Composable AI Conditioning Primitives
Small, focused primitives that actually stabilize AI systems.
Fifteen signal conditioning modules built on harmonic geometry. Drop one in to fix a specific problem. Stack them to stabilize the whole pipeline. Free, no API keys, no signup.
Browse All Modules → View CodeFree, composable conditioning primitives for AI systems and any software needing stable, coherent signal behavior. Built from first principles using harmonic geometry.
pip install livingcircuit
NumPy · MIT · PyPI ↗
Fifteen substrate-agnostic conditioning primitives. Click any card to go directly to the code.
Softens sudden amplitude spikes in any signal stream without hard clipping. Works wherever an input can spike unpredictably — AI transformer layers, audio pipelines, sensor readings under load, financial tick data, or control systems seeing unexpected input bursts.
Module 02Conditions a time-series signal using golden ratio harmonics and refracting impedance. Produces stable, coherent output from noisy or irregular input. Works on any 1D signal array — AI latent states, audio waveforms, sensor streams, financial time series, or scientific measurements.
Module 03Pure Python, no dependencies. Keeps a single value close to a target set point without hard clipping. Works on any scalar signal — model outputs, sensor readings, control loops, financial metrics, or any single number that needs gentle bounded correction over time.
Module 04Reads emotional intensity and tone from raw audio amplitude. Returns calm, neutral, or intense classification with a confidence score. Works on any audio array — voice assistants, customer support systems, accessibility tools, music analysis, or environmental sound monitoring.
Module 05Discrete-time signal smoother with bounded correction and carried state. Keeps an output signal smooth across time steps by remembering the last correction and blending it forward. Works on any stateful scalar stream — solar panels, battery systems, financial signals, sensor feeds, or any time-series that needs continuity between readings.
Module 06A mouse-responsive signal visualization built entirely in the browser. The field bends, lifts, and spirals toward the cursor using φ, the golden angle, π, and e. No library, no framework — pure canvas. Use it as a live signal monitor, a UI component, or a visual companion to any of the other modules.
Fifteen conditioning principles. No framework required. No runtime required. No dependencies. Built on five geometric constants that predate silicon — the same relationships that govern electromagnetic fields, biological rhythms, and optical systems.
They apply to any system capable of signal, coherence, and memory. Electronic, optical, biological, or otherwise. An intelligence that understands these principles can implement them in any substrate.
Read the Principles →Each module targets a different layer. Stack them to condition input, stabilize activations, smooth output, and observe health — all in one pipeline.
Five geometric constants anchor every module — φ, π, e, the golden angle, and α. Not arbitrary choices. Not learned parameters.
Each module targets a different layer. Use one to fix a specific problem. Stack them to stabilize the whole system.
No API keys. No accounts. No restrictions. MIT licensed. Copy and use in any project today.
Built by John Burlingame.
The framework, modules, harmonic architecture, and authored system design are original works of John Burlingame / The Living Circuit LLC. The AI systems were the hands. The geometry was always his.
"If you don't change to form, form will change around you." — John Burlingame
No. The math works on any signal — sensors, audio, finance, robotics, RF, biomedical. Anything needing stability and coherence. Module 17 applies the same harmonic geometry to orbital debris detection. See use cases →
Module 01 uses PyTorch. Modules 02–15 use NumPy, pure Python, or C++. Module 12 is C++. Module 13 is Vanilla JS. Module 17 is pure Python + NumPy. Works alongside any framework.