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aientity · one source so far

tesla t4

for ML engineers, cloud ops

Steadydevto
Signal score
20
Live items
1
Trajectory
Cooling
7-day est. ~0

Why this scored 20

every term, weighted
Velocity+0.0 / 40

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

Acceleration+12.5 / 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+7.3 / 10

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

Saturation penalty+0.0 / 30

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

Composite19.8

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

~0

range 012

14-day

~0

range 014

30-day

~0

range 019

Signal history

7-day window (free)
055

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
    22

    Gemma 4 on a Tesla T4, Part 2: The Minimum GCE VM and a Script to Drive It

    Shows how to run Gemma 4 on GCP using Tesla T4 GPUs for cheap inference. · for ML engineers, cloud ops

    devtoSteadyai58m ago
  2. 2
    12

    Gemma 4 on a Tesla T4: QAT Weights Decode 1.79x Faster Than bf16

    Gemma-4 on Tesla T4 with QAT decodes 1.79× faster than bf16. · for ML engineers, inference platform teams

    devtoCoolingai3d ago