Blog

Our philosophy regarding compute now, and in the future. Releasing our first 2 actually capable models.

Can tiny quantum neural networks outperform massive deep learning models? We don’t know (yet), but we collected everything you need to know about the topic, and through a set of experiments we explore the most promising application: PINNs.

A memory-efficient, single-GPU training and inference architecture for formalizing natural language mathematics into Lean 4 statement code using quantized Mixture-of-Experts, persistent Lean REPL compiler feedback, and dual-tier retrieval augmentation.