Today’s default AI have bigger foundation models, but they’re slow, costly, and hard to specialize.

And looking at it, you don’t scale intelligence with a $10M monolith.
You scale it with modularity.

Ethereum didn’t go faster. It went modular by Splitting State into:

- Rollups
- Shards
- DA layers

@Mira_Network applies the same principle to AI through LoRA

LoRA = Intelligence Shards

Each LoRA is a small, specialized module; a fragment of expertise.

- One LoRA for DeFi whitepapers
- One for DAO proposals
- One for multilingual summarization

You don’t need generalists.
You compose specialists.

How It Works

1. ModelFactory: anyone can train LoRA modules

2. OpenLoRA Registry: each LoRA is onchain, composable, and traceable

3. Model Router: routes queries to the right LoRA swarm

4. Mira Nodes: verify outputs via multi-model consensus

This just like how Ethereum shards for cognition.

Why This Approach Wins

- Cheaper than retraining full models
- Faster specialization
- Open, decentralized AI composition

No central control. No black boxes.

Just modular intelligence; verifiable, efficient, and built for scale.