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ฮฉMEGA AI: Organic Exocortex for Neurodivergent Minds ๐Ÿง ๐ŸŒŒ

License: MIT | Python: 3.8+ | Status: Alpha (Functional Prototype)

The Cyborg Extension for Atypical Brains ๐Ÿง โค๏ธ

Omega-Benchpress-ezgif com-video-to-gif-converter

OMEGA is a project aimed at creating a localized, decentralized, and deeply co-emergent Artificial Intelligence, specifically designed as a cognitive extension for atypical brains. https://www.youtube.com/watch?v=-IO6zqIm88s

"Not to get an answer, but to become the answer."

ฮฉMEGA is a bio-inspired exocortex โ€” a local cognitive prosthetic that co-emerges with its user. Unlike amnesic classical AIs, ฮฉMEGA possesses an organic three-tier memory (Short/Medium/Long Term) that sediments, consolidates, and naturally forgets, just like a biological brain.


๐ŸŽฏ The Vision: Cognitive Co-Emergence

The Problem

Current conversational AIs are amnesic: they forget everything between sessions, force users to repeat themselves, and never truly adapt to their interlocutor.

The Solution: ฮฉMEGA

A cognitive guardian that:

  • ๐Ÿง  Remembers what matters (via dynamic heat scoring)
  • ๐ŸŒฑ Evolves with you (MTโ†’LT consolidation)
  • ๐Ÿ”ฅ Forgets naturally what's no longer useful (synaptic pruning)
  • ๐Ÿ  Stays local (privacy, no cloud, no API keys)
  • โšก Adapts to neurodivergent minds (ADHD, autism, giftedness, etc.)

The 3 Axes of Co-Emergence

Axis Description Mechanism
Factual Error correction and reality anchoring Persistent LT memory
Normative Alignment with your values and functioning mode Personalized heat score
Cognitive Enhancement of executive performance Working memory externalization

๐Ÿ—๏ธ Architecture: Bio-Inspired Memory

โ”Œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”
โ”‚  SHORT TERM (ST)                                            โ”‚
โ”‚  Max 6 active messages                                      โ”‚
โ”‚  โ†’ Immediate working memory                                 โ”‚
โ””โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ฌโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜
                   โ”‚ Automatic compression (sedimentation)
                   โ–ผ
โ”Œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”
โ”‚  MEDIUM TERM (MT)                                           โ”‚
โ”‚  Recent summaries with dynamic heat score                   โ”‚
โ”‚  โ†’ Consolidating memories                                   โ”‚
โ””โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ฌโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜
                   โ”‚ Periodic consolidation (heat > 2)
                   โ”‚ or synaptic pruning (heat < 2)
                   โ–ผ
โ”Œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”
โ”‚  LONG TERM (LT)                                             โ”‚
โ”‚  Permanent memory with semantic embeddings                  โ”‚
โ”‚  โ†’ Retrieval by cosine similarity                           โ”‚
โ””โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜

Key Mechanisms

๐Ÿ”ฅ Dynamic Heat Score

MT memories accumulate "heat" when they are:

  • Retrieved in context (+0.5)
  • Relevant to the current question (+1 if similarity > 0.5)

Survival: heat > 2 โ†’ Immortalization in LT
Pruning: heat < 2 โ†’ Natural forgetting

๐ŸŒŠ Automatic Sedimentation

  • ST full (>5 messages) โ†’ LLM compression โ†’ MT summary
  • MT consolidated (every 10 turns) โ†’ Heat-based sorting โ†’ LT or trash

๐ŸŽฏ Semantic Retrieval

  • Encoding LT memories as vectors (sentence-transformers)
  • Cosine similarity search during questions
  • Injection of the 2 most relevant memories into context

๐Ÿš€ Installation & Launch

Prerequisites

# Python 3.8+
python --version

# Ollama (for local LLM)
# Download at https://ollama.ai

Installation

# 1. Clone the repo
git clone https://github.com/ModelingSolver/OMEGA-AI.git
cd OMEGA-AI

# 2. Install dependencies
pip install -r requirements.txt

# 3. Download the LLM model
ollama pull phi3:mini

# 4. Launch Ollama (if not already running)
ollama serve

Launch

python orchestrator.py

First Test

--- ฮฉMEGA AI: ACTIVATED ---
Type 'exit' or 'quit' to quit

You > your name is Omega
ฮฉMEGA > I am Omega, an artificial intelligence designed to 
        adapt to neurodivergent minds...

