Why This Automation Exists
Posting consistently on X (Twitter) sounds simple—until you try doing it daily.
You either:
- Manually track news
- Copy links from multiple sites
- Rewrite them into tweet-sized content
- Find or design a relevant image
- Then finally post
Doing this every day quickly becomes exhausting. And when breaking news drops, delays mean missed reach.I wanted a setup where:
- X keeps posting relevant updates automatically
- I can manually guide it when needed
- Everything stays real-time, accurate, and lightweight
So I built this automation using Make.com, WhatsApp, and Make’s new AI Web Search module.
What This Automation Does (At a Glance)
There are two behaviors built into this workflow:
- Auto mode – Posts ongoing updates based on a predefined topic
- Manual mode – I send a message on WhatsApp like:
“Latest AI updates”
The automation then:
- Pulls real-time web data
- Converts it into tweet-ready content
- Generates a matching image
- Posts everything to X automatically
Tools Used
- Make.com – Automation platform
- WhatsApp Business API – Input trigger
- Make AI Web Search – Real-time web data
- OpenAI – Content structuring (tweet + hashtags)
- Gemini – Image generation
- X (Twitter) module – Posting content
Step 1: WhatsApp – Watching Incoming Messages
This workflow starts with a WhatsApp Watch Events module.
Whenever a message comes in:
- The text becomes the topic instruction
- Example input: “Latest AI update”
This allows manual control without opening Make or editing scenarios.
WhatsApp setup requirements:
- Meta developer account
- Verified business
- WhatsApp Business API enabled
Step 2: Make AI Web Search – Real-Time Data
This is the most important part of the automation.
The incoming WhatsApp message is passed directly to Make AI Web Search.
What this module does:
- Searches the web in real time
- Extracts current, relevant updates
- Removes the need for scraping tools or third-party APIs
Each execution consumes:
- 1 operation
- ~10 AI credits (depending on output size)
Step 3: Structuring Content with OpenAI
Raw web data isn’t ready for X.
So the search result is sent to OpenAI, where the prompt:
- Summarizes the update
- Formats it into a tweet
- Generates relevant hashtags
- Creates a separate image-generation prompt
The output includes:
- X post text
- Hashtags
- Image prompt
Step 4: Image Generation with Gemini
Next, the image prompt is sent to Gemini.
Settings used:
- Aspect ratio optimized for X
- Output format: binary image
The generated image matches the topic of the tweet instead of using generic stock images.
Step 5: Posting to X (Text + Media)
Once both text and image are ready:
- Image is uploaded first
- Media ID is captured
- Tweet is created using text + media
This uses a custom X module with:
- Separate connections for text and media
- Read & write permissions
Live Result
After the workflow runs:
- A real-time update is posted on X
- Includes structured text, hashtags, and a relevant image
- No manual writing or posting involved
This allows consistent posting without sacrificing relevance or accuracy.[image]
The key takeaway here isn’t just posting to X automatically—it’s control.
Using WhatsApp as an input makes the system flexible, while Make AI Web Search ensures content stays current without relying on scraping tools.
This same pattern can be reused for other platforms, topics, or formats by changing only the input and prompts.
Hope this walkthrough helps you understand how real-time, AI-driven posting can be implemented cleanly inside Make.com.