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The Stack Under the Model: oMLX, llama.cpp, Hermes, and Why There Are So Many

Understanding ai model stacks

Steady
Signal score
10
as of 47d ago
Sources
2
agreeing on hermes
Trajectory
Too early
needs a few more snapshots

Why this scored 10

every term, weighted
Velocity+0.5 / 40

Engagement gained per hour since the last capture, against the fastest item on its own source

Acceleration+0.0 / 25

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

Cross-source spread+2.8 / 25

How many independent communities are talking about the same entity

Recency+7.9 / 10

Decays to zero over 14 days

Saturation penalty0.8 / 30

Subtracted once something is big and old — we rank what's next, not what's peaked

Composite10.4

Weights are hand-tuned, not learned — we're calibrating them against realized trends as history accumulates. On a topic'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

Signal history

7-day window (free)
Not enough history yet — the trend line fills in as snapshots accrue (every 30 min).

Entities

omlxllamacpphermes
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Drop this in a README or blog post — it updates automatically as the score moves.