Community · E2 · artifact verified
Score a whole novel by emotion, one passage at a time
Book Aurora sends every ~90-word passage of Frankenstein to Jev as ten parallel score questions - nine emotions plus overall intensity - and draws each answer as one feathered row of a full-book aurora strip.
01 · Role in the system
What Jev does here
Each passage becomes a fitted state carrying ten parallel typed questions: a 0-3 score for each of nine named emotions, plus overall intensity. Jev never sees pixels and never writes prose; it returns scores with calibrated probabilities, and the app draws one feathered row per passage, each emotion a curtain whose width and brightness follow its score. The author reports the whole 601-passage read - 6,010 judgments - finishing in 24.7 seconds for about three cents, with six requests in flight and a duplicate hedge fired when one is slow. When the book is done the strip is the book's emotional weather, exportable as a poster.
02 · Control boundary
Where Jev sits
Passages go out as fitted state with ten parallel score questions; deterministic code renders the returned scores as the aurora and owns concurrency, request hedging, and timeouts.
Code owns the loop, permissions, thresholds, validation, and side effects. Jev owns only the bounded judgments described above.
03 · Known limits
What this evidence does not prove
- The 24.7-second and three-cent figures are author-reported from the author's own run and were not independently reproduced here.
- Requests can go through OpenRouter (model typesafe/jev-1.13) or the native TypeSafe API; the README prices the OpenRouter path.
- Only public-domain texts ship with the repository; the server binds to localhost and refuses cross-site reads because every read costs money.
04 · Attribution
Public sources
This is a Community record: the project was published by a third-party community author.
- dani1005 ↗Community · github · public · checked 2026-09-20