Source URL: https://simonwillison.net/2025/Jul/4/gremllm/#atom-everything
Source: Simon Willison’s Weblog
Title: awwaiid/gremllm
Feedly Summary: awwaiid/gremllm
Delightfully cursed Python library by Brock Wilcox, built on top of LLM:
from gremllm import Gremllm
counter = Gremllm(“counter")
counter.value = 5
counter.increment()
print(counter.value) # 6?
print(counter.to_roman_numerals()) # VI?
You tell your Gremllm what it should be in the constructor, then it uses an LLM to hallucinate method implementations based on the method name every time you call them!
This utility class can be used for a variety of purposes. Uhm. Also please don’t use this and if you do please tell me because WOW. Or maybe don’t tell me. Or do.
Here’s the system prompt, which starts:
You are a helpful AI assistant living inside a Python object called ‘{self._identity}’.
Someone is interacting with you and you need to respond by generating Python code that will be eval’d in your context.
You have access to ‘self’ (the object) and can modify self._context to store data.
Via @awwaiid
Tags: python, ai, generative-ai, llms, llm
AI Summary and Description: Yes
Summary: The provided text discusses a Python library called “gremllm,” which utilizes a large language model (LLM) to dynamically generate method implementations based on method names. This situationally exemplifies the novel integration of LLM technology into programming, showcasing both its practical application and potential risks.
Detailed Description:
The text introduces an innovative Python library developed by Brock Wilcox, which leverages a large language model (LLM) to create a utility class capable of generating dynamic method implementations. Here are the key points:
– **Library Name**: The library is named “gremllm,” indicative of its focus on generative aspects of language models.
– **Functionality**:
– It allows users to define behavior by naming methods, which the LLM then interprets to create implementations on-the-fly.
– Code snippets demonstrate its usage, where basic operations like incrementing a counter and conversion to Roman numerals are performed.
– **Cautions**: The author humorously advises against using this library indiscriminately, hinting at the unpredictability or potential hazards associated with its capabilities.
– **Purpose**: Though the library has practical implementations, it evokes questions about reliability, safety, and the ability to handle unexpected results (termed “hallucination” in LLM terminology).
**Potential Implications for Professionals**:
– **AI Security Concerns**: The dynamic nature of code generation poses security risks, including the potential for executing harmful code if inputs are not meticulously validated.
– **Software Security**: Evaluating the integrity and trustworthiness of generated code is crucial to prevent vulnerabilities from emergent behaviors of the LLM.
– **Compliance and Governance**: Professionals in security and compliance need to consider the implications of automated code generation in their governance frameworks to ensure adherence to best practices and legal standards.
In summary, “gremllm” exemplifies the interplay of AI and software development, highlighting both innovative possibilities and the need for caution in adopting such tools in secure and compliant environments.