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CLOUD

the tool for alignment

theunpartycloud

Location: unparty-app/theunpartycloud
Status: Active Development
Primary Purpose: ML-powered word cloud visualization tool for founder alignment and business theme discovery

Overview

theunpartycloud is a specialized web application that helps founders and business teams discover patterns and alignment in their thinking through machine learning-powered word cloud analysis. The tool transforms user-input business-related words into interactive, colorful visualizations that reveal semantic relationships, thematic clusters, and hidden patterns in business thinking.

Tech Stack

  • Framework: Next.js 14 (App Router)
  • Language: TypeScript
  • Styling: Tailwind CSS
  • Visualization: D3.js + d3-cloud
  • Platform: Web (Vercel-ready)
  • Testing: Jest + React Testing Library
  • Linting: ESLint (Next.js configuration)

Key Features

Current Implementation

  • Interactive Word Cloud Visualization: D3.js-based word cloud rendering with dynamic sizing and coloring
  • Guided Prompt System: Structured business-focused questions to help users input meaningful words
  • Real-time Analysis: Instant visualization updates as users input data
  • Category-Based Organization: Predefined categories (Customer, Values, Benefits) for initial word grouping
  • Interactive Exploration: Zooming, tooltips, and word search game for engaging user experience
  • Responsive Design: Mobile-friendly interface with Tailwind CSS styling
  • Error Boundary Protection: Robust error handling for visualization components

Planned ML Enhancements

  • Semantic Word Embedding: Convert words into vector representations using Word2Vec, GloVe, or BERT
  • Intelligent Clustering: ML-driven theme identification using K-means or DBSCAN algorithms
  • Dimensionality Reduction: t-SNE or UMAP for semantic relationship visualization in 2D space
  • Importance Scoring: Combine frequency analysis with semantic significance for optimal word sizing
  • Dynamic Theme Detection: Replace predefined categories with ML-derived themes
  • User Feedback Loop: Continuous improvement through user ratings and model fine-tuning

Architecture

Code

Web Application (Next.js)
├── Public Routes
│   └── Main WordCloud Interface
├── Components
│   ├── FounderAlignmentWordCloud (Main UI)
│   ├── ErrorBoundary (Error handling)
│   └── WordCloud Visualization (D3.js)
├── Utilities
│   ├── Word Processing
│   └── Data Transformation
├── ML Pipeline (Planned)
│   ├── Word Embeddings
│   ├── Clustering Engine
│   ├── Theme Identification
│   └── Importance Scoring
└── Testing Suite
    └── Jest + React Testing Library

Data Flow

Getting Started

Prerequisites

  • Node.js 18+ or Bun
  • npm, yarn, pnpm, or bun package manager

Installation

  1. Clone the repository:

    bash

    git clone https://github.com/unparty-app/theunpartycloud.git
cd theunpartycloud

  2. Install dependencies:

    bash

    npm install
# or
yarn install
# or
pnpm install
# or
bun install

  3. Run the development server:

    bash

    npm run dev
# or
yarn dev
# or
pnpm dev
# or
bun dev

  4. Open your browser: Navigate to http://localhost:3000

Development Workflow

  • Start Development: npm run dev - Launches Next.js development server with hot reload
  • Build Production: npm run build - Creates optimized production build
  • Start Production: npm start - Runs production build locally
  • Run Linter: npm run lint - Checks code quality with ESLint
  • Run Tests: npm test - Executes Jest test suite
  • Watch Tests: npm run test:watch - Runs tests in watch mode

Testing

The project uses Jest with React Testing Library for comprehensive testing:

bash

# Run all tests
npm test

# Run tests in watch mode
npm run test:watch

# Run tests with coverage
npm test -- --coverage

Test files are located in src/app/__tests__/ directory.

Integration Points

Current

  • D3.js: Core visualization library for word cloud rendering
  • d3-cloud: Specialized layout algorithm for word cloud positioning
  • Vercel: Recommended deployment platform with automatic optimization

Planned

  • Word Embedding APIs: Integration with pre-trained ML models (Word2Vec, GloVe, BERT)
  • Analytics Services: User behavior tracking and model performance metrics
  • Feedback System: Structured data collection for model improvement

Business Value

ABOUT (Understanding)

  • Pattern Discovery: Helps founders understand their business focus by visualizing word relationships
  • Alignment Measurement: Reveals whether team members share common business priorities
  • Theme Identification: Surfaces hidden patterns in business thinking that might otherwise go unnoticed
  • Self-Reflection Tool: Provides data-driven insights into business strategy clarity

