Build a YouTube Growth AI App: Analytics, Thumbnails & More!

This article explores the process of building an AI-powered assistant for YouTube channels, leveraging the YouTube API and AI tools to analyze channel data and suggest improvements. The goal is to create an application that goes beyond simple analytics, offering actionable insights to content creators.

Project Overview: YouTube AI Channel Assistant

The project, named YouTube AI Channel Assistant, aims to streamline the process of analyzing YouTube channel data. Traditionally, this involved taking screenshots of analytics and thumbnails and manually analyzing them using tools like ChatGPT. The new assistant will automate this process. The core vision is to pull analytics from the YouTube API, analyze thumbnails, titles, and retention metrics, and then suggest improvements and new content angles. Instead of simply reporting what has happened, the AI will proactively suggest what to do next.

Technology Stack

The project will be built using the following technologies:

• Next.js: A React framework for building web applications.

• Supabase: An open-source alternative to Firebase, providing backend services.

• Vercel AI SDK: A toolkit for integrating AI features into the application.

• YouTube API: Used to access channel analytics and data.

Project Setup and Requirements

The initial steps involve setting up the project environment. This includes using mpx create latest to create a new Next.js project named youtube-assistant.ai.

Utilizing Kiro Agents

The project utilizes Kiro agents, specifically Kiro requirement agent, to help gather requirements and write user stories. This agent facilitates an iterative workflow from initial idea to implementation. The prompt was a prompt for doing the YouTube Analytics API and the YouTube data API, how to wire them together. And then I asked it to do Next.js for me. And then once I got Next.js, I asked it to write a product requirements for that so that I can then bring that into Cloud Code and Kiro requirements agent.

Design Phase and Technical Design Document

The design phase focuses on outlining the technical architecture and key design principles of the application. The design document highlights several important points:

• AI-First Architecture: Prioritizing AI integration throughout the application.

• Security First: Emphasizing security considerations in the design.

Key Components

The application architecture involves several key components:

• Supabase: Used for backend services, including authentication and database management.

• Vercel AI SDK: Employed for intelligent thumbnail analysis and content ideation.

• Google OAuth: Used for User Authentication

These architectural elements work together to facilitate data flow and functionality within the application.

Database and Storage

The architecture includes plans for using Blob Storage and Redis. It will use a Supabase database instead of Postgres as originally planned.

Implementation Plan and Task Generation

The implementation plan involves breaking down the project into smaller, manageable tasks. Kiro generates tasks based on the design and requirements, which are then used to guide the development process.

Key Tasks

Some key tasks include:

• Initializing the Next.js application.

• Configuring environment variables.

• Setting up the Supabase backend.

• Integrating the YouTube API.

• Implementing the frontend components.

Setting Up Supabase and Google OAuth

A crucial aspect of the project is setting up Supabase and integrating it with Google OAuth for authentication.

Steps for Supabase Setup

1. Create a new Supabase project.
2. Link the project using Supabase link --project ref.
3. Push the database schema using Supabase DBpush.

Configuring Google OAuth

Configuring Google OAuth involves several steps in Google Cloud Console:

1. Create an OAuth consent screen.
2. Create OAuth client ID.

3. Adding the YouTube APIs that I want to use.

   • Enabling the Data API

   • Enabling the Analytics API.

   • Add the application details, including the app name and support email.
   • Choose the "External" user type.
   • Add test users.

Finally, the Google OAuth client ID and client secret are added to Supabase under Authentication -> Google Provider. It also involves adding authorized redirect URLs.

Integrating YouTube API and OpenAI API

The application integrates with the YouTube API to fetch channel data and uses the OpenAI API for analyzing thumbnails and generating content ideas.

Channel Connection and Data Retrieval

The next step involves implementing the "Connect Channel" button functionality. This allows users to connect their YouTube channel to the application and authorize access to their data. The app uses youtube analytics api to retrieve the data.

Resolving Issues and Enhancements

During development, issues like incorrect user logins and security policy violations are encountered and addressed. The development process is an iterative and ongoing effort. There's also an error creating user profile, solved by prompting it to create the connection button to YouTube, pasting the image and the RLS policy.

Conclusion and Future Development

The project shows significant progress, with the application successfully connecting to a YouTube channel and retrieving basic analytics. The next steps involve:

• Fleshing out the frontend design.

• Implementing the thumbnail analysis feature.

• Developing content idea generation capabilities.