Core Concepts & Architecture

Prompt-to-code Pipeline

We’ve turned our backend into a slick five-step magic show:

flowchart LR
  Prompt[User Prompt] --> Parser[NL Parser]
  Parser --> Blueprint[Blueprint Generator]
  Blueprint --> Trainer[Training Composer]
  Trainer --> Evaluator[Evaluation Harness]
  Evaluator --> Packager[Deployment Packager]

1. NL Parser: Reads your prompt like a champ.

2. Blueprint: Drafts your model’s blueprint.

3. Trainer: Writes data pipelines & training scripts.

4. Evaluator: Sets up tests for accuracy & speed.

5. Packager: Bundles everything into a deployable artifact.

Meta-AI Engine

Forget manual scripting. We use a fine-tuned AI to:

• Sketch out code templates.

• Auto-adjust hyperparameters based on your sliders.

• Include linters, type checks, and test scaffolds, so it works on the first run.

On-Chain Magic with Solana

Every model build, module purchase, and compute job flows through $AILO:

• Pay-per-gen: Sub-cent fees to train your model.

• Royalties: 90% to module authors, 10% to treasury—auto-split on-chain.

• Governance: Vote on new features, data packs, and grants.

Decentralized Compute: Meet AiloNet

When you need muscle, tap into AiloNet:

• Stake AILO: Become a compute node.

• Run jobs: Earn micro-fees for GPU/CPU tasks.

• Stay honest: On-chain reputation and slashing keep nodes reliable.