I've built a proof of concept myself in Ruby, an agent that extends itself with new abilities at runtime based on code suggestions from ChatGPT until it deems i

I've built a proof of concept myself in Ruby, an agent that extends itself with new abilities at runtime based on code suggestions from ChatGPT until it deems itself capable of fulfilling an arbitrary task given in natural language. It then persists that „specialized“ version of the task was completed successfully. So far it was able to add an API to fetch me the local weather, return the capital of Venezuela, control brightness and volume of my MacBook and replicate itself at random locations (but tell me where, after). That being said, I added multiple human-in-the-loop points in the assess/suggest/patch/execute cycle and (given the nature of LLMs) would never use it outside of a sandbox without these safety rails.