Jevtown

a town of 10,000 personas computed from their id reads a post, listing, product or headline; one request asks Jev about 60 `Score` questions on who would care plus seven `Noul` moderation checks (0.5 keeps the text out of the public feed, 0.85 blocks it), batched `Choice` questions then return each persona's reaction in waves of 600, 1,500 and 3,000, and code sends the text to the next wave only while glad reactions outweigh sorry ones by at least 0.1 of the wave.

SCORE 호출은 이렇게 생겼다

이 질문 유형의 일반적인 뼈대이지 이 프로젝트의 실제 코드가 아니다.

from typesafe import TypeSafe

ts = TypeSafe()
result = ts.evaluate(
    state=candidate,
    questions={
        "relevance": {
            "type": "score",
            "levels": ["low", "medium", "high"],
            "instructions": "How relevant is this to the query?",
        }
    },
)
if result["relevance"] >= 0.7:
    keep(candidate)