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aientity · one source so far

llmmaster

for ai engineers, ml researchers

Steadygithub
Signal score
16
Live items
1
Trajectory
Cooling
7-day est. ~5

Why this scored 16

every term, weighted
Velocity+0.0 / 40

Its fastest-moving item, measured against the pace of its own source

Acceleration+12.0 / 25

Whether that velocity is itself speeding up, as a per-hour rate

Cross-source spread+0.0 / 25

How many independent communities its own items come from

Recency+7.7 / 10

Decays to zero over 14 days, counted from when we first saw it

Saturation penalty3.9 / 30

Subtracted once something is big and old — sized by its biggest item, aged from when we first saw it

Composite15.7

Weights are hand-tuned, not learned — we're calibrating them against realized trends as history accumulates. On an entity's first sighting there's no previous reading to compare against, so acceleration starts from a neutral prior rather than a measurement, and velocity falls back to engagement over its whole lifetime until a second reading exists. Full methodology

Outlook

low confidence · estimate, not a guarantee

7-day

~5

range 017

14-day

~0

range 014

30-day

~0

range 019

Signal history

7-day window (free)
568

projected trajectory (estimate, not a guarantee)

The evidence

The live items this entity's score aggregates — every community independently talking about it right now. This is the corroboration, shown, not claimed.

  1. 1
    9

    llm-master — 大模型(LLM)全栈学习路线与中文教程🔥:覆盖 Prompt Engineering、RAG、AI Agent、MCP、微调、模型部署、Transformer、AI 编程与大厂面试,从入门到生产实践。

    Chinese LLM curriculum covers prompt engineering, RAG, agents, fine-tuning and deployment. · for ai engineers, ml researchers

    githubSteadyai8 saturated3d ago