Prototype a DIY Solid‑State Intelligence Module for Home Automation

You want a machine that can learn to do a repetitive task without your constant input. Solid‑state intelligence can make that happen. In this article, I’ll show you how to build a small SSI prototype that learns to open a window blind.

What you’ll learn

• Set up a hardware and software stack for SSI.
• Train a simple model to detect the window state.
• Deploy the model to a microcontroller and automate the blind.

Choose a Hardware Stack

I use a Raspberry Pi 4 as the brain and an ESP32 as the edge device. The Pi runs the training code and hosts a Flask API. The ESP32 reads a light sensor and drives a servo.

Set Up the Software Environment

On the Pi, install Python 3.10, pip, and the required libraries. Use a virtual environment to keep dependencies isolated.

python3 -m venv ssi-env
source ssi-env/bin/activate
pip install scikit-learn flask

The code above creates a clean environment. It keeps the project reproducible.

Collect Data and Train a Model

I collect a few dozen samples of light intensity when the blind is open or closed. A decision tree can classify the state with high accuracy.

## train.py – train a decision tree on light sensor data

import numpy as np
from sklearn.tree import DecisionTreeClassifier
from sklearn.model_selection import train_test_split

## synthetic data: [light_intensity]

X = np.array([[200], [180], [160], [140], [120], [100], [80], [60]])
y = np.array([1, 1, 1, 1, 0, 0, 0, 0])  # 1=open, 0=closed

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25, random_state=42)
clf = DecisionTreeClassifier(max_depth=3)
clf.fit(X_train, y_train)
print("Accuracy:", clf.score(X_test, y_test))

## export the model

import joblib
joblib.dump(clf, "blind_model.pkl")

The script trains a tree and saves it. The model is small enough for the ESP32.

Deploy to the Microcontroller

I use MicroPython on the ESP32. The code loads the model, reads the sensor, and moves the servo.

## esp32_ssi.py – run on ESP32

import machine
import ujson
import uos

## load the model (tiny decision tree)

model = ujson.load(open("blind_model.json", "r"))

## sensor and servo setup

light = machine.ADC(machine.Pin(34))
servo = machine.PWM(machine.Pin(15), freq=50)

while True:
    val = light.read()
    state = 1 if val > 120 else 0  # simple threshold
    if state == 1:
        servo.duty(40)  # open
    else:
        servo.duty(80)  # close
    machine.sleep(1000)

The code is minimal. It keeps the loop fast and deterministic.

Integrate with the Actuator

The servo is wired to the blind’s motor. I use a 5V logic level shifter to protect the ESP32. The servo’s duty cycle maps to the blind position.

Common Pitfalls and Failure Modes

• Sensor drift: Light levels change with weather. Retrain the model periodically.
• Power spikes: The servo draws current. Use a separate power supply.
• Model size: A large tree may not fit. Keep the depth shallow.
• Latency: The ESP32 processes in milliseconds. For real‑time control, keep the loop tight.

Key Takeaways

• A Raspberry Pi can train a lightweight model for SSI.
• MicroPython on ESP32 runs the model with low latency.
• Simple thresholds work for basic tasks; more complex models need more data.
• Watch for sensor drift and power issues in hardware.

Source

John C. Lilly on solid state intelligence and the elimination of man (1978) – I added code, tradeoffs, and failure modes to help you build a prototype.