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MMR OFFICIAL@MR_0FFICIALL
ììŽì íž ìëíJEV
ðð²ð ðð ð¡ðŒð ðð¶ðð² ðŒð» ð.ðð ðð£ð: ð ð¡ð²ð ðªð®ð ð§ðŒ ð ð®ðžð² ðŠðŒð³ððð®ð¿ð² ðð²ð°ð¶ðð¶ðŒð»ð â¡
ðð²ð ðð ð¡ðŒð ðð¶ðð² ðŒð» ð.ðð ðð£ð: ð ð¡ð²ð ðªð®ð ð§ðŒ ð ð®ðžð² ðŠðŒð³ððð®ð¿ð² ðð²ð°ð¶ðð¶ðŒð»ð â¡
Most AI APIs are designed to generate something.
Text. Code. JSON. A response.
Jev takes a different approach.
From @typesafeai, Jev is a System One model designed specifically for structured decisions inside software.
Instead of asking it to generate text and then parsing the result, developers provide:
ð¹ App state â the information the system needs
ð¹ A typed question â what decision needs to be made
ð¹ A typed result â the decision, probability and confidence
No JSON prompting.
No fragile output parsing.
Just a structured decision that software can use directly.
ð§ What Can Jev Decide?
Jev evaluates three question types in parallel:
Choice â Select between predefined options.
Score â Produce a structured numerical assessment.
Noul â Make a structured binary-style decision.
That makes the model less about generating content and more about becoming a decision layer inside an application.
â¡ Built For Fast Decisions
According to the announcement, Jev responds in roughly 70â500ms and costs $0.042 per million input tokens, with output tokens free.
That pricing and latency profile makes the model particularly interesting for workloads where thousands of small decisions can happen continuously.
Think:
ð¹ Ticket routing
ð¹ Content moderation
ð¹ Risk scoring
ð¹ Agent branching
ð¹ Automated workflow decisions
ð¹ Classification inside software
Instead of building a pipeline around:
Prompt â Generated JSON â Parser â Validation â Application
the idea is closer to:
App State â Typed Question â Typed Decision
That is a meaningful architectural difference.
ð Now Available Through https://t.co/9CKQIEP2Jz
Jev is now available through the https://t.co/9CKQIEP2Jz API using:
Jev-1.13.0
Jev-Latest
For developers, this adds another specialized model to the https://t.co/9CKQIEP2Jz ecosystemâone designed not primarily to talk, but to help software decide.
ð Try it through https://t.co/9CKQIEP2Jz: https://t.co/IE5evqOJKL
ð Learn more: https://t.co/7bQibRC3KN
The interesting part of AI infrastructure is increasingly not just how well models generate.
It's how cleanly models can plug into software and perform a specific job.
#TRONEcoStar @justinsuntron @BAI_AGI
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