jevgrep (allebee)
filters logs and other text streams, including live `tail -f` output, by asking Jev one Noul per line against a plain-English question and printing lines at or above a probability threshold, with a hand-labelled benchmark against Claude in the repository.
WHAT IT DECIDES
CHOICE
Picks one option from a fixed set
SCORE
Rates against ordered levels
NOUL
Answers a yes / no question
Inferred from this project's own one-line summary in the community list — it reads as a SCORE decision. Read the source to confirm.
THE SHAPE OF A SCORE CALL
A generic skeleton for this question type, not this project's actual code.
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)MORE IN Data Labeling & Curation
jev-curatesifts synthetic JSONL and Parquet rows using Jev Noul checks and calibrated confidence scores, streaming passed records and rejections straight to disk.jev-align (Sutro)evaluates CSV, Parquet, and JSONL rows with Jev `Choice`, `Score`, or `Boolean` decisions, sends ambiguous and audit samples to a human, and uses accepted human labels to optimize the saved definition with GEPA.typeful-triagemultiplayer triage dashboard where Jev answers a fixed set of typed questions per issue — kind, severity, urgency, duplicate, and next step — and every human correction is kept and shown back to the model on later runs.jlinklinks records under a plain-English match rule using Jev Noul pair judgments, with local candidate blocking and match resolution.