World/Brain: an actual brain — procedural node+edge scaffold

Replaces the random per-id ellipsoid scatter with a fixed, deterministic brain
scaffold (frontend/src/components/world/brain.ts): two folded cerebral hemispheres
split by a longitudinal fissure (multi-octave noise = gyri/sulci), a cerebellum,
and a brain-stem — ~430 nodes wired into a k-nearest-neighbour edge network.

WorldCanvas Hierarchy renderer: the scaffold is one Points cloud + one LineSegments
(the dormant brain), with an activation field over the nodes. Agents seat into a
deterministic fixed region (agentRegion hash); their seat brightens on work and
fires activity-coloured signals along the real edges, which chain through the mesh
and light its pathways. Idle → a dim, gently-breathing brain. Same brain every
reload; orbit/pinch to see the 3D anatomy.

Co-Authored-By: Claude Opus 4.8 (1M context) <[email protected]>

frontend/src/components/world/WorldCanvas.tsx

@@ -20,6 +20,7 @@ import { useClawmatesLive, useLiveState } from "@/lib/live/useClawmatesLive";
 
 import { WorldFlow, type WorldItem } from "../dashboard/flow/WorldFlow";
 
+import { BRAIN, agentRegion } from "./brain";
 import { WorldEngine, type Formation, type WorldSeed } from "./engine";
 
 type ReplayEvent = { t: number; type: string; data: Record<string, unknown> };
@@ -69,27 +70,6 @@ function makeGlowTexture(): THREE.Texture {
   return tex;
 }
 
-/** A deterministic 3D position for an agent-neuron in the brain layout — a
- *  two-hemisphere ellipsoid keyed off the agent id. */
-function neuronPos(id: string): THREE.Vector3 {
-  let h = 2166136261;
-  for (let i = 0; i < id.length; i++) {
-    h ^= id.charCodeAt(i);
-    h = Math.imul(h, 16777619);
-  }
-  const r1 = ((h >>> 0) % 1000) / 1000;
-  const r2 = ((h >>> 7) % 1000) / 1000;
-  const r3 = ((h >>> 13) % 1000) / 1000;
-  const theta = r1 * Math.PI * 2;
-  const phi = Math.acos(2 * r2 - 1);
-  const rad = 95 + r3 * 80;
-  let x = rad * Math.sin(phi) * Math.cos(theta) * 1.35;
-  const y = rad * Math.sin(phi) * Math.sin(theta) * 0.92;
-  const z = rad * Math.cos(phi) * 0.78;
-  x += (x >= 0 ? 1 : -1) * 48; // split into two hemispheres
-  return new THREE.Vector3(x, y, z);
-}
-
 interface WorldCanvasProps {
   roots: WorldSeed[];
   selectedId: string | null;
@@ -285,19 +265,43 @@ export function WorldCanvas({ roots, selectedId, onSelect, expanded, onToggleExp
     );
     gourceGroup.add(beams);
 
