t4g
for ml engineers, cloud ops
Why this scored 18
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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Three Gemma 4 Deployments on One T4G for Under $3: What the Runtime Changes, and What It Doesn't
Shows cost-effective Gemma 4 inference on a single AWS T4g instance for ML workloads. · for ml engineers, cloud architects
devtoSteadyai56d ago - 218
Three Gemma 4 Deployments on One T4G for Under $3: What the Runtime Changes, and What It Doesn't
Deploying three Gemma 4 models on a single AWS T4g costs under $3, showing cheap runtime options. · for ml engineers, cloud ops
devtoSteadyai56d ago