elasticsearch
for security engineers, SOC analysts
Why this scored 9
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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Trust, but benchmark: How we let an AI agent optimize Elasticsearch
Demonstrates AI agents auto-tuning Elasticsearch for better performance. · for search engineers
lobstersCooling 2 · elasticsearchai39d ago - 218
Why we stopped using one Elasticsearch index per tenant and built our own
Switching from per-tenant Elasticsearch indexes to a custom solution improves scaling. · for backend engineers
devtoSteadydata39d ago - 315
Elastic Security webhook connector fails to push a case when `{{{case.id}}}` is used
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stackoverflowSteadysecurity22d ago - 412
Installing Elasticsearch, Logstash, and Kibana (ELK Stack) on Kubernetes
ELK stack on Kubernetes · for devops teams
devtoCoolingdevtools81d ago - 54
Storing time series data in Solr or ElasticSearch
Time series storage options · for data engineers
stackoverflowSteadydata92d ago