Y Combinator built its empire on software. Its latest investment thesis says the garage is no longer enough.

Introduction

In a significant shift from its traditional focus on software startups, Y Combinator's Summer 2026 Request for Startups emphasizes investments in hard tech—technology that requires physical capital, hardware, or both. This pivot reflects the growing importance of hardware-software integration in fields like agriculture, space technology, and defense systems. In this tutorial, you'll learn how to prototype a simple smart agriculture sensor using a Raspberry Pi and IoT sensors, similar to the AI-driven solutions YC is investing in.

This hands-on project will teach you how to interface hardware sensors with a Raspberry Pi, collect real-time environmental data, and send it to a cloud platform for analysis—key components of modern hard tech solutions.

Prerequisites

• Raspberry Pi 4 (or any Raspberry Pi model with GPIO pins)
• Environmental sensors: DHT22 temperature/humidity sensor, soil moisture sensor
• Basic knowledge of Python
• Micro USB cable and power adapter for Raspberry Pi
• breadboard and jumper wires for circuit connections
• Internet connection for cloud platform setup
• Free account on a cloud platform (we'll use Blynk, a free IoT dashboard platform)

Step-by-Step Instructions

1. Set Up Your Raspberry Pi

First, ensure your Raspberry Pi is running the latest version of Raspberry Pi OS. If not, update it using the following commands:

sudo apt update
sudo apt upgrade

This ensures your system is up to date and compatible with the libraries we'll use.

2. Install Required Libraries

We'll use the Adafruit_DHT library for reading from the DHT22 sensor and the gpiozero library for basic GPIO operations. Install them using:

pip install Adafruit-DHT
pip install gpiozero

These libraries simplify hardware interaction and reduce the risk of errors in GPIO control.

3. Connect Your Sensors

Connect your DHT22 sensor to the Raspberry Pi:

• VCC to 3.3V
• GND to GND
• DATA to GPIO 4

For the soil moisture sensor, connect:

• VCC to 3.3V
• GND to GND
• OUT to GPIO 17

These connections allow the Pi to read real-time data from the environment—critical for agricultural monitoring systems.

4. Write the Sensor Reading Script

Create a Python script to read data from the sensors:

import Adafruit_DHT
import time
from gpiozero import DigitalInputDevice

# Sensor setup
DHT_SENSOR = Adafruit_DHT.DHT22
DHT_PIN = 4
MOISTURE_PIN = 17

# Initialize moisture sensor
moisture_sensor = DigitalInputDevice(MOISTURE_PIN)

while True:
    # Read temperature and humidity
    humidity, temperature = Adafruit_DHT.read_retry(DHT_SENSOR, DHT_PIN)
    
    # Read soil moisture
    moisture = moisture_sensor.is_active
    
    if humidity is not None and temperature is not None:
        print(f"Temperature: {temperature:.1f}C, Humidity: {humidity:.1f}%")
        print(f"Soil Moisture: {'Wet' if moisture else 'Dry'}")
    else:
        print("Failed to read sensor data")
    
    time.sleep(5)

This script reads sensor data every 5 seconds and prints it to the console. It mimics the data collection phase used in smart agriculture systems.

5. Set Up Blynk for Data Visualization

Create a free account at Blynk and generate a new project. Note your Auth Token, which you'll use to connect your Pi to the dashboard.

Install the Blynk library:

pip install blynklib

Blynk provides a user-friendly interface to visualize sensor data—crucial for monitoring real-world hard tech systems.

6. Integrate Blynk with Your Script

Update your Python script to send data to Blynk:

import Adafruit_DHT
import time
import blynklib

# Blynk setup
BLYNK_AUTH = 'YOUR_BLYNK_AUTH_TOKEN'
blynk = blynklib.Blynk(BLYNK_AUTH)

# Sensor setup
DHT_SENSOR = Adafruit_DHT.DHT22
DHT_PIN = 4
MOISTURE_PIN = 17

# Initialize moisture sensor
moisture_sensor = DigitalInputDevice(MOISTURE_PIN)

@blynk.on("connected")
def blynk_connected():
    print("Blynk connected")

while True:
    # Read temperature and humidity
    humidity, temperature = Adafruit_DHT.read_retry(DHT_SENSOR, DHT_PIN)
    moisture = moisture_sensor.is_active
    
    if humidity is not None and temperature is not None:
        blynk.virtual_write(0, temperature)
        blynk.virtual_write(1, humidity)
        blynk.virtual_write(2, moisture)
    
    blynk.run()
    time.sleep(5)

This integration allows real-time monitoring of environmental conditions—similar to how YC-funded hard tech startups might visualize data from their field sensors.

7. Test Your Setup

Run your Python script and check the Blynk dashboard. You should see live updates of temperature, humidity, and soil moisture levels. This step simulates the real-time data flow that enables AI-driven agricultural decisions.

Summary

This tutorial demonstrated how to build a foundational smart agriculture system using Raspberry Pi and IoT sensors. By connecting hardware sensors to a cloud platform, you've created a prototype that mirrors the kind of hard tech solutions Y Combinator is investing in. The integration of hardware, software, and cloud platforms is essential for scalable, real-world applications in agriculture, defense, and space technology.

Next steps could include adding more sensors, implementing machine learning models for predictive analytics, or deploying the system in a real field environment—key elements of YC's hard tech investment strategy.