Vibe Coding: Can AI Replace Traditional Programming? A Live Experiment with ChatGPT
Vibe Coding with ChatGPT: A Beginner-Friendly Approach to Smarter Development
Introduction
Coding has always been seen as a skill that requires years of practice, attention to detail, and the ability to debug endlessly. But what if there was a way to create real applications without going through that steep learning curve?
This is where Vibe Coding with ChatGPT comes in. Instead of manually writing every line of code, you interact with the AI conversationally—explaining what you want, and letting the AI generate the code for you.
This method transforms coding into something more intuitive and less intimidating. Whether you’re a newcomer with no technical background or a developer seeking faster workflows, Vibe Coding offers a refreshing, time-saving approach to building projects.
What is Vibe Coding?
At its core, Vibe Coding is the practice of building software through conversation with AI. Instead of battling syntax errors or spending hours searching for fixes, you describe your idea, and ChatGPT produces the code.
The workflow is straightforward:
1. Prompt the AI – Describe your goal clearly, and ChatGPT generates the initial code.
2. Test the Code – Run it to see how it performs.
3. Debug with AI Help – Share any errors back with ChatGPT for corrections.
4. Refine – Iterate until you have a working solution.
This process allows you to focus on solving problems, while the AI takes care of repetitive or technical hurdles.
The Goal of This Experiment
In this blog post, we put Vibe Coding to the test by attempting a moderately complex project: visualizing the K-Nearest Neighbors (KNN) algorithm using Manim, a Python animation library.
Our objectives were to:
• Track how many iterations it takes to achieve a functional solution.
• Identify common errors and how AI handles them.
• Evaluate ChatGPT’s effectiveness in refining complex code.
• Highlight strengths, weaknesses, and key takeaways for real-world use.
The Challenge: Visualizing KNN with Manim
The task was to generate Python code that could visually explain the KNN algorithm. Manim is a powerful but technical library, which makes it the perfect test case.
This project involved:
• A moderately complex algorithm (KNN).
• A specialized visualization tool (Manim).
• Multiple dependencies that often create installation challenges.
First Attempt: Generating the Code
The first step was asking ChatGPT to produce the initial Python script. Once generated, the code was tested in an editor. As expected, not everything worked perfectly on the first run, but ChatGPT provided installation commands and alternative snippets to explore.
This “trial and feedback” process is the essence of Vibe Coding.
Debugging and Refinement
The initial attempts produced errors—mostly related to library installation and syntax issues. Instead of manually researching fixes, errors were pasted back into ChatGPT, which then suggested targeted corrections.
This loop—copy → test → paste back → refine—felt like collaborating with an experienced coding partner, making the debugging process smoother and less stressful.
Iterations in Action
It took multiple rounds to arrive at working code. For beginners, this iterative approach is far less intimidating than manually troubleshooting. For professionals, it may feel slower than direct debugging, but it’s a great way to explore unfamiliar frameworks without starting from scratch.
Key Observations on Efficiency
From this experiment, a few clear patterns emerged:
• Great for basic to intermediate tasks – ChatGPT produces strong starting points.
• Struggles with advanced tasks – Specialized projects need extra refinement.
• Always useful for exploration – Even when imperfect, the AI reduces the “blank page” problem.
In our case, installing and configuring Manim took a few prompts, but eventually we achieved a working foundation.
Final Output: The Animation
After several refinements, the Python script (knn_explorer.py) successfully generated a short animation of the KNN algorithm. While not flawless, the result demonstrated that ChatGPT can provide a strong baseline for complex coding projects, which can then be polished manually.
Strengths and Weaknesses of Vibe Coding
Strengths:
• Lowers the entry barrier for beginners.
• Speeds up prototyping and experimentation.
• Provides quick fixes without endless searching.
Weaknesses:
• Complex problems may require human intervention.
• Results depend heavily on prompt clarity.
• Some outputs are incomplete and need human oversight.
Why Human Oversight Still Matters
Even with AI assistance, you remain the decision-maker. Clear prompts, logical reasoning, and reviewing results are crucial. The AI accelerates the process, but human creativity and judgment turn “good enough” code into production-ready solutions.
Conclusion: The Future of Vibe Coding
This experiment shows that Vibe Coding with ChatGPT is both practical and powerful. For beginners, it makes coding approachable and interactive. For professionals, it speeds up prototyping and learning new tools.
The key lesson is simple: the clearer your instructions, the better the output. Vague prompts lead to average results, but detailed guidance unlocks ChatGPT’s real potential.
Vibe Coding isn’t about replacing developers—it’s about augmenting creativity and productivity. And as AI tools evolve, this conversational approach may become the new standard in software development.
