Friday, 4th July 2025
awwaiid/gremllm (via) 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.
The more time I spend using LLMs for code, the less I worry for my career - even as their coding capabilities continue to improve.
Using LLMs as part of my process helps me understand how much of my job isn't just bashing out code.
My job is to identify problems that can be solved with code, then solve them, then verify that the solution works and has actually addressed the problem.
A more advanced LLM may eventually be able to completely handle the middle piece. It can help with the first and last pieces, but only when operated by someone who understands both the problems to be solved and how to interact with the LLM to help solve them.
No matter how good these things get, they will still need someone to find problems for them to solve, define those problems and confirm that they are solved. That's a job - one that other humans will be happy to outsource to an expert practitioner.
It's also about 80% of what I do as a software developer already.