Article
What Is Vibe Coding? How I Built a Google Analytics Alternative With AI After a Decade Away From Coding
Not long ago, I realised I wanted a better understanding of my blog’s traffic.
I like clean and accurate data. But traditional analytics tools like Google Analytics come with serious downsides: inaccurate numbers (due to blocked consent requests and various tracking blockers), the need for a cookie banner, and questionable transparency around privacy.
That is why I decided to build my own analytics, with pure, unfiltered data and full respect for the privacy of my visitors. And since we are living in 2025, I built it with the help of generative AI.
When it comes to programming, I have always considered myself an eternal beginner, ever since I started with web development at the age of 12. But AI has opened up skills and opportunities for me that I never imagined I would have. I have already written here on my blog about how AI helps me improve my coding skills, explaining its steps and decisions along the way. Thanks to that, my horizons have expanded far beyond what I thought possible. One of the results is my own custom-built analytics tool.
There is a name for this way of working with AI: vibe coding. What is vibe coding? It means building software by describing what you want to an AI assistant in plain language and letting the model write the actual code, while you guide, test and correct it. The term was coined by Andrej Karpathy in February 2025. I am not a professional developer, yet this is exactly how my own analytics tool came to exist.
How vibe coding actually works: defining the goals and first prompts
When building a project like this today, you really need to have a clear idea of what you want it to do and why. Just saying "I want my own analytics" is not enough.
So we started by defining exactly what I wanted to measure: daily traffic, traffic sources, purchases of my premium articles, and a deeper analysis of visitor types (humans vs. suspicious activity vs. bots).
At first, creating the initial tables and charts seemed easy. But it did not take long before the first real challenges emerged.
The issues I had to solve
The first SQL queries we used were slow and inefficient. They dragged the whole website down. We gradually tuned them, replaced wasteful LIKE comparisons with exact matches, and optimised the logic to only process the data we really needed, and suddenly, it started to fly.
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Summary
Common questions on this article's topic
What is vibe coding?
Why is Google Analytics inaccurate?
Is it possible to build web analytics without cookies?
What percentage of web traffic comes from bots?
Can someone with limited programming experience build custom analytics using AI?
How does privacy-first analytics work without tracking individuals?
What were the biggest technical challenges in building custom analytics?
How do you build your own web analytics?
What is a good free Google Analytics alternative?
Is self-hosted analytics worth it?
Is Google Analytics GDPR compliant?
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