i helped Chris Callison-Burch design a class at upenn, called interactive fiction, which is a similar context to what Simon suggested. the real magic is that it reframes hallucinations as creative story telling. the usecase is SUPER fun if you imagine the LLM as a dungeon master telling a story that gets expanded over time.

the framework he and I built kept track of the game state over time and allowed saving and loading games as json. we could then send the full json to an LLM as part of the prompts to get it to react. the most neat part, imo, was when we realized we could have the LLM generate text for parts of the story, then analyze what it said to detect any items, locations, or characters not jn the game state, and then have it create json representations of the hallucinated objects that could be inserted into the game states. that sealed the deal for using hallucinations as creative story telling inside the context of a game.

i assure you the D&D context is very fun! the class website might give you more ideas too https://interactive-fiction-class.org/

i wasnt officially part of upenn at the time, so my name isnt listed on the site, but we wrote a paper about some of the things we did, such as this one, and you'll see me listed there https://www.cis.upenn.edu/~ccb/publications/dagger.pdf

Just want to say that I'm not an ai guy at all, but this has made me more excited about it than anything in a while. Really cool! Did you also do the one where you put "spells" in your code?

I share your feelings. What it most brings to mind for me is the infamous StackSort from the image alt text on XKCD comic 1185 (https://xkcd.com/1185/)

Awesome, now I don't have to write mocks for testing!

You know... Because if you get them wet they multiply, per the documentaries

It is using the llm library, so you do the plugin and model management through that. Let's say you've already gotten ollama installed and the `gemma3n:e2b` model. Then you use the llm cli to add the ollama extension:

  llm install llm-ollama

and then you use whatever model you like, anything llm has installed. See https://llm.datasette.io/en/stable/plugins/installing-plugin... for plugin install info.

Here is a sample session. You can't see it ... but it is very slow on my CPU-only non-apple machine (each response took like 30 seconds) :)

  >>> from gremllm import Gremllm
  >>> counter = Gremllm("counter", model="gemma3n:e2b")
  >>> counter.value = 5
  >>> counter.increment()
  >>> counter.value
  None
  >>> counter.value
  None
  >>> counter.value
  None
  >>> counter.what_is_your_total() 
  6
  6

... also I don't know why it kept saying my value is None :) . The "6" is doubled because one must have been a print and the other is the return value.