Testing classification assistance without handing policy decisions to a model
DiskDrive is the persistence layer, not the model or app itself. It gives a DiskDrive operator evaluating deterministic decision assistance a separate place for finished files, structured records, and explicit memories that should outlive one session. Each connected AI receives its own authorization and only the scopes the user approves.
An honest boundary
Connection does not mean unlimited access
An API or MCP pathway is only the technical beginning. A real connector still needs authentication, tenant isolation, permission checks, audit events, rate limits, and revocation. DiskDrive does not claim an app is connected merely because its name appears in this research library.
A believable everyday use
What this could look like in practice
A user asks an assistant to suggest possible memories from a project summary. An experimental Jev classifier may help identify candidates, but the user still accepts the memory and deterministic policy still controls who can read or write it.
1
Choose what is durable. Save finished source material, reviewed records, or a fact the user explicitly wants remembered—not an entire private conversation by default.
2
Authorize separately. Give this client the narrow read, search, or write permissions it needs. Write access never silently implies delete access.
3
Keep provenance. Preserve who created an item, where it came from, and what later corrected or superseded it.
Primary source
Check the provider’s current documentation
Product names, model availability, APIs, and MCP capabilities can change. This page links to the primary material used for the compatibility description.
DiskDrive is not affiliated with or endorsed by TypeSafe Jev. Product and company names belong to their respective owners. This page explains a possible data-portability workflow and does not promise a native connector unless the status above explicitly says one is available.