Ask every question the system might need in one request — including ones only some branches use — and let code discard the rest.
The fan-out pattern sends many questions in a single call and lets code decide what is relevant. The ticket-triage example asks a Choice category plus four speculative Score and Noul judgments, then escalates bugs above severity 1.5 with reproduction above 0.6 and flags refunds above 0.7.
Questions run in parallel, so adding them barely changes response time; the cost is the input tokens for each speculative question on every call. Fan out on questions several branches can reuse rather than on everything a workflow might ask.
Definitions that connect to this one.
Posts from builders shipping with Jev right now.
Post scoring with SuperX
Jev + SuperX = virality solved ✅ Every post gets 61 questions in ~1s for $0.0004 🤯 > fitted on 9,481 real posts from 207 creators > picks the viral post 2 in 3 times > never rewards reply bait So: write, score, rewrite, stop when it peaks. Free, no signup. try it below ↓
I spent the last 8 hours building a viral post classifier with Jev. It's now better at spotting viral posts than I am. And it's as good as Fable 5.1, but 100x faster.
A canvas you control by pointing and speaking
Jev is the future
Jev plays Smash Bros. against itself
jev is insane 🤯 here is jev playing smash bros against itself he is controlling all 4 different characters. and literally deciding whats the best move to play against itself all within a fraction of a second i used over 22 million tokens to play this match and it only cost Show more
A keystroke oracle that reads intent in ~100 ms
Also have been playing with @typesafeai Jev, insane! So many immediate use cases and new apps are possible. What a time to be a builder! Sharing some experiments here starting with: Keystroke oracle / predictive launcher: Your launcher ranks by aliases, fuzzy match, and Show more
A second-hand shopping agent
Claude me integró Jev con Playwright para buscar usados. Lee unos 26 artículos por minuto y decide qué hacer con cada uno en 406 milisegundos Descartó los que no encajaban con lo que busco, ofertó por los que sí y hasta les mandó un mensaje a los vendedores cuando faltaba algún Show more
Predictive spreadsheets
Another crazy @typesafeai Jev example: Predictive spreadsheets Spreadsheets recalculate numbers, not meaning. Jev reads intent. Type "Urgency" at the top of a column and, as you type, it figures out you want each row rated from "no follow-up needed" to "urgent" in ~100 ms.
Also have been playing with @typesafeai Jev, insane! So many immediate use cases and new apps are possible. What a time to be a builder! Sharing some experiments here starting with: Keystroke oracle / predictive launcher: Your launcher ranks by aliases, fuzzy match, and