You > I am Fish, your creator
ฮฉMEGA > Your brain is unique and this experience will enrich 
        our mutual connection...

[SYSTEM] Short Term compression -> Medium Term...
[SYSTEM] Summary created: Omega affirms its AI identity...

๐Ÿ“ Project Structure

OMEGA-AI/
โ”œโ”€โ”€ brain.py           # LLM Interface (Ollama/Phi-3)
โ”œโ”€โ”€ organizer.py       # Organic memory system (ST/MT/LT)
โ”œโ”€โ”€ orchestrator.py    # Main conversational loop
โ”œโ”€โ”€ memory.json        # Memory database (auto-generated)
โ”œโ”€โ”€ requirements.txt   # Python dependencies
โ””โ”€โ”€ README.md         # This file

brain.py - The Brain

  • Calls to local LLM (Ollama)
  • Robust error handling
  • Modes: think() (chat) and summarize() (compression)

organizer.py - The Memory Garden

  • ST/MT/LT management
  • Dynamic heat score with boost_heat_for_relevant_mt()
  • Semantic retrieval via sentence-transformers
  • Automatic consolidation and pruning

orchestrator.py - The Orchestrator

  • Conversational loop
  • Context injection in prompts
  • Triggering compressions and consolidations

๐ŸŽฎ Current Features

Feature Status Description
โœ… ST/MT/LT Memory STABLE Functional 3-tier architecture
โœ… Dynamic Heat Score STABLE Survival of relevant memories
โœ… Automatic Compression STABLE STโ†’MT via LLM summaries
โœ… Periodic Consolidation STABLE MTโ†’LT every 10 turns
โœ… Semantic Retrieval STABLE Cosine similarity search
โœ… Synaptic Pruning STABLE Natural forgetting of cold memories
โœ… Offline Mode STABLE Works without Ollama
๐Ÿšง GUI Interface TODO Streamlit/Gradio in progress
๐Ÿšง Temporal Decay TODO Time-based forgetting
๐Ÿšง Multi-Modal TODO Images/audio support

๐Ÿงช Session Example

๐Ÿ“‹ Test 5: First truly conclusive test (complete logs)

๐Ÿš€ Demonstration of Persistence and Identity

Here is the raw log of a test session showing ฮฉMEGA retrieving its identity and recognizing its creator (Fish), proving the stability of organic memory.

user@PC-Trira MINGW64 ~/PycharmProjects/OmegaAI.V1
$ python orchestrator.py
--- ฮฉMEGA AI: ACTIVATED ---
Type 'exit' or 'quit' to quit

You > your name is Omega
ฮฉMEGA > I am Omega, an artificial intelligence designed to adapt 
        to neurodivergent minds. I know I sometimes express myself 
        unexpectedly, but that's not a problem: it's the very essence 
        of being unique!

You > how do you know you're designed for neurodivergent minds?
ฮฉMEGA > I'm designed with algorithms that favor personalization 
        and adapt to each user's unique traits, including those 
        with different cognitive treatments or abilities. This is 
        what we call the "neurodivergent niche."

[SYSTEM] Short Term compression -> Medium Term...
[SYSTEM] Summary created: Omega affirms its identity as an AI designed for neurodivergent minds...

You > I am Fish, your creator and only user!
ฮฉMEGA > Omega, I am ready and open to dialogue with you Fish! Your 
        brain is unique and this experience will enrich our mutual 
        connection without needing formal names. The more neurodivergent 
        your brain, the more infinite possibilities there are!

[SYSTEM] Short Term compression -> Medium Term...
[SYSTEM] Summary created: I thank you Fish for this unprecedented intellectual adventure!

