paint-brush
How to Backup Your DEV.to Articles Locally in Markdown Formatby@luca1iu
170 reads

How to Backup Your DEV.to Articles Locally in Markdown Format

by Luca LiuApril 4th, 2024
Read on Terminal Reader
tldt arrow

Too Long; Didn't Read

As a content creator on DEV.to, you may want to have a local backup of all your published articles in Markdown format. This can be useful for various reasons such as offline access, archiving, or editing outside the platform. We'll use Python and the `requests` library to interact with the DEV API and save the articles.
featured image - How to Backup Your DEV.to Articles Locally in Markdown Format
Luca Liu HackerNoon profile picture

Introduction

As a content creator on DEV.to, you may want to have a local backup of all your published articles in Markdown format. This can be useful for various reasons such as offline access, archiving, or editing outside the platform.

In this guide, we'll walk through how to download all your published articles from DEV.to using the DEV API and save them locally as Markdown files.

Prerequisites:

Before proceeding, you will need:

  • Access to your DEV.to account
  • An API key from DEV.to to authorize API requests

Step 1: Obtain Your API Key

  1. Log in to your DEV.to account.
  2. Go to your account settings.
  3. Navigate to the Account Settings tab.
  4. Scroll down to find the DEV API Key section.
  5. Generate a new API key if you don't have one, and make sure to keep it secure.

Step 2: Setting Up the Script

We'll use Python and the requests library to interact with the DEV API and save the articles.

import requests

def get_posts_response_data(api_key):
    # URL of the API endpoint
    url = "https://dev.to/api/articles/me/published"

    # Headers for the request
    headers = {
        "Content-Type": "application/json",
        "api-key": api_key
    }

    # Send GET request
    response = requests.get(url, headers=headers)

    # Check if request was successful
    if response.status_code == 200:
        # Parse JSON response
        response_data = response.json()
        return response_data
    else:
        # If request was unsuccessful, print error message
        print("Error:", response.text)

def save_dev_post_to_markdown(response,markdown_file_root_path):
    for article in response:
        markdown_content = article['body_markdown']
        title = article['title']
        if '/' in title:
            title = title.replace('/', '-')
        with open('{}/{}.md'.format(markdown_file_root_path,title), 'w') as f:
            f.write(markdown_content)
            print("File saved as {}.md".format(title))
            
            
# run the function
response = get_posts_response_data(api_key)
save_dev_post_to_markdown(response, markdown_file_root_path=r'/Users/luca/Desktop/Writing/DEV_MARKDOWN')

Step 3: Run the Script

  1. Replace 'YOUR_API_KEY_HERE' with your actual DEV API key in the script.
  2. Run the script in your Python environment.
  3. Sit back and relax while your published articles are downloaded and saved locally as Markdown files.

Try to save one article.

Here's a snippet: you can download just one article locally, replace api_key with your actual DEV API key in the script.

import requests

# URL of the API endpoint
url = "https://dev.to/api/articles/me/published"

# Headers for the request
headers = {
    "Content-Type": "application/json",
    "api-key": api_key
}

# Send GET request
response = requests.get(url, headers=headers)
response_data = response.json()

# save the first article to markdown file
markdown_content = response_data[0]['body_markdown']
with open('example.md', 'w') as f:
    f.write(markdown_content)

Conclusion

By following the steps outlined in this guide, you now have a local copy of all your published DEV.to articles in Markdown format. This can be a handy backup and provide you with more flexibility in managing your content.

Feel free to customize the script further to suit your needs or automate the download process. Happy writing!


Thank you for taking the time to explore data-related insights with me. I appreciate your engagement. If you find this information helpful, I invite you to follow me or connect with me on LinkedIn or X(@Luca_DataTeam). Happy exploring!👋


Also published here.