-    // brain (hierarchy) — pathway edges between agent-neurons
+    // brain (hierarchy) — a FIXED procedural scaffold (nodes + edges shaped like a
+    // brain). Agents seat into fixed regions; activation diffuses through the edges.
+    const N = BRAIN.count;
+    const scaffoldGeom = new THREE.BufferGeometry();
+    scaffoldGeom.setAttribute("position", new THREE.Float32BufferAttribute(BRAIN.positions.slice(), 3));
+    const scaffoldColors = new Float32Array(N * 3);
+    scaffoldGeom.setAttribute("color", new THREE.BufferAttribute(scaffoldColors, 3));
+    const scaffoldPoints = new THREE.Points(
+      scaffoldGeom,
+      new THREE.PointsMaterial({ size: 5, map: glow, vertexColors: true, blending: THREE.AdditiveBlending, transparent: true, depthWrite: false, sizeAttenuation: true }),
+    );
+    brainGroup.add(scaffoldPoints);
+
+    const E = BRAIN.edges.length / 2; // edge count
+    const edgePos = new Float32Array(E * 2 * 3);
+    for (let e = 0; e < E; e++) {
+      const a = BRAIN.edges[e * 2];
+      const b = BRAIN.edges[e * 2 + 1];
+      edgePos[e * 6] = BRAIN.positions[a * 3];
+      edgePos[e * 6 + 1] = BRAIN.positions[a * 3 + 1];
+      edgePos[e * 6 + 2] = BRAIN.positions[a * 3 + 2];
+      edgePos[e * 6 + 3] = BRAIN.positions[b * 3];
+      edgePos[e * 6 + 4] = BRAIN.positions[b * 3 + 1];
+      edgePos[e * 6 + 5] = BRAIN.positions[b * 3 + 2];
+    }
     const brainEdgeGeom = new THREE.BufferGeometry();
+    brainEdgeGeom.setAttribute("position", new THREE.BufferAttribute(edgePos, 3));
+    const edgeColors = new Float32Array(E * 2 * 3);
+    brainEdgeGeom.setAttribute("color", new THREE.BufferAttribute(edgeColors, 3));
     const brainEdges = new THREE.LineSegments(
       brainEdgeGeom,
-      new THREE.LineBasicMaterial({ vertexColors: true, transparent: true, opacity: 0.95, blending: THREE.AdditiveBlending, depthWrite: false }),
+      new THREE.LineBasicMaterial({ vertexColors: true, transparent: true, opacity: 0.9, blending: THREE.AdditiveBlending, depthWrite: false }),
     );
     brainGroup.add(brainEdges);
-    const brainPositions = new Map<string, THREE.Vector3>();
+
-    const neuronAct = new Map<string, number>();
+    const activation = new Float32Array(N).fill(0.04); // dormant baseline
-    const neuronExp = new Map<string, number>(); // cumulative experience → neuron size
+    const neuronExp = new Map<string, number>(); // per-agent cumulative experience
-    const brainAdj = new Map<string, string[]>();
-    let brainEdgeIds: string[] = []; // flat [a0,b0,a1,b1,…] for per-edge lighting
-    let brainCount = -1;
 
     // particle system (sparks + trails): additive points, ring buffer, RGB→0 fade
     const MAXP = 1400;
@@ -403,108 +407,54 @@ export function WorldCanvas({ roots, selectedId, onSelect, expanded, onToggleExp
       particleGeom.attributes.color.needsUpdate = true;
     };
 
