The point of all this
No one else in the loop
Yours forever. No one can take it away. This page is the long form of that sentence — what you actually escape when the intelligence sits on your desk instead of someone else's datacentre.
The meter
Rented intelligence is billed per thought. An agent that loops is an agent that spends, so you ration it. Owned intelligence inverts the incentive: saturation is the goal, and an idle machine is the only waste.
The reader
Everything you send a provider transits their servers, lives in their logs under their retention policy, and is theirs to produce when lawfully ordered. Not malice — architecture. The only prompt nobody else can read is the one that never leaves your desk.
The kill switch
A ban, an outage, a deprecation, a terms update — capability you built a business on, revocable by email. The model on your machine ships with the machine. No one can take it away, because there is no one to take it.
The chat laws cannot reach your desk
This is not a hypothetical.
When regulators order a provider to scan or hand over conversations, the provider complies — that is what the law compels, and no privacy policy overrides a lawful order. Every cloud AI conversation exists somewhere a subpoena can go.
Nobody can compel us to hand over what we never had. Your chats live on your machine, on your premises, under your jurisdiction’s protections for your own property— a categorically stronger position than a provider’s promise.
Nothing to scan. Nothing to surrender. (So sorry.)
Privacy by architecture, not policy
A policy is a promise. An architecture is a fact.
No provider logs — because there is no provider — inference happens on silicon you own.
No training on your data — because no one else ever holds it. Not opt-out. Absent.
No retention policy — because retention is your filesystem, under your control, deletable by you.
No sub-processor list — because the data-processing chain has one link: you.
Questions, answered straight
- Is this legal? The models are uncensored.
- Running open-weight models on your own hardware is entirely lawful — the weights are published under licences like MIT and Apache-2.0. What you do with any tool remains your responsibility, same as a word processor. What changes is that no provider sits between you and the model deciding what you may ask.
- For regulated industries, does local actually satisfy compliance?
- It removes the hardest parts: no third-party processor, no cross-border transfer, no DPA negotiation, no sub-processor audits. Inference on premises is the cleanest possible answer to a data-residency question. Your regulator's specific requirements still apply — but the audit becomes 'here is the machine, nothing leaves'.
- What about the machine phoning home?
- The stack we install runs offline — model server, agents and chat app work with the network cable unplugged. OS and driver telemetry follow NVIDIA's DGX OS settings, which we walk through at handover so you decide what, if anything, reports.
- If DeepSeek's API is so cheap, why not just use it privately?
- Cheap, yes — private, no. API inference runs on the provider's servers under the provider's jurisdiction with your prompts in their logs. The machine is how you get the open-model price and the privacy at the same time. That combination is the product.