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aientity · corroborated across 2 sources

applesilicon

for mobile AI engineers

Steadygithubhackernews
Signal score
31
Sources
2
independent communities
Live items
2
Trajectory
Cooling
7-day est. ~12

Why this scored 31

every term, weighted
Velocity+3.4 / 40

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

Acceleration+11.8 / 25

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

Cross-source spread+12.5 / 25

How many independent communities its own items come from

Recency+7.8 / 10

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

Saturation penalty4.9 / 30

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

Composite30.6

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

~12

range 023

14-day

~0

range 014

30-day

~0

range 019

Signal history

7-day window (free)
1261

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
    33

    Laya on Mac M4 CoreML Offline

    Laya runs offline on Mac M4 using CoreML, enabling local inference without cloud. · for mobile AI engineers

    hackernewsSteady 3 · layaai2d ago
  2. 2
    33

    laya-mlx — Native MLX runtime for Laya typed decision models — 7–14 ms short decisions on M3 Max. No text generation, PyTorch, or cloud API.

    laya-mlx provides native MLX inference on Apple silicon for low-latency decision models. · for mobile ML engineers

    githubSteady 3 · layaai6 saturated3d ago