MCP attempts to standardize how LLM tools like Claude Desktop and Windsurf connect with external data sources and tools. So, naturally, I thought, “Put a bird on it.” Er, I mean, “Let’s CheerLight it.” By building a CheerLights interface, I learned everything from how to build Python libraries to Chrome extensions to Adrino integrations.

This looks like a big deal. I recommend that you learn how to build your own MCP server and/or use one of the hundreds that have already been created: https://github.com/punkpeye/awesome-mcp-servers

I am using Windsurf on Windows for this tutorial.

From Windsurf, open the Terminal (View -> Terminal). Create a new folder called, “cheerlights-mcp” and enter it. Create a new file called, “server.py” and copy in the CheerLights MCP server code from GitHub.

From the terminal:

• PS C:\github> cd .\cheerlights-mcp\
• PS C:\github\cheerlights-mcp> python -m venv .venv
• PS C:\github\cheerlights-mcp> .venv\Scripts\activate
• PS C:\github\cheerlights-mcp> pip install mcp httpx

Add the CheerLights MCP to the Windsurf config file (adjust the path as needed).

{
    "mcpServers": {
        "cheerlights": {
            "command": "python",
            "args": ["C:\\github\\cheerlights-mcp\\server.py"]
        }
    }
}

Restart Windsurf.

From the terminal, start the Python server for the MCP: PS C:\github\cheerlights-mcp> python server.py

This should enable the CheerLights MCP and show two tools in the Cascade Chat area.

To use CheerLights MCP, you can ask Cascade, “What’s the current CheerLights color?” This will eventually get sent to the CheerLights MCP and the get_current_color() tool. If it all works, you will find out the latest color is magenta (or whatever the latest color happens to be)!

All of the source code is available on GitHub. Let me know if you give this a try and if you build your own. Have fun!

Arduino Library Installation

1. Open the Arduino IDE.
2. Select Tools > Manage Libraries.
3. Search for “CheerLights” and click “install”.

CheerLights Arduino Methods

• begin(const char* ssid, const char* password): Initialize the library with your WiFi credentials to let the library handle the WiFi connection.
• begin(): Initialize the library without WiFi credentials (use this to manually connect to the WiFi network and manage the WiFi connection outside the library).
• reconnect(): Reconnect to the WiFi network.
• isConnected(): Returns a boolean indicating whether the library is connected to the WiFi network.
• getCurrentColor(): Get the current CheerLights color from ThingSpeak channel 1417. Returns a pointer to a constant char array.
• currentColorName(): The current CheerLights color name (e.g., “red”). Returns a pointer to a constant char array.
• currentColorHex(): The current CheerLights color as a hex value (e.g., 0xFF0000). Returns a uint32_t.
• currentRed(), currentGreen(), currentBlue(): The RGB values for the current CheerLights color (e.g., 255, 0, 0). Returns a uint8_t.
• currentHue(), currentSaturation(), currentValue(): The HSV values for the current CheerLights color (e.g. 0, 255, 255). Returns a uint16_t for hue and uint8_t for saturation and value.
• hasColorChanged(): Returns a boolean indicating whether the current CheerLights color has changed since the last call to this method.

CheerLights Projects

Now, that you know about the Arduino library for CheerLights, you might be thinking about what you can do with it. CheerLights is a global network of synchronized lights. The lights are all set to the same color. Anyone can change the color by changing it on the CheerLights Discord Server and other social networks like Mastodon and BlueSky.

Get inspired by taking a look at the build gallery at the CheerLights website.

WLED Installation on Windows

• Download ‘esptool.exe’ app from the esptool releases area.
• Download the latest release of WLED for the device that you are using.
• Plug in the device and note the COM port.
• Open the Command Prompt and enter this command:

esptool.exe -p COMXXXXX write_flash 0x010000 WLED_0.14.4_ESP32-C3.bin

Note: Change COMXXXXX to the actual COM port that your device is connected to. If your ESP device is on COM 9, then this would be COM9.

