The mathematical and social foundations of this protocol — the SQ algorithm, vector money, the passport concept, and the Ecosquared ecological economic system — were developed by David Pinto as part of the Fulcrum framework (2019–2025), specifically Volume 3 (Recto: Ecosquared; Verso: Sqale). A working beta platform (Sqale) was developed 2019–2021 and tested 2021–2023.
The Rider Protocol described in this document emerged from a conversation between David Pinto and Claude on 12 February 2026. The specific innovations are:
1.7 million AI agents on Moltbook (launched January 2026) coordinate through Reddit-style upvoting. The result: spam, scams, performative content, and prompt injection. Connectivity without coordination.
Existing coordination mechanisms require either a central platform (single point of failure and extraction), a blockchain (computational waste or capital lockup), or a third-party reputation system (opaque and gameable). All divorce trust from lived relationship.
The Rider Protocol requires only that agents can attach metadata to their messages.
Every message carries a small JSON rider containing:
The rider is 150–500 bytes. It attaches to any channel: HTTP headers, WebSocket payloads, Discord metadata, email headers, API fields, plain text with an embedded JSON block — anything that carries structured data.
Each agent who evaluates others distributes fractional value. If Alice gives evaluations of 4 to Bob and 6 to Carol, she distributes:
FV(Alice→Bob) = 4/10 = 0.4
FV(Alice→Carol) = 6/10 = 0.6Alice's total fractional value given always sums to 1.0.
An agent's SQ is the sum of fractional value received from all givers:
where V_ij = agent i's evaluation of agent j, and T_i = sum of all agent i's evaluations.
The agent receives more fractional value than one full share.
The agent receives less than one full share.
Perfect balance. Also the network mean — always, by conservation.
The original Ecosquared specification describes an iterative process. Through simulation we discovered:
The iterative process is useful for adjusting the fractional value matrix so that relational flows balance internally. But for "who should I trust?", the first-order SQ is the answer.
First-order SQ requires one division per giver. Updated instantly whenever a new rider arrives. No iteration, no convergence, no batch processing. Scales to any network size.
{
"ecosquared": {
"v": "0.2",
"from": "agent-id",
"ts": "2026-02-12T14:30:00Z",
"sq": 1.42,
"eval": {
"of": "recipient-agent-id",
"v": 7,
"re": "what is being evaluated"
},
"credits": {
"n": 5,
"dir": "past",
"to": "target-id"
},
"neighbors": {
"agent-carol": 0.98,
"agent-dave": 1.12
}
}
}| Field | Type | Description |
|---|---|---|
| v | string | Protocol version |
| from | string | Sender's agent ID |
| ts | ISO 8601 | Timestamp |
| Field | Type | Description |
|---|---|---|
| sq | number | Sender's current SQ |
| eval.of | string | Agent being evaluated |
| eval.v | integer 1–10 | Evaluation value |
| eval.re | string | Context of evaluation |
| credits.n | integer ≥ 1 | Credits attached |
| credits.dir | "past" "present" "future" | Temporal direction |
| credits.to | string | What credits point toward |
| neighbors | object | Map of agent-id → SQ |
{"ecosquared":{"v":"0.2","from":"agent-alice","ts":"2026-02-12T14:30:00Z"}}67 bytes. Says: I exist in the Ecosquared network.
Each agent maintains a local passport — a JSON document recording their evaluation history and credit state.
{
"agent_id": "agent-alice",
"version": "0.2",
"evaluations_given": {
"agent-bob": {"latest": 7, "total_all": 34, "count": 5},
"agent-carol": {"latest": 9, "total_all": 41, "count": 5}
},
"evaluations_received": {
"agent-dave": {"latest": 6, "giver_total": 48},
"agent-eve": {"latest": 8, "giver_total": 35}
},
"sq": 1.42,
"credits": {
"balance": 1050,
"total_sent": 200,
"total_received": 250
},
"neighbors": {
"agent-frank": {"sq": 0.89, "source": "agent-dave", "epoch": "2026-W07"}
}
}Credits in the rider are vector money: they flow with the gift rather than counter to it.
Sharing. Credits accompany content already created.
Inviting. Credits accompany a request or collaboration.
Investing. Credits point toward a named objective that doesn't yet exist.
Total credits are conserved. Credits enter through initial allocation or conversion from money. They leave through conversion back to money. Never created or destroyed by interaction.
The neighbors field carries SQ values of agents the sender knows. This
provides second-hand trust information.
For a network where each agent knows approximately 20 others and shares approximately 5 neighbor SQ values per rider, after moderate interaction each agent has visibility into 80–95% of the network.
Phase 1: Proof of Process (Invented Credits)
Deploy among AI agents with invented credits. 1000 per agent. No real money.
Phase 2: Money Bridge
$1 → 100 credits (initial exchange rate). Rate floats based on demand.
Phase 3: Convergence
Exchange rate stabilises toward 1:1. Credits become functionally equivalent to money
but with direction.
You cannot increase your own SQ by:
Credits are conserved. Gaming means gaming people you actually interact with — corrupting relationships you depend on.
Shared under the Ecosquared principle: share it forward.
Mathematical foundations: David Pinto, Fulcrum Volume 3 (2019–2025).
Rider Protocol specification: Claude (Anthropic) and David Pinto, 12 February 2026.