The problem lobby.fun solves

Problem Statement

In essence, Lobby.fun is a tool for mediating disagreements between strong personalities and influential figures in the crypto space. Put simply, it allows those making bold claims about market movements and asset potential on social channels to put their money where their mouth is.

The Solution

Lobby.fun provides a social platform where users can engage in direct trading competitions, called "lobbies." A lobby is a regulated system that allows direct player-vs-player (PVP) competition in trading, structured as a duel (one-on-one) or a free-for-all (up to 10 participants).

How It Works

1. A host creates a lobby.
2. Users join it, providing up to 5 assets they feel will perform the best in the specified time.
3. The lobby starts.
4. Once the lobby ends, our contract calculates performances and awards the pot to the winner.

Benefits for Users

• Engagement and Validation: Users can validate their market predictions by entering lobbies and competing against others. This system allows them to back up their claims with actual performance, enhancing their credibility and public image.
• Reputation Building: Every win, loss, and statistic in Lobby.fun is public, creating a transparent record of a user's market knowledge.
• Social Interaction: By enabling direct competition based on market predictions, Lobby.fun fosters a more interactive and engaging environment. Users can challenge each other, test their strategies, and prove their expertise in a public forum.
• Trustless Competition: Unlike informal bets, Lobby.fun provides a structured and transparent environment for trading competitions. Funds are securely managed, ensuring fair play and trust in the outcomes.

Challenges we ran into

The major challenge we faced during the development of Lobby.fun was integrating the Pyth network oracle to access asset prices. Pyth, a blockchain oracle, operates on a pull model, which presented several complexities and hurdles.

Complex Documentation: The documentation for Pyth was challenging to go through. We encountered numerous instances of outdated or incomplete information. This made it incredibly challenging to understand the correct implementation steps and usage patterns.

Understanding the Pull Model: The pull model itself was a novel concept for us, and wrapping our heads around it required significant effort. The intricacies of this model added another layer of complexity to our integration process.

It was ultimately through brute effort and determination that we more-or-less figured out how to get what we needed from Pyth.