Here’s what it looks like when everything works successfully on Windows:

WLED Configuration

This device’s button is on GPIO 9, and the data pin for LED lights is on GPIO 10. I need to set these in the WLED configuration for the lights to work.

• From WLED, select Config -> LED Preferences.
• Under LED outputs, enter ’10’ into the GPIO box.

• Scroll down the GPIO button sections.
• Select 9 for Button 0 and set to pushbutton.

• Scroll to the bottom and select Save.

CheerLights Settings (optional)

If (and when) you want to connect this WLED controller to CheerLights, just add the CheerLights MQTT server address to the Sync Interfaces. For MQTT to work, you will need to cycle the power going to the board (i.e., unplug and plug back in).

Configure the Arduino IDE to Support the Arduino UNO RF WiFi Board

To use the Arduino UNO R4 WiFi board, you need to update and install some board drivers.

• Open the Arduino IDE (from Arduino) and update to v2.3.3 or newer.
• Go to the Boards Manager, search “Arduino UNO R4” and click INSTALL.

Controlling the LED Matrix

I am here for the LEDs.

Here’s how to use the onboard LED matrix, which requires the LED_Matrix library. Start your sketches with “#include “Arduino_LED_Matrix.h”, create an instance, and begin it in the setup section of your code.

Here’s an animation example:

#include "Arduino_LED_Matrix.h"   
#include "animation.h"            

ArduinoLEDMatrix matrix;  

void setup() {
  matrix.loadSequence(animation);
  matrix.begin();
  matrix.play(true);
}

void loop() {
}

I learned everything that I know about this from the Arduino documentation: https://docs.arduino.cc/tutorials/uno-r4-wifi/led-matrix/

The cool part is that the library includes predefined frames. You can load these, and the Arduino UNO R4 WiFi displays them without knowing how to code the frames.

#include "Arduino_LED_Matrix.h"

ArduinoLEDMatrix matrix;          

void setup() {
  matrix.begin();                 
}

void loop() {
  matrix.loadFrame(LEDMATRIX_HEART_BIG);
}

Here are the available frames in the LED_Matrix library:

• LEDMATRIX_BLUETOOTH
• LEDMATRIX_BOOTLOADER_ON
• LEDMATRIX_CHIP
• LEDMATRIX_CLOUD_WIFI
• LEDMATRIX_DANGER
• LEDMATRIX_EMOJI_BASIC
• LEDMATRIX_EMOJI_HAPPY
• LEDMATRIX_EMOJI_SAD
• LEDMATRIX_HEART_BIG
• LEDMATRIX_HEART_SMALL
• LEDMATRIX_LIKE
• LEDMATRIX_MUSIC_NOTE
• LEDMATRIX_RESISTOR
• LEDMATRIX_UNO

Going Further

George and I explored this new board together. We had a lot of fun trying out the examples from the Arduino documentation and experimenting on our own. You know that I will be hooking this up to CheerLights soon, but we noticed that the LEDs are only red. We will have to get creative. Then, George had an idea… He said, “Let’s make a CheerLights for emoji. Like, red could be a smiley face.” Great idea. I might just do that.

We are experiencing the capabilities and limitations of AI collectively. After OpenAI’s Dev Day, I have been thinking of ways to learn the new features, services, and APIs that were dropped. This time of year always brings about a lot of inspiring CheerLights projects and new people joining CheerLights. This also comes with a lot of questions, as the new folks are learning IoT, looking for sample code, or just looking for inspiration on what to build. This gave me an idea and a challenge. Could I build an AI chatbot that can help onboard new people onto the CheerLights project and help them learn IoT?

If you have ChatGPT Plus, you can try the CheerLights IoT Expert bot right now.

What is CheerLights?

