We benchmarked uzu013ai against GPT-4 and Llama-2-70B in two distinct environments:

The hardware environment consisted of a clustered GPU array (NVIDIA A100s) and, notably, a lower-end consumer-grade rig to test efficiency claims.

| ID | Requirement | Priority | | :--- | :--- | :--- | | UZU-001 | User can select any text segment and assign it an "Anchor" status. | High | | UZU-002 | Anchored segments are displayed in a dedicated sidebar ("The Anchor Hold") for easy management. | Medium | | UZU-003 | Users can edit or delete Anchors at any time. | High | | UZU-004 | The system indicates when an Anchor is influencing a response (e.g., a subtle highlight or tag). | Low | | UZU-005 | Anchors persist across session boundaries (optional "Long-term Memory" toggle). | Medium |

Current LLMs suffer from "context drift." In a session exceeding 50 turns, the AI often forgets initial constraints, style guidelines, or specific data definitions provided at the start. Users currently have to repeat instructions, which disrupts workflow and increases token costs.