Github Trends®
9613 findingsmedian surprise 0.0043window 3 days
UNIT / TREND-MONITOR · REV 2.6
[ 3 days window ]
SOURCE: own snapshots
FINDING #751 · 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

SURPRISE0.0495
ENGAGEMENT0.39
FRESHNESS1.00
SCORE = SURPRISE × ENGAGEMENT^0.7 × FRESHNESS × VISIBILITY × CONFIDENCE
SURPRISE = WINDOW STARS / DAYS / (AUDIENCE + FLOOR)
3% OF STARS IN ARCHIVE

Growth Telemetry

VELOCITY /D
8.33
ACCEL
-6.50
RETENTION
39.3%
PEAK 2026-08-18 · FORK-RETENTION 0.0% · 25 STARS / WINDOW

Author Audience

AUDIENCE
128
FOLLOWERS
34
OWNER ★
944

Engagement Signals

FORKS
126
ISSUE AUTH
0
PR AUTH
0
UNIQUE STARGAZERS 25 / 25 (DIVERSITY 1.00)

Why This Is A Finding

dimastatz/whisper-flow собрал 25 звёзд за окно, тогда как у автора всего 34 подписчиков — эффективная аудитория ≈ 128. Это даёт surprise-индекс 0.0495 (звёзды относительно охвата автора, а не в абсолюте). Удержание форков 0.0% и 0 внешних контрибьюторов отделяют реальный инструмент от разовой вспышки. Акселерация отрицательная — внимание остывает после пика.

METRICS IN CONTEXT

MEDIAN ACROSS ALL 9613 FINDINGS · Δ vs MEDIAN · PERCENTILE = SHARE RANKED BELOW
METRICVALUEMEDIANΔ MEDPERCENTILE
SCORE0.020.00+0.02ABOVE 92%
VELOCITY8.335.67+2.67ABOVE 65%
RETENTION39.3%42.9%-3.6 PPABOVE 45%
FORKS126324-198ABOVE 32%
SURPRISE0.050.00+0.05ABOVE 88%