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Here is a detailed summary of how Open Interpreter works, with both technical and layman's explanations: - Open Interpreter provides an interactive conversational interface powered by large language models like GPT-3/4 and Codex. - It allows having a conversation with an AI assistant by exchanging messages back and forth. - The main Interpreter class handles the conversation flow: - It stores a message history in `self.messages` as a list of dicts with keys like "role" and "content". - It connects to GPT-3/4 via litellm or Codex locally via llama-cpp. - Technically it sets API keys and endpoints to call the APIs. - In plain terms, it connects to the artificial intelligence brain power. - It displays messages in the terminal using MessageBlock and CodeBlock. - MessageBlock displays plain text chat messages. - CodeBlock displays code snippets. - They use Rich for nice formatting. - It runs code in different languages with CodeInterpreter. - Each language like Python or JavaScript has its own CodeInterpreter. - The CodeInterpreter executes code and captures the output. - It truncates long messages and injects system info into the prompt. - This keeps prompt under token limits and personalizes it. - It handles commands like %help, %reset, etc. - These allow controlling the experience like resetting or getting help. - It can save and load conversations as JSON. - This persists conversations to disk and loads them back. - It uses helpers like get_hf_llm() to download and set up local AI models smoothly. - Checks for models on disk, installs llama-cpp if needed, configures GPU usage, etc. - Makes using large local models easy. - It asks for confirmation before running code for safety. - Requires user to approve code execution. - Prevents accidental or unauthorized code running. So in summary, it provides a conversational interface to AI with code execution, safety checks, command handling, and more to make the experience smooth.