Frame Zero

The Problem

Crypto analysts, comms teams, and protocol researchers spend most of their week doing the same work manually: skimming Google Alerts, copying paragraphs into Notion, cross-referencing Twitter, posting summaries into Telegram, generating briefs for leadership, and trying to remember which article contained which claim when someone asks, “Are you sure that number is right?”

Three things break:

1. The work doesn’t compound
   Every week starts from zero. The graph of who-funded-whom, who-regulates-whom, and what-was-said-when exists only in the analyst’s head and scattered documents.

2. The output isn’t verifiable
   When leadership questions a claim, the analyst has to chase the original article from memory. There’s no signed receipt proving which sources and models generated a brief.

3. The tools are scattered
   Google Alerts, Notion, Twitter lists, RSS readers, Telegram, Discord — every workflow is fragmented across different tools and contexts.

The crypto-native ecosystem feels this pain even more because information moves fast, sources are unconventional (Twitter threads, governance forums, on-chain events), and compliance expectations are increasing. Existing AI tools only help with summarization; they ignore the compounding layer entirely. Every summary is generated once and forgotten immediately.

Frame0
Transforms fragmented crypto information into a typed knowledge graph that compounds over time, with every inference and artifact anchored to 0G for verifiability.

Users enter a topic. The agent classifies it, creates a persistent watcher, and begins ingesting from selected sources. Every run produces:

• An editorial brief written in the analyst’s voice

• A growing typed knowledge graph with evidence-backed relationships

• Alerts triggered by material changes

• A signed inference trace for every LLM call

• A verifiable content hash anchored to 0G Storage

When an alert fires, it pushes to Telegram. When someone questions a claim, users can click directly into the graph to inspect the original evidence and source article. When auditors ask where a number came from, the platform provides the trace ID and 0G Storage root hash.

Frame0 is built on 0G because the primitives required for a verifiable analyst desk already exist:

• 0G Compute for signed LLM inference

• 0G Storage for low-cost immutable anchoring

• 0G Chain for on-chain payments and access control

Knowledge Graph

• Commission a topic
  A single text input is classified into one of seven entity types:

• Typed relationships
  Sixteen relationship types are supported, including:

• Articles as graph nodes
  Every processed article becomes an event:article-* node connected through mentions edges to related entities, ensuring sparse extractions still build navigable graphs.

• CoinGecko enrichment
  Token and protocol entities include:

  • Live price

  • Market cap

  • 24h / 7d change

  • All-time high

Editorial Agent

• Per-run briefs
  The system selects relevant articles and generates concise editorial summaries persisted alongside inference trace IDs.

• 7-day executive briefing
  The last seven days of activity are condensed into an executive-level summary.

• Chat with your graph
  Users can query their commission directly. Responses are grounded strictly in:

  • Entities

  • Relationships

  • Briefs

  • Uploaded datasets

  The system refuses answers outside available context.

Sources and Ingestion

• Bring-your-own RSS and YouTube sources
  Sources are attached per commission and merged into a shared ingestion pipeline.

• Google News fallback
  When user-defined sources return nothing, the system automatically dispatches targeted Google News RSS queries.

• CSV upload pipeline

  • Up to 20 rows per file

  • Each row passes through the same typed extraction engine

  • SHA-256 hash computed before inference begins

Editorial Agent Loop (The Core Flow)

Commission a topic → classify the entity → fetch news from cache, BYO RSS feeds, and Google News fallback → process up to 8 articles → generate editorial briefs → extract typed entities and relationships → persist with provenance → evaluate alerts → deliver notifications to app logs, webhooks, or Telegram.

Typed Knowledge Graph

• 7 entity types

  • Token

  • Protocol

  • Company

  • Person

  • Jurisdiction

  • Event

  • Topic

• Schema-aware relationships
  Domain/range validation rejects invalid relationships between entity types.

• Articles as graph nodes
  Every article becomes an event:article-* node connected through mentions edges, allowing sparse extractions to still create navigable graphs.

• CoinGecko enrichment
  Token and protocol entities include:

  • Market cap

  • 24h / 7d price change

  • All-time high

User-Facing Surfaces

• /dashboard
  Live force-directed graph with commissions, entity exploration, and timeline view.

• /chat
  Per-commission Q&A grounded only in:

  • Entities

  • Relationships

  • Briefs

  • Uploaded datasets

  Responses include model trace IDs and refuse when data is missing.

• /sources
  RSS feeds and YouTube ingestion merged with the global news cache.

• /vault
  Unified table of briefs, uploads, and content hashes with anchored-on-0G statistics.

• /agent
  Filterable audit log for:

  • Inference calls

  • Brief generations

  • Alert triggers

  • Upload events

  • Source additions

Alerts and Delivery

• Delivery channels

  • In-app logs

  • Webhooks

  • Telegram

• Telegram subscriptions
  Separate toggles for:

  • Alerts

  • Brief digests

  Digests bundle multiple article updates into a single message.

• Telegram onboarding bot
  @ogtimes_bot onboarding through /start.

Building this as a frontier in Knowledge Graphs and AI infra on 0g.
Applied for a16z and booked calls with clients on exploring the usecase beyond current compass.