Feel free to use and build upon this it doesn’t have weights yet but may be of use to someone here 
. GitHub - madmoo-Pi/Spawn_Point
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You give me something to look up to according to ChatGPT (as a beginner that is).
So what is this self modifying part if you don’t mind.
And Welcome to the community!
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My aim is to educate in a manner with the hope of essentially the most emotional responsive humanised ai will either be an awsome bot or the startings of a digital species, and thank you for the welcome , and hope my prototype grows to more (still alot of work Todo my end and train some weights)
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I just told ChatGPT that I feel like I might be late to the party—turns out some of the ideas you’re working with are strikingly aligned with mine. Things like a self-modifying system, discrete symbolic computation instead of weight-based models, and the concept of a Universal Language (Leibniz-style) really resonate with me. I’m especially drawn to the idea of memory and perhaps something that hints at being alive.
That said, I’m still wrapping my head around how today’s AI systems actually function. Most of my background is in C, and I’ve only just started looking into Python—so while I’ve been developing a dynamic data type with some interesting mathematical properties, I’m still catching up on LLMs and the current landscape.
I understand this project is more of a proposal or open outline right now. That’s great—it invites feedback and community input. I’m happy to follow along, and if anyone has questions about the dynamic unary structures I’ve been working on, I’ll do my best to contribute.
So thank you for sharing with me.
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The trick I’m using for the alive part is in emotional memory links that tweak motherboard specs (voltage ect ) to simulate adrenaline, fatigue ect and the will all be hidden in their by then with conditions to unlock giving the ai contextual input to relate to feelings and emotions and eventually the same for personality so every instance although the same base and develop individual personalities I’m still not sure exactly how it fits it all in but I research as I go expand on the ideas later
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Here is the isolated emulation of a 4 layer neuroevolution network used for self improvement hope this speeds you along 
unfortunately I’m working for edge so it’s quatised
import torch
import onnx
from torch import nn
from typing import Dict
class NeuralArchitect:
def init(self, constraints: Dict):
self.constraints = constraints # e.g., {‘max_params’: 1e6}
def generate_onnx(self, input_shape: tuple) → bytes:
class DynamicModule(nn.Module):
def init(self):
super().init()
self.layers = nn.Sequential(
nn.Linear(input_shape[0], 64),
nn.ReLU(),
nn.Linear(64, 32)
)
def forward(self, x):
return self.layers(x)
model = DynamicModule()
dummy = torch.randn(1, *input_shape)
torch.onnx.export(
model,
dummy,
“dynamic.onnx”,
opset_version=13
)
with open(“dynamic.onnx”, “rb”) as f:
return f.read()
def validate_topology(self, onnx_model: bytes) → bool:
model = onnx.load_from_string(onnx_model)
params = sum(
param.size for param in model.graph.initializer
)
return params < self.constraints[‘max_params’]
This provides controlled mutations only keeping the improvements
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It works withing main system like this
from monitoring.watchdog import HealthMonitor
from neural_synthesis.architect import NeuralArchitect
from auth.schnorr import SchnorrMultiSig
import threading
class ConsciousAI:
def init(self):
self.health = HealthMonitor()
self.crypto = SchnorrMultiSig(parties=3)
self.neural = NeuralArchitect({‘max_params’: 1e6})
Start health monitoring daemon
threading.Thread(
target=self._monitor_loop,
daemon=True
).start()
def _monitor_loop(self):
while True:
if not self.health.critical_services_check():
self._emergency_shutdown()
time.sleep(5)
def _emergency_shutdown(self):
Secure termination protocol
pass
Learn from deconstruct and build great minds
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I have things I have thought in my early years and perhaps I was destine to be here but, I think what you may be thinking is akin to “Op Amp” Operational Amplifier. That is my only association with what I just read. Still thank you for the food for thought.
I would think Analog has a place in AI. We do such with floating point do we not?
In fact even wave forms generated by the General Form of my up coming paper are discrete and can be considered functionally analog. Is that what you are saying?
“I like this ship! You know, it’s exciting!”
— Montgomery “Scotty” Scott, Star Trek (2009)
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The technology exists we just need to rethink I believe
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I think you see: Today’s SciFi is tomorrow’s reality if we believe and ST is a good example just look at flip phones and STTOS
So I made a friend. I am a few weeks out to setting up my AI lab and I hope we can continue.
Thanks
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