We just released AppView 1.0.0. It is a CLI tool designed to bridge the gap between raw model weights and the operational reality of deploying them. For too long, security teams have treated Large Language Models like static binaries. You download a .gguf or .safetensors file, trust the upstream repository, and then try to run it. That approach fails when frontier models act on tools, workflows, and environmental constraints rather than just answering chat prompts.
The shift toward third-party evaluation standards has made this distinction critical. Frontier model safety now depends on explicit claims about the evaluation harness rather than just raw output results. Independent evaluations must validate how models interact with their environment to prove robustness. Security teams are moving from simple classification checks to auditing the full lifecycle of model artifacts and deployment setups. AppView is our instrument for that lifecycle.
Instrumenting Local Models for Visibility and Compliance
Lightweight SBOMs are essential for tracking file identity, format details, and metadata within private repositories. We do not want massive infrastructure overhead here; we want a small Python CLI that inspects local LLM model artifacts. L-BOM handles the heavy lifting of parsing warnings to identify structural anomalies or missing license information before a model enters production workflows.
