GOGO History Simulator

Inspiration

GOGO History Simulator was inspired by the experience of entering a Japanese pachislot hall in the evening and making decisions from incomplete information.

In real halls, players often do not know the hidden settings. They only see machine history, bonus counts, odds, coin difference, and the slump graph. I wanted to turn that decision-making process into a browser-based simulator.

This is not meant to be a real-money gambling game. It is a decision simulator about uncertainty, limited information, and reviewing your own choices.

What it does

The player chooses an entry time, checks machine data one machine at a time, and decides whether to play, move to another machine, or quit.

The app simulates hidden settings, bonus probability, coin flow, machine history, and final results. At the end, the hidden settings are revealed so the player can compare their decisions with the actual machine conditions.

How I built it

The project is a static browser app built with HTML, CSS, and JavaScript. It uses LocalStorage for saved progress and GitHub Pages for deployment.

Codex was used to implement and debug the app, including the simulation logic, responsive UI, machine switching, result screens, local storage, and GitHub Pages deployment.

GPT-5.6 was used to review the product concept, refine the decision-simulation framing, and improve the submission narrative.

Challenges

The hardest part was turning a real-world decision process into a playable simulator.

I had to balance realism and playability, design a UI that works on both desktop and mobile, manage hidden settings without revealing them too early, and keep machine history consistent as the player moves between machines.

Another challenge was communicating the project clearly. The topic is inspired by Japanese pachislot, but the core idea is broader: making decisions from incomplete information.

What I learned

I learned how to guide Codex through a real iterative development process: writing specifications, testing the app, finding UI issues, revising the design, and preparing a public GitHub Pages release.

I also learned that AI-assisted development works best when the human provides domain knowledge, tests the result carefully, and keeps refining the product experience.

What's next

I want to improve the simulator with better visual design, more polished mobile UI, more machine styles, and articles that analyze simulated decision-making results.

Built With

• browser
• codex
• css
• decision
• game
• github
• gpt-5.6
• html
• javascript
• localstorage
• pages
• simulation