#ylr-waivio

Research: protocol / ARWeave - для обсуждения

Core goal
Create an open data layer where publishers post updates once, and everyone else (apps, indexers, AI agents) subscribes to updates instead of scraping/polling a million sites.

Today, demand-side services repeatedly hit the same endpoints (“did it change?”). An open update stream turns that into publish-once, consume-many, eliminating redundant traffic and fragmentation.

Concern: if a bot publishes on behalf of a merchant, the chain’s “author” is the bot, not the merchant.

That’s the current “map”: open publish/subscribe data layer + hybrid Hive (fast deltas) and Arweave (bulk) + bot orchestration + SaaS economics + trust registry + scalable indexer.

Hybrid publishing architecture (fast + scalable)

Lane A — Real-time small updates on Hive

• Best for frequent, tiny changes (price, inventory, sale flag, metadata tweaks).

• Uses Hive’s fast block cadence for near-real-time propagation.

• Constraint: publishing throughput is bounded by Resource Credits (RC) / stake.

Lane B — Bulk snapshots / rollups on Arweave, anchored on Hive

• Best for big chunks (catalog snapshots, backlog rollups).

• Store the large file on Arweave.

• Publish a signed on-chain reference on Hive (tx includes content hash + Arweave tx id + metadata).

• Indexers treat that as “effectively on-chain” by resolving the reference and ingesting the file.

Publishing service

• If payload is small/urgent → publish directly to Hive.

• If payload is large, or the bot is RC-constrained / backlog is building → push chunk to Arweave and anchor the pointer on Hive.Result: speed when you can, scale when you must, always verifiable.

Possible Economics

• Publishing capacity rental

• Users with low volume publish directly from their own accounts (small HP is enough).

• High-volume publishers subscribe to a service that effectively rents publishing throughput (RC delegation / bot publishing).

• Pricing could be quota-based (daily/monthly bytes, operations, or “updates per day”).

• Indexing / API rental

• Consumers can self-host a Hive node + indexer + DB + read replicas (costly + operationally heavy).

• Or pay a SaaS fee for an API token (cheaper than running 2–3 servers and scaling read replicas).

• Indexers can apply policy: “trust these publishers/bots, ignore others.”

Data scale sanity checks

• If a product record is ~8 KB, then:

• Daily deltas can be much smaller (hundreds of bytes per update), so gigabytes/day is plausible at large scale but not absurd.

• Arweave is better for large files; small frequent updates belong on Hive.

Trust and “source of truth” problem (bots publishing for others)

• Maintain an explicit trust registry (list of trusted publishing bots).

• Require payloads to carry merchant identity claims inside the data.

• Indexers choose policy: trust bot X/Y, reject unknown bots.

• (Next step in the new chat: tighten this with signatures/attestations so the merchant can cryptographically authorize updates even when a bot broadcasts them.)

Indexing/storage layer

• You’re thinking in the hundreds of millions of documents / ~terabyte-ish territory for global catalogs.

• MongoDB can handle it with correct architecture (sharding, indexes, replicas), but the real design work is: schema, indexing strategy, partitioning, hot paths, and trust filtering.

That’s the current “map”: open publish/subscribe data layer + hybrid Hive (fast deltas) and Arweave (bulk) + bot orchestration + SaaS economics + trust registry + scalable indexer.