Github Trends®
9594 findingsmedian surprise 0.00639window 30 days
UNIT / TREND-MONITOR · REV 2.6
[ 30 days window ]
SOURCE: own snapshots
FINDING #1532 · UNIT ID 809767236
dimastatz/whisper-flow
Whisper-Flow is a framework designed to enable real-time transcription of audio content using OpenAI’s Whisper model. Rather than processing entire files after upload (“batch mode”), Whisper-Flow accepts a continuous stream of audio chunks and produces incremental transcripts immediately.
[ PYTHON ][ GITHUB ↗ ]
SURPRISE SCORE
0.00

Score Breakdown

SURPRISE0.022
ENGAGEMENT0.31
FRESHNESS1.36
SCORE = SURPRISE × ENGAGEMENT^0.7 × FRESHNESS × VISIBILITY × CONFIDENCE
SURPRISE = WINDOW STARS / DAYS / (AUDIENCE + FLOOR)
12% OF STARS IN ARCHIVE

Growth Telemetry

VELOCITY /D
3.73
ACCEL
+0.43
RETENTION
15.7%
PEAK 2026-08-16 · FORK-RETENTION 50.0% · 112 STARS / WINDOW

Author Audience

AUDIENCE
130
FOLLOWERS
34
OWNER ★
955

Engagement Signals

FORKS
127
ISSUE AUTH
0
PR AUTH
0
UNIQUE STARGAZERS 112 / 112 (DIVERSITY 1.00)

Why This Is A Finding

dimastatz/whisper-flow собрал 112 звёзд за окно, тогда как у автора всего 34 подписчиков — эффективная аудитория ≈ 130. Это даёт surprise-индекс 0.022 (звёзды относительно охвата автора, а не в абсолюте). Удержание форков 50.0% и 0 внешних контрибьюторов отделяют реальный инструмент от разовой вспышки. Акселерация положительная — рост ещё не выдохся.

METRICS IN CONTEXT

MEDIAN ACROSS ALL 9594 FINDINGS · Δ vs MEDIAN · PERCENTILE = SHARE RANKED BELOW
METRICVALUEMEDIANΔ MEDPERCENTILE
SCORE0.010.00+0.01ABOVE 84%
VELOCITY3.733.57+0.17ABOVE 52%
RETENTION15.7%23.8%-8.0 PPABOVE 34%
FORKS127144-17ABOVE 47%
SURPRISE0.020.01+0.02ABOVE 78%