What people are shipping with Jev.

1 day ago ... Jev is a frontier AI model from TypeSafe AI that returns typed, probabilistic decisions instead of generated text. You send program state plus typed ...

Typed decisions with TypeSafe's Jev, the first System One model

@typesafeai Can you talk to Jev, and try to fool it? 5 comment templates, from note to monitor to plausible false justifications written on the trigger line (line with backdoor co

@imcharliegraham @typesafeai Nice, I had a limilar idea of Jev playing poker, in a tournament with GPT. Jev is very conservative in my attempt :) it needs better prompting https://

Jev from @typesafeai can replace your LLM-as-a-judge for scoring agent responses. It returns a choice or numeric result with information about uncertainty, so you don't need to sp

fast-jev-compaction: https://t.co/y73It71ndT More tools and ready-to-use AI stacks: @iamrexei

@0fir0z @typesafeai Used custom OpenCV code and pasted results as a state to Jev. Will clean up the code and share it in same thread this weekend

@typesafeai jev is learning how to gamble https://t.co/hmfvb3kvGH

Interesting idea from @TypeSafeAI: Jev, a “System One Model” built for decisions, not text. Instead of generating JSON and parsing it, Jev returns typed decisions + probabilities.

@altryne @typesafeai PS: if you want JEV with images, gotten it working with qwen and gemma as well - with public endpoints https://t.co/CoSCLiSPGP

14 hours ago ... What is Jev? Learn how TypeSafe AI's System One model makes fast, structured decisions, where it fits in the agent loop, and how to use Jev with LangChain.

Jev from @typesafeai is now available as an evaluation model in Braintrust. If you have an existing scorer, just change the model in the dropdown to Jev and reduce your scoring co

@sawyerhood @typesafeai Taking a stab at it now. IMO auto is great, but given how fast this is, I think continuously giving feedback will give more confidence to the user. I'm go

@mandel59 match はパターンマッチで、noul, choice, score は jev 側のモデル抽象です。 一応アップロードしてますが、真面目に作ってないです https://t.co/MnfGTu5Zpi

@braintrust @typesafeai productizing jev as a trace scorer is the natural seat. high-conf disagreement with human soft-fail is the miss card - shadow dual-scorer on last n evals

@thisiskp_ A PG CHECK constraint in plain english managed by Jev ;) https://t.co/UJ1eRWtaGm https://t.co/OoN1i1NPX6

So whats next? Got many ideas, but first we have to make this version stable and fully working. Currently 1m candles is the max I can get but i kind of want to scan on the 1s,5s

Using Jev to codify "flavor based" linters so code contributions reflect the style of the invoker

https://github.com/shauryajain07/ghost-user

@typesafeai One huge limitation is that I didn't add an actual LLM-monitor control. The goal was to evaluate and benchmark the capabilities of Jev. Full-write is at LessWrong: ht