# psyc — demo run-sheet

A ~5-minute walk-through of the platform; ~10 min including setup.

## 0. Setup (once)

```bash
python3 -m virtualenv .venv
.venv/bin/pip install -e .
.venv/bin/psyc init
```

## 1. Start the services

Separate terminals — the third is optional and needs an NVIDIA GPU:

```bash
# terminal 1 — operator cockpit
.venv/bin/psyc serve --port 8767

# terminal 2 — stand-in CERT / abuse-API receiver
.venv/bin/psyc mock-cert --port 8770

# terminal 3 — live model behind the Classifier bot (optional)
docker run --gpus all --rm -p 8771:8771 --entrypoint python \
    -v $(pwd)/data:/data -v $(pwd)/scripts:/scripts \
    psyc-trainer /scripts/serve_model.py --adapter /data/adapters/psyc-v4/final
```

## 2. Run the pipeline

```bash
.venv/bin/psyc fetch-all     # ingest URLhaus + CISA KEV + Feodo Tracker
.venv/bin/psyc demo          # one case end-to-end; prints the cockpit links
```

## 3. The walk-through

1. **Case Queue** — http://127.0.0.1:8767/cases
   30+ cases across three feeds, with severity + TLP badges. *"Three sources,
   one normalized case object."*

2. **Worker Mesh** — open the journey link `psyc demo` printed. This is the
   centerpiece: seven robot agents, a case token flowing through, each bot
   waking to perform its action and speak its real answer. Hit **▶ replay**.
   - **Classifier bot** carries a live verdict from the fine-tuned psyc-v4
     model — green when the model agrees with the rule, amber when it differs.
   - **Sealer** — evidence encrypted to authority public keys (PyNaCl sealed box).
   - **Router** — destinations cleared vs. policy-blocked (TLP ceiling, country).

3. **Ledger** — http://127.0.0.1:8767/ledger
   Every submission and every blocked route, immutably recorded.

4. **Trainline** — http://127.0.0.1:8767/train
   The four task datasets and the trained adapters with their loss curves.

## Talking points

- **Defensive only** — psyc never amplifies stolen data or contacts criminal
  actors; routing is gated by TLP, jurisdiction, and incident type.
- **Rules + model** — deterministic work is rule-based; the fine-tuned model
  handles judgment. One bot is genuinely a live model, not animation over rules.
- **Honest about limits** — psyc-v4 evals 7/8 on severity; the one miss is a
  documented data-scarcity case (one online-botnet example), not a bug, and was
  not gamed away.