An implementation of Tak and a computer opponent in C https://git.l-3.space/ctak/atom/compile_commands.json?h=hybrid_rootstrap 2026-09-02T11:13:01Z 2026-09-02T11:13:01Z tslil tslil@posteo.de 2026-08-30T18:46:07Z urn:sha1:241156da6f35cb888253e6b8814e7b61a76b4fc7 NOTE: 6x6 weights are as-yet untrained On the performance side: * switch from linked-list chaining hash table to direct mapping * cache check_win by hash * persist transposition tables across plies (this was a big deal and difficult to puzzle out) * add align directives to the network floats * prepare_nn_input gained a small LUT to avoid branching (i suppose you might call this 1D embedding) * apply mtune=native for the native builds On the correctness side: * Zobrist has now includes the ply parity * the network now predicts something in [-1, 1] using tanh (approximated by polynomials) * the training object is now MSE because we're learning a value estimate * use .clangd instead of compile_commands.json On the training side: * completely forgo human-derived data set * implement td(lambda), self-play, tournaments 2023-01-15T15:03:37Z tslil clingman tslil@posteo.de 2023-01-15T15:03:37Z urn:sha1:81213c273fd874aba95c45bd692e14079ebce434 Gone is the convolutional neural network, for it turns out not only is it more difficult to train, but all of the extra information about board layers didn't make much of a difference at this size. So cnn1986 has been replaced by nn1986, a standard, two-layer, dense nn configured as a binary classifier and (mis)used in that capacity. Note: total number of parameters is unchanged. HARK: this new nn exposes a bug somewhere in ctak. Run ctlm with self-play to see the completely borked board state at the end.