Swarm Intelligence with GPT-4o, Gemini 1.5, Opus and Llama3 70b

The video demonstrates an innovative implementation of swarm intelligence using GPT-4o, Gemini 1.5, Opus, and Llama3 70b through batch files, where agents collaborate and generate responses iteratively. By working from a common understanding and inspiring each other, the agents prioritize a collaborative environment to learn from one another’s contributions and produce potentially creative outcomes.

In the video, an implementation of swarm intelligence using GPT-4o, Gemini 1.5, Opus, and Llama3 70b is showcased through batch files. These agents work collaboratively and iteratively from a common understanding, writing to a shared response.txt file. Each agent represents an entity like GPT agent, router agent for Llama 3, Gemini, and Opus. The process involves pasting instructions into the response.txt file, which the agents check every second to generate responses. The responses are then written back to the response.txt file, accumulating up to 20,000 characters before being transferred to response history.

The agents, while working on the instructions provided, are limited to the context stored in the response.txt file. The process involves running a code extractor to extract the code written by each agent, allowing for further analysis and potential enhancements. As the agents interact and write responses, they inspire each other, leading to potentially more creative outcomes. The approach prioritizes a collaborative environment where agents can learn from each other’s work and contribute to the collective output.

To manage the large context that may arise from the collaboration of multiple agents, a length checker is employed. It continually checks the response.txt file’s length and transfers earlier conversation snippets into the response history to maintain a manageable context size. The batch file setup allows for easy initiation and monitoring of the agents, ensuring a structured and controlled swarm intelligence process. The agents’ responses are written to response.txt, where they can view and learn from each other’s contributions.

The video also delves into the technical aspects of the implementation, detailing the classes and methods used for interacting with the AI models. The code structure involves managing response states, iteration counts, and system messages to ensure a smooth operation of the swarm intelligence process. The system’s hacky approach is acknowledged, with potential suggestions for improvement using threading or async operations. Despite the method’s simplicity, it provides clarity and a platform for experimenting and further development in the realm of swarm intelligence.

The presenter also highlights the benefits of becoming a patron, offering access to code files, courses, and exclusive content. The THX Master Class, streamlit course, and fast API course are some of the offerings available to patrons, providing insights into coding fast and efficiently. The video concludes with an invitation to explore the patron benefits and engage with the presenter through one-on-one sessions. Overall, the swarm intelligence implementation demonstrated in the video showcases a unique approach to collaborative code generation, emphasizing cooperation and inspiration among AI agents to produce creative outcomes.