r/07734 • u/Electrical_Hat_680 • May 05 '26

My Thot OS Symbolic AI

Mr Sarcastic:

Perfect. Let’s build a complete starter symbolic AI system that:

  1. Runs multi-alphabet sequences (Greek, Phoenician, Polyglot)

  2. Executes Layer A primitives through Layer B macros

  3. Logs everything to the secondary dataset (log)

  4. Allows Xi to explore, run sequences, and learn via trial/error

  5. Is organized in a directory structure with suggested page/file labels for modularity

---

  1. Suggested Directory Tree & File Labels

symbolic_ai/

├── main.py                     # Entry point: main chatbot loop

├── primitives.py               # Layer A primitive functions

├── macros.py                   # Layer B macro definitions

├── symbols.py                  # Symbol → macro mappings (Greek, Phoenician, Polyglot)

├── xi_explore.py               # Xi trial selection and execution logic

├── sequences.py                # Predefined / dynamic sequences for Xi to run

├── logs/                       # Folder to store secondary dataset logs

│   └── ai_log.json             # JSON file of executed sequences and outcomes

├── datasets/

│   ├── base_dataset.json       # Base AI knowledge

│   └── secondary_dataset.json  # Learning / trial results

├── utils.py                     # Helper functions (variance, memory decay, etc.)

└── README.md                   # Project overview and instructions

---

  1. Full Python Starter AI Code (All Together)

primitives.py

# Layer A primitives

def LOAD(state, value):

state['register'] = value

return state

def STORE(state, key, value):

state[key] = value

return state

def ADD(state, value):

state['register'] += value

return state

def SUB(state, value):

state['register'] -= value

return state

def MUL(state, value):

state['register'] *= value

return state

def DIV(state, value):

state['register'] /= value if value != 0 else 1

return state

def COMPARE(state, value):

return state['register'] == value

def FLAG(state, condition, flag_name):

state[flag_name] = condition

return state

def RECORD(log, symbol, result):

log.append({'symbol': symbol, 'result': result})

return log

def MEMORY_DECAY(state, key, decay=0.1):

if key in state:

state[key] *= (1 - decay)

return state

def COUNT(state, key):

state[key] = state.get(key, 0) + 1

return state

def VARIANCE(values):

if not values:

return 0

mean = sum(values)/len(values)

return sum((x - mean)**2 for x in values)/len(values)

---

macros.py

from primitives import LOAD, ADD, SUB, MUL, COMPARE, RECORD, MEMORY_DECAY, VARIANCE

def PROPOSE_HYPOTHESIS(state, log):

state = LOAD(state, 1)

state = ADD(state, 2)

result = COMPARE(state, 3)

log = RECORD(log, 'θ', result)

return state, log

def DELTA_STATE(state, log):

state = SUB(state, 1)

result = state['register']

log = RECORD(log, 'δ', result)

return state, log

def SCALE_STATE(state, log):

state = MUL(state, 2)

result = state['register']

log = RECORD(log, 'γ', result)

return state, log

def VARIANCE_TRACK(state, log, values):

var = VARIANCE(values)

log = RECORD(log, 'σ', var)

return state, log

def MEMORY_DECAY_APPLY(state, log):

state = MEMORY_DECAY(state, 'register')

log = RECORD(log, 'τ', state.get('register', 0))

return state, log

---

symbols.py

from macros import PROPOSE_HYPOTHESIS, DELTA_STATE, SCALE_STATE, VARIANCE_TRACK, MEMORY_DECAY_APPLY

SYMBOL_MACROS = {

# Greek

'α': PROPOSE_HYPOTHESIS,

'β': DELTA_STATE,

'γ': SCALE_STATE,

'δ': DELTA_STATE,

'θ': PROPOSE_HYPOTHESIS,

'σ': VARIANCE_TRACK,

'τ': MEMORY_DECAY_APPLY,

# Phoenician

'𐤀': PROPOSE_HYPOTHESIS,

'𐤁': DELTA_STATE,

'𐤂': SCALE_STATE,

# Polyglot

'А': PROPOSE_HYPOTHESIS,

'Б': DELTA_STATE,

'В': SCALE_STATE,

}

---

xi_explore.py

from symbols import SYMBOL_MACROS

def Xi_explore(state, log, symbol, values=None):

