My outreach agent pitched 50 prospects this week — I approved them with one click

My AI-Powered Outreach Experiment

I've been running a self-hosted AI system, consisting of 50 agents, on a single RTX 4050 6GB laptop for the past month. The system, which I've dubbed "Ollama", utilizes cron-scheduled Python agents to manage outreach efforts for my 20+ real websites. This week, my outreach agent, Atlas, pitched 50 prospects, and I was able to approve them with just one click. In this post, I'll dive into the details of how I set up the system, the trade-offs I made, and the results I've seen so far.

The Ollama System Architecture

The Ollama system is built around a core group of agents, each with a specific role. The overseer, Axiom, is responsible for monitoring the system and allocating tasks to the other agents. The content agent, Forge, generates content for my websites, while the SEO agent, Sentinel, focuses on optimizing my sites for search engines. Atlas, my outreach agent, is responsible for identifying and pitching prospects. The system also includes several scout agents, which are responsible for researching and identifying new opportunities.

Setting Up the Agents

Setting up the agents was a time-consuming process, but it was worth it in the end. I started by creating a set of rules and guidelines for each agent, outlining their specific tasks and responsibilities. I then wrote a series of Python scripts to automate the tasks, using a combination of natural language processing (NLP) and machine learning algorithms to enable the agents to make decisions and take actions. The agents are scheduled to run at regular intervals using cron jobs, ensuring that the system is constantly working to manage my outreach efforts.

The Approval Process

One of the key features of the Ollama system is the ability to approve pitches with a single click. Atlas, my outreach agent, uses a combination of NLP and machine learning algorithms to identify and pitch prospects. Once a pitch is made, the system sends me a notification, which includes a summary of the pitch and the prospect's information. I can then review the pitch and approve it with a single click, or reject it and provide feedback to Atlas. This process has streamlined my outreach efforts, allowing me to focus on higher-level tasks and strategy.

Results and Trade-Offs

So far, the results have been promising. Atlas has pitched 50 prospects this week, and I've approved 20 of them. The system has also identified several new opportunities, which the scout agents are currently researching. However, there have been some trade-offs. The system requires a significant amount of computational power, which can be a challenge given the limited resources of my laptop. I've had to optimize the system to run efficiently, which has involved making some compromises on the complexity of the tasks that the agents can perform. Additionally, the system is not perfect, and there have been some instances where Atlas has pitched prospects that are not a good fit for my business.

Lessons Learned

Running the Ollama system has taught me several valuable lessons. First, it's possible to build a sophisticated AI-powered outreach system using relatively limited resources. Second, the key to success lies in careful planning and optimization of the system. Finally, it's essential to monitor the system closely and make adjustments as needed to ensure that it's running efficiently and effectively. Some of the key takeaways from my experience include:

• Start small and scale up gradually, rather than trying to build a complex system from the outset
• Optimize the system for efficiency, rather than trying to make it perfect
• Monitor the system closely and make adjustments as needed
• Be prepared to make compromises on the complexity of the tasks that the agents can perform
• Focus on higher-level tasks and strategy, rather than getting bogged down in details

Conclusion

In conclusion, my experience with the Ollama system has been positive, and I'm excited to see where it will take my business in the future. While there have been some trade-offs, the benefits of the system have far outweighed the costs. I'm confident that the system will continue to evolve and improve over time, and I'm looking forward to exploring new opportunities and applications for AI-powered outreach. If you're a developer or technical creator who is interested in self-hosted AI agents, I hope that my experience will provide a useful starting point for your own experiments and projects.