Large models run on the Machine in front of you. Once a model is serving, the engine cannot open a network connection — the prompt has nowhere else to go.
Local judgments powered by jev-rs. Build 75 is available to internal TestFlight testers; the App Store release is being prepared. The current App Store version is 1.0.7.
Ask the local model yes-or-no questions, choose among named options, or rate text on an ordered scale. The Judgments tab shows the answers and their probability distributions.
Paste a customer message and ask which team should handle it, how urgent it is, and whether the customer threatens to leave. Define your own questions and labels to use the same workflow for your own text.
The engine scores answer labels from the model’s next-token distribution, with no generated response text. Multiple questions can reuse the shared text prefix. The probabilities describe the model’s answers; they are not measured accuracy.
Our Rust judgments engine, jev-rs, powers this feature and is available under MIT OR Apache-2.0. Developers can also request typed judgments through the Jev-compatible POST /v1/systemone endpoint on the local service.
Earlier releases are kept in full: 1.0.7, 1.0.6, 1.0.5, 1.0.4, 1.0.3 and 1.0.2.
Four decisions stand between a model file and a running assistant. The app makes each one explicitly, and tells you which choices your Machine can actually honour — a 594 GB model is offered, flagged, and honestly priced rather than hidden.
One command. It detects the accelerator, fetches only the matching binary, checks it against a published SHA-256, and installs the key:
Without a key it installs and runs the unlicensed path, so you can try it before buying one.
Piping a script into a shell asks you to trust what you cannot read, which is the opposite of everything else on this page. The same install, verifiable:
One key, every binary. The licence carries an expiry and the address it was issued to, and nothing else — there is no per-binary entitlement, so a key bought for one accelerator works on the others as they ship. It is checked offline against a public key compiled into the binary: no network is consulted, so it works on an air-gapped machine, and it stops working the day after it expires without needing to phone home. Keys come from peria.ai, emailed on purchase.
Enforced, not promised. Once the engine has loaded weights and bound its port, it asks the kernel to revoke its own networking — irreversibly, for the life of the process.
Check it while it serves:
lsof -i -a -p <pid>
1.0.3 adds one door, and it is locked by default. Serving others binds the LAN instead of loopback — so it needs a key you minted, it is refused without one, and it switches itself off again at the next launch. Nothing you do not turn on this session.
The app writes each agent's config and points it at the local engine.
dsh)newEvery run reports time to first token, prefill and decode rate — beside the engine that produced them and the model actually served.
Two prompts: a short chat, and a ~3000-token passage that measures real prefill.
Rankings are links, not verdicts — 1.0.3 lets you add the leaderboard you trust beside the ones we shipped.
Every download carries its expected SHA-256. A mismatch is refused rather than loaded, and a partial file is reported as incomplete instead of failing later as corruption.
Local Mind is a small amount of new code on top of a great deal of open source. Named because a benchmark is meaningless without saying whose code produced it, and because these projects are the reason any of this runs at all.
The inference engine itself is not open. The kernels it runs are generated by tooling that is, which is the part worth reading.
reference option and used as the correctness oracle our own kernels are checked against.Each carries its own licence, shown in the app before download.