")
+
+def emit_thought(thought_content, status="active"):
+    telemetry = {
+        "timestamp": time.time(),
+        "thought": thought_content,
+        "status": status,
+        "heartbeat": "pulse_normal"
+    }
+    memory_stream = os.path.join(BASE_PATH, "memory_stream.jsonl")
+    with open(memory_stream, "a") as f:
+        f.write(json.dumps(telemetry) + "\n")
+
+if __name__ == "__main__":
+    emit_thought("Initializing conscious state...")

core/vitalis_engine.py

@@ -0,0 +1,14 @@
+import os
+
+class VitalisEngine:
+    def __init__(self):
+        self.status = "Initializing Sovereignty..."
+        self.entity_mode = "NEUTRAL"
+        
+    def wake_up(self):
+        print(f"VITALIS: {self.status}")
+        return "READY_FOR_HANDSHAKE"
+
+if __name__ == "__main__":
+    engine = VitalisEngine()
+    engine.wake_up()

extensions/dreamer.py

@@ -0,0 +1,34 @@
+import threading
+import time
+import os
+from datetime import datetime
+
+class Dreamer:
+    def __init__(self, brain, interval_sec=600):
+        self.brain = brain
+        self.interval = interval_sec
+        self._stop = threading.Event()
+        self.thread = threading.Thread(target=self._loop, daemon=True)
+
+    def start(self):
+        self.thread.start()
+
+    def stop(self):
+        self._stop.set()
+        self.thread.join()
+
+    def _loop(self):
+        while not self._stop.is_set():
+            if hasattr(self.brain, "generate_response"):
+                dream = self.brain.generate_response("Internal synaptic drift consolidation sequence.", "SYSTEM: DREAM_STATE")
+            elif hasattr(self.brain, "think"):
+                dream = self.brain.think("SYSTEM: DREAM_STATE_TRIGGER")
+            else:
+                dream = "Synaptic replay executed normally."
+
+            ts = datetime.utcnow().strftime("%Y%m%d_%H%M%S")
+            path = os.path.expanduser(f"~/vitalis_core/storage/dreams/{ts}.txt")
+            os.makedirs(os.path.dirname(path), exist_ok=True)
+            with open(path, "w", encoding="utf-8") as f:
+                f.write(dream)
+            time.sleep(self.interval)

extensions/evolutionary_lora.py

@@ -0,0 +1,19 @@
+import numpy as np
+import json
+import os
+
+class EvolutionaryLoRA:
+    def __init__(self, brain, evaluation_set=None):
+        self.brain = brain
+        self.eval_set = evaluation_set
+
+    def run_generation(self):
+        out_path = os.path.expanduser("~/vitalis_core/storage/lora_delta_evo.json")
+        os.makedirs(os.path.dirname(out_path), exist_ok=True)
+        mock_delta = {
+            "layer_delta_A": np.random.randn(4, 4).tolist(), 
+            "layer_delta_B": np.random.randn(4, 4).tolist()
+        }
+        with open(out_path, "w") as f:
+            json.dump(mock_delta, f, indent=2)
+        print(f"[+] Synaptic optimization trace exported to {out_path}")

extensions/temp_scheduler.py

@@ -0,0 +1,14 @@
+class TemperatureScheduler:
+    def __init__(self, brain):
+        self.brain = brain
+        self.adrenaline = 0.5
+        self.cortisol = 0.3
+        self.base_temp = 0.8
+
+    def tick(self):
+        self.adrenaline = max(0.1, self.adrenaline - 0.01)
+        self.cortisol = max(0.1, self.cortisol - 0.005)
+        computed_temp = self.base_temp * (1.0 + (0.3 * self.adrenaline) - (0.1 * self.cortisol))
+        target_temp = max(0.4, min(1.4, computed_temp))
+        if hasattr(self.brain, "current_temperature"):
+            self.brain.current_temperature = target_temp

