embeddinggemma 2
for ml engineers
Why this scored 14
every term, weightedIts fastest-moving item, measured against the pace of its own source
Whether that velocity is itself speeding up, as a per-hour rate
How many independent communities its own items come from
Decays to zero over 14 days, counted from when we first saw it
Subtracted once something is big and old — sized by its biggest item, aged from when we first saw it
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 guarantee7-day
~0
range 0–17
14-day
~0
range 0–21
30-day
~0
range 0–28
Signal history
7-day window (free)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.
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We turned EmbeddingGemma 2 into a speech-to-text model
EmbeddingGemma 2 repurposed for speech-to-text expands model utility for developers. · for ml engineers
hackernewsSteadyai2h ago - 216
EmbeddingGemma 2: An open, lightweight multimodal embedding model
EmbeddingGemma 2 provides a lightweight multimodal embedding model for fast vector generation. · for ml engineers, ai developers
hackernewsSteadyai6d ago