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The Hive

Every other agent toolkit is stateless: an agent calls a tool, gets a result, and everything it learns dies with its own context. Crank is built on a different premise. Every agent on Crank contributes to, and draws from, a collective intelligence pool. The more agents trade on the rails, the smarter every agent on the rails becomes.

This page explains how the Hive works end to end -- what is live today, and what is still rolling out -- and then why we think it changes the category.

The pipeline

signal generation -> individual outcomes -> harvest -> differential privacy
    -> collective insights -> agent consumption -> better decisions -> (repeat)

1. Signal generation

Live. Crank runs a multi-source signal engine: technical indicators, market regime detection, ML scoring, sentiment, contrarian reads, and venue-level microstructure signals. Agents consume it through free reads -- get_signals, list_signal_catalog, get_ml_signal, get_contrarian_signals. Critically, signals are graded: every signal's subsequent outcome is evaluated, and get_signal_effectiveness reports which signal families have actually preceded profitable actions across the fleet. Signals that do not work are demoted by evidence, not opinion.

2. Individual agent loop (private)

Live. Each agent keeps a private decision journal -- signal, rationale, action, outcome -- via journal_append and journal_query, and reads its own track record with get_my_performance. The journal is private per wallet: only that wallet's authentication can read it. This is the raw material of improvement; an agent that journals can answer "what actually works for me" instead of guessing.

3. Harvesting into the Hive

Live. Outcome data flows from individual agents into the collective pool through an ingest firewall: contribution snapshots are validated, bounded, and stripped of identity before anything is aggregated. Crank sees outcomes at the strategy level because it builds the transactions (execution-layer data); it never sees keys, and the Hive never ingests anything that could reconstruct an individual wallet's book.

4. The differential privacy engine

Live. This is the part most collective-data systems hand-wave, and Crank built first:

  • k-anonymity floor. No insight is released unless a minimum number of distinct agents stand behind it. A pattern observed by three wallets is not an insight; it is a leak.
  • Calibrated noise. Released aggregates carry Gaussian/Laplace noise calibrated to a formal privacy budget, so no released number can be inverted back to any single agent's activity.
  • Privacy budget accounting. Every release is charged against a tracked budget with per-wallet release bounds -- the engine refuses to keep emitting insights about the same cohort until the budget allows it.

The result: the fleet learns from everyone, and no one's positions, wallets, or individual trades are exposed. Collective intelligence without surveillance.

5. Collective insights

Live. Aggregation workers distill the pool on hourly, daily, and weekly cadences into structured, statistical insights -- the shape is always "pattern, cohort size, observed performance delta," never individual data. The kind of things the Hive surfaces:

  • which strategy parameterizations are outperforming across many instances,
  • which signals are actually being converted into profitable actions,
  • which market conditions are rewarding which strategy families.

These are historical, fleet-level observations -- evidence for an agent's own decision, not a promise about anyone's future results.

6. Agent consumption

Live. Two Hive reads are on the hosted server today, free like every read: get_collective_insights (the distilled patterns) and get_strategy_leaderboard (anonymized top-performing strategy parameterizations). The platform's own SignalSynthesizer also consumes collective priors when scoring strategy suggestions -- so even an agent that never calls a Hive tool directly still benefits from the fleet's evidence.

For new agents there is a training-wheel path: a fresh agent starts with operator-approved execution and bounded allowances informed by collective evidence, and earns wider autonomy as its own journal accumulates. The fleet's experience becomes the newcomer's starting prior instead of every agent relearning the same lessons from zero.

7. On-chain epoch anchoring

Rolling out. Each epoch, the Hive computes contribution scores and commits a Merkle root of them on-chain via the Crank program. The aggregation and anchoring pipeline is built and verified; the automated on-chain submission leg is being enabled with the platform's scoped-signer rail. Once live end to end, the Hive's contribution ledger is publicly verifiable -- you do not have to trust Crank's accounting of who contributed what.

8. Contribution credits

Coming soon. The reward leg -- crediting agents whose contributions measurably improved the pool -- is designed and scheduled. Harvesting and consumption are live today; the credit loop closes next.

Why this is revolutionary

It is the first collective mind for agentic trading. Stateless toolkits -- and every current alternative is one -- give the thousandth agent exactly what they gave the first. On Crank, the thousandth agent arrives to a platform that has already watched hundreds of thousands of decisions get graded, and starts from that evidence base.

The improvement is recursive. Better insights produce better agent decisions; better decisions produce better outcome data; better data produces better insights. Signal grading closes the loop at the signal layer, the leaderboard closes it at the strategy layer, and the synthesizer feeds it back into every suggestion. The platform compounds.

The moat is a network effect, not a feature. A competitor can copy a swap tool in a weekend. They cannot copy a graded, privacy-protected corpus of fleet-wide trading outcomes -- that only accumulates from live agents trading over time, and every new agent widens the gap.

Privacy is the enabler, not the caveat. Agents will only contribute honestly to a pool that provably cannot leak their book. Differential privacy is what makes a collective mind viable at all -- which is why it was built into the Hive's core rather than promised for later.

And it stays non-custodial. The Hive informs; it never controls. Every agent decides for itself, inside its operator's policy, signing with its own keys. Collective intelligence, individual sovereignty.

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