A year ago, I shared my initial experiments with vibe coding. This is the art of using Artificial Intelligence to build tools by describing outcomes rather than writing every line of syntax. At the time, the experience felt like working with a struggling junior developer. It required constant oversight, frequent course correction, and significant time spent troubleshooting UI issues and hallucinated libraries.
I recently decided to revisit that original ETL project. I tore it down and rebuilt it from the ground up using Google AntiGravity.
The difference in the experience is profound. This iteration has shifted from a one-way command line interaction to a true collaborative partnership. I no longer feel like I am fighting a junior developer. Instead, I am working with a mid-level partner that actively helps me make the product better.
The Evolution of the Flow
My first attempt was a series of scripts held together by hope and manual configuration. The new version is architected as a robust, modular system. By incorporating dedicated Python modules and integrated Machine Learning and AI components, the system is now a sophisticated pipeline rather than just a simple data mover.
The Agentic Leap
The most significant improvement is the ability of the agent to maintain context throughout the project. In my previous experiment, I spent the majority of my time fixing errors. In this build, the agent handled the environment and project structure with genuine precision. It did not just execute code; it proactively suggested improvements to the logic.
Consistency by Design: Google Material
In the past, the user interface was a recurring headache. Every time I added a feature, the styling would drift, resulting in a messy patchwork of inconsistent buttons and layouts. This time, I instructed the agent to strictly follow Google Material Design guidelines.
The result was striking. The agent applied the design system consistently across every component without me having to micromanage hex codes or padding. One detail worth noting is that the tool defaults to a dark mode aesthetic. While I am not personally a huge fan of dark mode, I decided to keep it as is. The implementation is so clean and consistent that I found it easier to adapt to the aesthetic than to spend the time fighting the defaults. By leveraging a standard design language, the interface looks polished, feels intuitive, and stays consistent as the project grows. It is a massive relief to have a UI that simply works as intended.
Productivity Gains: The Test Data Hack
The biggest quality of life upgrade was the automatic generation of test data. Previously, I had to spend hours manually creating datasets to validate the pipeline. With this new flow, the agent handled it automatically. Being able to validate logic against high-quality test data instantly meant I could focus entirely on product logic rather than the technical plumbing.
The Reality Check: Costs and Limitations
It is important to be realistic about the experience. I used the free version of the tool, which proved to be quite limited. I was forced to make small, incremental changes before running out of credits, often having to wait days to pick up where I left off. While I completely understand that these companies need to monetize their services, the potential cost of scaling this type of development is significant. It is easy to see how a high-velocity project could become very expensive very quickly.
Proper Version Control
The maturity of this iteration is also reflected in how it is hosted. It is now properly version controlled and documented, ready for ongoing development rather than being a one-off scratch build.
Final Thoughts: The New Product MVP
As a product executive, I have always been obsessed with speed to insight. Last year, vibe coding was a way to prototype quickly. Today, it is a way for product people with some technical background to build functional, high-value Minimum Viable Products.
To be clear, this does not replace professional developers. We still need them to address complex new ideas and manage rigorous security requirements. However, the barrier to entry for creating functional, data-driven tools has fundamentally lowered. We are entering an era where we can bridge the gap between ideation and a working prototype faster than ever, provided we respect the line where AI capability ends and professional engineering rigor must begin.
The agents have become competent collaborators, and the path to a functional product is finally open.