Automate Blog to Social Media Using AI and Make

Every time I finished writing a new blog post, the same routine followed: summarize it, write captions for each platform, grab the featured image, and publish it manually to LinkedIn, X (Twitter), and Facebook.

It worked, but it quickly became a chore.

As the pace of my publishing increased, I wanted to stop repeating this manual work. The captions still had to fit each platform’s tone and character limit, so a straight copy-paste to all three was out.

So I built an automation using Make.com, Claude, OpenAI, and Airtable.

The Goal

I wanted a system that would:

Automatically detect when I publish a new blog post
Generate social media captions using AI
Customize those captions per platform
Post automatically to Facebook, LinkedIn, and X (Twitter)
Track everything in Airtable so I always know what’s been published

The Flow of the Automation

The steps, in order:

A new blog post is published on my site.
Make.com picks it up, pulls metadata and the featured image.
The post is logged in Airtable.
Claude writes a clean, informative summary.
ChatGPT adapts the summary into different tones for each platform.
Make.com posts them directly to my social accounts.
Airtable is updated with a status for each platform.

The whole thing in Make.com:

Detecting New Blog Posts (WordPress → Make)

The automation starts with a Watch Posts trigger in Make.com. This listens for new published posts on my WordPress site.

Once a new post is detected, a second WordPress module fetches the featured media. Since some platforms don’t preview links properly without a native image, I download the featured image via the HTTP module to attach it manually during posting.

This part of the workflow gathers everything I need to build a complete social post:

Title
URL
Featured Image
Category

Logging Posts in Airtable

I use Airtable as a lightweight backend to store metadata and status info about each blog post. Make.com first checks if the post already exists (in case I update something), then either creates or updates the record.

This lets me:

Track which platforms have received the post
Retry or fix issues without reposting everything

It also makes it easy to pause or disable individual platforms per post.

Generating the Captions (Claude + ChatGPT)

I use Claude API to generate an initial post caption. I send the blog post title and excerpt from the “Watch Post” module into Claude, along with a short instruction like:

“Summarize this and start your output with a headline.”

Claude returns a clean, neutral caption that works as a base for all platforms.

Then, for each platform, I run a second AI step using OpenAI’s API, I’ve created an OpenAI Assistant for each Social Media platform.

Each assistant is tuned slightly differently:

Facebook: A little more relaxed and conversational
LinkedIn: Professional and technical, and very sensitive about formatting (especially parentheses)
X (Twitter): Short and concise (under 280 characters)

Two models in sequence read better than one model doing both jobs.

Platform-Specific Posting

Each platform has its own route in Make.com:

Facebook Pages: Posts the AI caption, the link, and attaches the featured image.

LinkedIn: Creates an article post with the AI caption, the link, the title, and attaches the featured image via the HTTP module.

X (Twitter): Posts the caption and link (link is included in the caption), keeping it under 280 characters.

Each post happens automatically. I added Break modules after each one to avoid rate limits and give me space to troubleshoot if needed.

After a post is made, Make.com updates the Airtable record to mark that platform as completed.

If Twitter fails or another error occurs, the Airtable record will show a ❌ instead of a ✅.

Handling Errors and Retries

One thing I learned early: Make.com scenarios will fail silently if you don’t plan for it.

To avoid that, I built in:

Breaks and delays with Retry logic with Make’s built-in error handling
Airtable status flags to prevent double-posting

If something fails (e.g. Twitter API rate limit), I can fix it and re-run only that part of the flow.

Lessons Learned

Don’t rely on a single AI model. Claude + ChatGPT together produce more human-like captions.
Keeping the image native (especially on Facebook) increases reach.
Airtable is good for state tracking. Keeping that state inside Make was worse.
Test everything on dummy accounts first, especially for LinkedIn and Facebook.
Rate limits matter. Breaks save you headaches.

Final Thoughts

This automation has saved me hours every month. I no longer write the same caption three times after publishing.

Make.com keeps it running in the background. If you publish technical content regularly, it is worth building.

I share more automation projects here on the blog, including Make.com workflows, n8n automations with AI agents, and real-world IT setups.
Code and examples go on GitHub.

If you want to talk about setting up your own workflow, reach out.