Vibe Coding: Can AI Replace Traditional Programming? A Live Experiment with ChatGPT

Introduction

Overview of Vibe Coding

In this blog post, we will explore Vibe Coding using ChatGPT—a method that eliminates the need for prior coding knowledge. Instead of writing code from scratch, you can simply provide prompts to the AI, guiding it to generate the necessary code for your project.

The process is straightforward:

1. Prompt the AI – Describe what you need, and ChatGPT generates the code.
2. Test the Code – Run the provided code to check if it works as expected.
3. Debug with AI Assistance – If the code produces errors or incorrect outputs, simply copy and paste the issue back into ChatGPT.
4. Iterate Until Success – ChatGPT refines the code through multiple iterations until a functional output is achieved.

This approach shifts the focus from learning and debugging to building a product efficiently. Instead of getting stuck on the technical aspects, you leverage AI to accelerate the development process.

Now, let’s put this into practice and see how well ChatGPT can assist us in Vibe Coding!

Objectives

In this comprehensive exploration, we will undertake the challenge of solving a complex problem through the application of Vibe Coding in collaboration with ChatGPT. Our primary objective is to meticulously examine and document the number of iterative cycles required to refine and develop a fully functional solution. By carefully analyzing each stage of the process, we aim to gain deeper insights into the efficiency and adaptability of Vibe Coding as a problem-solving methodology.

Beyond merely tracking iterations, we will delve into the various challenges that may arise, assessing the potential limitations and constraints inherent in this approach. This analysis will allow us to critically evaluate the practicality of Vibe Coding, highlighting both its strengths and areas that may require refinement. By addressing these aspects, we seek to provide a well-rounded perspective on its effectiveness, shedding light on the potential pitfalls that practitioners may encounter.

Ultimately, this study will serve as a valuable resource for those interested in leveraging Vibe Coding for problem-solving, offering a nuanced understanding of its real-world applicability, efficiency, and overall impact.

Problem Statement: Generating a KNN Algorithm Video with Manim

We will generate Python code utilizing the Manim library to visually illustrate the K-Nearest Neighbors (KNN) algorithm through dynamic and engaging animations. Manim, a powerful mathematical animation package, will serve as the foundation for crafting an intuitive and visually appealing explanation of this fundamental machine learning algorithm.

Given the intricacies of both the KNN algorithm and the Manim framework, this task presents a moderately complex challenge. As we proceed, we will closely observe and assess the performance of Vibe Coding in handling this problem. Our focus will be on identifying the number of errors encountered, the nature of these errors, and the iterative refinements required to achieve a fully functional and visually coherent representation of KNN.

Through this process, we aim to gain deeper insights into the effectiveness of Vibe Coding in addressing complex programming and visualization tasks. By tracking the number of iterations needed for debugging and refinement, we will evaluate the strengths and potential limitations of this approach, ultimately providing a clearer understanding of its practicality in real-world coding scenarios.

First Iteration: Generating Initial Code with ChatGPT

As you navigate through the ChatGPT interface while practically following the instructions outlined in this post, you will notice a key feature that enhances usability—once the code generation process reaches completion, an option is available to execute it immediately. This functionality streamlines the workflow by allowing users to test the generated code without delay.

When the code is fully prepared, our next step will involve copying and pasting it for further use, ensuring that it is properly transferred and ready for execution. Additionally, ChatGPT has generated another supplementary code snippet, which serves as an alternative or complementary solution. We will take the time to carefully review this additional snippet, assess its relevance, and, if applicable, execute it as well.

This approach allows for a more thorough exploration of the generated code, providing us with an opportunity to evaluate multiple outputs, compare their functionality, and determine the most effective solution for the given problem.

Handling Errors and Refining the Code

Installing Manim and Resolving Dependencies

Now, let’s advance to the next phase of the process. ChatGPT has supplied a command specifically designed to install the necessary package using pip install. At this juncture, we will refrain from adding any extra prompts or modifications. Instead, our approach will be straightforward—we will execute the provided command exactly as it is and carefully monitor the results.

By doing so, we aim to assess how the installation proceeds, identifying any potential issues or confirmations required during the process. Once the command runs successfully, we will analyze the outcome and determine the appropriate next steps, ensuring a smooth and structured progression in our workflow. Let’s go ahead and execute it to move forward from here.

Observing Logical Issues in KNN Explanation

Now, we have encountered an issue. Instead of trying to resolve it manually, we will copy and paste the error back into ChatGPT and ask for a solution. As you can see, ChatGPT has now suggested a different approach. Let’s implement this new suggestion and see if it works.

