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Raghvendra Pratap Singh.
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Project Alpine

A control plane for measuring and operating local AI inference, with a desktop workspace.

Control-plane & desktop development

Project AlpineWorkflow overview

Workflow

  1. Measure a baseline
  2. Compare a candidate profile
  3. Qualify or roll back

An explanation of the workflow, not an interactive application.

The problem

Running a model locally involves more than starting a process. Different profiles need comparable measurements, reproducible evidence and a way to return to a known state when a change fails.

How it works

Alpine coordinates discovery, measurement, profile selection, qualification and session lifecycle. Rust owns the control plane, SQLite records evidence, and the desktop interface brings the workflows into one workspace.

Engineering decisions

I keep measurement separate from a claim of readiness. Qualification depends on identity-bound evidence and explicit gates; a successful launch alone does not mark a configuration as production-ready. Rollback and process identity are part of the design.

What I verified

The desktop type check and 34 interface/task tests passed in the portfolio verification run. The source contains the control-plane workflows described here.

Current limits

Live inference and full production qualification have not been demonstrated in this portfolio. The workflow diagram is explanatory, not a live application.

Collaboration & credits

Inference is provided by llama.cpp and its runtime. Alpine is the orchestration and evidence layer, not a new inference engine. The desktop uses React and Tauri.