glm5.3-flash
for ml engineers, ai product teams
Why this scored 0
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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Turning GLM-5.3-Flash into a Jev-like decision model
Adapts GLM-5.3-Flash for decision modeling, useful for ML engineers building inference pipelines. · for ml engineers, ai product teams
hackernewsSteady 3 · jevai15d ago - 228
GLM-5.3-Flash
New GLM-5.3-Flash model promises faster inference and lower latency. · for ml engineers
hackernewsSteady 2 · glm5.3-flashai47d ago - 312
Turn GLM-5.3-Flash into a Jev-like System One model
Turning GLM-5.3-Flash into a Jev-like system enables faster custom inference pipelines. · for ml engineers
lobstersCoolingai15d ago - 40
zai-org/GLM-5.3-Flash (image-text-to-text)
GLM-5.3-Flash model enables image-to-text generation using a transformer architecture. · for ml engineers, multimodal AI researchers
huggingfaceSteadyai−21 saturated47d ago