What Offline & Private AI Actually Means

Clawdbot and similar tools are a useful way to get ahead in understanding how LLMs work. What concerns me is how often "on-device" is treated as synonymous with "offline and private."

In default setups, your data is still:

• Logged and retained. More than often unencrypted.
• Stored at rest on third-party infrastructure.
• Likely used for training.

This is not unusual — it remains the quiet default.

Running models locally is not especially difficult if you stay within the OpenAPI pattern and use tools like LM Studio or equivalents. But local execution alone does not imply privacy, security, or control. You also need powerful hardware to do it.

At MLNavigator, Donella Cohen and I are building security-first systems designed for the highest possible level of in-house control.

• Offline by default.
• Private by default.
• Provable token accounting and deterministic behavior.

We're calling it "adapterOS."

Current systems can't fully explain inference end-to-end. Ours can, with a receipt.

Choose systems that let your data work for you without leaving your control.

That is what offline & private AI actually means.