Using LLMs as Recommendation Engines: Finding Books by Character Traits
devanshmehta · x · 2026-09-29
The author shares a novel LLM use case: after reading The Goldfinch, they asked an LLM to grasp the protagonist's "self-destructive dragon" and recommend another book whose protagonist has an "impatience dragon" instead. This kind of semantic matching on abstract character traits is impossible for traditional recommenders, highlighting LLMs as open-dimension recommendation engines.
More from Apps
- Open-source xvr AI aligns live X-ray with 3D CT at submillimeter accuracy, published in Nature — Dr_Alex_Crimi · 2026-09-29
- Seth Rosen pitches structured agent memory over markdown — and Notion's CEO coyly responds — ivanhzhao · 2026-09-29
- Notion ships column permissions as CEO vows databases built for humans and agents alike — ivanhzhao · 2026-09-29
- Dev builds a personal status page: live Oura data answers "how are you?" — TejasKumar_ · 2026-09-29
- Manus 2.0 turns agent into a platform: video editing, multiplayer games, phone-controlled agents — The Decoder · 2026-09-29
- Topaz Labs ships faster video upscaling on web, SDR-to-HDR in Premiere, and Android debut — azed_ai · 2026-09-29