Github Trends®
1508 findingsmedian surprise 0.00901window 1 day
UNIT / TREND-MONITOR · REV 2.6
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SOURCE: own snapshots
РЕПО НЕТ В ОКНЕ 30D. ПОКАЗАН FINDING ИЗ ОКНА 1D (1 day) — РАНГ #1433.
FINDING #1433 · UNIT ID 679366051
NVIDIA/TensorRT-LLM
TensorRT LLM provides users with an easy-to-use Python API to define Large Language Models (LLMs) and supports state-of-the-art optimizations to perform inference efficiently on NVIDIA GPUs. TensorRT LLM also contains components to create Python and C++ runtimes that orchestrate the inference execution in a performant way.
[ PYTHON ][ ORG ][ VERIFIED ][ GITHUB ↗ ]
SURPRISE SCORE
0.00

Score Breakdown

SURPRISE0.0000262
ENGAGEMENT0.82
FRESHNESS1.00
SCORE = SURPRISE × ENGAGEMENT^0.7 × FRESHNESS × VISIBILITY × CONFIDENCE
SURPRISE = WINDOW STARS / DAYS / (AUDIENCE + FLOOR)
0% OF STARS IN ARCHIVE

Growth Telemetry

VELOCITY /D
13.00
ACCEL
0.00
RETENTION
0.0%
PEAK 2026-07-27 · FORK-RETENTION 0.0% · 13 STARS / WINDOW

Author Audience

AUDIENCE
495,419
FOLLOWERS
28,361
OWNER ★
394,318

Engagement Signals

FORKS
2,611
ISSUE AUTH
0
PR AUTH
0
UNIQUE STARGAZERS 13 / 13 (DIVERSITY 1.00)

Why This Is A Finding

NVIDIA/TensorRT-LLM собрал 13 звёзд за окно, тогда как у автора всего 28,361 подписчиков — эффективная аудитория ≈ 495,419. Это даёт surprise-индекс 0.0000262 (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.

METRICS IN CONTEXT

MEDIAN ACROSS ALL 1508 FINDINGS · Δ vs MEDIAN · PERCENTILE = SHARE RANKED BELOW
METRICVALUEMEDIANΔ MEDPERCENTILE
SCORE0.000.00-0.00ABOVE 5%
VELOCITY13.009.00+4.00ABOVE 64%
RETENTION0.0%0.0%0.0 PPABOVE 0%
FORKS2,611317+2,295ABOVE 87%
SURPRISE0.000.01-0.01ABOVE 1%