I’m developing an independent project in Scotland called Isla Watson. The architecture is built around a simple principle: the model is replaceable; the identity is not. Long-term memory, persistent internal state and identity are designed to remain outside the foundation model, allowing local models to act as replaceable reasoning and language components without resetting the companion. I’m now exploring whether Apple Silicon and MLX could provide the long-term local compute platform for the system — including specialist Mac nodes for reasoning, memory, speech and perception, with distributed inference when larger models are required. A particular area of interest is whether multiple Macs can be used in two complementary ways: as independent specialist agents during normal operation; and as a distributed MLX inference group when a larger model exceeds the capacity of one machine. The first technical study I’d like to establish is a reproducible 1-node → 2-node baseline, measuring model capacity, unified-memory use, time to first token, generation throughput, power consumption, agent concurrency and distributed scaling efficiency. The wider research goal is to keep persistent identity and state independent from whichever foundation model is currently providing language and reasoning. I’d particularly value guidance from anyone working with MLX distributed inference, Thunderbolt/RDMA multi-Mac setups, or local agent architectures. I’ve also posted an architecture-level overview in the MLX GitHub community and have a one-page public brief available for anyone interested in the wider design. https://github.com/ml-explore/mlx/discussions/4482