BUILD (Creation)

  • Interactive Experience: Engaging visualization that encourages exploration and discovery
  • Guided Input System: Structured prompts help users articulate business concepts effectively
  • Real-time Feedback: Instant visualization updates create rapid iteration cycles
  • ML Infrastructure: Foundation for sophisticated semantic analysis and theme clustering

CONNECT (Sharing)

  • Multi-Founder Comparison: Enables alignment discussions between business partners
  • Visual Communication: Word clouds serve as shareable artifacts for team discussions
  • Insight Generation: ML-derived themes provide conversation starters for strategic planning
  • Collaborative Decision-Making: Data visualization supports evidence-based business discussions

Relationship to Ecosystem

theunpartycloud is part of the broader UNPARTY ecosystem:

Ecosystem Position

  • Category: Analytics & Visualization Tool
  • User Type: Founders, business teams, strategic planners
  • Platform: Web-based (complementary to native UNPARTY apps)

Cross-Repository Connections

  • theunpartyrunway: Could integrate cost tracking for ML API usage and development velocity metrics
  • theunpartycrawler: Potential synergy for conversation analysis and theme extraction from chat data
  • theunpartyapp: Could be embedded as a feature module within the main web platform
  • theunpartyunppp: Word cloud insights could inform journal suggestion algorithms

Data Flow

Documentation

Project Documentation

External Resources

Deployment

Vercel (Recommended)

  1. Connect Repository:

    • Import the repository in Vercel Dashboard
    • Automatic detection of Next.js framework

  2. Configure Settings:

    • Build Command: npm run build
    • Output Directory: .next
    • Install Command: npm install

  3. Deploy:

    • Push to main branch triggers automatic deployment
    • Preview deployments for pull requests

Alternative Platforms

  • Netlify: Configure build command and publish directory
  • AWS Amplify: Connect repository with Next.js preset
  • Self-Hosted: Use npm run build && npm start with PM2 or Docker

Project Status

Current State

  • ✅ Core Visualization: Functional D3.js word cloud implementation
  • ✅ User Input System: Guided prompts and word collection interface
  • ✅ Basic Interaction: Zoom, tooltips, search functionality
  • ⚠️ ML Integration: Not yet implemented (see roadmap below)
  • ⚠️ User Experience: Ongoing improvements to accessibility and usability

Roadmap

Phase 1: Foundation (Complete)

  • Next.js project setup
  • D3.js integration
  • Basic word cloud visualization
  • User input interface
  • Testing infrastructure

Phase 2: ML Integration (In Progress)

  • Word embedding model integration
  • Clustering algorithm implementation
  • Dimensionality reduction (t-SNE/UMAP)
  • Importance scoring system
  • Dynamic theme detection

Phase 3: Enhancement (Planned)

  • User feedback mechanism
  • Model fine-tuning pipeline
  • Advanced visualization options
  • Multi-founder comparison mode
  • Export and sharing features

Phase 4: Ecosystem Integration (Future)

  • Integration with theunpartyapp
  • API for external consumption
  • Real-time collaboration features
  • Analytics dashboard

Contributing

Development Guidelines

  1. Follow existing code structure and TypeScript conventions
  2. Write tests for new features using Jest + React Testing Library
  3. Run linter before committing: npm run lint
  4. Update documentation for significant changes
  5. Keep commits focused and descriptive

Code Quality

  • TypeScript: Use strict type checking
  • Components: Functional components with hooks
  • Styling: Tailwind CSS utility classes
  • Testing: Minimum 80% coverage for new code
  • Accessibility: Follow WCAG guidelines

Success Metrics

As outlined in the ML Integration Plan:

  1. Accuracy: Thematic clustering compared to human-labeled groupings
  2. User Satisfaction: Self-reported value from insights provided
  3. Engagement: Usage metrics vs. non-ML baseline version
  4. Alignment Correlation: ML themes vs. user's stated business priorities

Creator Ownership & Privacy

This tool respects UNPARTY core values:

  • Ownership: Users retain full ownership of input data and generated insights
  • Privacy: No data transmission to third-party services (when ML runs client-side)
  • Cost-Sensitivity: Optimized for minimal computational overhead and hosting costs
  • Transparency: Clear explanations of ML-derived insights and their sources

License

[To be specified by UNPARTY LLC]

Contact & Support

Last Updated: 2025-10-29
Version: 0.1.0
Status: 🚧 Active Development

Focus: Measurable user progress through ABOUT → BUILD → CONNECT while protecting creator ownership, privacy, and cost-sensitivity.