Using Jev as a routing layer for pump.fun trade monitoring
The author uses Jev as a cheap classifier to screen on-chain trade events and sends only flagged cases to GPT-6 Astra for deeper analysis, claiming tests showed it 201x faster and 456x cheaper.
Jev Engineering can make a http://pump.fun monitoring system faster and cheaper by keeping expensive models out of routine decision loops up to 201x faster and 456x cheaper in tests these are reported benchmark results and measured TRENCHNET performance figures here's how I applied this idea to tracking traders: observed trade → structured wallet history → graph context → Jev classifies the event → flag / skip / request deeper analysis → GPT-6 Astra reviews flagged cases → decision journal → Telegram alert with transaction links the expensive model doesnt need to write an essay about every buy Jev gets narrow questions: does this trade match the wallets profile? does it change the situation were watching? is there enough evidence to send an alert? does this need Astra? the graph adds context several wallets buying one token may have a long history of moving together that matters before treating their buys as independent signals Astra builds detailed trader profiles and explains the cases that require reasoning Jev helps route incoming events using that context thats the Jev Engineering approach I break down in TRENCHNET: give each decision layer a specific job, then measure its speed, cost and errors full architecture in my article - open the post below ↓
This post quotes another post: “TRENCHNET trading bot uses Jev to time on-chain buys”