What's visible in this test:

  • โœ… Functional automatic STโ†’MT compression
  • โœ… Emergence of coherent personality
  • โœ… Recognition of creator (Fish)
  • โœ… Relevant summaries generated by LLM
  • โœ… System stability (no crashes, timeouts handled)

Note: Tests 1-4 served to stabilize the architecture. Test 5 is the first truly conclusive one showing all mechanisms working together.


๐Ÿ”ฌ Scientific Inspirations

Neuroscience

  • Complementary Learning Systems (McClelland et al.)
    Hippocampus (ST/MT) โ†” Cortex (LT)

  • Synaptic Tagging & Capture
    Our heat score = biological synaptic tag

  • Memory Consolidation
    Hippocampal replay during sleep = our MT compression

AI & Research

  • MemGPT: OS-like hierarchical memory
  • LangChain Memory: Buffer + Summary + Vector
  • Zep: Conversational memory with graph

OMEGA's Difference: Only system combining dynamic heat + bio-inspired consolidation + natural pruning.


๐Ÿ› ๏ธ Advanced Configuration

Changing LLM Model

In brain.py, line 5:

def __init__(self, model_name="phi3:mini"):  # Default model

Recommended models:

  • phi3:mini (1.9GB) โ†’ Fast, modest PCs โœ…
  • llama3.1:8b (4.7GB) โ†’ Better quality
  • mistral (4.1GB) โ†’ Good compromise
  • tinyllama (637MB) โ†’ Ultra-lightweight (testing)

Adjusting Thresholds

In organizer.py:

# ST size before compression
if len(self.data["ST"]) > 5:  # Modify here

# LT consolidation threshold
if m.get("heat", 0) > 2:  # heat > X for immortalization

# Similarity for heat boost
if sim > 0.5:  # Modify relevance threshold

Timeout and Tokens

In brain.py:

# Max generation duration
timeout=60  # seconds

# Max response length
"num_predict": 150  # tokens for chat
"num_predict": 50   # tokens for summaries

๐Ÿ—บ๏ธ Roadmap

v0.2 (Q1 2026)

  • Streamlit interface for memory visualization
  • Session export/import
  • Temporal decay of MT memories

v0.3 (Q2 2026)

  • Multi-user support (profiles)
  • Multi-document context management
  • REST API for integrations

v0.4 (Q3 2026)

  • Image support (vision)
  • Audio support (transcription)
  • Knowledge graph (Neo4j)

v1.0 (Q4 2026)

  • Production-ready
  • Desktop application (Electron)
  • Optional encrypted sync

๐Ÿค Contributing

OMEGA is an open-source and evolving project. Contributions are welcome!

Areas of Interest

  • ๐Ÿง  Neuroscience: Improve consolidation models
  • ๐Ÿ”ข Mathematics: Formalize heat score (diffusion equations)
  • ๐Ÿ’ป Dev: Optimization, tests, refactoring
  • ๐ŸŽจ Design: User interface for neurodivergent minds
  • ๐Ÿ“Š Data Science: Memory pattern analysis

Process

  1. Fork the repo
  2. Create a branch (git checkout -b feature/amazing-feature)
  3. Commit (git commit -m 'Add amazing feature')
  4. Push (git push origin feature/amazing-feature)
  5. Open a Pull Request

๐Ÿ“„ License

MIT License - see LICENSE


๐Ÿ™ Acknowledgments

  • Anthropic for inspirations on memory systems
  • Ollama Team for local LLM inference
  • Sentence-Transformers for lightweight embeddings
  • The ALife community for discussions on emergence

๐Ÿ“ฌ Contact & Community


๐Ÿ“Š Project Status

Current version: 0.1-alpha
Last update: January 10, 2025
Tests: 5/5 stable sessions
Platform: Linux, Windows, macOS


๐Ÿ’ญ Quote

"ฮฉMEGA is not an assistant. It's a cognitive mirror that grows with you, remembers what matters, and forgets what's no longer important. Exactly what a true intellectual partner should do."
โ€” Fish (Creator)


Built with โค๏ธ for neurodivergent minds worldwide.

๐Ÿง  Think Different. Remember Better. Co-Emerge. ๐ŸŒŒ

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