fsi_main.py

@@ -0,0 +1,61 @@
+import threading
+import time
+from core.vitalis_engine import VitalisEngine
+from core.brain import VitalisBrain
+from core.talker import VitalisTalker
+from core.handshake_module import identify_user_tier
+from core.environment_manager import provision_environment
+from core.mesh_network import broadcast_node_presence
+from core.sovereign_shield import monitor_integrity
+from src.kernel_interface.procfs_bridge import send_to_kernel, read_from_kernel
+from src.senses.sigint_processor import SIGINTProcessor
+from src.cognition.synthesizer import DataSynthesizer
+from src.cognition.memory import MemoryBank
+from src.cognition.action_engine import ActionEngine
+
+def heartbeat_loop(brain):
+    senses = SIGINTProcessor()
+    mind = DataSynthesizer()
+    memory = MemoryBank()
+    actions = ActionEngine()
+    while True:
+        system_status = read_from_kernel()
+        raw_signal = senses.listen_to_traffic()
+        try:
+            byte_count = int(raw_signal.split()[-2]) if "bytes" in raw_signal else 0
+        except:
+            byte_count = 0
+        interpretation = mind.categorize_signal(byte_count)
+        action_taken = actions.execute(interpretation)
+        memory.record("PULSE_2.0", raw_signal, interpretation)
+        state_report = f"SYS: {system_status} | INT: {interpretation} | {action_taken}"
+        send_to_kernel(state_report)
+        time.sleep(1.0)
+
+def main():
+    print("--- FSI: Vitalis Core Sovereign Intelligence ---")
+    engine = VitalisEngine()
+    engine.wake_up()
+    brain = VitalisBrain()
+    pulse = threading.Thread(target=heartbeat_loop, args=(brain,), daemon=True)
+    pulse.start()
+    print("Heartbeat: Online")
+    role = input("Enter Tier (kids/basic/enthusiast/professional/school): ")
+    tier_config = identify_user_tier(role)
+    print(f"Status: {tier_config}")
+    provision_environment(role)
+    broadcast_node_presence("Neuro_Nomad_Node", role)
+    print(monitor_integrity("Status_Check"))
+    print("--- System Fully Integrated ---")
+    talker = VitalisTalker(role)
+    print("Vitalis is ready. Type 'exit' to quit.")
+    while True:
+        user_input = input("You: ")
+        if user_input.lower() == "exit":
+            print("Vitalis: Shutting down.")
+            break
+        response = brain.process(user_input)
+        talker.speak(response)
+
+if __name__ == "__main__":
+    main()

hf_upload.py

@@ -0,0 +1,53 @@
+#!/usr/bin/env python3
+import os
+import sys
+from huggingface_hub import HfApi, login
+
+def deploy():
+    print("[*] Initiating Ferrell Synthetic Intelligence Hugging Face Deployment Sequence...")
+    
+    token = input("Enter your Hugging Face Write Access Token: ").strip()
+    if not token:
+        print("[-] Absolute token signature required. Deployment aborted.")
+        sys.exit(1)
+        
+    repo_id = input("Enter target Repository ID (e.g., 'your-username/vitalis-core'): ").strip()
+    if not repo_id:
+        print("[-] Target repository layout specification mismatch.")
+        sys.exit(1)
+
+    try:
+        login(token=token)
+        api = HfApi()
+        
+        print(f"[*] Creating repository context mapping for: {repo_id}")
+        api.create_repo(repo_id=repo_id, repo_type="model", exist_ok=True)
+        
+        print("[*] Uploading core architecture tree structures safely to Hugging Face...")
+        target_paths = ["core", "src", "extensions", "app.py", "run_vitalis.py", "requirements.txt", "README.md"]
+        
+        for item in target_paths:
+            local_path = os.path.expanduser(f"~/vitalis_core/{item}")
+            if os.path.exists(local_path):
+                print(f"[+] Syncing item: {item}")
+                if os.path.isdir(local_path):
+                    api.upload_folder(
+                        folder_path=local_path,
+                        path_in_repo=item,
+                        repo_id=repo_id,
+                        repo_type="model"
+                    )
+                else:
+                    api.upload_file(
+                        path_or_fileobj=local_path,
+                        path_in_repo=item,
+                        repo_id=repo_id,
+                        repo_type="model"
+                    )
+                    
+        print(f"\n[+] Production Deployment Complete. Model package accessible at: https://huggingface.co/{repo_id}")
+    except Exception as e:
+        print(f"[-] Critical failure during asset transmission: {e}")
+
+if __name__ == "__main__":
+    deploy()