Source URL: https://simonwillison.net/2024/Nov/24/open-interpreter/#atom-everything
Source: Simon Willison’s Weblog
Title: open-interpreter
Feedly Summary: open-interpreter
This “natural language interface for computers" project has been around for a while, but today I finally got around to trying it out.
Here’s how I ran it (without first installing anything) using uv:
uvx –from open-interpreter interpreter
The default mode asks you for an OpenAI API key so it can use gpt-4o – there are a multitude of other options, including the ability to use local models with interpreter –local.
It runs in your terminal and works by generating Python code to help answer your questions, asking your permission to run it and then executing it directly on your computer.
I pasted in an API key and then prompted it with this:
find largest files on my desktop
Here’s the full transcript.
Since code is run directly on your machine there are all sorts of ways things could go wrong if you don’t carefully review the generated code before hitting "y". The team have an experimental safe mode in development which works by scanning generated code with semgrep. I’m not convinced by that approach, I think executing code in a sandbox would be a much more robust solution here – but sandboxing Python is still a very difficult problem.
Via Hacker News
Tags: llms, ai, generative-ai, uv, sandboxing
AI Summary and Description: Yes
Summary: The provided text discusses the “open-interpreter” project, which serves as a natural language interface for running code generated by AI models like GPT-4 directly on a user’s machine. It emphasizes the security risks associated with executing code without review and highlights ongoing efforts to develop safer execution methods, such as code scanning and sandboxing.
Detailed Description: The text delves into the functionalities and security concerns related to the open-interpreter project, designed to enhance user interaction with AI models for executing code. Here are the key points:
– **Overview of open-interpreter**:
– A project that allows users to interact with computers through a natural language interface.
– Users can run it using a command (`uvx –from open-interpreter interpreter`) without needing to install additional software.
– **Interaction with AI**:
– The tool requires an OpenAI API key for accessing models like GPT-4.
– Provides options for using local models for generating code.
– **Functionality**:
– Runs in the terminal and generates Python code based on user prompts (e.g., seeking to find the largest files on a desktop).
– Prompts users for permission before executing any generated code, necessitating careful review to mitigate risks.
– **Security Concerns**:
– Notable risks arise from executing potentially harmful code generated by the AI.
– Suggestion of existing code scanning features using semgrep, though skepticism about its effectiveness is noted.
– **Proposed Improvements**:
– The author argues for the need for a sandboxing solution as a more robust approach to security compared to code scanning, highlighting the complexities involved in sandboxing Python effectively.
– **Tags Noting Relevance**:
– Mentions tags related to LLMs, AI, generative AI, and sandboxing, indicating a focus on ensuring security while interacting with AI-generated content.
This discussion is significant for security and compliance professionals as it underscores critical considerations regarding the safe execution of AI-generated code, emphasizing the need for robust safety mechanisms in AI applications. It raises awareness about potential vulnerabilities that could arise from careless usage and the importance of rigorous review processes in AI-driven environments.