PSSA is a non-transformer language model written in Rust from scratch that uses recurrent state-space layers, episodic memory, and plastic weights instead of attention. On matched parameters and corpus, it learns faster and generates text 12 times quicker than transformers, achieving better generalization with lower perplexity on held-out data.