Show HN: Garden Horizons Calculator – Built by a Beginner via "Vibe Coding"
gardenhorizonscalculator.proHi HN,
I’m a programming beginner, and lately, I’ve been hopelessly addicted to a farming simulation on Roblox called Garden Horizons.
The core loop is simple: plant, grow, and harvest. However, the game uses a complex, non-linear economic system where crop value scales with the square of its weight.
Sell Price = Base Price × (Weight / Base Weight)² × Ripening × Color × Mutation × ...
Calculating the optimal time to harvest became a total ROI nightmare.
So, I built this tool to solve that: https://gardenhorizonscalculator.pro/
Data-Driven ROI: I collected raw data from the game and used Regression Fitting to verify the underlying math. The resulting model is highly consistent with in-game values.
Integrated Wiki: I aggregated scattered info from Discord and community forums into a structured, mobile-friendly guide for plants and mutations.
The Struggle: "Vibe Coding" and its Limits I built this using Next.js through "Vibe Coding" (heavy AI assistance). While the AI was incredible for getting me from 0 to 1, I hit a massive wall during deployment.
I fell into "Deployment Hell"—failed over a dozen times. At one point, both the AI and I were just staring at the terminal, completely stuck. I realized then that while AI can write code, technical fundamentals are the ultimate safety net. It was only after diving into the documentation and developer forums that I understood the "why" behind the failure and finally got it live.
The Tool: It’s live, free, no sign-up required, and designed specifically for mobile-first use (since most players have it open on their phones while gaming).
I’d love to hear your feedback, especially from anyone interested in non-linear game economies or those who have thoughts on how a beginner can better navigate the "Vibe Coding" workflow.
The "Deployment Hell" section is the most instructive part of this writeup. The AI could write the code but couldn't navigate the deployment environment — and you found that it was only by understanding the "why" behind the failures that you could actually get unstuck.
This is a pattern that shows up consistently in vibe coding experiences: AI excels at generating implementation within a well-understood context, but breaks down at system boundaries where implicit knowledge matters most. Deployment, authentication flows, third-party API quirks — these are places where the gap between "code that looks right" and "code that works in your specific environment" is largest.
What you discovered intuitively — that technical fundamentals are the "safety net" — is exactly what the Agile Vibe Coding Manifesto articulates: "humans remain accountable for software systems" and "generated systems remain understandable and maintainable." The beginner who learns why things fail, not just how to prompt around them, ends up with something genuinely theirs.
Great example of vibe coding used as a learning accelerator rather than a shortcut: https://agilevibecoding.org