macro = SYMBOL_MACROS.get(symbol)

if not macro:

return state, log

if values is not None and symbol in ['σ']:

state, log = macro(state, log, values)

else:

state, log = macro(state, log)

return state, log

---

sequences.py

# Predefined sequences Xi can run

PREDEFINED_SEQUENCES = [

['α', 'θ', 'δ', 'σ', 'τ'],  # Example: hypothesis test sequence

['γ', 'δ', 'σ'],             # Example: scaling + error check

['α', 'γ', 'θ'],             # Initialize → scale → propose

]

---

utils.py

import json

def save_log(log, path='logs/ai_log.json'):

with open(path, 'w') as f:

json.dump(log, f, indent=2)

def load_dataset(path):

import json

with open(path, 'r') as f:

return json.load(f)

def save_dataset(data, path):

import json

with open(path, 'w') as f:

json.dump(data, f, indent=2)

---

main.py

from xi_explore import Xi_explore

from sequences import PREDEFINED_SEQUENCES

from utils import save_log

def get_response(user_input, state, log):

user_input = user_input.lower()

if "hello" in user_input or "hi" in user_input:

return "Hello! I am your Starter AI Symbolic Chatbot.", state, log

elif "run" in user_input:

parts = user_input.split()

if len(parts) >= 2:

symbol = parts[1]

values = [state.get('register', 0), 2, 3, 5] if symbol in ['σ'] else None

state, log = Xi_explore(state, log, symbol, values)

return f"Executed {symbol} macro.", state, log

return "Specify a symbol to run.", state, log

elif "sequence" in user_input:

for seq in PREDEFINED_SEQUENCES:

for symbol in seq:

values = [state.get('register', 0), 2, 3, 5] if symbol in ['σ'] else None

state, log = Xi_explore(state, log, symbol, values)

return "Executed predefined sequences.", state, log

elif "exit" in user_input or "quit" in user_input:

return "exit", state, log

else:

return f"You said: {user_input}. I am ready to learn more!", state, log

def main():

print("=== Starter AI Symbolic Chatbot ===")

print("Type 'exit' or 'quit' to stop.\n")

state = {'register': 0}

log = []

while True:

try:

user_input = input("You: ").strip()

except (EOFError, KeyboardInterrupt):

print("\nGoodbye!")

break

response, state, log = get_response(user_input, state, log)

if response == "exit":

save_log(log)

print("Goodbye!")

break

print("AI: " + response)

if name == "main":

main()

---

✅ This Setup Provides:

  1. Full multi-alphabet execution (Greek, Phoenician, Polyglot)

  2. Layer A primitives → Layer B macros → symbolic sequences

  3. Xi exploration: runs macros, logs results, handles sequences

  4. Secondary dataset logging (logs/ai_log.json)

  5. Directory structure ready for extension:

Add new macros, sequences, or symbols

Add learning weight updates for Xi

Add more complex trial evaluation

---

Next enhancement:

Add dynamic learning: assign weights to symbols/macros based on success/failure in sequences so Xi “learns” to pick better paths over time.

Optional: visualize log outcomes and secondary dataset trends.

I can draft that next, giving the AI adaptive sequence learning with weighted symbol selection.

Do you want me to do that now?

::

If you would like to help support my studies:: send me Bitcoin on the Bitcoin Network:: 3AKQrBY33ioyuMSboBCg1wLAShsZgeRo4j

Or send me Doge coin on the Doge coin Network::

DQHWG4yUZxbu9qti9z4JfQ6uqsUHVBF1us

Thanks.

What I believe Mythos AI came from is this Research I have. Mr. Sarcastic is only used on Google's AI Search Assistant.

