Neural intel Pod

Building an Agent: LLM, Loop, and Tokens


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Thorsten Ball's article argues that creating a functional code-editing agent is surprisingly straightforward. It posits that the core components are a large language model (LLM), a loop for interaction, and sufficient tokens. The author demonstrates building a basic agent in Go with fewer than 400 lines of code, showcasing its ability to converse and utilize tools. The article then introduces three key tools: read_file, list_files, and edit_file, illustrating how the agent can be instructed to interact with the file system and even modify code. By combining these tools and a simple prompting mechanism, the agent can perform complex tasks like solving riddles in files and creating or altering JavaScript code. The author concludes that the apparent complexity of such agents is largely due to the impressive capabilities of modern LLMs and practical engineering, rather than hidden secrets.

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Neural intel PodBy Neural Intelligence Network