A Stanford team used JEV to triage 40 billion data points every 15 minutes Instead of asking an LLM to explain everything, JEV decides which results are actually worth deeper analysis, cutting latency and inference waste

JEV triage seems ideal for filtering massive datasets before expensive language-model analysis.

Smarter data triage can reduce unnecessary inference while preserving deeper analysis where it matters, a direction @DigiMaaya finds valuable for scalable systems.

40b every 15 min only works if jev throws most of it away

What’s the purpose of these posts? Clickbait on X? @X have a look at this

bullllshiiiit

Routing billions of data points before expensive inference is the right pattern: use cheap computation to reserve premium models for high-value cases. @fluence_project can make that execution layer more distributed and auditable

how?

stanford running 40 billion points through this and the graphs still look calmer than my own logs

this is the exact efficiency crypto infra needs right now

The next AI breakthrough may be knowing what not to analyze. 40 billion data points is impressive. Knowing which 0.01% actually matter is the real intelligence. 👀

to much BS in here!