Github Trends®
9778 findingsmedian surprise 0.00756window 7 days
UNIT / TREND-MONITOR · REV 2.6
[ 7 days window ]
SOURCE: own snapshots
FINDING #7384 · UNIT ID 1196855016
elizabetht/100-days-of-inference
100 days of LLM inference engineering — daily posts, experiments, and visualizations
[ JUPYTER NOTEBOOK ][ GITHUB ↗ ]
SURPRISE SCORE
0.00

Score Breakdown

SURPRISE0.0692
ENGAGEMENT0.45
FRESHNESS1.00
SCORE = SURPRISE × ENGAGEMENT^0.7 × FRESHNESS × VISIBILITY × CONFIDENCE
SURPRISE = WINDOW STARS / DAYS / (AUDIENCE + FLOOR)
12% OF STARS IN ARCHIVE
[BOT] SUSPECTED STAR BOT — SCORE PENALIZED. SIGNATURES:
S2 · NO EXTERNAL ISSUE/PR AUTHORS DESPITE 100+ STARS
S7 · THIS REPO IS ~ALL OF THE OWNER'S STARS
S10 · DEAD CODE, ZERO CONTRIBUTORS, YET STARS KEEP DRIPPING

Growth Telemetry

VELOCITY /D
20.00
ACCEL
-4.21
RETENTION
19.8%
PEAK 2026-09-02 · FORK-RETENTION 63.6% · 140 STARS / WINDOW

Author Audience

AUDIENCE
249
FOLLOWERS
120
OWNER ★
1,289

Engagement Signals

FORKS
123
ISSUE AUTH
0
PR AUTH
0
UNIQUE STARGAZERS 140 / 140 (DIVERSITY 1.00)

Why This Is A Finding

elizabetht/100-days-of-inference собрал 140 звёзд за окно, тогда как у автора всего 120 подписчиков — эффективная аудитория ≈ 249. Это даёт surprise-индекс 0.0692 (звёзды относительно охвата автора, а не в абсолюте). Удержание форков 63.6% и 0 внешних контрибьюторов отделяют реальный инструмент от разовой вспышки. Акселерация отрицательная — внимание остывает после пика.

METRICS IN CONTEXT

MEDIAN ACROSS ALL 9778 FINDINGS · Δ vs MEDIAN · PERCENTILE = SHARE RANKED BELOW
METRICVALUEMEDIANΔ MEDPERCENTILE
SCORE0.000.00-0.00ABOVE 24%
VELOCITY20.003.43+16.57ABOVE 88%
RETENTION19.8%30.0%-10.2 PPABOVE 37%
FORKS123116+7ABOVE 51%
SURPRISE0.070.01+0.06ABOVE 93%