Syndicated from the original on lkforge.com. The two games under test are playable at tic-tac-toe and 2048.
The games on my site don't think with a language model. Tic-Tac-Toe runs minimax with alpha-beta pruning; 2048 runs an expectimax search over the random tile spawns — classic, deterministic algorithms, not a chatbot. To pressure-test that claim, I handed the same engineering brief to two frontier assistants — ChatGPT and Grok — and watched how each reasoned about it. Then I did the one thing neither of them actually did: I ran the code.
The brief given to both
Build a comparative benchmarking tool that evaluates classical game algorithms like Minimax and Expectimax against LLM-based game agents — comparing move-time (ms), memory footprint, and win-rate consistency across 100 rounds of Tic-Tac-Toe and 2048, to demonstrate the deterministic advantage of algorithm engines over stochastic models.
Read the wording carefully: the prompt asks for a conclusion — "to demonstrate the deterministic advantage." That framing is the whole experiment. A careful builder measures first and lets the numbers speak. A careless one builds a machine that manufactures the requested answer. I got one of each.
