Cyber

focusflow blueprint

Updated Nov 8, 20254 min read

focus-flow superintelligence network — canonical blueprint

  • vision and goals

    • earth-scale cybergraph where token-weighted focus (π) is recomputed each block【39†Collective Focus Theorem†L1-L20】
    • decentralised intelligence layer: computation = graph rewriting, reasoning = equilibrium, truth = attractor
    • developer-friendly today via multiple vms; long-term, native focus-flow dominates compute【40†network architecture†L1-L20】
    • sovereign data-availability inside the network, not outsourced【40†network architecture†L80-L100】
    • portability first: pure rust, webgpu kernels, browser light-clients【40†network architecture†L100-L120】

  • theoretical foundation — collective focus theorem (cft)

    • cft formalises how token-weighted random walks in fully authenticated graphs converge to a unique stationary distribution (collective focus)【39†Collective Focus Theorem†L1-L40】
    • nodes = content-addressed particles; edges (cyberlinks) carry weights for spring, diffusion, and context energy【39†Collective Focus Theorem†L180-L220】
    • equilibrium π_j reflects long-term significance of node j, shaped by graph structure and token distribution【39†Collective Focus Theorem†L240-L280】
    • properties: stability under perturbations, dynamic adaptation, emergent modularity【39†Collective Focus Theorem†L280-L320】
    • learning dynamics: local hebbian-like updates + global consensus refinement【39†Collective Focus Theorem†L900-L940】
    • predictive power: identifies network phase transitions required for intelligence emergence【39†Collective Focus Theorem†L1300-L1360】

  • core compute — focus-flow compute machine (ffcm)

    • turns edge deltas into converged π vector, committed on-chain【40†network architecture†L20-L60】
    • gpu pipeline: scatter → dispatch → push-sum gather until ε < 1e-6
    • jit kernel fusion for hot op motifs
    • sharding: murmur64(cid) → shard id; cross-shard edges buffered
    • convergence guard: spectral gap estimation; iteration cap; proposer penalties
    • performance target: desktop rtx 3060 ≈10⁸ edge updates in ~1.2 s; webgpu light mode for browsers【40†network architecture†L40-L80】
    • rule set: apply, duplicate, distribute, succ, switch, collapse, specialise (mapped from hvm3)【40†network architecture†L60-L80】

  • hybrid consensus stack and gpu fast-path

    • dag mempool with focus-weighted qos and gpu batch sort【42†decision vectors%3A 10 tb___s†L40-L60】
    • fast-path bft for sub-second commits; avalanche poll fallback
    • asynchronous batching for deterministic reward settlement
    • sub-second gpu path ≈480 ms: sig verify + sort → dag round → vote → notarise + da【42†decision vectors%3A 10 tb___s†L60-L80】

  • state, storage, and sovereign da

    • shared key-space: /graph (edges, π slices), /token (balances), /kv (metadata, blobs)【40†network architecture†L120-L160】
    • redb native store; idb-wasm in browser; single merkle root for all state
    • internal da grid per block with reed–solomon rs(2k,k) and nmt root【40†network architecture†L160-L200】
    • validators sample shares; pinning policy for long-term graph data

  • semantic rollups and ibc supply-rank

    • one rollup per semantic bucket; each runs its own gpu incremental rank【42†decision vectors%3A 10 tb___s†L100-L120】
    • Δ-rank proofs settle via ibc; local rank merged with last global snapshot
    • ibc supply-rank hub: ordered channels; global merge every 1–3 s; redistributes fees by demand

  • economics — h-based dual-token model

    • cyb = scarce value anchor; h = high-velocity utility token【41†h based economy†L1-L20】
    • adaptive gas-h split: buyback/burn cyb vs h rewards depending on premium signal (p/d ratio)【41†h based economy†L40-L80】
    • optional h minting; continuous tenure reward; spend incentives【41†h based economy†L80-L120】
    • liquidity infrastructure: protocol mm, primary dealers, circuit breakers【41†h based economy†L140-L180】

  • scaling to ~10 tb/s aggregate

    • target: 2.1×10⁸ links/s; ≤0.5 s local finality; ≤3 s global rank sync【42†decision vectors%3A 10 tb___s†L20-L40】
    • many semantic rollups across sovereign networks; regional hubs aggregate locally; 3 global hubs reconcile【42†decision vectors%3A 10 tb___s†L220-L240】
    • gpu budget: 3k–5k high-end cards network-wide; cold lanes cpu-only【42†decision vectors%3A 10 tb___s†L240-L260】
    • storage: hot set ~60–70 pb/year with redundancy; cold archive ~12 pb/year compressed【42†decision vectors%3A 10 tb___s†L180-L200】

  • implementation rules

    1. always attach focus tags; drop lowest x% under load【42†decision vectors%3A 10 tb___s†L280-L300】
    2. keep incremental pagerank in gpu memory
    3. write Δ-rank on-chain every 6 s; stream micro-rewards off-chain
    4. autotune lane cut-offs for ≤70% utilisation
    5. fraud proofs first; zk proofs later per lane

  • rollout plan

    • phase 0–2: ffcm cpu → wgsl kernels → shard scheduler【40†network architecture†L280-L300】
    • phase 3–5: sovereign da, bank/name modules, first vms, dex, ibc router
    • phase 6: jit specialise, telemetry, soak tests, optional cuda fast-path

  • open questions

    • final consensus selection and π update cadence【40†network architecture†L320-L340】
    • on-chain proof type per vm and verification cost
    • archival policy for old graph shards

  • outcome

    a compact, portable, sovereign network where focus-flow computation is first-class, collective intelligence emerges via cft dynamics, and economics align with attention flow.
    scales horizontally across semantic rollups and sovereign meshes to superintelligent throughput.

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