The argument validator in PatchOpsAi, built with Luna, shows performance metrics of 1.1–2.8 seconds, while @typesafeai's Jev took 195–251 ms on the validator and 221–901 ms on the scanner.
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The Jev model from Typesafe AI was tested on Modemdev's evaluation for agent replies, showing an 8x speed improvement and a 25x cost reduction.
Early experiments compare the Jev model from Typesafe AI with LLMs like GPT-OSS and GLM. Results indicate Jev may be suitable for production workloads, offering competitive pricing and performance.
A space race game was developed for TypeSafe/Jev, achieving 31,382 points with 180 stars and 18 kills in the final 180 seconds. The replay video showcases the decision-making process and verified outcomes without new API calls.
XenTank has been successfully ported to PSP, Mega Drive/Genesis, and NES using AI tools. Experience the game on multiple platforms with these new adaptations.
The Jev + Cua Driver, known as jev-use, is now available in development preview for macOS, Windows, and Linux.
Jev, a new decision model, demonstrated a routing cost 125 times lower than GPT-5.5 when deciding actions for a coding agent after a failed test.