Leveraging AI for Automated Professional Content Generation on Twitter
Shmulik Willinger
Leading Solution-Architects and Engineers Group | Active R&D Leader | Hands-on AI-Driven engineer
In this article, I'll guide you through an innovative project that harnesses the power of AI to automatically generate and post professional content to your Twitter feed. This solution combines cutting-edge technologies to create a seamless, hands-off content creation pipeline.
Project Overview
Our project utilizes a robust technological stack, including:
The main steps will be:
The Content Generation Process
Step 1: Sourcing Content Ideas
We begin by using OpenAI's GPT-3.5-turbo model to generate a random website URL within specified domains (AI, crypto, or quantum computing). This provides a fresh topic for each content piece.
I used Perplexity to generate the Python code, as Perplexity is much better with coding (For more info visit this article )
In the first call, we ask for a random website URL of a company from the specific requested domain
Model Selection: We're using the "gpt-3.5-turbo" model, known for its balance of performance and cost-effectiveness.
Temperature Setting: The temperature parameter is set to 0. This low temperature results in more deterministic and focused responses, reducing randomness. It's particularly useful when we need consistent, fact-based outputs.
Domain Specification: In this example, we've asked for websites in the AI, crypto, or quantum computing domains. However, this is entirely customizable. You can modify the prompt to focus on any industry, topic, or niche that aligns with your content strategy.
Step 2: Crafting Professional Summaries
The selected URL is then fed back into GPT-3.5-turbo, which generates a concise, professional summary suitable for a tweet (limited to 218 characters).
The outcome is a professional summary that encapsulates the company's business objectives and goals.
Generate relevant image
To enhance engagement, we utilize DALL-E 3 to generate a relevant image based on the tweet summary. This visual component adds depth to our content.
Posting to Twitter
Using the Tweepy library, we automate the process of uploading the generated image and posting the tweet with both text and visual content.
Before we can include an image in our tweet, we need to upload it to Twitter's servers and obtain a media ID. Here's how we accomplish this:
After uploading the image and obtaining its media ID, we use the Twitter API v2 to create a tweet that includes both text and the uploaded image. Here's a breakdown of the process:
Automating the Workflow with AWS
To ensure regular content generation without manual intervention, we leverage AWS services:
领英推荐
Step-1: Create Lambda function
To create a Lambda function, navigate to the AWS Lambda console and click "Create function". Choose "Author from scratch", name your function, and select Python as the runtime (choose the appropriate version). Finally, create or select an execution role with the necessary permissions to run your function.
To upload your Python script, navigate to the Lambda function page and scroll to the "Code source" section. Paste your Python code into the lambda_function.py file. If desired, you can rename this file to better reflect your function's purpose.
Configure EventBridge (CloudWatch Events) to trigger the Lambda function: Go to the Amazon EventBridge service, and Click "Create rule".
Name your rule in EventBridge. Choose "Schedule" under "Define pattern" and select "Cron expression". Enter '0 /12 ? ' to run the script every 12 hours. Under "Select targets", choose your Lambda function to be called by the scheduler.
Test your function:I n the Lambda console, click the "Test" button to manually trigger your function. Check the CloudWatch Logs to see the output and any potential errors.
The Final outcome
The Lambda function executes the Python script, which generates tweets on your feed every few hours. You can view the results on my Twitter account: https://x.com/SamuelWillinger .
The Power of Open Source
One of the most compelling aspects of this project is its reliance on open-source and free-tier services. This approach demonstrates how powerful automation tools can be created without significant financial investment.
Getting Started
To implement this system, you'll need to set up accounts and obtain API keys for:
Troubleshooting
While Perplexity's engine provided the foundation for much of the initial code, I encountered several challenges during the implementation phase. To overcome these hurdles, I leveraged AI-powered coding assistants integrated into my IDE, such as Tabnine and CodeiumAI . These tools proved invaluable, offering context-aware suggestions and helping to resolve many of the issues I faced. Their effectiveness in streamlining the coding process and problem-solving capabilities make them highly recommendable for developers working on similar projects.
A significant challenge I encountered was managing Twitter's rate limits, particularly when using the basic and free versions of the Twitter API. Due to the restricted number of API calls allowed, I implemented a crucial function in the Jupyter Notebook: a Python function that monitors our remaining API calls within the current time window. This addition proved invaluable in optimizing our API usage and preventing unexpected interruptions due to exceeding rate limits. For more information about the Twitter rate limit visit the X developer portal
?
Conclusion
This project showcases the potential of AI-driven content creation and automation in social media management. By combining various AI services with cloud automation, we've created a system that generates professional, engaging content consistently and effortlessly. For those interested in exploring the code further, the Jupyter Notebook is available here . I encourage you to experiment with this framework and adapt it to your specific needs. If you encounter any challenges or have questions, feel free to reach out through comments or direct messages. I'm here to help you navigate this exciting intersection of AI and social media automation.
Thanks for reading. Now it's your turn to revolutionize your content strategy with AI!