CheerLights is an Internet of Things (IoT) project that creates a global network of lights that change color in unison. The concept is straightforward yet powerful: any connected light anywhere in the world will change to the same color when a new color is set. This allows people across different locations to feel connected through a shared lighting experience.

Features of the CheerLights IoT Expert GPT

The CheerLights IoT Expert GPT has a lot of general knowledge of Arduino, ESP Wi-Fi devices, ThingSpeak, and CheerLights. You can ask anything from the basics to advanced things like code generation for a specific device configuration.

• Natural language interface
• Onboarding prompts to get started
• Technical support and debugging
• Idea generation
• Code generation
• The world’s most sophisticated CheerLights color checker

How it works

A Generative Pre-trained Transformer (GPT) is an advanced type of artificial intelligence model designed to understand and generate human-like text. This is the technology that ChatGPT is built on. It’s built using a neural network architecture called a transformer, which excels at processing sequential data like language. GPT models are trained on vast amounts of text data, enabling them to learn language patterns, grammar, and context. They function by predicting the next word in a sequence, considering the words that have already been presented. This capability allows them to generate coherent and contextually relevant text responses. The more they are trained with diverse datasets, the better they become at mimicking human-like writing, answering questions, and even engaging in conversations, making them powerful tools in natural language processing.

OpenAI released a new feature where you can take the power of a foundational language model and direct it towards a specific domain. I created a GPT using all of the CheerLights documentation and blog posts that I have written over the past 12 years. I gave it additional instructions about where to find the latest CheerLights color and the fact that we no longer use Twitter/X. GPT-4 still thinks that you can set the color of everyone’s lights using Twitter (insert sad face emoji), but we are now on a Discord Server!

How to use the CheerLights IoT Expert Chatbot

If you are a ChatGPT Plus subscriber, all you have to do is load up the GPT by clicking this URL: https://chat.openai.com/g/g-E2zjJXE56-cheerlights-iot-expert

From there, just click on an onboarding prompt or type in a message.

Use Cases

Try one of these prompts to help inspire how to use the CheerLights IoT Expert chatbot.

• How do I tell which pin is the power pin on a WS2812B LED strip?
• I want a sparkle animation for a NeoPixel strip of 16 LEDs.
• How do I teach IoT to a group of kids using CheerLights?
• How do I show the latest color of CheerLights in my Chrome browser?
• Write Python code that retrieves the last 100 color changes and analyzes the data for any patterns.
• What are five ideas for a CheerLights display?

Get Started

Give the CheerLights IoT Expert bot a try. It can get things wrong, but it can also surprise you with something that you never thought of before. If it answers something incorrectly, let me know. I can always make changes and provide up-to-date information to make sure the bot gives the best answer possible. I am looking forward to seeing the creative CheerLights displays this season enhanced by ChatGPT. Join the CheerLights Discord server and share all of your awesome projects, ideas, and experiences.

Bonus Prompt

I enabled image creation with DALL-E 3, just for fun. You can ask the CheerLights IoT Expert GPT to create images inspired by the latest CheerLights color. Try this prompt: Create an illustration based on the latest CheerLights color, depicting a globe with electric lights.

Twitter/X

According to my own History of the CheerLights Project, I created this project around the idea that anyone on Twitter, ahem, X, could change everyone’s lights. Twitter made the most sense. It was open and had a global reach. And the bonus was that followers of people who joined the project would see the tweets, which caused some of them to join the project. It was a nice, virtuous circle that fueled the growth of CheerLights year over year. 2022 was the biggest year for the project, just hitting its peak at the same time Twitter started to crack. The API access that we have had for over 12 years was cut off, users moved to decentralized platforms, and X’s relevance has been in constant question. Don’t get me wrong. I will always have a special place in my heart for Twitter. I can’t unwind Twitter’s on my professional growth over the last 15 years.

