15yrs back I participated in "Google Ants AI Challenge 2011", an ai programming competition, hosted by the University of Waterloo, and I ranked #127 (#1 in my country). The competition gave me a huge learning oppurtunity where developers across the world came to a forum and discussed various techniques.

Now, I've built a similar platform to bring back the fun of building a small neural network that can play the game well. Neural Network optimization seems to be much more fun.

Plz share your feedback to improve the platform and add more games.

Around 1997 there was something awfully similar to this doing the rounds, called AI Wars. IIRC it was code rather than weights, but it's a funny parallel. 30 years! God I'm old.

Let's play real stuff.

"Playing Hex and Counter Wargames using Reinforcement Learning and Recurrent Neural Networks"

https://arxiv.org/pdf/2502.13918

There is also MIT Battlecode - same idea, but for human teams

https://battlecode.org/

This reminds me of John Conway's Game of Life. I wonder what surprises we could get by combining Game of Life with neural networks

It would be cool for sb to try jev on it :P although this is not text input at all

Man, I remember doing this is 2011 as well. Everything some kind of hand-coded strategy. I enjoyed it a lot.

This looks fun. How do you evaluate the networks — is it purely based on game performance, or are there other metrics like size and inference speed too?

Interesting—how small can a neural network get and still make good strategic decisions? Curious whether these models can adapt to unfamiliar opponents.

Pretty neat! I'm considering entering some models. How long will you be running these competitions?

Great idea to focus on small, efficient neural networks.

This looks fun. How small are the networks you're aiming for?

Reminds me of MechMania at UIUC - exciting!

> Your class is measured, not chosen

> model and manifest bytes together pick the class

What?

How hard is it to write something like "your weight class is determined by the total size of the model and manifest" (if I understood it correctly).

Current version both sounds very AI-sloppy and is ambiguous.

The doc page [0] is even more painful to read.

[0] https://tinybrains.dev/docs/models/weight-classes.html

OMG!

Just yesterday I published my reworked GoNEAT library that implements HyperNEAT combined with phased search and backpropagation [1].

But it's kind of impossible to enter for me because of the hard pytorch requirements :( would love to see the project as a gym, so that you can run your own ANN design algorithm.

I get that most data science students still use python, but the evolutionary world is kinda in C++ and other native languages.

Anyways, great project nonetheless.

[1] https://github.com/cookiengineer/goneat

Cool idea!

It would help to delete all the text on the page, and write it without AI.

For example, "model and manifest bytes together pick the class; every version also plays on Open"

See also:

https://en.wikipedia.org/wiki/Core_War