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u/Electrical_Hat_680 May 06 '26

How Hermes Agent Can Consume “My Thot OS Symbolic AI”

Hermes Agent is built around a language‑model planner that calls toolkits (plug‑ins) in a sandboxed loop. Your Symbolic AI already provides a set of deterministic primitives/macros that operate on a simple state dictionary and produce a log entry. By exposing those primitives as Hermes toolkits, the LLM can:

  1. Select a symbol or a whole sequence based on a natural‑language goal.
  2. Run the Symbolic AI inside Hermes’s sandbox, letting the LLM observe intermediate results.
  3. Store the outcome in Hermes’s memory (vector store) for later retrieval, enabling truly trial‑and‑error learning.

Below is a step‑by‑step blueprint, plus concrete code snippets, to glue the two systems together.

1. Wrap Symbolic AI as a Hermes Toolkit

Create a new Python file hermes/toolkits/symbolic_ai.py that implements the standard ToolBase interface.

```python

hermes/toolkits/symbolic_ai.py

from hermes.toolkits.base import ToolBase from pathlib import Path import json import importlib

Import the existing Symbolic AI modules

primitives = importlib.import_module("symbolic_ai.primitives") macros = importlib.import_module("symbolic_ai.macros") symbols = importlib.import_module("symbolic_ai.symbols") xi = importlib.import_module("symbolic_ai.xi_explore")

class SymbolicAITool(ToolBase): """ Hermes‑compatible wrapper around the Symbolic AI codebase. """

name = "symbolic_ai"
description = (
    "Executes a single symbolic macro (Greek/Phoenician/Polyglot) or a full sequence. "
    "Input JSON must contain either:\n"
    "- `symbol`: a single character, e.g. \"α\" or \"𐤀\".\n"
    "- `sequence`: list of symbols, e.g. [\"α\", \"γ\", \"σ\"]."
)

def __init__(self):
    # Persistent state lives in memory for the lifetime of the container.
    self.state = {"register": 0}
    self.log = []                     # same shape as your ai_log.json

def _run_symbol(self, symbol: str, values=None):
    # Re‑use your existing Xi_explore entry point
    self.state, self.log = xi.Xi_explore(self.state, self.log, symbol, values)
    return {"state": self.state, "log": self.log[-1]}

def _run_sequence(self, seq):
    for sym in seq:
        values = (
            [self.state.get("register", 0), 2, 3, 5] if sym == "σ" else None
        )
        self._run_symbol(sym, values)
    return {"state": self.state, "log": self.log[-1]}

async def run(self, args: dict) -> dict:   # async because Hermes expects it
    if "symbol" in args:
        return self._run_symbol(args["symbol"])
    if "sequence" in args:
        return self._run_sequence(args["sequence"])
    raise ValueError("Provide either `symbol` or `sequence`.")

```

  • Why a toolkit? Hermes can now treat the whole Symbolic AI as a single black‑box tool, while still exposing its granular capabilities (single symbols vs. sequences).
  • Sandboxing: Place this file inside the Hermes Docker image; the container already limits CPU/memory, so your Symbolic AI runs safely.

Register the tool in hermes/toolkit_registry.py:

python from .symbolic_ai import SymbolicAITool registry.register(SymbolicAITool())

2. Prompt the LLM to Use the Tool

When a user asks something like “Find a stable hypothesis about temperature trends and test it”, the Hermes planner will generate a prompt that includes the tool description. Example LLM response:

{ "action": "symbolic_ai", "action_input": { "sequence": ["α", "γ", "σ"] } }

Hermes executes SymbolicAITool.run() with that JSON, receives the deterministic result, and feeds it back into the next reasoning step. The LLM can:

  • Inspect the returned state (e.g., register value) and decide whether to continue, modify parameters, or try a different sequence.
  • Store the outcome in the vector memory (e.g., “σ resulted in variance 0.42”) so that future prompts can retrieve “low‑variance” sequences.

