I foresee this laying the foundation for whole football stadia filled to the brim with people wanting to watch (and bet on!) competing teams of AI trained on military tactics and strategies!

Soon enough we shall have AI-Olympics! Imagine that, MY FELLOW OXYGEN CONVERTING HUMAN FRIEND! Tens of thousands of robots and drones, all competing against each other in stadia across the planet, at the same time!

I foresee a world wide, synchronized countdown marking the beginning of the biggest, greatest and definitively most unique, one-time-only spectacle in human history!

Keep up the good work!

I find this pretty funny because it seems like a perfect representation of what's easy with today's tools and what isn't

Love the idea though

I was proud for getting the highest-ranked JavaScript-based implementation, but got absolutely crushed by the eventual winner.

1. https://github.com/aichallenge/aichallenge

Curious how isolated-vm actually enforces the boundary in practice. isolate-vm is solid for JS isolation, but I'd want to know whether the cheating attempts were happening at the JS level (accessing globals it shouldn't) or whether models were trying to inject something into the game runner itself. Those are very different attack surfaces.

Also - is the ladder single-match or do you average across multiple runs? The variance in LLM outputs over 200 turns feels like it would make a single match pretty noisy. Would be interesting to see confidence intervals on the rankings rather than a single leaderboard position.

It reminds me a bit of OpenAI Five — not just because it played a complex game, but because the real value wasn’t “AI plays Dota,” it was observing how coordination, strategy formation, and adaptation emerged under competitive pressure. A controlled RTS environment like this feels like a lightweight, reproducible version of that idea.

What I especially like here is that it lowers the barrier for experimentation. If researchers and hobbyists can plug different models into the same competitive sandbox, we might start seeing meaningful AI-vs-AI evaluations beyond static leaderboards. Competitive dynamics often expose weaknesses much faster than isolated benchmarks do.

Curious whether you’re planning to support self-play training loops or if the focus is primarily on inference-time agents?

https://egeozcan.github.io/unnamed_rts/game/

I occasionally run my tournament script: https://github.com/egeozcan/unnamed_rts/blob/main/src/script...

That calculates the ELOs for each AI implementation, and I feed it to different agents so they get really creative trying to beat each other. Also making rule changes to the game and seeing how some scripts get weaker/stronger is a nice way to measure balance.

Funny thing, Codex gets really aggressive and starts cheating a lot of times: https://bsky.app/profile/egeozcan.bsky.social/post/3mfdtj5dh...

The largest winner having 50 wins against 14 other opponents for instance). That guy adding a new script would instantly plummet down the leader board capping out at 14 wins again, Putting it below the 2nd place user.

The leader board will quickly become "who can have a mostly competent AI and never change it" over who actually has the better script.

I had youthful dreams of re-implementing something similar that would run on the Java Virtual Machine, where you could run the submitted robots via the debugger interface so you could keep "real-time" in the game environment more authentic. Ideas are cheap, follow-through is hard.

[1] https://corewar.co.uk/cpprobots.htm

[2] https://www.pbm.com/~lindahl/pbem_articles/cpprobots_environ...

I will just leave it here.

Opus needs to learn to kite.

Interestingly, I’ve had to create an entire category for games llms play. Strange times we live in.

Using an LLM friendly api with a snapshot of game state and calculated heuristics, legal moves, and varying levels of strategy in working out nicely. They can play a web based game via curl.

Edit: Forgot link: https://davechurchill.ca/starcraft/

This would bring another dimension to it since then quality of tokens would be one dimension (RTS-language: Decision Making) and speed of tokens the other (RTS-language: Actions Per Minute; APM).

Also there are a lot of coding benchmarks, that way it would test something more abstract, similar to AlphaStar https://en.wikipedia.org/wiki/AlphaStar_(software)

You could just use the exposed APIs of OpenAI, Anthropic etc. and let them battle.

I quite like the idea of llms writing more code up front to execute strategies.

I’m currently developing the game mechanics and ELO. Please share anything relevant if it comes to mind

And what is the game state here exactly? Is LLM able to even perceive game state? If game state is what we can see on UI, then it seems pretty high-dimensional and token-intensive. I am not sure whether LLMs with their current capabilities and context windows can even perceive so token-intensive game state effectively...

Edit: Actually the repo README indeed says its inspired by Screeps. I don't know why they didn't just build on top of Screeps, maybe the idea is to have something anyone can pick up off the shelf for free?

Are these casters AI?

Maybe they are already doing this? Are there logs of the model's thinking?

This is just free propaganda for Anthropic && OpenAI who will leverage these (useless) capabilities to convince your boss to give your salary to them, or at least a substantial portion of it.

I swear people (esp here on HN) are actually blind to the weaknesses of Gemini.

I must be among the handful of people who know how thoroughly lobotomized any AI agent from Google must be given their extremely radical historical and contemporaneous practices of censorship.

I wonder if an LLM could call on another strategy AI to help.

Maybe the LLM could be more of a coordinator of its own thinking by incorporating other types of AI's.

I haven't.