Github Trends®
9651 findingsmedian surprise 0.00326window 7 days
UNIT / TREND-MONITOR · REV 2.6
[ 7 days window ]
SOURCE: own snapshots
FINDING #8634 · 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

SURPRISE0.00585
ENGAGEMENT0.60
FRESHNESS1.00
SCORE = SURPRISE × ENGAGEMENT^0.7 × FRESHNESS × VISIBILITY × CONFIDENCE
SURPRISE = WINDOW STARS / DAYS / (AUDIENCE + FLOOR)
1% 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
2.29
ACCEL
-1.07
RETENTION
21.4%
PEAK 2026-10-11 · FORK-RETENTION 0.0% · 16 STARS / WINDOW

Author Audience

AUDIENCE
350
FOLLOWERS
156
OWNER ★
1,944

Engagement Signals

FORKS
245
ISSUE AUTH
0
PR AUTH
0
UNIQUE STARGAZERS 16 / 16 (DIVERSITY 1.00)

Why This Is A Finding

elizabetht/100-days-of-inference собрал 16 звёзд за окно, тогда как у автора всего 156 подписчиков — эффективная аудитория ≈ 350. Это даёт surprise-индекс 0.00585 (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 9651 FINDINGS · Δ vs MEDIAN · PERCENTILE = SHARE RANKED BELOW
METRICVALUEMEDIANΔ MEDPERCENTILE
SCORE0.000.00-0.00ABOVE 11%
VELOCITY2.293.14-0.86ABOVE 32%
RETENTION21.4%16.7%+4.8 PPABOVE 56%
FORKS245277-32ABOVE 48%
SURPRISE0.010.00+0.00ABOVE 60%