Beacon

When the river crests and the towers go dark, a hundred people end up stranded in a school gym with no signal and no way to call for help. A volunteer nurse faces a growing line of the sick and injured with no one to consult. A teacher manages sixty frightened kids alone. A family doesn't know if their water is safe to drink. Every one of them is holding a phone with a powerful on-device NPU, but cloud AI dies the instant the network does, and no single phone has the memory or compute to run a frontier-grade LLM by itself. Beacon is built around this constraint from the start: the model is pre-sharded before disaster strikes, not after. Users opt in ahead of time, downloading a layer-wise slice of a large language model's weights onto their device, a contiguous block of transformer layers sized to that phone's available memory and NPU class. These shards sit dormant on the device, costing nothing until they're needed. When the network goes down, phones nearby connect over a peer-to-peer hotspot network: one phone hosts, others join directly, with no router or internet infrastructure required. Beacon assembles an inference cluster from whichever pre-loaded layer shards happen to be present in the room, sequencing them in the correct layer order for a forward pass. The hotspot link only needs to negotiate which layers are available, route activations between phones in sequence, and reroute around a phone that drops out or runs out of battery. The heavy lifting, distribution, was done in advance, when everyone still had a connection. The result is a cluster that can assemble in seconds during an emergency, because the only real-time job is discovery and coordination, not download. The nurse gets triage guidance. The teacher gets crisis-management support. The family gets a real answer about their water. The help didn't arrive; it was already pre-positioned in their pockets, just waiting to be switched on.