aws ec2
for ml engineers, cloud developers
Why this scored 5
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
Signal history
7-day window (free)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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Pure JAX on G5g: Serving Gemma 4 on Graviton and a T4G
Shows how to serve Gemma 4 on ARM Graviton and T4G GPUs using AWS G5g instances. · for cloud ml engineers
devtoSteadyai47d ago - 216
Plain Gemma 4 26B vs Jev on One EC2 L4: 2.1 Points Behind Overall, Level on Yes/No, 4.5 Behind on Multiple Choice
Gemma 4 26B outperforms Jev on EC2 L4, showing better accuracy on multiple-choice tasks. · for ml engineers, cloud developers
devtoSteadyai21d ago