-    // Brain pathway graph: connect each neuron to its 3 nearest (rebuilt on count change).
+    // Brain (hierarchy): the fixed scaffold is the dormant mesh; agents seat into
-    const buildBrainGraph = (ids: string[]) => {
+    // fixed regions whose activity diffuses through the real edges + fires signals.
-      brainAdj.clear();
+    const seatPos = new THREE.Vector3();
-      const pos = ids.map((id) => brainPositions.get(id)!);
-      const segs: number[] = [];
-      const pairs: string[] = [];
-      for (let i = 0; i < ids.length; i++) {
-        const ds = ids.map((_, j) => ({ j, d: i === j ? Infinity : pos[i].distanceToSquared(pos[j]) }));
-        ds.sort((a, b) => a.d - b.d);
-        const k = Math.min(3, ids.length - 1);
-        const nbrs: string[] = [];
-        for (let n = 0; n < k; n++) {
-          const j = ds[n].j;
-          nbrs.push(ids[j]);
-          if (i < j) {
-            segs.push(pos[i].x, pos[i].y, pos[i].z, pos[j].x, pos[j].y, pos[j].z);
-            pairs.push(ids[i], ids[j]);
-          }
-        }
-        brainAdj.set(ids[i], nbrs);
-      }
-      brainEdgeIds = pairs;
-      brainEdgeGeom.setAttribute("position", new THREE.Float32BufferAttribute(segs, 3));
-      brainEdgeGeom.setAttribute("color", new THREE.Float32BufferAttribute(new Float32Array(segs.length), 3));
-      brainEdgeGeom.attributes.position.needsUpdate = true;
-    };
-
-    // Neural-brain view: agents are neurons in a dormant mesh; active agents fire
-    // signals along pathways to neighbors (chaining), so the brain comes alive.
     const renderBrain = (dt: number) => {
-      // Neurons = agents (bright, drive firing) + structure nodes (org/company/
+      // decay activation toward the dormant baseline + a resting shimmer so the
-      // team) as a dim support mesh, so even a near-idle brain has body.
+      // brain keeps breathing when idle
-      type Neuron = { id: string; color: string; fire: string; agent: boolean; boost: number };
+      for (let i = 0; i < N; i++) activation[i] = Math.max(0.04, activation[i] - dt * 0.6);
-      const neurons: Neuron[] = [];
+      for (let k = 0; k < 3; k++) {
+        const i = (Math.random() * N) | 0;
+        activation[i] = Math.min(0.6, activation[i] + 0.12);
+      }
+
+      const seen = new Set<string>();
       for (const p of engine.pawns.values()) {
-        const recent = p.idle < 2.5 ? 1 : 0; // a recent touch lights the neuron
+        seen.add(p.id);
-        neurons.push({ id: p.id, color: p.color, fire: p.fireColor ?? p.color, agent: true, boost: (p.status === "working" ? 0.9 : 0) + recent });
+        const seat = agentRegion(p.id, N);
-      }
+        const recent = p.idle < 2.5 ? 1 : 0;
-      for (const n of engine.nodes.values()) {
+        const boost = (p.status === "working" ? 0.9 : 0) + recent;
-        if (n.tier === "org" || n.tier === "company" || n.tier === "team")
+        activation[seat] = Math.min(1, activation[seat] + boost * dt * 1.6);
-          neurons.push({ id: n.id, color: n.color, fire: n.color, agent: false, boost: n.heat });
+        const act = activation[seat];
-      }
+        let exp = neuronExp.get(p.id) ?? 0;
-      const ids = neurons.map((n) => n.id);
+        exp = Math.min(1, exp + act * dt * 0.15);
-      const seen = new Set(ids);
+        neuronExp.set(p.id, exp);
-      for (const ne of neurons) {
+        let g = neuronGlows.get(p.id);
-        if (!brainPositions.has(ne.id)) brainPositions.set(ne.id, neuronPos(ne.id));
-        const pos = brainPositions.get(ne.id)!;
-        let g = neuronGlows.get(ne.id);
         if (!g) {
           g = newGlow(brainGroup);
-          neuronGlows.set(ne.id, g);
+          neuronGlows.set(p.id, g);
         }
-        let act = neuronAct.get(ne.id) ?? (ne.agent ? 0.06 : 0.03);
+        seatPos.set(BRAIN.positions[seat * 3], BRAIN.positions[seat * 3 + 1], BRAIN.positions[seat * 3 + 2]);
-        act = Math.min(1, act + ne.boost * dt * 1.3);
+        g.position.copy(seatPos);
-        if (Math.random() < dt * 0.5) act = Math.min(1, act + 0.08); // resting shimmer → the brain breathes
+        const gs = 18 + exp * 14 + act * 30;
-        act = Math.max(ne.agent ? 0.06 : 0.025, act - dt * 0.22);
