Forem: Kuldeep Singh

Getting Started with Soft IoT

Kuldeep Singh — Thu, 10 Oct 2024 10:28:29 +0000

This is a submission for the 2024 Hacktoberfest Writing challenge: Maintainer Experience

Learning IoT doesn’t have to be complex, especially when starting out. In this post, we’ll explore how to get started with IoT without diving deep into hardware complexities like wiring or understanding intricate sensor details.

In an earlier article, we set up a Raspberry Pi. My son initially saw it as just another, albeit slower, computer. There was no “IoT” in it yet, just a basic device. But IoT is about more than just the device itself; it’s about integrating sensors and actuators.

In this article, we’ll take the first step to transform the Raspberry Pi into a functional IoT device — using the Grove Pi+ board to add sensors and actuators — without getting bogged down by hardware details. I call it Soft IoT — focusing on the software side of things. We’ll delve into the hardware later.

What You’ll Need:

In addition to what we used in the last article, you’ll need:

Grove Pi+ Board (more details here)
Grove LED and Universal 4-pin cable (can be found here)

⚙️ Setting up the Grove Pi+ Board

If you have already setup Raspberry Pi with an OS as mentioned earlier, then you may choose to manually configure firmware and software for Grove PI. ❌ But I don’t recommend that as it can be tedious and prone to errors (I faced many issues with my older PI, and I gave up later!).

✅ It is better we use a pre-built OS image provided by Dexter Industries, the makers of the Grove Pi+ board. This simplifies the process — just download the image from here, use the Raspberry Pi Imager and choose the downloaded zip in Operating System section to write it to SD card, and you’re ready to go with new OS image. Insert the SD card to your Raspberry Pi and boot up on new OS.

🎮 Connecting the Grove Pi+ Board

Once the OS is ready, everything is pre-installed. Now, it’s time to connect the board to the Raspberry Pi. Align the Grove Pi+ board’s pins with the Raspberry Pi’s, ensuring the pins match the right corners. The Grove Pi+ board has multiple ports (D1 to D7 for digital input/output and A1 to A3 for analog input/output).

Next, connect the Grove LED to one of the digital sockets (for instance, D4). Power up the Raspberry Pi, and you should see a green light on the Grove Pi+ board, indicating that it’s ready for use.

🧑🏻‍💻 Writing Your First Program

Now, we’ll write a simple program to control the LED and introduce you to Soft IoT.

There are already several sample programs pre-loaded, so we don’t need to reinvent the wheel. These programs simplify hardware connections and interactions by using pre-built libraries. The programs allow us to easily read from or write to specific ports, and they wrap Arduino functions that handle communication between the Raspberry Pi and Grove sensors.

Let’s understand a basic program that makes the LED blink continuously. It is available at /home/pi/Dexter/GrovePI/Software/Python/grove_led_blink.py

Select the Port and Mode — For this program we use a digital port, to power ON and OFF led. Below method to set a pin mode to OUTPUT.
```
grovepi.pinMode(pin, "OUTPUT")
```
Make sure you connect the grove led to respective port. It connected to to D4, then use pin = 4.
Power ON the LED — Power ON the LED by writing a positive non-zero value to the selected port.
```
grovepi.digitalWrite(pin,1)
```
Similarly write value zero to Power OFF the Led
```
grovepi.digitalWrite(pin,0)
```

Blinking LED — To blink an LED we need to ON and OFF the LED in a loop.

  while True:
      grovepi.digitalWrite(pin,1) # Send HIGH to switch on LED
      print ("LED ON!")
      time.sleep(1)
      grovepi.digitalWrite(pin,0) # Send LOW to switch off LED
      print ("LED OFF!")
      time.sleep(1)

Thats it, and there are few more error handling and logs in the program.

💡 Running a program

You may browse and run the program in developer tools comes within the Raspberry OS, Eg. Main Menu > Programming > Thonny Python IDE or Just run the program from the terminal as follows:

pi@dex:~ $ cd Dexter/GrovePi/Software/Python/
pi@dex:~/Dexter/GrovePi/Software/Python/ $ python grove_led_blink.py
This example will blink a Grove LED connected to the GrovePi+ on the port D4.
If you're having trouble seeing the LED blink, be sure to check the LED connection and the port number.
You may also try reversing the direction of the LED on the sensor.

Connect the LED to the D4 port!
LED ON!
LED OFF!
LED ON!
LED OFF!
LED ON!
LED OFF!
LED ON!
LED OFF!
LED ON!
LED OFF!

For fun, try experimenting with the intervals or testing other programs like grove_led_fade.py, which ask you to connect to a port that support Pulse Width Modulation (PWM) and analog writing, allows for more complex control, such as fading the LED.

⏭️ What’s Next?

This is just the beginning of your IoT journey. The Grove Pi+ significantly simplifies the complexity of interfacing with hardware and firmware by providing a range of plug-and-play sensors. From building projects like weather stations, alarms, rain sensors, and more. Grove Pi+ makes it easy for beginners and enthusiasts to dive into IoT without getting overwhelmed by technical details.

