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Tesseract

A tap-to-trade app that lets users place fast bets on a live chart with a simple, game-like experience. It helps make short-term trading easier to access.

视频

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技术栈

React
Next
Web3
Solidity
Node

描述

Tesseract turns short-term crypto trading into a simple tap-based experience. Instead of using complex trading panels, users interact directly with a live token price line and place positions by tapping on the chart. The goal is to reduce the learning curve of trading and create a flow that feels closer to a game than a traditional exchange, while still preserving real on-chain execution.

The user flow is straightforward. A user logs in, deposits them into the vault, and starts placing taps on the moving chart. Each tap creates a market position with predefined risk, multiplier logic, and payout behavior. The interface is designed to feel instant, so users experience immediate action on tap, while the backend and relayer handle the chain-facing execution in the background. This allows the app to feel responsive even on a slower network.

What makes the implementation interesting is that it combines a consumer-style UX with real smart contract infrastructure. The chart is driven by a live token price feed, positions are translated into on-chain actions, and the token and vault system create a usable loop for demo trading, rewards, and liquidity. Instead of exposing users to raw blockchain friction, the app abstracts that complexity away and focuses on speed, clarity, and interaction design.

DCAI L3 is a meaningful part of the system because it is where the app’s core mechanics actually run. It is used for the faucet flow, demo token economy, vault deposits, contract interactions, and trade settlement. In other words, DCAI is not just mentioned as infrastructure, it is the execution layer that makes the app function. That makes the project useful as both a trading product concept and a practical example of how DCAI L3 can support interactive, on-chain consumer applications.

队长
HHo Shao Mun
项目链接
赛道
DeFiGamingAI