-        neuronAct.set(ne.id, act);
-        let exp = neuronExp.get(ne.id) ?? 0;
-        exp = Math.min(1, exp + act * dt * 0.15); // experience grows with activity
-        neuronExp.set(ne.id, exp);
-        g.position.copy(pos);
-        const base = (ne.agent ? 16 : 8) + exp * (ne.agent ? 16 : 7);
-        const gs = base + act * (ne.agent ? 36 : 16);
         g.scale.set(gs, gs, 1);
         const gm = g.material as THREE.SpriteMaterial;
-        gm.color.set(ne.color);
+        gm.color.set(p.color);
-        gm.opacity = (ne.agent ? 0.16 : 0.06) + act * (ne.agent ? 0.84 : 0.5);
+        gm.opacity = 0.3 + act * 0.7;
-        // fire a signal along a pathway (chaining through the mesh)
+        // fire a signal along a real edge to a neighbour (chaining through the mesh)
-        const nbrs = brainAdj.get(ne.id);
+        const nbrs = BRAIN.adjacency[seat];
-        if (nbrs && nbrs.length && Math.random() < act * dt * (ne.agent ? 7 : 3.5)) {
+        if (nbrs.length && Math.random() < act * dt * 8) {
-          const tid = nbrs[(Math.random() * nbrs.length) | 0];
+          const tj = nbrs[(Math.random() * nbrs.length) | 0];
-          const tp = brainPositions.get(tid);
+          const dx = BRAIN.positions[tj * 3] - seatPos.x;
-          if (tp) {
+          const dy = BRAIN.positions[tj * 3 + 1] - seatPos.y;
-            const dx = tp.x - pos.x;
+          const dz = BRAIN.positions[tj * 3 + 2] - seatPos.z;
-            const dy = tp.y - pos.y;
+          const len = Math.hypot(dx, dy, dz) || 1;
-            const dz = tp.z - pos.z;
+          const speed = 130;
-            const len = Math.hypot(dx, dy, dz) || 1;
+          spawn(seatPos.x, seatPos.y, seatPos.z, p.fireColor ?? p.color, (dx / len) * speed, (dy / len) * speed, (dz / len) * speed, speed / len + 0.3);
-            const speed = 150;
+          activation[tj] = Math.min(1, activation[tj] + 0.2);
-            spawn(pos.x, pos.y, pos.z, ne.fire, (dx / len) * speed, (dy / len) * speed, (dz / len) * speed, speed / len + 0.25);
-            neuronAct.set(tid, Math.min(1, (neuronAct.get(tid) ?? 0.05) + 0.14));
-          }
         }
       }
-      // light up pathways by endpoint activity (lit pathways stand out; gradient
-      // from each end so the brighter neuron dominates its edges)
-      const ecol = brainEdgeGeom.getAttribute("color") as THREE.BufferAttribute | undefined;
-      if (ecol) {
-        const arr = ecol.array as Float32Array;
-        for (let e = 0; e + 1 < brainEdgeIds.length; e += 2) {
-          const va = 0.05 + (neuronAct.get(brainEdgeIds[e]) ?? 0) * 0.95;
-          const vb = 0.05 + (neuronAct.get(brainEdgeIds[e + 1]) ?? 0) * 0.95;
-          const ia = e * 3;
-          const ib = (e + 1) * 3;
-          arr[ia] = 0.43 * va;
-          arr[ia + 1] = 0.71 * va;
-          arr[ia + 2] = va;
-          arr[ib] = 0.43 * vb;
-          arr[ib + 1] = 0.71 * vb;
-          arr[ib + 2] = vb;
-        }
-        ecol.needsUpdate = true;
-      }
       for (const [id, g] of neuronGlows) {
         if (!seen.has(id)) {
           brainGroup.remove(g);
@@ -512,10 +462,27 @@ export function WorldCanvas({ roots, selectedId, onSelect, expanded, onToggleExp
           neuronGlows.delete(id);
         }
       }
-      if (ids.length !== brainCount) {
+
-        buildBrainGraph(ids);
+      // scaffold point + edge colours from the activation field (lit pathways)
-        brainCount = ids.length;
+      for (let i = 0; i < N; i++) {
+        const v = 0.05 + activation[i] * 0.95;
+        scaffoldColors[i * 3] = 0.42 * v;
+        scaffoldColors[i * 3 + 1] = 0.66 * v;
+        scaffoldColors[i * 3 + 2] = v;
       }
+      scaffoldGeom.attributes.color.needsUpdate = true;
+      for (let e = 0; e < E; e++) {
+        const va = 0.04 + activation[BRAIN.edges[e * 2]] * 0.96;
+        const vb = 0.04 + activation[BRAIN.edges[e * 2 + 1]] * 0.96;
+        const i0 = e * 6;
+        edgeColors[i0] = 0.42 * va;
+        edgeColors[i0 + 1] = 0.66 * va;
+        edgeColors[i0 + 2] = va;
+        edgeColors[i0 + 3] = 0.42 * vb;
+        edgeColors[i0 + 4] = 0.66 * vb;
+        edgeColors[i0 + 5] = vb;
+      }
+      brainEdgeGeom.attributes.color.needsUpdate = true;
     };
 