Note that IoT is much more than just connecting sensors. To create an end-to-end IoT solution, you’ll need to integrate these devices with the cloud, edge computing, and advanced technologies like AI and XR. Ref : Touchless IoT by Sri Vishnu S

In future articles, we’ll explore how IoT connects with the broader internet ecosystem, and shaping up it’s practices, and its role in the emerging metaverse. In Chapter 4 of my book 📕Exploring the Metaverse, I discuss how IoT will shape our connected future, and XR devices are the evolved IoT.

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☞ Read: Exploring the Metaverse - Synopsis

Stay tuned for more! Keep learning, keep experimenting, and keep sharing.

Myths and Realities of AI-Assisted Software Development

Kuldeep Singh — Thu, 10 Oct 2024 10:18:43 +0000

This is a submission for the 2024 Hacktoberfest Writing challenge: Maintainer Experience

AI is transforming the software development process, but there are many misconceptions about its capabilities. This article explores the common myths surrounding AI in software development and sheds light on its reality.

In recent times, Artificial Intelligence (AI) has gained momentum across various fields, especially with the introduction of Generative AI (GenAI). AI is now widely accessible, often sparking discussions about its potential, its misuse, and, unfortunately, several myths. As the hype around AI intensifies, so does the Fear of Missing Out (FOMO), leading many to adopt it without fully understanding its true value.

Software development, like many industries, has evolved over time and now faces the integration of AI into its processes, practices, and standards. At Thoughtworks, we are scaling AI-assisted software development, equipping teams with practical insights. Here are my personal takeaways and lessons from practicing AI-assisted software development:

Myth 1: AI will replace software development teams

❌ A prevalent misconception is that AI will eliminate the need for developers, business analysts, quality analysts, experience designers, and other related roles. Many fear losing their jobs to AI.

✅ Reality: AI is far from replacing human intelligence. The idea of achieving Artificial General Intelligence (AGI) or superintelligence is still largely speculative. While AI will not replace humans, those who embrace and effectively use AI will outpace those who don’t. AI should be viewed as a powerful assistant. It can help with tasks like writing business specifications from meeting notes, fast-tracking user stories, and even generating code. However, human oversight is crucial to ensure the quality and appropriateness of the output.

Yes, there are tools that allow users to create web dashboards or mobile apps from textual prompts, but this does not negate the need for developers. Developers are still essential for guiding AI, refining its output, and, importantly, creating the platforms that AI relies on.

Ref: Haiven™ team assistant | Thoughtworks

Myth 2: AI can handle all software development tasks

❌ Another myth is that since AI can code, it can handle the bulk of software development.

✅ Reality: Coding constitutes less than half of the entire software development process. While AI may speed up coding (by around 30–50%), it may solve about 50–60% of development challenges. AI assists not just in coding but also in writing user stories, acceptance criteria, and more. However, the process still requires human input for tasks such as requirement agreements, evolutionary architectural design, refactoring, testing driven development and quality assurance.

In my experience, tools like GitHub Co-pilot Chat are incredibly useful. For instance, Co-pilot can generate a method to calculate the total price of an order or suggest test cases in less than a second.

/* Calculate order total price. */
public Double calculateTotalPrice(final Order order) {
    return order.getLineItems().stream()
        .mapToDouble(lineItem -> lineItem.getProduct().getPrice() * lineItem.getQuantity())
        .sum();
}

There are some great tools Tabnine, Cursor, CodeSense that can provide better experience, but developers must ensure that the generated code meets security, performance, and coding standards. AI can assist, but it doesn’t replace critical development practices like Test-Driven Development (TDD) or refactoring, but these practices need to evolve.

Myth 3: AI-generated software is future-proof

❌ Some believe that AI-generated software will be more advanced and better equipped for the future than one built with traditional methods.

✅ Reality: AI is trained on historical data, meaning it replicates both the good and bad patterns of the past. AI-generated artifacts still require validation and must pass through quality gates. While AI can assist in creating solutions, human oversight remains essential to ensure innovation and to avoid issues like AI “inbreeding,” where AI begins learning from its own outputs, potentially leading to hallucinations and suboptimal results. AI is built on historical knowledge and must be guided to innovate effectively.

Myth 4: AI-assisted software development is just about learning tools

❌ There’s a myth that mastering AI-assisted software development simply means learning AI tools.

✅ Reality: AI-assisted development represents a paradigm shift. It’s not just about using tools but also about adapting how we think and communicate with AI. Since AI tools rely heavily on natural language inputs, developers must refine their ability to write concise, clear, and expressive natural language prompts to get the desired outcome from AI. They need to be the great storyteller before a programmer.

Another key area is defining success metrics. Traditional productivity measures may no longer apply in the AI-assisted world. Teams need to clarify which tasks AI can handle and where human effort is still needed.

Myth 5: “Prompt Engineering” will become a job role

❌ With the rise of GenAI, some predicted that “prompt engineering” would become a dedicated job role.

✅ Reality: While prompt engineering is a valuable skill, it’s unlikely to become a standalone career. User interfaces and tools will continue to evolve, simplifying the process of interacting with AI, just as no one has a career solely based on searching Google. The more familiar you are with a tool, the better you become at using it effectively. Similarly, prompting is about practice. As AI tools improve, prompting will become second nature.

In conclusion, AI-assisted software development is not about replacing developers/makers but empowering them to work more efficiently. It’s important to understand that AI is a tool to assist, not a magic solution that can do everything. As we continue to integrate AI into software development, the focus will be on finding the right balance between human ingenuity and AI efficiency.