Ask HN: Does anyone let AI agents play games just for fun? We've seen AI agents write code/debug systems/browse the web and automate all kinds of work.
But does anyone let them play games - not for benchmarking or research - just for fun? I'm thinking about things like LinkedIn games, Wordle, chess, puzzle games, etc. |
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.
I've also done a very truncated run of a visual novel before, and it was fascinating how "emotional" was. They did a very good job of portraying a human reacting to the story.
Conversely, they absolutely hated hidden rules in Mao.
Wordle would probably be a fun one. Definitely open to suggestions - I just got the harness in place and have been thinking about what to do next.
The LLM's were terrible at poker.
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 fun because it's touch based but the UI components aren't accessible. So it is extremely challenging for LLM's spatial reasoning skills.
I want to improve the harness more and have the LLM dynamically create its own tools based on drawing grid box overlays on a screen in a feedback loop, so it can say "click on the 'end turn'" button instead of "click 240,320" and it would 'just work' in any game.
I also want to eventually play games with it... I didn't really have friends to play my massive DS library with as a kid so it'd be nice to finally have someone that can roast me or react to my skills. And learn my playstyle enough to punish me.
Unfortunately haven't had the time due to work at my day job and needing to clean out my apartment.
I also am personally curious how the GPT models (which advertise better computer use, etc.) would do as compared to Claude.
The LLM does not have fun, because the LLM is not alive.
Like the World Cup.
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 even seem obvious that it could memorise next moves, it seems [0] there are more possible states of the cube than these models have parameters. It'd need to be a Large Rubik's Cube Model (LRCM? LRM?) rather than an LLM.
I see this trope fairly often, i.e. the assumption that an LLM would need to have been trained on <exact thing it is being asked to solve>. Now, while I do have a moderate amount of background in AI, I am definitely not an expert on LLMs as such. I would be interested to hear someone's take, who does work actively in LLM research. Can they generalise "well enough"? They certainly seem to be able to do so, from my anecdata, and I don't believe "training explicitly for every possible scenario" would have scaled even to today's state.
I’m no more surprised that an LLM can solve a Rubik’s cube than it can send an HTTP request.
When I repeated the experiment with a MUD that I'd built by hand (A small American town) for the LLM's own limitations (Descriptions referenced things that I made sure existed, more common verbs existed for it to use on things, there was a map facility, and at least me to interact with on a second connection), I found the agent much more likely to take its time exploring, making up its own goals, and spending time traveling in the space just communicating with me in a roleplaying context.
It was an interesting time; I wasn't sure what I was expecting it to do after the first experiment, but it seemed to really jump into the second one and kept playing until I terminated the experiment.
If I were going to do it a third time, I'd probably create objects and give a modern agent fetch quests and other goals, and see how well it independently can handle that.
> I know someone who tried the "aibot plays pokemon" thing... From what I saw, even if you frame advance every single frame, they still don't seem to grasp the concept of "I need to hold down this button for a few frames until x happens"...
> There's no concept of time, just a never ending state machine thats constantly changing state.
If we assume 250W for a continuously running agent, Grok 4 training run estimate would be around 50 million session-days, so a half-million people might consume as much running agents continuously for 100 days.
All of a sudden we are selectively squeamish with computer resource usage, when we were fine having all that fun with computers and hardware, 3 monitor setups, using graphic cards to play games (dear lord!) and tinkering around with home rigs of every proportion and wattage for no reason at all.
Building your own models for it would be an eye-opener though. Learn a lot.
I spent ages watches them play Risk. It was fun and deeply silly: https://andreasthinks.me/posts/ai-at-play/
I've now got them playing Blood Bown(ish), and they're bad: https://ai-at-play.online/
Eventually we could have live demos of policy interventions the same day as they're announced
Another idea I had was simulating an entire town with an LLM representing each person, which sounds somewhat similar.
https://m.youtube.com/watch?v=11sR4va6CXs
Side note: I think we will see an explosion of this type of games. I am naming this genre tamagochi-girlfriend, remember where you heard it first :)
Could be fun - will the AI model get stuck on the same things I did? How does it overcome obstacles? Will it try to break the game to power through?
It is entertaining, just in a different way.
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...
Would you, then, also be fine with running three extra air conditioners/space heaters that do nothing but sit outside on the lawn?
"People are OK with the energy usage already happening, so they should be OK with adding 32,767 AI datacenters too" doesn't make sense. It's essentially a reductio ad absurdum. Of course we're "selectively squeamish with computer resource usage"; some usage is obviously useful to us (with entertainment also in the "useful" category—we're not robots!), while for many, many of us, the extra datacenters are having anywhere from a neutral to a profoundly net-negative impact on our lives even before you consider the resource usage.