One app.
Your machines, connected.

A model runs on your computer.

Pick a model from the library and download its weights. Potluck estimates what fits your hardware and verifies the download before installing it. Your CPU or GPU then generates the answer locally.

After the model is installed, local inference can work offline. No account is required for local use. Model downloads, update checks, optional account features, and network routes still need a connection.

The other computer can do the work.

Pair computers you own inside the app. Potluck includes a WireGuard mesh: an encrypted connection between those machines, with connection setup and diagnostics built in. You do not need a separate VPN account.

1. Install Potluck on each machine and pair them using the in-app pairing flow.
2. On the computer that will answer, load the model and enable serving from the Network view.
3. Check that the machine is reachable and ready, then select My Machines as the routing scope in chat.

The request can be served by an eligible paired machine with the requested model loaded. The model runs on the serving machine; ordinary routing does not merge the memory of every GPU into one pool.

WireGuard protects traffic between the machines. The computer generating the answer necessarily processes the prompt in plaintext. Only pair machines you control and trust.

Your context survives the session.

The bundled Model Context Protocol (MCP) server lets compatible coding tools save and retrieve project facts, preferences, decisions, and active tasks. Your coding agent stays in the tool you use; Potluck supplies the memory.

In Settings → Connect tools, choose a client and copy its generated configuration. Restart that client, then ask it to save a project fact. You can inspect and edit the result in Potluck’s memory browser.

Local chat selects relevant saved context. You can pin a memory to keep an applicable standing instruction in retrieval. Recognizable credential patterns are redacted, but the memory store is not a password manager.

The agent activity view displays tool-call metadata. It is an observation surface, not an agent runtime or a log of external agents’ raw prompts.

A wider network is the next step.

The intended routing scopes are your machines, an invited trusted circle, and an open contributor pool. The current desktop release supports local use and your own paired machines. Trusted-circle inference and a dependable open contributor network are still in development.

The coordinator code includes blinded job dispatch: account identity is left out of the job sent to a contributor. That is one part of the design, not proof that the full public service is ready.

Contribution credits, shared-network pricing, and member governance are future work. They are not required to run the local app. The roadmap tracks the remaining work.