Bolt.new AI and Lynda (LinkedIn Learning) integration: Step-by-Step Guide 2025

 

Preparing Your Project for Lynda (LinkedIn Learning) Integration

 

This guide explains how to integrate Lynda (LinkedIn Learning) API into your Bolt.new AI project using TypeScript. We will create new files and add code snippets step by step. Since Bolt.new AI does not have a terminal, you must add dependencies manually by updating your code as instructed below.

 

Step: Adding Required Dependencies

 

In your project, open the file where you can define your package dependencies (usually this is a file called package.json). Since you cannot run a terminal, add the following entry in the "dependencies" section. This ensures you have the HTTP client library required for making requests (for example, axios). If you already have a "dependencies" section, simply add the axios package inside it.

{
  "dependencies": {
    "axios": "^0.27.2"
    // … other dependencies in your project
  }
}

You may need to trigger a project refresh for the changes to take effect.

 

Step: Creating the Lynda API Service File

 

Create a new file in your project directory named "LyndaService.ts". This file will handle all communication with Lynda (LinkedIn Learning) API. Paste the following code snippet:

// LyndaService.ts

import axios, { AxiosInstance } from 'axios';

export class LyndaService {
  private apiClient: AxiosInstance;
  private accessToken: string;

  constructor(accessToken: string) {
    this.accessToken = accessToken;
    this.apiClient = axios.create({
      baseURL: 'https://api.lynda.com/v1/',  // Use the actual Lynda API endpoint
      headers: {
        'Authorization': Bearer ${this.accessToken},
        'Content-Type': 'application/json'
      }
    });
  }

  // Example method: Fetch course details
  public async getCourseDetails(courseId: string): Promise {
    try {
      const response = await this.apiClient.get(courses/${courseId});
      return response.data;
    } catch (error) {
      console.error('Error fetching course details:', error);
      throw error;
    }
  }

  // Add additional methods as needed to integrate other Lynda API endpoints
}

Make sure to replace 'https://api.lynda.com/v1/' with the actual Lynda API base URL if it differs. This file creates a service class that uses axios to perform HTTP requests.

 

Step: Using LyndaService in Your Main Application File

 

Open your main project file (for example, "app.ts" or "index.ts") and import the LyndaService. Add the following code snippet where you want to initialize and call the service. Adjust the access token value accordingly from your configuration or environment variable (remember Bolt.new AI might require you to define these values in your code-based config):

import { LyndaService } from './LyndaService';

// Replace 'YOURACCESSTOKEN' with your actual Lynda API access token.
const accessToken = 'YOURACCESSTOKEN';
const lyndaService = new LyndaService(accessToken);

// For example, fetch details for a course with courseId '12345'
(async () => {
  try {
    const courseDetails = await lyndaService.getCourseDetails('12345');
    console.log('Course Details:', courseDetails);
    // You can now use these details in your Bolt.new AI project
  } catch (error) {
    console.error('Failed to fetch course details', error);
  }
})();

Insert this snippet in the main function or at the appropriate logical place where you want the integration to occur.

 

Step: Managing Environment Variables

 

Since Bolt.new AI does not provide a terminal for setting environment variables, include your API token value directly in the code as shown in the previous step. Make sure you manage your secrets carefully. For a production environment, consider using a configuration file or a secure secret management method provided by your hosting solution.

 

Step: Testing the Integration

 

To test the integration, simply run your Bolt.new AI project using the provided run configuration. The code snippet in your main file will call the Lynda API and log the course details. Check the console logs to ensure that the data is received correctly.

 

Step: Extending the Integration

 

If you need to access more endpoints from the Lynda API, add additional methods in the "LyndaService.ts" file similar to the getCourseDetails method. Always wrap your API calls in try-catch blocks and log errors appropriately.

 

By following these steps and inserting the code snippets into the proper files, you will successfully integrate your Bolt.new AI project with Lynda (LinkedIn Learning).