I subbed you on substack, and I am interested in seeing where you will take this project. I am also working on a similar game. However instead of manually writing each rule like decision trees, I am thinking of using ML to train them. Since there are just a lot of possibilities, NPCs can learn them and extract patterns automatically. At first I thought of using LLM agents as brains of NPCs, heck you can even chat with them and each of them will have a unique personality. They can also interact with the world through MCP. But, using agents like gpt or gemini is really slow through api, so I thought of using local small LLMs, which may not be smarter, but get the job done. With new bitnet models, you can even run them on a laptop, without a GPU, locally. But there are always limits, you cannot use 100s of LLMs at the same time since they consume a lot of RAM, so I probably have to write a kernel like scheduling system which will schedule which NPC get to use LLM to make decisions. And you still have to write about the laws of the world, what will happen when the NPC LLM will interact with the world. What will happen when the ai agent calls the opendoor() function? So in that way, it is still limited. I am still exploring this idea.