How to build an automated AI content manager for free using Make.com. Walkthrough tutorial.
TLDRIn this tutorial, the creator shares a step-by-step guide on building an automated AI content manager using Make.com, a visual workflow building platform. The system utilizes Google Sheets as a database, pulls curated stories through an RSS feed, and employs web scraping and the Open AI API for sentiment analysis and content creation. The process involves approving news stories, rewriting them with a positive sentiment, and publishing them on a WordPress site. Additionally, the system generates social media posts for Twitter and LinkedIn, scheduling them with Buffer. This comprehensive content management solution is designed to run with minimal human intervention, offering a cost-effective alternative to traditional content creation methods.
Takeaways
- 🤖 The presenter has built an automated AI content manager to create fresh and relevant news content about the AI industry without spending much money.
- 📈 The content manager uses a variety of free services including Make.com, Google Sheets, RSS feed, Rapid API, and Open AI API to function.
- 📋 Google Sheets is utilized as a database to store and manage the content and sentiment analysis results.
- 📰 The system pulls curated stories through an RSS feed and uses a web scraping app via Rapid API to gather content.
- 📉 The content manager filters out neutral or promotional news, focusing only on positive news stories.
- 📝 The presenter has the opportunity to approve or reject a story before it gets written, adding a layer of manual control.
- 📅 The system is designed to automate the entire process, from content creation to publishing and social media sharing, saving significant time and effort.
- 🔄 The process includes steps for sentiment classification, manual approval, text acquisition, validation, rewriting, image generation, and publishing.
- 🖼️ Open AI is used to generate images based on prompts, which are then uploaded to Google Drive and associated with the blog post.
- 🗓️ Approved content is scheduled for publishing with a delay to space out the posts over time.
- 📢 Social media posts (tweets and LinkedIn posts) are automatically created and scheduled to coincide with the blog post publication using Buffer.
- 💡 The entire system operates with minimal human intervention, showcasing a cost-effective solution for content management.
Q & A
What is the main purpose of the automated AI content manager built in the tutorial?
-The main purpose of the automated AI content manager is to create fresh and relevant news content about the AI industry continuously, with minimal cost, and to allow the user to approve stories before they are written and published.
Which platform is used to build the backend of the content manager?
-Make.com, a visual workflow building platform, is used to build the backend of the content manager.
How does the content manager ensure that only good news is published?
-The content manager uses an open AI API to classify the sentiment of news stories. It only selects positive news stories for publishing and flags neutral or promotional content for deletion.
What is the role of Google Sheets in this content manager system?
-Google Sheets is used as a database to store and manage the information about the news stories, including their titles, sentiment analysis, and approval status.
How does the system handle the manual approval of news stories?
-The system sends a message to the user's Slack channel for each story that requires approval. The user can then choose to approve or not approve the story using buttons provided in the Slack message.
What is the final output of the content manager after a story is approved and rewritten?
-The final output is a blog article published on a WordPress site with all the metadata, accompanied by tweets and a LinkedIn post with a link to the blog.
How does the content manager generate images for the articles?
-The content manager creates an image prompt using the information from the approved news story and sends it to the DALL-E model via the open AI API to generate a landscape image, which is then saved on Google Drive.
What is the significance of the 'Readability' API in the content manager's workflow?
-The 'Readability' API is used to extract the text content from the approved news stories' URLs. This text is then sent to open AI to check if it has the structure of a news or blog article before proceeding with rewriting.
How does the system ensure that the rewritten articles are published at different times?
-The system uses a time incrementation feature that delays the publishing of each approved story by a set number of minutes (e.g., 20 minutes) to spread out the publication times.
What is the role of Buffer in the content manager's process?
-Buffer is used to schedule and distribute the social media posts (tweets and LinkedIn posts) related to the published articles at specified time intervals.
How does the content manager keep track of the stories that have been processed?
-The content manager updates a Google Sheet with various details for each story, including the article title, tweet text, image prompt, document ID, primary keyword, meta description, excerpt text, LinkedIn text, and a flag to mark stories for deletion if necessary.
