A few weeks ago, I set out to create an AI agent to track LinkedIn posts mentioning “venture studios” and compile them daily into a Google Doc. Inspired by Ben van Sprundel’s video on automating work with AI agents, I tried implementing this with ChatGPT. After experimenting with various tools like LinkedIn’s API, PhantomBuster, and Relevance AI, I faced several challenges, including managing data formatting and inconsistent outputs. Ultimately, I built a simple agent that sends updates through Slack. This journey highlighted the immense potential of AI agents to automate tasks, and I encourage everyone, especially in startups, to explore how they can enhance and streamline processes.
A Deep Dive into Building AI Agents for LinkedIn Insights
Recently, I focused on creating an AI agent for tracking LinkedIn posts about “venture studios.” The idea was to compile these mentions into a daily Google Doc. Sounds straightforward, right? But the journey was anything but simple.
Inspiration struck after watching a video by Ben van Sprundel, who demonstrated the power of AI agents. I began with a clear prompt in ChatGPT, detailing what I wished to achieve. The plan involved gathering LinkedIn posts from the last 24 hours, including data such as the poster’s name, post content, timestamp, and the post URL, and putting all of it in a document.
However, the first hurdle was extracting data from LinkedIn. ChatGPT suggested two main options: using the LinkedIn API, or web scraping. Although the API seemed like the ideal solution, I ran into issues accessing the specific data I needed. Scraping tools like PhantomBuster eventually provided success but came with their own set of challenges.
Once I obtained the data, my next step was to summarize and categorize the LinkedIn posts. Here, I experimented with various tools, including Relevance AI, Make.com, and Zapier. While Zapier was familiar, it had limitations with looping functionalities, causing me to abandon it in favor of Relevance AI.
In Relevance AI, I created a simple agent designed to run a tool that processes the LinkedIn data. The agent would summarize posts and categorize them for better readability. While I had to navigate some complicated workflows, I thrived on simplifying them into manageable steps.
I finally set up the AI agent to trigger via email at the same time every day. This required a bit of creativity since I needed a recurring email setup, but with the help of a tool called Right Inbox, I successfully automated the process.
Every evening, an email activates the agent, which processes the latest LinkedIn posts and sends a summary via Slack. While it may not be flawless, this system works effectively enough to streamline my daily insights on venture studios.
This endeavor highlights the potential of AI agents to enhance productivity. With experimentation and persistence, I’ve learned that anyone can develop their own AI solutions, regardless of their technical background. As businesses increasingly adopt AI, the focus should shift toward exploration and innovation in workflows to stay competitive.
For startups and organizations, investing time in AI tools can yield substantial returns in efficiency and effectiveness. The future holds promise as automation takes the wheel, allowing human creativity and instinct to focus where they are most needed.
In summary, the experience has broadened my perspective on AI’s role in streamlining processes. Whether you’re a leader in a startup or an established corporation, leveraging AI agents is a crucial step in enhancing operations and keeping pace with evolving technologies.
Primary keyword: AI Agents
Secondary keywords: LinkedIn Insights, Automate Processes
What are simple AI agents?
Simple AI agents are computer programs that can perform tasks or make decisions without needing constant human help. They can solve problems, answer questions, or even interact with users in a basic way.
How can I build a simple AI agent?
To build a simple AI agent, start by choosing a clear goal. You can use programming languages like Python and frameworks such as TensorFlow or PyTorch. Then, gather data and create the logic or rules for how your agent should respond.
Do I need to be a programmer to create an AI agent?
While having some programming skills helps, you don’t need to be an expert. Many online resources and tutorials can guide beginners through the process of building simple AI agents without needing advanced coding knowledge.
What can I use my AI agent for?
You can use your AI agent for various purposes, such as answering FAQs on a website, assisting with customer service, or even personal tasks like setting reminders. Their versatility makes them useful in many fields.
What are the benefits of using AI agents?
AI agents can save time and effort by automating repetitive tasks. They can handle multiple requests at once and provide consistent responses. This can improve efficiency and allow people to focus on more complex problems.
