Put the machine on screen
An AI product that hides its workings is asking to be taken on trust. This one shows the runners-up on every decision, every rule that overruled the model, which brain is answering, and what the session has cost so far.
There is a standard way to present a model's output, confidently and as a finished thing, and it is the wrong one for anything a person is supposed to reason about. Four things are on screen here that usually are not.
The near-miss
approach_host 0.71 · hold_court 0.25 · withdraw 0.02. An almost-did you can see, taken straight from the distribution instead of from a model's account of itself. It is the difference between watching somebody decide and watching a token move.
The override, in amber
Every time a rule overrules the model, the board says which rule and what it changed. Most systems treat that as plumbing and hide it. Here it is the most interesting thing on the screen, because it is the only place where the two halves of the system disagree in public.
Which brain is answering
Live model or house brain, on a badge, always. A product that degrades to a heuristic without saying so is lying by omission, and the fallback is much less embarrassing than the concealment would be.
What it has cost
A running total, like a buy-in. It makes the economics an argument instead of a claim, and it teaches a watcher how small this kind of system can be.
The rule behind all four
Show the thing that would embarrass you slightly. The near-miss, the override, the fallback and the bill are each a small admission, and together they are the reason a watcher can believe the parts that are impressive.
Read next
coerce(): when the code overrules the model
coerce() is the last word on every decision. It drops forbidden actions, enforces gates and confidence floors, refuses moves the character cannot afford, and puts every override on screen in amber.
Using distributions instead of picks
Because every answer is a distribution, the engine can scale an outcome by how sure a character was. A 0.95 read of an open host earns a bigger opening than a 0.55 one, so not every decision is equally committed.
The house brain: running with no model at all
The house brain is a deterministic personality heuristic that answers the same nine questions and synthesises its own distributions, so the simulation stays watchable, and tunable, with no API key and no network.
What a live LLM simulation actually costs
A Parlour party runs five or six minutes on roughly 250–300 evaluation calls of about 3,000 input tokens each, which is three to four cents. Every watcher of a room shares one party, so the bill does not grow with the audience.
What makes a simulation worth watching
One shared world instead of a private instance, a visible deadline so something is always about to happen, rules you can learn by watching them fire, and permission to walk away and come back to a story that carried on.