This iterative process of debugging—testing, reporting errors, and receiving corrections—can feel tedious for experienced programmers who are already familiar with coding. However, for those who are new to a particular library or lack coding experience altogether, this method can be highly effective. It eliminates the need to start from scratch and provides a structured way to troubleshoot and refine the code efficiently.

Multiple Iterations: Debugging and Improving Output

Now, we have encountered an issue. Instead of trying to resolve it manually, we will copy and paste the error back into ChatGPT and ask for a solution. As you can see if you are practicalizing, ChatGPT has now suggested a different approach. Let’s implement this new suggestion and see if it works.

This iterative process of debugging—testing, reporting errors, and receiving corrections—can feel tedious for experienced programmers who are already familiar with coding. However, for those who are new to a particular library or lack coding experience altogether, this method can be highly effective. It eliminates the need to start from scratch and provides a structured way to troubleshoot and refine the code efficiently.

Observations on Efficiency and Complexity Handling

Evaluating ChatGPT's Performance in Vibe Coding

Based on my observations, ChatGPT performs well for basic coding tasks but struggles with more complex, intermediate-level problems. While it provides useful guidance, it may not always be the most efficient solution for advanced coding challenges.

Now, let's test whether it can successfully install Manim using pip install. This time, it looks like we have successfully completed the installation. This demonstrates that while ChatGPT can be helpful, it may require multiple iterations to achieve the desired outcome, especially for more intricate tasks.Evaluating ChatGPT's Performance in Vibe Coding

Based on my observations, ChatGPT performs well for basic coding tasks but struggles with more complex, intermediate-level problems. While it provides useful guidance, it may not always be the most efficient solution for advanced coding challenges.

Now, let's test whether it can successfully install Manim using pip install. This time, it looks like we have successfully completed the installation. This demonstrates that while ChatGPT can be helpful, it may require multiple iterations to achieve the desired outcome, especially for more intricate tasks.

Final Output Evaluation

Assessing the Quality of the Generated Video

It seems that the installation process has successfully completed. Now, let’s proceed by rerunning the code to determine at what stage we encounter any obstacles this time. From the initial execution, it appears that the code has run, but there are some issues arising.

Upon closer inspection, I can identify the exact nature of the problem—the system is prompting us to save this specific piece of code into a file before proceeding further. To address this, rather than making assumptions, I will consult ChatGPT directly for clarification. I will provide the exact error message and ask for guidance on the next steps. Let’s see what ChatGPT suggests as the best course of action to resolve this issue efficiently.

Strengths and Weaknesses of Vibe Coding

As instructed, we need to save the file as knn_explorer.py, so we will go ahead and complete that step as well. At this point, my primary goal is to assess the number of iterations required to successfully execute the process. Up until now, we have gone through three to four rounds of copying and pasting code, but I have no prior experience with Manim.

Given this lack of familiarity, diving into extensive study and learning everything from the ground up would be a time-consuming endeavor. Instead, I find that a hands-on approach—experimenting in real-time, troubleshooting errors as they arise, and refining the process incrementally—offers a far more efficient and practical way to grasp the workings of this system. This method not only accelerates learning but also provides valuable insights into how iterative problem-solving can streamline complex tasks.

The Importance of Human Oversight

We will create a new file as it has mentioned: knn_explorer.py. So we will copy-paste the code here now and save it as mentioned by ChatGPT. We log in, track rerunning the code. I think some execution has started and a video is created. What it has generated, as you can see—classification as A. It's not correct. It's incomplete. As I can see, classified as A. So it is just giving a seven-second video. Let me prompt it a bit more to update the code for us.

Conclusion and Future Experiments

As you just saw, if you were following pratically ChatGPT was able to generate a decent solution. This highlights an important takeaway: if you're working on an objective problem and can clearly articulate what you need, your initial prompt can be a game-changer in Vibe Coding. The better you define your request, the more effective and accurate the AI's response will be.

Limitations of ChatGPT’s Coding Abilities

As you've noticed, the prompt I provided was relatively short and straightforward, and this simplicity had a direct effect on the result we received. Because the input was minimal, the output was also fairly basic. However, this outcome is consistent with what we would expect—the amount of detail included in your prompt plays a crucial role in shaping the quality and complexity of the generated result. The more specific and thorough the input, the more detailed and refined the output is likely to be.

Future Testing with Cursor and Claude 3.5

We will be exploring Vibe Coding with other tools as well. In the next blog post, we’ll try it out with Cursor to see how it compares. Stay tuned!