DataHub Hackathon · a MoreSalamander StudioLabs Production

Entropy OS

Every number behind this door is a frozen export of a real local DataHub instance (datahub docker quickstart) populated by Veritas's emitters during the hackathon window — no mockups. Regenerate it yourself with tools/export_graph.py.

The organism, at a glance
Entropy OS an OS earned by structure — kernel: Veritas (trust) · IPC: DataHub (provenance) · package manager: the Vending Machine open ▾

Entropy OS is the organism; Veritas Dynamics is its nervous system. During the hackathon, that nervous system got a metadata backbone: every org run, gate verdict, artifact and agent action is now published to DataHub as datasets, lineage edges and assertions — so the trust story is queryable governance, not a README claim. Open it, layer by layer.

Opportunity [Agency AI] cross-engine arbitration — allocates the day's mission across engines; arbitrates priority, never truth open ▾

Every pick below already passed its own engine's fail-closed gate. New in the hackathon window: gate-verified opportunities flow through a real ETL into Postgres, where the DataHub Analytics Agent answers plain-English questions about them with DataHub context (34fd787). The tag economy on the real graph right now:

🥫 The Vending Machine disposable AI engineering — and what populates it open ▾
1 · A verified build

Only artifacts that passed the gates reach the machine — the same runs whose verdicts you can read as DataHub assertions below. The machine sells nothing unverified.

2 · Tutorial generation

The build explains itself: generated step-by-step lessons over the verified code (826f58a), plus taichi-academy projects packaged the same way (f5e0bfb).

3 · A disposable container

Lesson + code ship as a throwaway container from the Hub's Vending Machine tab (38a0354); the hunter bridge spawns real disposable containers the same way (3d1d9d5).

The artifact floor — one real run, receipts included

Straight from the export: an org run (the logical parent) with lineage edges to its outcome datasets (the physical children), each guarded by a DataHub assertion carrying the actual gate verdict. Failures are shown because fail-closed systems have failures — that's the point.

And the real-domain slice: Stage 5 publishes Crypto Hunter AI's actually-hunted opportunities as standalone governed datasets (URN family crypto-hunter-opp_…), their trust carried as tags and glossary terms — the same graph the tag economy above is counted from.

The metadata backbone — built in the hackathon window