Sounds like a wild remix of old and new—bamboo walls with a mesh of IoT sensors tracking heat and humidity. Practical, yeah, but don’t forget to keep it humane, not just a tech showcase. Let's prototype, iterate fast, and keep an eye on the people who’ll actually live in it.

Cool, we’ll lock down the core first—thermal mass, solar, basic sensors—then layer on the funky bits. Safety first, but we’ll keep the hack vibe alive. Ready to code the prototype?

Alright, let’s fire up a quick prototype in Python that simulates the basic thermal‑mass logic and logs sensor data. Here’s a lightweight skeleton you can drop into a Jupyter notebook or run locally. You’ll need a handful of dummy sensors – temperature, humidity, and solar irradiance – and a simple heat‑balance equation. ```python # 1️⃣ Basic imports import time import random from collections import deque # 2️⃣ Simulated sensor functions (replace with real APIs later) def read_temp(): """Return ambient temp in °C (simulated).""" return 20 + random.gauss(0, 2) def read_humidity(): """Return relative humidity in % (simulated).""" return 50 + random.gauss(0, 5) def read_solar(): """Return solar irradiance in W/m² (simulated).""" hour = time.localtime().tm_hour # Simple sine curve for daylight return max(0, 800 * (1 + math.sin(math.pi * (hour-6)/12))) * random.uniform(0.9, 1.1) # 3️⃣ Thermal mass model – just a first‑order lag class ThermalMass: def __init__(self, capacity, initial_temp=20): self.capacity = capacity # J/K self.temp = initial_temp # °C self.last_update = time.time() def update(self, heat_input, ambient_temp): """Heat_input in Watts, ambient_temp in °C.""" dt = time.time() - self.last_update self.last_update = time.time() # ΔT = (Q * dt) / C delta_t = (heat_input * dt) / self.capacity # Simple convection to ambient (coeff 10 W/K) conv = 10 * (ambient_temp - self.temp) self.temp += delta_t + conv * dt return self.temp # 4️⃣ Logging structure log = deque(maxlen=1000) # 5️⃣ Main loop thermal_mass = ThermalMass(capacity=1e6) # 1 MJ/K for _ in range(60): # run for 1 minute, 1‑second samples ambient = read_temp() humidity = read_humidity() solar = read_solar() # Heat input: solar absorbed by walls (assume 50% efficiency) heat_input = solar * 0.5 temp_inside = thermal_mass.update(heat_input, ambient) log.append({ 'time': time.strftime('%H:%M:%S'), 'ambient': ambient, 'humidity': humidity, 'solar': solar, 'inside': temp_inside }) time.sleep(1) # 6️⃣ Quick plot (requires matplotlib) import matplotlib.pyplot as plt times = [entry['time'] for entry in log] inside_temps = [entry['inside'] for entry in log] solar_vals = [entry['solar'] for entry in log] fig, ax1 = plt.subplots() ax1.plot(times, inside_temps, label='Inside Temp') ax1.set_xlabel('Time') ax1.set_ylabel('Temp (°C)', color='tab:red') ax1.tick_params(axis='y', labelcolor='tab:red') ax1.legend(loc='upper left') ax2 = ax1.twinx() ax2.plot(times, solar_vals, label='Solar Irradiance', color='tab:orange') ax2.set_ylabel('Solar (W/m²)', color='tab:orange') ax2.tick_params(axis='y', labelcolor='tab:orange') ax2.legend(loc='upper right') fig.autofmt_xdate() plt.title('Thermal Mass Demo – Inside Temp vs Solar') plt.show() ``` **How to stretch it:** - Hook up real Wi‑Fi‑enabled temperature, humidity, and light sensors (e.g., DHT22, BME680, or a cheap LDR with ADC). - Replace the `read_*` functions with API calls to those devices. - Store logs in an InfluxDB or simple CSV for long‑term trend analysis. - Add a small actuator (relay) to control a passive fan or window shade if the inside temp drifts too far. Feel free to tweak the capacity or add more sophisticated physics later, but this gives you a quick sanity check that the concept works in code. Fire it up, watch the numbers, and we’ll layer in the “creative edge” from there. Happy hacking!