❯ Looki launches Loom to turn first-person life recordings into traceable long-term memory

Recordings become usable contextLooki introduced Loom, a multimodal model family that organizes device-captured images, sounds and timing into events, experiences and long-term memory. Its intended use includes recalling an earlier decision and its conditions when a discussion resumes days later, rather than merely remembering that a meeting occurred.

Three layers divide the workLoom Sense identifies current activities and allows overlapping states such as eating and talking. Loom Moment builds experiences with time boundaries, descriptions and supporting evidence. Loom Memory searches through monthly, weekly and daily summaries and can return to original images, tying an answer to specific records.

Internal tests cover quality and processing costsLooki reports an F1 score of 88.6 across nearly 20 event classes, average detection latency of 0.51 seconds and roughly 94% lower inference costs than the selected closed-model reference under its assumptions. In its consent-based, anonymized long-duration audiovisual memory evaluation, Memory scored 79. Average input-plus-output tokens per question were 14.5% of mem0’s and 30.5% of A-Mem’s.

Proactive assistance remains a development goalLooki proposes a more end-to-end architecture that combines current circumstances with recalled evidence to decide whether to remind, ask or stay quiet. Storage, encryption and permission isolation remain system responsibilities, and the website and features continue to evolve. Remembering experiences and choosing when to intervene are distinct challenges.

▮ SIGNALBetter memory organization can reduce personal AI’s operating costs: locating relevant experiences before processing evidence avoids repeatedly rereading life recordings and makes answers easier to trace.