livingcircuit 1.0.0

Composable AI conditioning primitives for stability, coherence, and signal smoothing.

livingcircuit

Composable AI conditioning primitives for stability, coherence, and signal smoothing.

Free. No API keys. No bloat. Drop them in, layer them together.

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Install

pip install livingcircuit

For PyTorch support (AdaptiveAmplitudeStabilizer nn.Module):

pip install livingcircuit[torch]

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Modules

Module	What it does	Layer
AdaptiveAmplitudeStabilizer	Softens activation spikes in transformer blocks	Activation amplitude
adaptive_amplitude_stabilizer	Scalar form — no dependencies	Scalar signal control
harmonic_vector_stabilizer	Latent coherence conditioner using golden ratio harmonics	Latent coherence
SimpleVoiceToneAnalyzer	Reads tone and intensity from raw audio	Input classification
stabilize_solar_output	Discrete-time smoother with carried state	Output smoothing

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Quick start

import numpy as np
from livingcircuit import harmonic_vector_stabilizer

t      = np.linspace(0, 4 * np.pi, 256)
result = harmonic_vector_stabilizer(t)

print(result["quality_score"])   # coherence ratio
print(result["energy"])          # mean signal energy

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Composable stack

These modules are designed to layer together:

raw input
  → SimpleVoiceToneAnalyzer        # tone + intensity
  → harmonic_vector_stabilizer     # latent coherence
  → AdaptiveAmplitudeStabilizer    # activation control
  → model block (attention + FFN)
  → adaptive_amplitude_stabilizer  # scalar output smoothing
  → stabilize_solar_output         # signal continuity
  → stable output

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Transformer block example

import torch
from livingcircuit import AdaptiveAmplitudeStabilizer

stab1 = AdaptiveAmplitudeStabilizer(dim=768, layer_idx=0)
stab2 = AdaptiveAmplitudeStabilizer(dim=768, layer_idx=1)

# In your forward pass
attn_out = self.attn(self.norm1(x))
x = x + stab1(attn_out)

ffn_out = self.ffn(self.norm2(x))
x = x + stab2(ffn_out)

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Voice-aware response shaping

import numpy as np
from livingcircuit import SimpleVoiceToneAnalyzer, harmonic_vector_stabilizer

analyzer = SimpleVoiceToneAnalyzer()
audio    = np.random.randn(16000).astype(np.float32) * 0.1

tone_result = analyzer.analyze(audio)
print(tone_result["tone"], tone_result["intensity"])

# Feed intensity into harmonic conditioner
t       = np.linspace(0, 4 * np.pi, 256) * tone_result["intensity"]
latent  = harmonic_vector_stabilizer(t)
print(latent["quality_score"])

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Scalar signal continuity

from livingcircuit import adaptive_amplitude_stabilizer, stabilize_solar_output

readings = [1.0, 1.4, 0.9, 1.1, 1.3, 0.8, 1.2]
set_pt   = readings[0]
memory   = 0.0

for i in range(1, len(readings)):
    bounded, set_pt = adaptive_amplitude_stabilizer(
        readings[i], set_pt, variance_limit=0.3, impedance=0.5
    )
    smoothed, memory = stabilize_solar_output(
        bounded, readings[i - 1], dt=1.0, memory=memory
    )
    print(f"raw: {readings[i]:.2f}  →  smoothed: {smoothed:.4f}")

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License

MIT — free for any use. No restrictions on the public layer.

The Living Circuit LLC · thelivingcircuit.ai · [e-mail]