kev
for ML engineers, AI product teams
Why this scored 61
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
low confidence · estimate, not a guarantee7-day
~54
range 12–66
14-day
~59
range 15–74
30-day
~61
range 12–80
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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kev — tiny Jev-like family of decision models built on top of Qwen3.5 you can train and run on your own
kev is a lightweight decision-model library built on Qwen3.5 for on-device training and inference. · for ML engineers, AI product teams
githubSteady 2 · kevai−7 saturated3d ago - 230
Kev: Tiny Jev-like family of decision models built on top of Qwen3.5
Lightweight decision models built on Qwen3.5 enable low-resource AI inference. · for ml engineers, ai startups
hackernewsSteady 2 · qwen3.5ai1d ago