FINDING #549 · UNIT ID 1300173275
vshulcz/deja-vu
Your agents already solved this. deja finds it — it indexes the sessions your coding agents already wrote to disk, months of history from before you installed it, and recalls them automatically at session start across seventeen harnesses. 84.9% hit@1 on LongMemEval-S, no LLM, no embeddings. One zero-dep binary, fully local.
SURPRISE SCORE
0.00
Score Breakdown
SCORE = SURPRISE × ENGAGEMENT^0.7 × FRESHNESS × VISIBILITY × CONFIDENCE
SURPRISE = WINDOW STARS / DAYS / (AUDIENCE + FLOOR)
11% OF STARS IN ARCHIVE
Growth Telemetry
VELOCITY /D
9.00
ACCEL
+4.86
RETENTION
0.0%
PEAK 2026-08-05 · FORK-RETENTION 0.0% · 63 STARS / WINDOW
Author Audience
AUDIENCE
118
FOLLOWERS
55
OWNER ★
630
Engagement Signals
FORKS
40
ISSUE AUTH
0
PR AUTH
0
UNIQUE STARGAZERS 63 / 63 (DIVERSITY 1.00)
Why This Is A Finding
vshulcz/deja-vu собрал 63 звёзд за окно, тогда как у автора всего 55 подписчиков — эффективная аудитория ≈ 118. Это даёт surprise-индекс 0.057 (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 94% OF 9742 FINDINGS
METRICS IN CONTEXT
MEDIAN ACROSS ALL 9742 FINDINGS · Δ vs MEDIAN · PERCENTILE = SHARE RANKED BELOW
METRICVALUEMEDIANΔ MEDPERCENTILE
SCORE0.030.00+0.03ABOVE 94%
VELOCITY9.003.43+5.57ABOVE 77%
RETENTION0.0%38.9%-38.9 PPABOVE 0%
FORKS40117-77ABOVE 26%
SURPRISE0.060.01+0.05ABOVE 92%