ARC-Neuron LLMBuilder: Building a Local-First AI Model Growth and Evaluation Runtime

I’m building ARC-Neuron LLMBuilder , a local-first AI model lifecycle framework focused on small-model improvement, benchmark receipts, dataset-connected training paths, and governed candidate promotion.

The goal is not just to wrap an existing model. The goal is to build a repeatable system where model candidates, datasets, evaluations, receipts, promotion decisions, and archive lineage can all be tracked in a way that is inspectable and reproducible.

What ARC-Neuron LLMBuilder is

ARC-Neuron LLMBuilder is designed as a local-first framework for building and improving AI models through a governed lifecycle.

The core idea is:

datasets → candidates → evaluations → receipts → promotion gates → archived lineage → next candidate

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Instead of treating model building as a black box, the project focuses on making each step visible:

what data was used

what candidate was…