Github Trends®
9733 findingsmedian surprise 0.00869window 7 days
UNIT / TREND-MONITOR · REV 2.6
[ 7 days window ]
SOURCE: own snapshots
FINDING #392 · 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

SURPRISE0.0868
ENGAGEMENT0.31
FRESHNESS1.00
SCORE = SURPRISE × ENGAGEMENT^0.7 × FRESHNESS × VISIBILITY × CONFIDENCE
SURPRISE = WINDOW STARS / DAYS / (AUDIENCE + FLOOR)
11% OF STARS IN ARCHIVE

Growth Telemetry

VELOCITY /D
14.71
ACCEL
-6.39
RETENTION
15.7%
PEAK 2026-08-16 · FORK-RETENTION 57.1% · 103 STARS / WINDOW

Author Audience

AUDIENCE
130
FOLLOWERS
34
OWNER ★
955

Engagement Signals

FORKS
127
ISSUE AUTH
0
PR AUTH
0
UNIQUE STARGAZERS 103 / 103 (DIVERSITY 1.00)

Why This Is A Finding

dimastatz/whisper-flow собрал 103 звёзд за окно, тогда как у автора всего 34 подписчиков — эффективная аудитория ≈ 130. Это даёт surprise-индекс 0.0868 (звёзды относительно охвата автора, а не в абсолюте). Удержание форков 57.1% и 0 внешних контрибьюторов отделяют реальный инструмент от разовой вспышки. Акселерация отрицательная — внимание остывает после пика.

METRICS IN CONTEXT

MEDIAN ACROSS ALL 9733 FINDINGS · Δ vs MEDIAN · PERCENTILE = SHARE RANKED BELOW
METRICVALUEMEDIANΔ MEDPERCENTILE
SCORE0.040.00+0.04ABOVE 96%
VELOCITY14.713.57+11.14ABOVE 85%
RETENTION15.7%34.4%-18.7 PPABOVE 28%
FORKS12798+29ABOVE 56%
SURPRISE0.090.01+0.08ABOVE 94%