     const col = new THREE.Color();
@@ -567,8 +534,8 @@ export function WorldCanvas({ roots, selectedId, onSelect, expanded, onToggleExp
         renderBrain(dt);
         updateParticles(dt);
         if (!userInteracted) {
-          controls.target.lerp(new THREE.Vector3(0, 0, 0), 0.05);
+          controls.target.lerp(new THREE.Vector3(0, -18, 0), 0.05);
-          camera.position.lerp(new THREE.Vector3(0, 0, 520), 0.04);
+          camera.position.lerp(new THREE.Vector3(0, -18, 460), 0.04);
         }
         controls.update();
         bloom.strength = 0.75;
@@ -803,6 +770,9 @@ export function WorldCanvas({ roots, selectedId, onSelect, expanded, onToggleExp
       edgeGeom.dispose();
       beamGeom.dispose();
       brainEdgeGeom.dispose();
+      (brainEdges.material as THREE.Material).dispose();
+      scaffoldGeom.dispose();
+      (scaffoldPoints.material as THREE.Material).dispose();
       particleGeom.dispose();
       glow.dispose();
       composer.dispose();

frontend/src/components/world/brain.ts

@@ -0,0 +1,136 @@
+// A procedural, deterministic brain scaffold for the Hierarchy view: a fixed
+// node + edge network shaped like an actual brain — two folded cerebral
+// hemispheres (split by a longitudinal fissure), a cerebellum, and a brain-stem.
+// Agents seat into fixed regions of it (agentRegion); their activity diffuses
+// through the real edges. Seeded so the brain is identical on every reload.
+
+const R = 120; // overall brain radius scale
+
+function mulberry32(seed: number): () => number {
+  let s = seed >>> 0;
+  return () => {
+    s = (s + 0x6d2b79f5) | 0;
+    let t = Math.imul(s ^ (s >>> 15), 1 | s);
+    t = (t + Math.imul(t ^ (t >>> 7), 61 | t)) ^ t;
+    return ((t ^ (t >>> 14)) >>> 0) / 4294967296;
+  };
+}
+
+// Multi-octave sinusoidal "folds" over (theta, phi) → gyri/sulci wrinkles.
+function folds(theta: number, phi: number): number {
+  return (
+    0.1 * Math.sin(6 * theta + 1.3) * Math.cos(5 * phi) +
+    0.07 * Math.sin(9 * theta + 2.1) * Math.sin(7 * phi + 0.5) +
+    0.05 * Math.cos(13 * theta) * Math.sin(11 * phi + 1.1)
+  );
+}
+
+export interface BrainScaffold {
+  positions: Float32Array; // count*3
+  adjacency: number[][]; // per-node neighbour indices (for signal propagation)
+  edges: Uint16Array; // deduped index pairs (for line rendering)
+  count: number;
+}
+
+export function buildBrainScaffold(): BrainScaffold {
+  const rng = mulberry32(0x9e3779b9);
+  const pts: number[] = [];
+  const push = (x: number, y: number, z: number) => pts.push(x, y, z);
+
+  // Two cerebral hemispheres — an ellipsoid (longer front↔back, a bit wider than
+  // tall), folded to one side of a longitudinal fissure and wrinkled by folds().
+  const cere = { x: 0.86, y: 0.78, z: 1.12 };
+  const gap = 9; // fissure half-gap
+  const HEMI = 175;
+  for (const side of [-1, 1]) {
+    for (let i = 0; i < HEMI; i++) {
+      const u = rng() * 2 - 1; // cos(phi)
