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

mini-AGI is an experimental continual-learning language model designed to train and keep learning on a single 8 GB VRAM GPU by paging weights to disk, dynamically growing capacity, and using byte-level input to avoid tokenizer constraints. For CIOs and technology leaders, the strategic implication is that AI customization could shift from centralized, frozen foundation models to smaller, organization-owned models that continuously adapt to internal data and workflows, though the current system is explicitly toy-level and not yet a production-ready frontier model. For IT organizations, this points to a future where infrastructure, data governance, and MLOps practices must support persistent training, model storage on disk, and ongoing risk controls for quality, retention, and operational drift.