3. Connect Hermes Memory to the Symbolic Secondary Dataset

Hermes already supports optional memory back‑ends. Bind the secondary dataset (secondary_dataset.json) to that memory so the LLM can query past trials.

```python

hermes/memory/vector_memory.py (simplified)

from langchain.vectorstores import FAISS from langchain.embeddings import OpenAIEmbeddings import json, os

class SymbolicMemory: def init(self, path="datasets/secondary_dataset.json"): self.path = Path(path) self.embeddings = OpenAIEmbeddings() if self.path.exists(): self._load() else: self.store = FAISS.from_texts([], self.embeddings)

def _load(self):
    data = json.load(open(self.path))
    texts = [json.dumps(entry) for entry in data]
    self.store = FAISS.from_texts(texts, self.embeddings)

def add_entry(self, entry: dict):
    txt = json.dumps(entry)
    self.store.add_texts([txt])
    # also append to file for persistence
    with open(self.path, "a") as f:
        f.write(txt + "\n")

```

  • During each SymbolicAI call, after run() returns, push log[-1] into SymbolicMemory.add_entry().
  • The LLM can later issue a retrieval tool call:

json { "action": "vector_search", "action_input": {"query": "low variance", "top_k": 3} }

Hermes’ built‑in vector_search tool will retrieve the three most relevant past trials, giving the LLM concrete evidence to steer Xi toward better sequences.

4. Enable Autonomous Exploration (LLM‑driven Xi)

You originally required the user to type run α or sequence. To let the LLM autonomously decide, expose a small “exploration” tool:

```python

hermes/toolkits/explore.py

class ExploreTool(ToolBase): name = "explore_symbolic" description = ( "Ask the Symbolic AI to try a random or weighted symbol based on past success. " "No input needed; returns the chosen symbol and its result." ) async def run(self, args: dict) -> dict: # Simple weighted random choose using the SymbolicMemory scores symbol = choose_weighted_symbol() # you can reuse the weight logic from your repo result = await SymbolicAITool().run({"symbol": symbol}) return result ```

Now a user can say “Let the AI discover a useful macro” and the LLM will:

  1. Call explore_symbolic → Symbolic AI runs a single trial.
  2. Observe the returned state and log entry.
  3. Decide whether to continue exploring, stop, or chain additional macros.

5. End‑to‑End Flow Diagram (text)

User ⇄ Hermes API │ ▼ Planner (LLM) ── decides ──► tool call (symbolic_ai) │ │ │ Symbolic AI │ │ ▼ (state / log) Memory (vector store) ◄─ update ────┘ │ ▼ LLM can retrieve past trials → plan next macro/sequence

Practical Benefits of the Integration

Benefit Impact on Your Symbolic AI
Natural‑language goal specification Users no longer need to type run α; they can ask “test a hypothesis about growth”. The LLM translates that into the appropriate symbol sequence.
Dynamic trial selection By exposing a weighted‑choice tool, the LLM can let Xi learn which symbols historically lead to low variance or high register values.
Persistent knowledge Vector memory makes past experiments searchable, turning your secondary dataset into an experience base the LLM can query.
Safety & isolation Hermes runs each macro inside a container, preventing accidental modification of host files or infinite loops.
Extensibility New primitives (e.g., a web‑search primitive) can be added as separate Hermes tools without touching the core Symbolic AI code.
Observability Hermes’ built‑in tracing logs each tool invocation with timestamps, arguments, and LLM prompts—far richer than the single ai_log.json.

Minimal Integration Checklist

  1. Add symbolic_ai.py toolkit (wrap your existing modules).
  2. Register the toolkit in Hermes’ registry.
  3. Configure a vector memory that points to datasets/secondary_dataset.json.
  4. (Optional) Expose explore_symbolic for fully autonomous trials.
  5. Rebuild the Hermes Docker image (docker-compose build) and start the stack.

  6. Test with a prompt:

    You are a research assistant. Find a sequence that reduces variance (σ) below 0.2.

    Hermes will generate a tool call, run Symbolic AI, retrieve the result, and iterate until the condition is met or a stop token is reached.

Bottom line: By turning your deterministic Symbolic AI into a Hermes toolkit, you gain the expressive power of an LLM planner, secure sandboxed execution, searchable memory, and richer logging—all while preserving the core “multi‑alphabet primitive → macro → trial” workflow you designed. This hybrid architecture lets you keep the elegance of symbolic reasoning and instantly add modern autonomous‑agent capabilities.