Kush Bhuwalka, of Puffle, with the two-line observation several people then built: retrieve as usual, run Jev over every chunk, delete the ones that are not relevant. Precision in RAG has been the half nobody could afford at retrieval time, since the fix was another model call per chunk.
Kush Bhuwalka
also did anyone realize @typesafeai jev solved precision in RAG?
you can run jev on all the chunks it retrieves and delete the irrelevant ones.
Andre Landgraf's showcase of the yes/no gate, several worked examples at the link.
Andre Landgraf
2h
Ok, jev is really cool. So many great use cases where a fast and accurate yes/no gate can deliver meaningful performance gains.
Played around and built some showcase examples here →
Vipul Sharma's Minecraft agents ask Jev whether to fight or run, from what the agent can see, the mob count, its weapon and its health; one broke off mid-fight as the odds turned.
Vipul Sharma
2h
I hooked up Jev by @typesafeai to a few agents on my Minecraft world. Look at how Jev decides when they should fight and when they should run away.
Jev rates the odds from what the agent can see, mob count, weapon, how hurt it is, and runs away when they are bad. One broke off mid-fight as its odds turned.
Mahmoud's simulation of fifteen autonomous drones through an asteroid field, Jev deciding for each in under 300 ms, all fifteen arriving.
Mahmoud
3h
Jev can control a swarm of 15 autonomous drones in real time!
I built a simulation to test it navigating an asteroid field:
• Decision latency: < 300ms
• Survival rate: 100% (all 15 reached the destination)
Built with @typesafeai by @CompleteSkeptic
