Show HN: Mini-AGI – Dynamic continual learning model trained on 8GB VRAM

Mini-AGI is a continual learning byte-level language model that dynamically assembles its own architecture and trains on a single 8GB VRAM GPU. It manages weights by storing them on disk and paging them to VRAM as needed, allowing parameter count to be limited by disk space. The model can grow new capacity during training and prunes unused components. It uses the same forward pass for both generating and reading, with writing costing more depth than reading. Training on a single stream can lead to catastrophic forgetting, as seen when learning chess impacted other subjects.