Choose drone tactics from camera-derived state
A simulated quadrotor uses Jev for low-frequency tactical judgments while classical vision, flight control, and safety reflexes remain in code.
Independent capability index · 2026
Jev is TypeSafe's System One model. It does not chat. It returns typed decisions your code can act on. Explore what has been publicly demonstrated, with evidence and limitations.
Unofficial and independent. Every published capability links to evidence and shows how it was verified.
Public evidence ledger
13 of 13 records
A simulated quadrotor uses Jev for low-frequency tactical judgments while classical vision, flight control, and safety reflexes remain in code.
A reproducible harness lets Jev direct combat, exploration, and economy actions in the original StarCraft shareware campaign.
A computer-use loop combines OCR and accessibility data, then asks Jev which bounded action should move the Mac toward a plain-English goal.
A browser agent turns the visible DOM into an indexed action space and uses Jev to choose the next operation and compatible target.
A Home Assistant integration exposes Jev probabilities, choices, and scores as entities and action responses that automations can use.
A trading loop reads the Kuru MON-USDC order book and asks Jev for a buy-or-sell judgment before code places a post-only limit order.
An MCP server gives compatible agents tools for classification, scoring, checking, matching, screening, and custom typed Jev questions.
A Pi extension asks Jev to flag destructive, exfiltrating, or out-of-scope tool calls and to classify failures in command output.
An experimental controller translates NES telemetry into object-centric JSON and lets Jev choose the next legal controller macro.
A Pi extension uses Jev to decide which old tool calls and results still matter while keeping conversation text verbatim.
A Chrome extension finds ad-shaped DOM candidates and asks Jev whether each candidate is a paid advertisement before code removes it.
A staged review workflow uses Jev to identify risky areas, select evidence, classify mechanisms, score severity, and route follow-up checks.
Foreman runs an independent observation loop that asks Jev whether a coding worker is progressing, stuck, complete, or ready for verification.
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