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JevMade field notes / Local deployment and evaluation guide

Run a local Jev-style decision API with Rizzo Flow

Rizzo Flow documents a local llama.cpp runtime that exposes the bounded decision programming model through a Jev-compatible API, with quickstarts, demos, and caveated measurements.

Original by Rizzo AI AcademyIntegrationsGitHub README and APISource reviewed

Before you dive in

What you’ll find in the original

  1. Point compatible client code at localhost by changing the base URL while retaining choice, noul, and score request shapes.
  2. Select a llama.cpp backend appropriate to Metal, CUDA, Vulkan, ROCm, SYCL, or CPU rather than assuming one hardware path.
  3. Reproduce the demos and measurements on target hardware; the README explicitly distinguishes individual recordings from benchmarks.
Worth knowing

Requires downloading and running a local model with substantial hardware-dependent latency and memory use. The project reproduces an interface pattern, not Jev's architecture or training, and makes no quality-parity claim.