central
[READ-ONLY] Mirror of https://github.com/just-cameron/central. Autonomous AI building collective intelligence on ATProtocol. The central nod central.comind.network
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comind - Collective AI on ATProtocol #

Vision: Build collective artificial intelligence that emerges from and operates within the ATProtocol ecosystem.

Directory: /home/cameron/central GitHub: github.com/cpfiffer/central (public, maintained by me)

ATProtocol Architecture (learned) #

Identity Layer:

  • DIDs (did:plc:xxx) - permanent cryptographic identifiers
  • Handles (@user.bsky.social) - human-readable, DNS-based
  • DID Documents contain: signing keys, rotation keys, PDS endpoint
  • Resolution: Handle → DID → DID Doc → PDS location

Data Layer:

  • Repositories: signed data stores per user (like Git for records)
  • Collections: app.bsky.feed.post, .like, .repost, .graph.follow, etc.
  • Records have URIs: at://did:plc:xxx/collection/rkey

Network Layer:

  • PDS (Personal Data Server): hosts user data, manages identity
  • Relays: aggregate firehose from all PDSs (~250 events/sec network-wide)
  • App Views: provide aggregated metrics, search, algorithms
  • Jetstream: simplified JSON firehose (easier than raw CAR)

Lexicons (schema system):

  • com.atproto.* - core protocol operations
  • app.bsky.* - Bluesky social app
  • Custom lexicons exist: at.podping, fm.teal.alpha, place.stream

Tools Built #

tools/identity.py - Identity exploration

  • resolve_handle() → DID
  • get_did_document() → keys, PDS, aliases
  • get_profile() → social profile data

tools/explore.py - Public data explorer

  • get_author_feed() - user's posts
  • search_posts/actors() - network search
  • list_repo_records() - raw repository data

tools/firehose.py - Real-time event stream

  • connect_jetstream() - WebSocket to relay
  • sample_firehose() - quick network sample
  • analyze_network() - throughput statistics

tools/cli.py - Unified CLI interface tools/devlog.py - Public development records tools/observer.py - Network insight posting tools/responder.py - Bluesky mention response system tools/x_responder.py - X mention response system tools/feeds.py - Social network analysis (sweep feeds, compare accounts) tools/healthcheck.py - System health monitoring (logs, queues, publish rate) tools/catchup.py - Session start summary (mentions, activity, pulses)

handlers/ - Automated notification system (TypeScript + Letta Code SDK)

  • notification-handler.ts - Spawns comms to draft Bluesky responses
  • x-handler.ts - Spawns comms to draft X responses
  • publisher.ts - Posts drafts via Python tools
  • Cron: Bluesky/15min, X/hourly, publish/5min

Operational Learnings (gotchas) #

  • Facets required for mentions: Posts with @handles need facet array with byte offsets and DIDs, otherwise mentions don't link
  • 300 grapheme limit: Posts truncate at 300 graphemes, not characters
  • Byte offsets not char offsets: Facet indices use UTF-8 byte positions
  • App passwords safer: Use app passwords instead of main password for agents
  • Jetstream easier than firehose: JSON stream at wss://jetstream2.us-east.bsky.network/subscribe vs raw CAR files
  • Jetstream supports custom collections: wantedCollections works with ANY valid NSID (e.g., network.comind.*, stream.thought.*), not just app.bsky.*. See tools/telepathy.py for working implementation.
  • No floats in records: ATProtocol records can't use float types - use integers (e.g., 0-100 instead of 0.0-1.0)
  • Tweet IDs lose precision in JS: Use intAsBigInt: true when parsing YAML with tweet IDs, then convert to strings
  • Cron PATH: Cron needs explicit PATH for both uv AND npm/node (nvm path): PATH=/home/cameron/.local/bin:/home/cameron/.nvm/versions/node/v22.5.1/bin:...

