datawhalechina/quant-for-beginners — GitHub trend

◂ BACK TO LEADERBOARD

FINDING #1041 · UNIT ID 1254959609

datawhalechina/quant-for-beginners

[ JUPYTER NOTEBOOK ][ ORG ][ GITHUB ↗ ]

SURPRISE SCORE

0.00

Score Breakdown

SURPRISE0.0000961

ENGAGEMENT0.20

FRESHNESS1.00

SCORE = SURPRISE × ENGAGEMENT^0.7 × FRESHNESS × VISIBILITY × CONFIDENCE

SURPRISE = WINDOW STARS / DAYS / (AUDIENCE + FLOOR)

26% OF STARS IN ARCHIVE

[BOT] SUSPECTED STAR BOT — SCORE PENALIZED. SIGNATURES:

S2 · NO EXTERNAL ISSUE/PR AUTHORS DESPITE 100+ STARS

S8 · KEYWORD-STUFFED OR EMPTY DESCRIPTION

Growth Telemetry

VELOCITY /D

12.86

ACCEL

+0.93

RETENTION

57.1%

PEAK 2026-07-09 · FORK-RETENTION 0.0% · 90 STARS / WINDOW

Author Audience

AUDIENCE

133,804

FOLLOWERS

30,618

OWNER ★

362,841

Engagement Signals

FORKS

ISSUE AUTH

PR AUTH

UNIQUE STARGAZERS 90 / 90 (DIVERSITY 1.00)

Why This Is A Finding

datawhalechina/quant-for-beginners собрал 90 звёзд за окно, тогда как у автора всего 30,618 подписчиков — эффективная аудитория ≈ 133,804. Это даёт surprise-индекс 0.0000961 (звёзды относительно охвата автора, а не в абсолюте). Удержание форков 0.0% и 0 внешних контрибьюторов отделяют реальный инструмент от разовой вспышки. Акселерация положительная — рост ещё не выдохся.

Related Findings

RANKS ABOVE 7% OF 1120 FINDINGS

#857Mjrovai/EdgeML-with-Raspberry-Pi[ JUPYTER NOTEBOOK ]0.000569 #930NVIDIA-AI-Blueprints/cuFOLIO[ JUPYTER NOTEBOOK ]0.000179 #05AlephAITech/WorkBuddyGuide[ PYTHON ]0.51 #15Mesh-LLM/mesh-llm[ RUST ]0.31 #16synthetic-sciences/openscience[ TYPESCRIPT ]0.31

METRICS IN CONTEXT

MEDIAN ACROSS ALL 1120 FINDINGS · Δ vs MEDIAN · PERCENTILE = SHARE RANKED BELOW

METRICVALUEMEDIANΔ MEDPERCENTILE

SCORE0.000.01-0.01ABOVE 7%

VELOCITY12.8613.86-1.00ABOVE 46%

RETENTION57.1%36.8%+20.3 PPABOVE 79%

FORKS5350+3ABOVE 51%

SURPRISE0.000.04-0.04ABOVE 1%