FINDING #2662 · UNIT ID 19868085
rlabbe/Kalman-and-Bayesian-Filters-in-Python
Kalman Filter book using Jupyter Notebook. Focuses on building intuition and experience, not formal proofs. Includes Kalman filters,extended Kalman filters, unscented Kalman filters, particle filters, and more. All exercises include solutions.
SURPRISE SCORE
0.00
Score Breakdown
SCORE = SURPRISE × ENGAGEMENT^0.7 × FRESHNESS × VISIBILITY × CONFIDENCE
SURPRISE = WINDOW STARS / DAYS / (AUDIENCE + FLOOR)
0% OF STARS IN ARCHIVE
Growth Telemetry
VELOCITY /D
21.33
ACCEL
-14.50
RETENTION
32.1%
PEAK 2026-09-10 · FORK-RETENTION 45.5% · 64 STARS / WINDOW
Author Audience
AUDIENCE
4,300
FOLLOWERS
1,914
OWNER ★
23,860
Engagement Signals
FORKS
4,527
ISSUE AUTH
0
PR AUTH
0
UNIQUE STARGAZERS 64 / 64 (DIVERSITY 1.00)
Why This Is A Finding
rlabbe/Kalman-and-Bayesian-Filters-in-Python собрал 64 звёзд за окно, тогда как у автора всего 1,914 подписчиков — эффективная аудитория ≈ 4,300. Это даёт surprise-индекс 0.00492 (звёзды относительно охвата автора, а не в абсолюте). Удержание форков 45.5% и 0 внешних контрибьюторов отделяют реальный инструмент от разовой вспышки. Акселерация отрицательная — внимание остывает после пика.
Related Findings
RANKS ABOVE 70% OF 8833 FINDINGS
METRICS IN CONTEXT
MEDIAN ACROSS ALL 8833 FINDINGS · Δ vs MEDIAN · PERCENTILE = SHARE RANKED BELOW
METRICVALUEMEDIANΔ MEDPERCENTILE
SCORE0.000.00+0.00ABOVE 70%
VELOCITY21.335.67+15.67ABOVE 86%
RETENTION32.1%45.9%-13.9 PPABOVE 37%
FORKS4,527344+4,183ABOVE 92%
SURPRISE0.000.00+0.00ABOVE 52%