higgsfield-ai/higgsfield — GitHub trend

FINDING #352 · UNIT ID 134999697

higgsfield-ai/higgsfield

Fault-tolerant, highly scalable GPU orchestration, and a machine learning framework designed for training models with billions to trillions of parameters

[ JUPYTER NOTEBOOK ][ ORG ][ VERIFIED ][ GITHUB ↗ ]

SURPRISE SCORE

0.00

Score Breakdown

SURPRISE0.000438

ENGAGEMENT0.59

FRESHNESS1.25

SCORE = SURPRISE × ENGAGEMENT^0.7 × FRESHNESS × VISIBILITY × CONFIDENCE

SURPRISE = WINDOW STARS / DAYS / (AUDIENCE + FLOOR)

1% OF STARS IN ARCHIVE

Growth Telemetry

VELOCITY /D

1.90

ACCEL

-0.16

RETENTION

11.1%

PEAK 2026-09-18 · FORK-RETENTION 35.7% · 57 STARS / WINDOW

Author Audience

AUDIENCE

4,302

FOLLOWERS

1,046

OWNER ★

8,141

Engagement Signals

FORKS

1,074

ISSUE AUTH

PR AUTH

UNIQUE STARGAZERS 57 / 57 (DIVERSITY 1.00)

Why This Is A Finding

higgsfield-ai/higgsfield собрал 57 звёзд за окно, тогда как у автора всего 1,046 подписчиков — эффективная аудитория ≈ 4,302. Это даёт surprise-индекс 0.000438 (звёзды относительно охвата автора, а не в абсолюте). Удержание форков 35.7% и 0 внешних контрибьюторов отделяют реальный инструмент от разовой вспышки. Акселерация отрицательная — внимание остывает после пика.

Related Findings

RANKS ABOVE 47% OF 663 FINDINGS

#154Nicolepcx/ai-agents-the-definitive-guide[ JUPYTER NOTEBOOK ]0.00259 #305Lordog/dive-into-llms[ JUPYTER NOTEBOOK ]0.000526 #334lyogavin/airllm[ JUPYTER NOTEBOOK ]0.000431 #590rasbt/LLMs-from-scratch[ JUPYTER NOTEBOOK ]0.0000247 #650microsoft/generative-ai-for-beginners[ JUPYTER NOTEBOOK ]<0.000001

METRICS IN CONTEXT

MEDIAN ACROSS ALL 663 FINDINGS · Δ vs MEDIAN · PERCENTILE = SHARE RANKED BELOW

METRICVALUEMEDIANΔ MEDPERCENTILE

SCORE0.000.00-0.00ABOVE 47%

VELOCITY1.902.77-0.87ABOVE 23%

RETENTION11.1%11.1%-0.0 PPABOVE 50%

FORKS1,0741,805-731ABOVE 39%

SURPRISE0.000.00-0.00ABOVE 43%