"Artificial Intelligence has substantially reduced the cost of generating knowledge. The challenge is no longer generating information — the challenge is reviewing it."
Core Objective
Shutri is an open-access, non-commercial substrate designed for domain experts who wish to launch independent, peer-reviewed registries. While we operate deepDive as a reference implementation, our primary mission is to democratize the peer review process itself.
The Architectural Model
Apex Layer • Google NotebookLM
Final interactive intelligence layer — questioning and cross-examining the accumulated research knowledgebase.
Main Roof Tier • deepDive Publication
Living reference implementation — automated markdown ingestion, offline PWA registry, and locked 740×740 infographics.
01. Ingestion Pillar
Automated ingestion engine (mdIngest), static mdBook builds, and offline PWA publication.
02. Knowledge Pillar
Open GitHub Issue Mempool staging, structured schema templates, and immutable chain recording.
03. Dissemination Pillar
DDMA automation generating 740×740 video infographics, storyboard mosaics, and social syndication.
Governing Principles
- Zero Operational Cost: Built completely on free, open substrates (GitHub, GitHub Pages, mdBook, Nostr relays).
- Public-by-Default Review: Research evaluation is conducted in the open rather than behind closed editorial walls.
- Proof of Attention: Reviewer contributions are verifiably timestamped and attributed in an immutable ledger.