Speak in sentences compiled from decisions

01 · Role in the system

What Jev does here

Each turn, one request asks Jev roughly thirteen parallel questions - intent, topic, emotion, which speech act fits, which way a judgment leans, which finite claim, what caveat, what tone, how long - each answered as a distribution over finite options plus scores for confidence and emotion intensity. Those answers normalize into a typed Semantic IR, and a deterministic compiler assembles the reply from layered phrase banks: acknowledgement, short answer, claim with hedge, qualification, follow-up. Confidence is made visible in the wording itself - below 0.5 Jev declines or asks which part you mean, 0.6 becomes "I think", 0.97 becomes "yes" - so the model cannot bluff. The README's cleanest evidence that language is only a rendering layer: switch the locale pack and the same decisions come out in Chinese, with nothing about the questions, IR, or planner changed.

02 · Control boundary

Where Jev sits

One batched request of parallel questions per turn; answers normalize into a typed Semantic IR and a deterministic, locale-swappable compiler renders it - templates decide wording, the IR decides meaning.

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

• Templates decide wording, so what can be said is bounded by the phrase banks and the IR vocabulary.
• Conversation memory is structured state over the last N turns, not open-ended recall.
• The README states no generative model runs in the reply path; the author used Claude as a development tool to build the codebase.

04 · Attribution

Public sources

This is a Community record: the project was published by a third-party community author.

• MM-sheng ↗Community · github · public · checked 2026-09-20
• MM-sheng ↗Community · demo · public · checked 2026-09-20