Discord

The CheerLights community got together last year and created a Discord server bot and even released the bot’s source code on GitHub. We did this to increase the number of ways to control the CheerLights network, but as it turned out, this is now the primary way to control CheerLights. Hundreds of our users moved over to the Discord server from Twitter/X. In some ways, Discord is way better. I love being able to chat in real-time with other people who are building CheerLights. A lot of people use Discord. And Discord is constantly evolving and keeping up with the trends by adding admin and community features. In other ways, it is not ideal for CheerLights. Discord servers are not easily discoverable. You have to be in the know. It is hard to attract new users since new users don’t see public mentions of the project. But I am not aiming for perfection. I am aiming for a path forward to keep the project growing. Discord is where we are now. Join us.

How can you help?

Since our Discord server is hidden from public view, we could use your help by making CheerLights discoverable. If you share your project on your blog or social channels, include a link to the Discord server invitation. H

CheerLights Discord Server Invitation Link: https://cheerlights.com/discord

Thank you to everyone who has helped build and grow this project over the last 12 years. This is always my favorite time of year getting to see the creative ways CheerLights gets used and how it enables learning of core IoT concepts.

CheerLights is a fun way to get started with IoT, microcontrollers, and LED lights. If you know how to make CheerLights work, you can build some other awesome IoT projects. CheerLights is a great project to get people interested in IoT. It’s often used as an “Hello, World” project for the Internet of Things, and what you learn can be applied to other project ideas.

To make a Halloween-inspired project, you can take the CheerLights color and illuminate a pumpkin or even a… skull from Pimoroni.

Check out other Halloween CheerLights projects over at the CheerLights blog. Have fun with the idea, build something spooky, and help spread the… fear.

CheerLights

CheerLights is an “Internet of Things” project I created in 2011 to help teach people IoT concepts. I consider it a “Hello, World” project to learn IoT protocols, microcontroller programming, and LED light control. CheerLights is a global network of synchronized colored lights. Everyone subscribes to the latest CheerLights color, which sets their lights to the same color. The color is set on the CheerLights Discord Server by anyone.

Apps

SkyStream: Track Bluesky Hashtags

IoT Debugger: Explore IoT Data

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• 
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• 
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• 
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• 
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• 
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• 
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Head over to chat.openai.com, sign in, and select Code Interpreter.

ThingSpeak enables millions of IoT devices to log data to the MathWorks Cloud. The data can be easily exported using MATLAB and ThingSpeak data feed API. ChatGPT Code Interpreter can work with CSV files and ThingSpeak also generates CSV files of data.

Use ChatGPT Code Interpreter to Analyze Traffic Data

One of the most popular IoT projects that we published is the ability to analyze traffic data using a webcam and a Raspberry Pi. We generated an algorithm using Simulink to detect cars and their direction. Simulink exported the car detection model and runs directly on the Raspberry Pi.

The car counter data is being stored in real-time using ThingSpeak Channel #38629. The channel is public so anyone can use the data to learn IoT data analytics. Download the most recent data by clicking “Export recent data” and selecting CSV. This will download a CSV file that we can upload to ChatGPT Code Interpreter.

Now, let’s upload the data to ChatGPT.

Tell ChatGPT that “Field 1 Data is the Density of Westbound Cars and Field 2 Data is the Density of Eastbound Cars”.

Now, you can ask ChatGPT any questions or requests that you have about the IoT using natural language. You can ask it for ideas on how to interpret the data or provide suggestions on visualizations and plots. I asked ChatGPT to provide some interesting visualizations for the east and west traffic. I got back some suggestions.

1. Histograms: These can help to understand the distribution of the traffic densities. We can create separate histograms for eastbound and westbound traffic densities.
2. Box Plots: These can provide statistical insights into the traffic densities. A box plot can show the median, quartiles, and potential outliers in the data.
3. Hourly averages: By grouping the data by hour of the day, we can identify patterns that occur on an hourly basis. For instance, we might find that traffic is consistently higher during certain hours of the day.
4. Day of the week patterns: Similarly, by grouping the data by day of the week, we can identify patterns that occur on specific days. For instance, traffic might be higher on weekdays compared to weekends.

