f1449af45b
psyc demo now closes with cockpit links pointing at the Worker Mesh and reports whether the live model server is up. README rewritten to current state — Worker Mesh, inference server, model-in-operation, the three services, accurate code layout. Adds docs/demo.md, a one-page run-sheet. Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
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2.3 KiB
psyc — demo run-sheet
A ~5-minute walk-through of the platform; ~10 min including setup.
0. Setup (once)
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:
# 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
.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
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Case Queue — http://127.0.0.1:8767/cases 30+ cases across three feeds, with severity + TLP badges. "Three sources, one normalized case object."
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Worker Mesh — open the journey link
psyc demoprinted. 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).
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Ledger — http://127.0.0.1:8767/ledger Every submission and every blocked route, immutably recorded.
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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.