ARCHITECTURE DIAGRAM
CODEX / CLAUDE CODEOFFICIAL TYPESAFE SKILLWORKFLOW CODE + DOCS
Overview & Result
MajoSayo describes LogJev as a Node.js and Python implementation for Jev-style bounded decisions using the author's own models, with text, image, or audio input and choices or scores from logprobs.
How Jev fits in the loop
- The application supplies a bounded decision question and its input state.
- The decision layer scores the available choices or outputs a score.
- The surrounding agent or application consumes the typed result and owns the next action.
How to reproduce
- Open the repository and verify the current Node.js and Python setup instructions.
- Run one harmless text classification with a fixed option set and inspect the returned choice or score.
- Only then test image or audio inputs if the selected model supports the required modality and logprobs.
Why this build matters
It gives JevForAgents a concrete local/open implementation to compare with hosted decision APIs, while keeping the evidence boundary explicit.
Limitations
- The reviewed post does not provide a benchmark or a reproducible latency measurement.
- Support depends on the selected model exposing the required modality and logprob behavior.
- Local-model parity with TypeSafe Jev is not established by this post.