What are the potential cost savings of using this automated content manager?
-The automated content manager can potentially save companies thousands of dollars per year that would otherwise be spent on hiring someone to perform similar content management tasks manually.
Outlines
🤖 Automating AI Content Management
The speaker introduces an automated AI content manager that generates fresh and relevant AI industry news content continuously, aiming to save time and money. The system is cost-effective, as it uses free services and requires minimal manual intervention. It involves using Make.com for workflow, Google Sheets as a database, an RSS feed for story curation, web scraping through Rapid API, and OpenAI API for various tasks including sentiment analysis and content creation. The process also includes approval steps and publishing on a WordPress site with metadata, as well as social media integration.
📊 Sentiment Analysis and Manual Approval
The system classifies stories for sentiment, looking for positive news to share. It uses a webhook to listen for completed sentiment analysis and then sends approved stories to the user's Slack for manual approval. Approved stories are then passed for rewriting, while those marked for deletion are scheduled to be removed. The process ensures that only desired content moves forward in the pipeline.
📝 Article Rewriting and Validation
Approved stories are checked for their article structure using OpenAI. If the structure is correct, the story is approved for rewriting. The rewritten content, including a new title, tweet, image prompt, primary keyword, LinkedIn text, and meta description, is then saved as variables and used to create a Google Doc. The system updates the Google Sheet with this new information for future reference.
🖼️ Image Generation for Articles
For articles that have been rewritten and validated, the system generates an image using the image prompt text. It utilizes DALL-E, an AI model, to create a landscape image that is then saved to Google Drive and linked to the article in the Google Sheet. This step ensures that each article has a visually appealing image to accompany the text.
📅 Scheduling Blog and Social Media Posts
The system creates a WordPress blog post using the approved Google Doc content, including the title, text, excerpt, and metadata. It also uploads the generated image as the featured image for the post. The post's publication date is set to the current time, and the system schedules social media posts, including tweets and LinkedIn updates, using Buffer. The system staggers these posts to avoid simultaneous publishing and updates the Google Sheet with the post's link and other relevant information.
🚀 Complete Automation and Cost-Effectiveness
The speaker concludes by emphasizing the cost-effectiveness and automation of the content manager. It operates with minimal human intervention and can potentially replace the need for a full-time content manager, saving thousands of dollars annually. The speaker invites viewers to like, subscribe, and comment on the video to support the channel's growth.
Mindmap
Keywords
💡Automated AI Content Manager
💡Make.com
💡Google Sheets
💡RSS Feed
💡Rapid API
💡Open AI API
💡Sentiment Analysis
💡Slack
💡WordPress
💡Buffer
💡Metadata
Highlights
Automated AI content manager created to generate fresh and relevant news content about the AI industry.
The system is designed to work with minimal costs, using free services to maximize efficiency.
Google Sheets is utilized as a database to store and manage content.
Content curation is achieved through an RSS feed, ensuring up-to-date stories are included.
Web scraping is implemented via Rapid API for data extraction.
Open AI API is used for sentiment analysis and content rewriting.
Only positive news stories are selected for approval, with an option to approve or reject before writing.
Manual approval process is facilitated through Slack for a personalized content selection.
Approved stories are automatically rewritten by Open AI to create fresh content.
The system generates a concise tweet, a detailed LinkedIn post, and a meta description for each article.
A Google Doc is created for each article, formatted with the new content and saved on Google Drive.
Featured images for articles are generated using the DALL-E model from Open AI.
WordPress integration allows for the automatic creation and publishing of blog posts.
Buffer is used to schedule and distribute social media posts, such as tweets and LinkedIn updates.
The entire process is designed to be hands-off, requiring minimal human intervention.
The content manager can potentially save thousands of dollars compared to hiring a human content manager.
The system is scalable and can be adapted for various content management needs.
All services used in the content manager are free, making it an accessible solution for many.
The walkthrough tutorial provides a step-by-step guide for replicating the automated AI content manager.
