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I fine-tuned GPT-2-XL with LoRA to generate playable levels for my Bloxorz-inspired puzzle game (Mindcraft).

Based on the "Level generation through large language models" paper (NYU, 2023) which did this for Sokoban. I adapted their approach to work with block-rolling puzzles.

The interesting part: I didn't give it any solution data during training - just level layouts and metadata (grid size, move count, gimmick types). After 10k steps, it generated 22% valid+novel levels. With 50k steps on levels with glass tiles, that jumped to 64%.

The model learns what makes a level solvable just from seeing enough examples. It's not perfect (grid size accuracy is low), but the generated levels work in the actual game.

Trained on RTX 4080 (16GB) using LoRA to keep it feasible on consumer hardware.

Hello.

I teach students at a university. Recently, in my JavaScript class, I wanted to explain mouse and keyboard input in a visual way, but I couldn't find a suitable example.

So I created one myself, and I'm sharing it here in case it might be helpful to you as well.

Please test it out and let me know if you have any feedback.

Thank you.

I didn't realize that! Thanks for the clarification.

Yes, that's right. I'll have to update my blog with additional information about this. Thank you!

Thank you. I didn't see that!

Hello Hacker News!

Thank you for your interest in my previous post. This time, I've written a blog post about the game and the process of creating it.

In the original Minesweeper, there are inevitable 50/50 moments where you have to rely on luck. In the game I created, 'Explainable Minesweeper,' I eliminated these guessing situations. However, I also prevented the maps from becoming too easy! How? By using logical deduction, you can solve puzzles that initially appear to be luck-based. The blog post explains the process in more detail.