Github Trends®
4503 findingsmedian surprise 0.00322window 3 days
UNIT / TREND-MONITOR · REV 2.6
[ 3 days window ]
SOURCE: own snapshots
FINDING #3342 · UNIT ID 136202695
mlflow/mlflow
The open source AI engineering platform for agents, LLMs, and ML models. MLflow enables teams of all sizes to debug, evaluate, monitor, and optimize production-quality AI applications while controlling costs and managing access to models and data.
[ PYTHON ][ ORG ][ GITHUB ↗ ]
SURPRISE SCORE
0.00

Score Breakdown

SURPRISE0.000493
ENGAGEMENT0.33
FRESHNESS1.00
SCORE = SURPRISE × ENGAGEMENT^0.7 × FRESHNESS × VISIBILITY × CONFIDENCE
SURPRISE = WINDOW STARS / DAYS / (AUDIENCE + FLOOR)
0% OF STARS IN ARCHIVE

Growth Telemetry

VELOCITY /D
4.00
ACCEL
-6.00
RETENTION
0.0%
PEAK 2026-07-25 · FORK-RETENTION 0.0% · 12 STARS / WINDOW

Author Audience

AUDIENCE
8,078
FOLLOWERS
1,205
OWNER ★
28,338

Engagement Signals

FORKS
6,059
ISSUE AUTH
0
PR AUTH
0
UNIQUE STARGAZERS 12 / 12 (DIVERSITY 1.00)

Why This Is A Finding

mlflow/mlflow собрал 12 звёзд за окно, тогда как у автора всего 1,205 подписчиков — эффективная аудитория ≈ 8,078. Это даёт surprise-индекс 0.000493 (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 4503 FINDINGS · Δ vs MEDIAN · PERCENTILE = SHARE RANKED BELOW
METRICVALUEMEDIANΔ MEDPERCENTILE
SCORE0.000.00-0.00ABOVE 26%
VELOCITY4.005.33-1.33ABOVE 33%
RETENTION0.0%0.0%0.0 PPABOVE 0%
FORKS6,059457+5,602ABOVE 91%
SURPRISE0.000.00-0.00ABOVE 20%