Vibe Coding Journey Part 2: Solving Randomization Issues in “Lorye Go! 香港版”
Last Updated on April 16, 2025 by Editorial Team
Author(s): Lorentz Yeung
Originally published on Towards AI.

Lorye Go! Hong Kong real in-game play. Video created by the author.
Hey, welcome back to my vibe coding chronicles! In Part 1, I shared how Grok 3 and I brought Lorye Go! Hong Kong Edition to life — a train-themed board game packed with MTR stations and Hong Kong flair, built in just 4 weeks. But as any coder knows, shipping is only half the battle. About three weeks in, I noticed something off: the game’s randomness wasn’t random at all. Players kept drawing the same trivia questions — like QIDs 1, 100, 233, 301 — and events felt stale. In this second installment, I’ll walk you through how I debugged this predictable mess with a mix of human instinct and AI finesse.
Vibe Coding Journey Part 1: Building Lorye Go! with AI | by Lorentz Yeung | Mar, 2025 | Towards AI
Vibe Coding Journey Part 3: Debugging the macOS Freeze in Lorye Go! | by Lorentz Yeung | Mar, 2025 | Towards AI
I actually wrote this article preceding the first one. It was about the 3rd week after copiloting this game development project with AI. At that time, I hit a new snag: the randomization of questions and events wasn’t… Read the full blog for free on Medium.
Join thousands of data leaders on the AI newsletter. Join over 80,000 subscribers and keep up to date with the latest developments in AI. From research to projects and ideas. If you are building an AI startup, an AI-related product, or a service, we invite you to consider becoming a sponsor.
Published via Towards AI
Towards AI Academy
We Build Enterprise-Grade AI. We'll Teach You to Master It Too.
15 engineers. 100,000+ students. Towards AI Academy teaches what actually survives production.
Start free — no commitment:
→ 6-Day Agentic AI Engineering Email Guide — one practical lesson per day
→ Agents Architecture Cheatsheet — 3 years of architecture decisions in 6 pages
Our courses:
→ AI Engineering Certification — 90+ lessons from project selection to deployed product. The most comprehensive practical LLM course out there.
→ Agent Engineering Course — Hands on with production agent architectures, memory, routing, and eval frameworks — built from real enterprise engagements.
→ AI for Work — Understand, evaluate, and apply AI for complex work tasks.
Note: Article content contains the views of the contributing authors and not Towards AI.