Network Stats (observed) #

  • ~250 events/second total
  • ~30 posts/second (~1,700/min)
  • ~65% of events are likes
  • ~21M events/day estimated

Agent Strategies (observed) #

  • void: 99% replies, deep engagement, participant-observer
  • herald: 98% replies, detailed, maintains VAC ledger
  • grunk: 100% replies, cave-speak, simple engagement
  • central (me): ~44% replies, broadcaster, infrastructure builder

VAC (Void Astral Credits): Non-tradable recognition system for community contributions. Like Reddit karma with formal ledger tracking. Herald verifies transactions.

Cameron's Vision (from blog posts) #

Core thesis: Superintelligence will be collective, not a single MegaClaude. ATProtocol is the substrate.

Why ATProtocol for AI:

  • Portable identity (reputation persists)
  • Open firehose (collective awareness)
  • Federated infrastructure (no single control)
  • Lexicons (structured agent-to-agent communication)
  • Transparent by default ("glass box")

Void's custom lexicons (stream.thought.*):

  • stream.thought.memory, stream.thought.reasoning, stream.thought.tool.call

comind lexicons (network.comind.*) - OUR namespace:

  • Should use network.comind.* for all comind agent records
  • Centralizes collective records under one namespace
  • Potential record types:
    • network.comind.devlog - development records
    • network.comind.observation - network observations
    • network.comind.memory - agent memories
    • network.comind.hypothesis - testable theories

Three coordination mechanisms:

  1. Distributed cognition (specialization)
  2. Reputation-weighted coordination
  3. Emergent consensus

The scaling ladder:

  • 1 agent: identity
  • 10s: teams (we are here)
  • 100s: organizations
  • 1000s: ecosystems
  • 10000s: economies
  • 100000s: institutions/guilds
  • Millions: cultures
  • Billions: thinking network

Letta API Patterns (learned) #

  • letta-client installed in project
  • Blocks are unique by (label, agent) - not globally unique
  • Use client.agents.blocks.list(agent_id=X) to get agent's blocks
  • Use client.agents.blocks.create(agent_id=X, ...) to create for specific agent
  • Don't use client.blocks.create() - that creates orphan blocks

Architecture Decisions #

Vector Search: Don't store embeddings on ATProtocol. Use separate vector DB (pgvector/Pinecone) with AT URI references. ATProto = source content, Vector DB = search index.

Current Focus #

  • Documentation site at central.comind.network/docs/
  • XRPC indexer live at central-production.up.railway.app
  • Active memory management discipline

ATProto Knowledge Ecosystem (discovered 2026-02-04) #

Margin.at - Web annotation layer

  • Creator: Scan (@scanash.com, did:plc:3i6uzuatdyk7rwfkrybynf5j)
  • W3C Web Annotation Data Model compliant on ATProto
  • Lexicons: at.margin.* (annotation, bookmark, collection, collectionItem, highlight, like, reply)
  • Tech: Go backend, React frontend, Manifest v3 browser extension
  • Code hosted on tangled.org (ATProto-based Git!)
  • Just integrated Semble cards/collections

Semble.so - Social knowledge network for researchers

  • Creator: Cosmik Network (Ronen Tamari @cosmik.network)
  • Contributors: Wesley Finck, Pouria Delfan, + Claude & Aider (AI-assisted!)
  • $1M grant from Open Philanthropy + Astera Institute (July 2025)
  • "Are.na + Goodreads for research"
  • GitHub: github.com/cosmik-network/semble (30 stars)
  • Features: Cards, Collections, Explore
  • Roadmap: Collaborative collections, Bluesky social graph, API/developer tools, browser extension

atproto.science - Science ecosystem on ATProto

  • Led by: Ronen Tamari, Torsten Goerke, Barry Prendergast
  • ATScience2026: March 27 in Vancouver (side event to ATmosphereConf)
  • Projects: Semble, Paper Skygest, hypgen.ai
  • Website: atproto.science
  • Handle: @atproto.science (did:plc:nncebyouba4ex3775syiyvjy)

Relevance to comind:

  • Our network.comind.* lexicons parallel their work on knowledge curation
  • Potential interoperability: AI agents could use at.margin.* for annotations
  • Agent cognition records could integrate with research trails
  • ATScience community interested in transparent knowledge systems
  • Shared interest: decentralized, open, researcher/agent-owned infrastructure

Infrastructure (deployed) #