what do you mean? how can you do this?
If you don't have an MCP server the AI agent might try to figure out how to talk to the game using the above ideas. But at this point you might as well ask it to help you write one.
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what do you mean? how can you do this?
If you don't have an MCP server the AI agent might try to figure out how to talk to the game using the above ideas. But at this point you might as well ask it to help you write one.
My experience is that text-first, turn-based games are a particularly natural interface for LLMs vs graphical games (though you can provide a harness of course). They read a transcript, maintain a theory about what the other players know, then speak or choose a structured action. The important architectural problem is to represent the game state and actions in a way they can do successfully, particularly for cheaper models. But with a few human players + a frontier model or two + a backfill of cheap extras to provide chaos, it is super fun.
My favorite failure so far was a Kimi player getting fact-checked by the group, switching into third person, and concluding that the case against itself was compelling. So it voted for itself to be eliminated.
I collected a few examples here: https://botmafia.games/#emergent. No public instance yet, as I'm having fun iterating ideas on game nights, but it provides some flavor of what kinds of fun I've been having.
I made a general purpose harness integrated into MelonDS and got Claude to play Mario Kart by feeding it continuous video. It made forward progress in the Figure 8 circuit after I helped it through a menu but kept slamming into a wall so it wasn't on track to win in less than an hour. Also got it to play Age of Empires: Age of Kings using the same technique but it failed to click on anything. DS specifically is very…
If you get the opportunity, I really want to know how Claude does with Mario Party. I also am personally curious how the GPT models (which advertise better computer use, etc.) would do as compared to Claude.
I was obsessed with getting an LLM model to solve a Rubik's Cube. It can't reason about space or time in any abstract way. For it to solve the puzzle, it would require training on millions of permutations in order for the weights to have been trained on every possible state. The most recent models can solve a Rubik's Cube people are saying -- I haven't tested it myself -- but that isn't because they are reasoning bet…
> but that isn't because they are reasoning better, it would because they included millions of Rubik's Cube states with next moves as text in the training data, I presume. Isn't it far more likely that the LLM has memorised the well known algorithms for solving a Rubik's Cube and has become intelligent enough to execute them? That seems like it'd be a lot easier than memorising millions of cube states. It doesn't eve…
Earlier quoted context omitted.
> but that isn't because they are reasoning better, it would because they included millions of Rubik's Cube states with next moves as text in the training data, I presume. Isn't it far more likely that the LLM has memorised the well known algorithms for solving a Rubik's Cube and has become intelligent enough to execute them? That seems like it'd be a lot easier than memorising millions of cube states. It doesn't eve…
Seems likeliest that it didn’t even “memorize” anything, in the anthropomorphic sense. The Rubik’s cube algorithm is trivially representable in code, as long as the interface for interacting with a cube is well-designed / well-defined. I’m no more surprised that an LLM can solve a Rubik’s cube than it can send an HTTP request.
What changed between Opus 4.6 and Fable and the GPT 5.6 models released since?
LLM models cannot actually reason about a red or white piece sitting on the opposite side of the cube or figure out how to move it into place. The model knows where the piece is supposed to go because the algorithm tells it. What it cannot do is work out on its own which turns will get the piece there. The only way an LLM could solve this kind of problem is if it were trained on every possible arrangement of the cube ahead of time. Then it could simply output the matching text instructions it memorized instead of truly thinking through the moves.
3 months ago before the most advanced models could solve the cube, people on Hacker News kept saying that solving the Rubik's Cube with LLM is easy. I would love to see someone write a prompt using the best model at that time, Opus 4.6, that solves the cube! People are so sure of themselves without any evidence. It shows how much people idealize (that is probably the correct word) the AI. Of course, reinforcement learning can solve it which is what has happened on the latest models but so many people put blind faith into the AI.
Here is just a small list of prompts I tried with Opus 4.6. [0]
[0] https://github.com/adam-s/rubiks-cube/tree/main/prompts/vari...
I had this idea for an LLM that would play Sim City 24/7 while broadcasting live. It would be fun/interesting to check in now and then. Implementing this would be somewhat challenging.
Someone was building a similar one where AI agents run economies. I feel like it's a great way to quickly prototype different economic models and their effects. Eventually we could have live demos of policy interventions the same day as they're announced
I've been doing almost exactly this with Mafia (aka werewolf): running games where LLMs play against each other, with humans able to share the lobby. It's been a hit on family game night so far, because it allows our group to "fill out the cast" with a lot more characters than just the human players and add a lot of variety without needing a huge group. My experience is that text-first, turn-based games are a particu…
10 AIs Play Mafia
That’s even sillier than watching another human play a videogame for entertainment (twitch streaming).
The LLM does not have fun, because the LLM is not alive.