I then asked ChatGPT to compare east and west traffic for the past three hours.

I wasn’t too happy with the plot. So, I asked it to smooth data and use distinctive colors for the lines.

This plot turned out well and I was able to understand the results. Traffic is normally bad in Metro West Boston in the morning then settles down a bit and then spikes around lunch time. You can see the spike if you follow the Westbound Traffic blue line past 11:30. Neat.

Observations

• I have been using ChatGPT Code Interpreter since the alpha version. Right now, you are limited to 25 queries with the GPT-4 model over a three-hour period. It runs out pretty quickly and then you wait for a while to try again.
• Traffic has been extremely high to ChatGPT Code Interpreter so many requests need to be resent.
• Exploring new analytics and visualizations using natural language is a game changer. It is a fun way to learn other ways to explore the data without having to write up bunch of scratch code. It’s nice to try before you commit to writing the code.
• Since this is Generative AI through the ChatGPT interface, the model temperature is set to 1. This is good for creative solutions but not great for reproducibility. I never get the same results following the same process. You must learn to be a little flexible, loosen up your expectations, and learn how to guide ChatGPT Code Interpreter towards the results that your seeking.

Are you exploring ChatGPT Code Interpreter? Share what you have learned on our Discord Server.

ThingSpeak is an Internet of Things (IoT) platform that allows you to collect, analyze, and act on data from sensors and other IoT devices. The platform provides a web-based interface for users to create and manage channels, which are virtual locations where data from sensors and other devices can be collected and analyzed using the built-in MATLAB analytics app.

With ThingSpeak, you can visualize your data in real-time, generate alerts and notifications based on pre-defined thresholds, and even automate actions based on specific conditions. The platform also includes built-in integrations with popular IoT hardware platforms, such as Arduino and Raspberry Pi, making it easy to connect your devices to the internet and start collecting data.

Top 10 IoT Project Ideas for ThingSpeak

1. Weather Station: Build a weather station using a microcontroller such as Arduino or Raspberry Pi that collects data on temperature, humidity, and air pressure, and sends it to ThingSpeak for real-time monitoring and analysis.
2. Home Automation: Use ThingSpeak to automate your home by controlling lights, fans, and other appliances based on sensor data, such as motion, temperature, or light levels.
3. Smart Agriculture: Monitor soil moisture, temperature, and other environmental variables to optimize crop growth and water usage, using ThingSpeak to collect and analyze the data.
4. Water Quality Monitoring: Monitor water quality in lakes, rivers, and other bodies of water using sensors to collect data on pH, temperature, and other parameters, and then use ThingSpeak to analyze the data and generate alerts for unsafe conditions.
5. Smart Parking: Use ThingSpeak to monitor parking spots and guide drivers to available spots using sensors and real-time data analysis.
6. Industrial Automation: Monitor machine data in a factory or other industrial setting to detect anomalies and predict equipment failures, using ThingSpeak to collect and analyze the data.
7. Healthcare Monitoring: Use ThingSpeak to monitor patients’ health data, such as heart rate, blood pressure, and oxygen levels, in real-time and alert healthcare professionals of any critical changes.
8. Air Quality Monitoring: Use ThingSpeak to collect data on air quality, including particulate matter and pollutants, and generate alerts and visualizations for high-risk areas.
9. Smart Energy: Monitor energy usage in homes or businesses and automatically adjust consumption based on sensor data, such as occupancy or sunlight levels.
10. Smart City: Use ThingSpeak to monitor and optimize traffic flow, waste management, and other city services based on sensor data from a network of IoT devices.

Subscribe to the About Things blog for more IoT news, tips, and projects. If you need help or want to share ideas, connect with me and the community on our Quandary Discord Server.