Local-first AI
Desktop and local workflows are treated as the center of gravity, with optional model plugins and isolated work areas.
Experimental prototype
Brain / AI Systems
Experimental local AI architecture for memory, learning, and model coordination.
Brain is an experimental local AI and learning architecture exploring persistent memory, multi-model coordination, async learning, and QELM-inspired systems.
Research boundary
Brain does not claim GPT-4+ accuracy or production-grade performance without benchmark evidence. It is an experimental architecture and roadmap for local learning systems.Conceptual architecture
Local learning systems with clear validation boundaries
Persistent memory
Memory concepts focus on continuity across sessions while remaining auditable, inspectable, and benchmarked.
Async learning
Background learning, scheduler behavior, and checkpointing are roadmap concepts that need clear validation.
QELM roadmap
Brain is positioned as a possible place for QELM-inspired learning systems, not as a finished super-model.
Model plugins
Multiple local or remote models can be coordinated as tools if the surrounding safety and data boundaries are clear.
Benchmark discipline
Claims should be tied to reproducible tests, evaluation sets, and visible limitation reports.