# Introduction

Ludo Cities is a Web3-powered evolution of the classic Ludo game, blending nostalgic gameplay with blockchain-driven ownership and rewards. Players can stake LUDC tokens to join competitive matches, earn from victories, and unlock NFT-based city ownership for passive income.

Each NFT city acts as a dynamic hub in our in-game economy — glowing in real-time to show local matches, revenue, and rankings. Built on the Solana blockchain, Ludo Cities offers low fees, fast gameplay, and true asset ownership.

Whether you’re a casual player or a Web3 investor, Ludo Cities lets you play, own, and profit.

### Backstory

Ludo is a modern adaptation of the ancient Indian game Pachisi, which dates back to the 6th century and was played in royal courts, including that of the Mughal emperor Akbar. The British later simplified and commercialized it as “Ludo” in 1896. In a futuristic digital metaverse of cities and regions, players become competitive game masters seeking influence and passive income through Ludo matches. City NFTs act as real economic zones, generating yield from match activity.

### Genre

Ludo Cities is a multiplayer casual skill-based board game enriched with Web3 and Play-to-Earn (P2E) elements: on-chain staking, PvP competitions, tournaments, and a passive NFT-based economy. Other web2 games like Ludo King and Yalla Ludo do not allow users to earn rewards from the game.

### Game Classification

Ludo Cities is a Traditional skill-based board game with a stochastic layer. Strategy arises from token placement and risk/reward decision-making. Dice introduce chance, but long-term success depends on skilful movement, timing, and social bluffing.


---

# Agent Instructions: Querying This Documentation

If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question.

Perform an HTTP GET request on the current page URL with the `ask` query parameter:

```
GET https://ludo-cities.gitbook.io/ludo-cities-whitepaper/readme.md?ask=<question>
```

The question should be specific, self-contained, and written in natural language.
The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.

Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.