In this article, the author shares their journey of building an AI agent to track LinkedIn posts about venture studios and compile them daily in a Google Doc. They encountered challenges using LinkedIn’s API and ended up using PhantomBuster for data extraction. Despite facing setbacks with tools like Relevance AI and Zapier, they simplified their approach and created a functional agent that sends Slack updates with relevant posts. The experience highlighted the potential of AI agents in automating tasks, improving workflows, and driving efficiency for startups. The author encourages others to explore building their own AI agents as the technology advances.
Building an AI Agent to Track LinkedIn Posts
Recently, I embarked on a journey to create an AI agent with one clear mission: track all LinkedIn posts that mention "venture studios" and compile them into a Google Doc every day. While this might sound straightforward, the process turned out to be an intricate challenge.
The inspiration for this project came from Ben van Sprundel’s video, “This 20+ AI Agent Team Automates ALL Your Work.” After watching, I was motivated to see if I could apply similar automation to my needs.
Getting Started with AI Development
I initiated the project by crafting a prompt for ChatGPT, seeking guidance on how to extract LinkedIn posts efficiently. Here’s what I aimed for:
- Daily search for posts mentioning "venture studio."
- Collect data such as the poster’s name, post content, comments, timestamp, and a summarization in a Google Doc.
ChatGPT provided a comprehensive implementation plan, but I quickly realized that retrieving LinkedIn post data was more complicated than anticipated.
Navigating Data Extraction Methods
ChatGPT suggested two primary methods for data extraction:
- LinkedIn API: This is the preferred option since it allows for structured access to post data.
- Web Scraping: If the API route fails, tools like PhantomBuster can scrape LinkedIn pages directly.
I tried to work with LinkedIn’s API initially, but encountered several roadblocks. Ultimately, I opted for PhantomBuster after some trial and error. With the help of their customer support, I managed to pull the data successfully into a CSV file.
Streamlining Processes with Relevance AI
Next, I explored using Relevance AI to handle the data processing. Initially, this led me down a path filled with Python code complexities that seemed overwhelming. I aimed for a user-friendly interface instead of deep coding immersion, which made my progress slow.
I played around with other tools like Make.com and Zapier. Make was too complex for a beginner, while Zapier proved to be a bit counterintuitive, causing me to rack up unnecessary costs without delivering desired results.
Returning to Relevance AI, I discovered a simple prompt to create an agent. This agent was designed to run a sequential tool to convert CSV files into a human-readable format. Despite challenges with the formatting and categorization of posts, I persisted.
Embracing the Learning Curve
As I built my AI agent, I encountered consistent hurdles – from configuring tools to ensuring data was formatted correctly. Debugging became my daily routine and required significant patience. However, each challenge taught me invaluable lessons about the potential and limitations of AI in automating tasks.
Ultimately, I created a reliable process:
- Fetch LinkedIn posts using PhantomBuster.
- Transform that data into a cohesive output utilizing Relevance AI.
- Send the results directly to Slack for real-time updates, ensuring I’m always informed of the latest insights.
Refining the Workflow
The initial agent I created was basic, prompting me to rethink its functionality. I explored ways to trigger the agent automatically, finally settling on using an email scheduler to activate the agent daily.
This daily ritual now efficiently collects and summarizes pertinent posts about venture studios, which saves me time while enhancing my knowledge of industry trends.
Conclusion: The Power of AI Agents
Despite the initial frustrations, I have successfully built an AI agent that meets my needs. The experience has heightened my interest in automating processes and exploring AI’s role in enhancing workflows.
If you’re involved in startups or business, consider the potential of integrating AI agents. The ability to streamline operations and extract valuable insights from data is not just a convenience; it’s becoming essential in our fast-paced world. Experimenting with AI can open doors to innovation that you may have never considered before.
Keywords: AI Agent, LinkedIn Automation, Venture Studios
Secondary Keywords: Data Extraction, Relevance AI, AI Automation
What is a simple AI agent?
A simple AI agent is a program designed to perform specific tasks automatically. It can help with things like answering questions, making recommendations, or managing repetitive duties. Building such agents can be a fun and educational experience, especially for beginners.
How do I start building an AI agent?
To start building an AI agent, you need to first choose a programming language. Python is very popular for this. Then, learn the basics of AI concepts, such as natural language processing and machine learning. There are many online resources and tutorials that can guide you through the process step by step.
What tools or libraries are helpful for creating AI agents?
There are several useful tools and libraries for building AI agents. Some popular ones include TensorFlow, PyTorch, and scikit-learn for machine learning. If you’re into natural language processing, libraries like NLTK and SpaCy can be very helpful. Many of these tools come with plenty of example projects to help you get started.
Are there any challenges in making AI agents?
Yes, there can be challenges when creating AI agents. Some common issues include gathering quality data, understanding how the algorithms work, and dealing with unexpected behavior. It’s important to be patient and willing to troubleshoot problems as they arise. Learning from mistakes is part of the process.
Can I build an AI agent without coding experience?
While coding experience is helpful, it’s not strictly necessary. Many platforms offer user-friendly interfaces that allow you to create simple AI agents without writing code. However, some basic understanding of programming concepts will enhance your ability to build and customize your agent effectively.
