# Setup for a new user

This kit is local-first. No credential is included and no cloud service is required for
the Genesis Engine path.

## Offline path

1. Install Python 3.10 or newer and PyTorch.
2. Open [`genesis_engine/README.md`](genesis_engine/README.md).
3. Run `genesis.py`, then `serve.py` to open the local browser UI.
4. Keep each user's `config.json`, memory, and ledger local to that user.

## Quantum path

The IBM Quantum account is optional. To use your own hardware archive, set the token in
your local environment or configuration and never commit it:

```text
IBM_QUANTUM_TOKEN=your_token_here
```

The trainer can also consume an archive produced by your own measurement scripts. Every
record used as hardware provenance should carry a backend name, job identifier, shot
count, and an explicit simulation flag. Simulator records must be labelled as simulator
data, never as IBM hardware shots.

## Train and evaluate

Read [`TRAINING.md`](TRAINING.md) completely before starting a long run. Begin with a
small smoke test, keep a held-out split, compare the plain and CST arms with identical
seeds, and publish the result only after the run finishes and the checkpoint hash is
recorded.

## Privacy checklist

- Never upload `.env`, API tokens, camera/audio captures, private experience logs, or
  unreviewed dialogue memory.
- Review the file list before every Hub upload.
- Treat a model trained on private conversations as private until memorization and license
  review are complete.