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I trained a sprite model with agents. The data was the bottleneck.

DEV Community·Daniel King·27 days ago
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I just published pixel-llm , a small autoregressive transformer that generates 32x32 pixel art sprites of reef sea creatures. About 2.9 million parameters, a 64-colour palette, runs on consumer hardware. Built end to end through agent sessions, with me steering rather than typing. The output is sub-par. I am sharing it anyway, because the way it failed taught me something I did not expect. The setup was narrow on purpose. I picked sea creatures because the visual vocabulary is constrained: a few zones (shallows, twilight, midnight, abyss, hadal) and a few categories (reef fish, grazer, coral, jellyfish, cephalopod, plus an abyssal catch-all). A small, well-defined domain felt like the right shape for a small model. Six categories, five zones, thirty cells in the grid. Tractable on paper. The model itself fell out fast. Agents wrote the transformer, the KV-cache inference loop, the sprite breeding via partial completion, and the post-process palette-aware shader. That last piece is the strongest output.…

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