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Signalcrest
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aientity · one source so far

tinker

for AI engineers, compliance teams

Steadydevto
Signal score
9
Live items
1
Trajectory
↓ Cooling
7-day est. ~0

Why this scored 9

every term, weighted
Velocity+0.0 / 40

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

Acceleration+6.2 / 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+5.1 / 10

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

Saturation penalty−2.3 / 30

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

Composite9.1

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

medium confidence · estimate, not a guarantee

7-day

~0

range 0–12

14-day

~0

range 0–14

30-day

~0

range 0–19

Signal history

7-day window (free)
075

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
    26

    Fine-Tuning Qwen3-8B with Tinker: From 50% to 76% Accuracy, and Burning Through My Budget

    Fine-tuning Qwen3-8B with Tinker boosts accuracy but raises compute costs. · for ml engineers, ai researchers

    devtoSteadyai6d ago
  2. 2
    16

    Everyone Clicks 'I Agree.' No One Reads Them: I Fine-Tuned a 4B Model with Tinker to Catch Unfair Terms of Service in 30 Seconds

    Fine-tuned a 4B model with Tinker to flag unfair TOS in seconds. · for AI engineers, compliance teams

    devtoSteadyai2h ago