Model selection
Choose authorized local or external models according to task, privacy settings, and available resources.
AURA Mind is being designed to coordinate models, memory, agents, and available computing resources in one permission-aware workspace.
Choose authorized local or external models according to task, privacy settings, and available resources.
Scoped memory references help preserve work without indiscriminately exposing private history.
Future hybrid scheduling for local GPUs, CPUs and other user-approved devices.
Carry task goals, evidence, blockers, and review status between permitted tools.
Designed to measure total work, verification quality, costs, and regressions—not just tokens per second.
Approval controls should determine what can cross model and service boundaries.
An architectural direction—not a diagram of deployed services. The user sets permissions around a proposed coordination layer and each connected capability.
All platform capabilities shown here remain planned or experimental. See the milestones and current status.
Concepts map to private workspace and expanded compute milestones.
A user-approved task brief for goals, constraints, permissions and evidence carried across authorized handoffs.
Research into choosing compute based on quality, latency and cost, subject to user settings and approval.
Long-term evaluation research for measured system improvement; no self-modifying production system is implied.
Cloudflare serves this public website. Any future private workspace or compute service would have a separately governed backend; website hosting does not imply access to AWS credentials or private data.