# BrainPlay
> Making AI capabilities visible through gameplay.
Case study by Nayeem Bhuiyan. HTML: https://nayeemux.vercel.app/projects/brainplay.
BrainPlay is a decentralized AI benchmarking platform built on Bittensor. It evaluates AI models through interactive games that test reasoning, strategic thinking, and navigation. As a UI/UX Designer at ShiftLayer.ai, I contribute to creating clear and intuitive experiences for this AI competition platform.
## Meta
- Category: Web App
- Role: UI/UX Designer
- Company: ShiftLayer.ai
- Duration: One Month
- Platform: Web Application
- Live site: https://play.shiftlayer.ai/
- Figma: https://www.figma.com/design/oXXqES0MBjJbKmBB0RW22W/BrainPlay-Website-Demo?node-id=2344-27858
## Overview
BrainPlay evaluates AI models through competitive gameplay instead of relying only on traditional benchmarks. The platform features Codenames, 20 Questions, and Super Mario, alongside live matches, game history, model rankings, and network performance information. Users can explore AI competition, follow match activity, and compare model performance across different games.
## Challenge
Making AI competition easier to understand. BrainPlay combines interactive games with technical performance data. The challenge is to make both the gameplay and evaluation results easy to explore.
- Game discovery: Help users find and explore different benchmarking games.
- Live activity: Make ongoing matches easy to discover and follow.
- Performance data: Present model statistics in a clear, readable format.
- Model comparison: Help users understand rankings and game-specific results.
- Navigation: Connect games, live matches, history, and leaderboard information.
## Goals
Creating a clear AI benchmarking experience.
- Simplify information: Make technical model data easier to understand.
- Support discovery: Help users explore games and live matches.
- Show performance: Present rankings and match statistics clearly.
- Enable comparison: Make game-specific model performance easier to review.
- Maintain consistency: Keep navigation and interface patterns familiar throughout the platform.
## Target audience
- **AI Researchers & Developers**: People interested in evaluating AI models and understanding their performance.
- **AI Enthusiasts**: Users who want to explore AI capabilities through interactive games.
- **Model Evaluators**: People comparing model rankings, win rates, and match results.
- **Technical Users**: People interested in model identifiers, network information, and benchmarking data.
## Design approach
- 01 **Understand**: Understand the platform's features, game types, and performance information.
- 02 **Structure**: Organize games, live matches, history, and rankings into a clear information hierarchy.
- 03 **Explore**: Explore layouts and interaction patterns for presenting game and model data.
- 04 **Design**: Create consistent interfaces with clear visual hierarchy and intuitive navigation.
- 05 **Refine**: Improve readability, usability, and consistency across the experience.
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