James Montemagno shares his experience creating a podcast analytics dashboard in just minutes with GitHub Copilot and Claude Sonnet 4.5, highlighting how AI handled both architecture and implementation.

Vibe Coding a Podcast Analytics Dashboard with GitHub Copilot and AI

Author: James Montemagno

Overview

James Montemagno demonstrates the power of modern AI-assisted development by building a complete podcast analytics dashboard in minutes using GitHub Copilot and Claude Sonnet 4.5, directly within Visual Studio Code. This case study reveals a practical, outcome-focused approach to “vibe coding,” where conversational prompts guide AI in generating both the technical architecture and the code for a full-stack application.

The Challenge

Starting with a CSV file containing podcast metrics from nearly 500 episodes, James wanted to avoid the traditional, time-consuming process of dashboard development. Instead, he used VS Code, engaged GitHub Copilot’s chat powered by Claude Sonnet 4.5, and gave a natural-language prompt describing his outcome:

“In the attached file is all of our podcast metrics for every episode. Create a beautiful website that helps visualize, search, finds topics, and more. Come up with a bunch of ideas for what a podcast creator would want and build it out. Use a vite based app that I can use and publish on github pages.”

Solution Choices by AI

The AI immediately picked appropriate technologies and patterns:

  • React + TypeScript for robust, modern UI development
  • Vite for fast project setup
  • Tailwind CSS for easy styling
  • Recharts for data visualization
  • Lucide React for icons
  • Clear component structure (Dashboard, Episode List, Topic Analysis, etc.)
  • React Hooks for state management
  • Utility and type definition modules

Project Scaffolding Example

// Project file structure
├── src/
│   ├── App.tsx // Main app
│   ├── components/
│   │   ├── Dashboard.tsx
│   │   ├── EpisodeList.tsx
│   │   ├── TopicAnalysis.tsx
│   │   ├── PerformanceCharts.tsx
│   │   └── EpisodeDetail.tsx
│   ├── utils.ts // Parsing helpers
│   └── types.ts // Type definitions
├── package.json
├── vite.config.ts
└── tailwind.config.js

Handling Issues and Iteration

  • CSV Parsing Bug: The original parser failed for titles with commas inside quotes. After James described the bug to Copilot, the AI refactored the parser with correct quote handling.

    const parseCSVLine = (line: string): string[] => {
  const result: string[] = [];
  let current = '';
  let inQuotes = false;
  for (let i = 0; i < line.length; i++) {
    const char = line[i];
    if (char === '"') inQuotes = !inQuotes;
    else if (char === ',' && !inQuotes) { result.push(current.trim()); current = ''; }
    else current += char;
  }
  result.push(current.trim());
  return result;
};

  • TypeScript Build Errors: Unused imports caused deployment failures. Copilot automatically cleaned up imports and adjusted code for best practices.

  • Deployment: A GitHub Actions workflow was generated by referencing a previous project, automating build and deployment to GitHub Pages.

Key Features Built

  • Dashboard Analytics: Overall statistics, top episodes, performance timelines
  • Advanced Search & Filtering: Customizable with React hooks and memoization for efficiency
  • AI-Powered Topic Extraction: Recognizes episode topics and clusters analytics by theme
  • Retention Analytics: Calculates listening curves per episode
  • Interactive Charts: Responsive, with Recharts component examples

Development Methodology Insights

  • Outcome-Driven Prompts: Focused on what’s needed, rather than how to implement
  • AI as Architect and Implementer: From technology decision-making to code generation
  • Iterative Debugging: AI handled refactoring and bug fixes
  • Type Safety First: Leverages TypeScript for reliability

What Developers Can Learn

  • How to leverage AI to skip boilerplate and focus on outcomes
  • Using natural language prompts to drive solution design and code generation
  • Building, deploying, and refining modern apps using GitHub Copilot as both pair programmer and architect

Conclusion

James’ experience illustrates how AI tools can co-own implementation while developers retain ownership of outcomes and quality—enabling rapid, creative, and highly productive software development workflows.

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