Consensus
Query multiple language models in parallel and compare their outputs, with a synthesized consensus response for faster model evaluation.
Explore the Project
Project Overview
Consensus is a multi-LLM comparison platform that demonstrates the power of aggregating responses from multiple AI models to generate more comprehensive and reliable answers. The application allows users to select from various Gemini models, submit a single query, and receive responses from all selected models simultaneously, culminating in a synthesized "consensus" answer that combines the best insights from each model.
This project showcases modern web development skills while exploring the fascinating concept of AI model ensembling. Unlike simple voting mechanisms, Consensus uses semantic analysis to merge responses, providing users with both individual model perspectives and a unified, comprehensive answer.
Key Features
Multi-Model Selection: Choose from a comprehensive list of Gemini models including 1.5 Pro, 2.0 Flash, and experimental variants. Users can activate multiple models simultaneously to compare their reasoning approaches and response styles.
Real-Time Response Comparison: View responses from all selected models side-by-side, with individual loading states and collapsible panels for each model's output. Each response includes full markdown and LaTeX rendering support.
Consensus Generation: Automatically synthesizes a unified answer by analyzing all successful responses, identifying common themes, resolving disagreements, and presenting the most comprehensive solution.
Advanced UI/UX: Clean, intuitive interface with dark/light mode support, responsive design, and smooth animations. Features include model selection dropdown, persistent settings, and keyboard navigation.
Technical Implementation
Frontend Architecture
Built with React and Vite for optimal development experience and performance. The frontend handles complex state management for multiple concurrent API calls, dynamic UI updates, and responsive design patterns. Features modern CSS with custom properties and smooth transitions.
Core Technologies
React, JavaScript, Vite, CSS3
UI Features
Responsive Design, Dark/Light Mode, Animations, LaTeX Rendering
Backend & API Integration
Express.js backend serves as a lightweight API gateway, handling authentication with Google's Gemini API and managing concurrent requests to multiple models. The server implements proper error handling, rate limiting awareness, and efficient response processing.
Backend
Node.js, Express.js, REST API
Integration
Gemini API, Concurrent Processing, Error Handling
Project Impact & Learning
This project demonstrates advanced concepts in modern web development while exploring cutting-edge AI integration patterns. The challenge of coordinating multiple asynchronous API calls, managing complex state updates, and creating an intuitive user experience for comparing AI responses required careful architectural planning and implementation.
Beyond the technical implementation, Consensus explores the fascinating concept of AI model ensembling at the application level. By allowing users to compare different models' reasoning approaches and see how a consensus emerges, the project provides insights into the diverse strengths and perspectives of different AI architectures.
The consensus generation algorithm itself is a form of meta-AI reasoning, where one model analyzes and synthesizes the outputs of others. This approach could have broader applications in decision-making systems, research analysis, and any domain where multiple perspectives need to be consolidated into actionable insights.