+      const t = rng() * Math.PI * 2; // theta
+      const s = Math.sqrt(1 - u * u);
+      const nx = Math.abs(s * Math.cos(t)) * side; // fold to one hemisphere
+      const ny = u;
+      const nz = s * Math.sin(t);
+      const f = 1 + folds(t, Math.acos(u));
+      let x = nx * cere.x * R * f;
+      let y = (ny * cere.y - 0.06) * R * f;
+      const z = nz * cere.z * R * f;
+      if (y < 0) y *= 0.82; // flatter underside
+      x += side * gap;
+      push(x, y, z);
+    }
+  }
+
+  // Cerebellum — a denser, more wrinkled ellipsoid at the back-bottom.
+  const CB = 64;
+  for (let i = 0; i < CB; i++) {
+    const u = rng() * 2 - 1;
+    const t = rng() * Math.PI * 2;
+    const s = Math.sqrt(1 - u * u);
+    const f = 1 + 1.8 * folds(t * 1.7, Math.acos(u));
+    push((s * Math.cos(t)) * 0.5 * R * f, (u * 0.34 - 0.92) * R, (s * Math.sin(t) * 0.42 - 0.64) * R);
+  }
+
+  // Brain-stem — a short tapering column descending from the bottom-centre.
+  const ST = 16;
+  for (let i = 0; i < ST; i++) {
+    const tt = i / ST;
+    const r = (1 - tt) * 10 + 3;
+    const a = rng() * Math.PI * 2;
+    push(Math.cos(a) * r, (-0.74 - tt * 0.52) * R, (-0.5 + Math.sin(a) * 0.04) * R);
+  }
+
+  const positions = new Float32Array(pts);
+  const count = positions.length / 3;
+
+  // Edges: each node to its K nearest neighbours within MAXD (so edges hug the
+  // surface — no cross-brain chords). Build a node-edge network + adjacency.
+  const K = 4;
+  const MAXD = 50;
+  const adjacency: number[][] = Array.from({ length: count }, () => []);
+  for (let i = 0; i < count; i++) {
+    const ix = positions[i * 3];
+    const iy = positions[i * 3 + 1];
+    const iz = positions[i * 3 + 2];
+    const near: { j: number; d: number }[] = [];
+    for (let j = 0; j < count; j++) {
+      if (j === i) continue;
+      const dx = ix - positions[j * 3];
+      const dy = iy - positions[j * 3 + 1];
+      const dz = iz - positions[j * 3 + 2];
+      near.push({ j, d: dx * dx + dy * dy + dz * dz });
+    }
+    near.sort((a, b) => a.d - b.d);
+    for (let n = 0; n < near.length && adjacency[i].length < K; n++) {
+      if (Math.sqrt(near[n].d) > MAXD) break;
+      adjacency[i].push(near[n].j);
+    }
+  }
+  const seen = new Set<string>();
+  const edges: number[] = [];
+  for (let i = 0; i < count; i++) {
+    for (const j of adjacency[i]) {
+      const a = Math.min(i, j);
+      const b = Math.max(i, j);
+      const key = `${a}_${b}`;
+      if (!seen.has(key)) {
+        seen.add(key);
+        edges.push(a, b);
+      }
+    }
+  }
+
+  return { positions, adjacency, edges: new Uint16Array(edges), count };
+}
+
+/** Deterministic fixed "seat" node for an agent (FNV-1a hash). */
+export function agentRegion(id: string, count: number): number {
+  let h = 2166136261;
+  for (let i = 0; i < id.length; i++) {
+    h ^= id.charCodeAt(i);
+    h = Math.imul(h, 16777619);
+  }
+  return (h >>> 0) % count;
+}
+
+/** The single shared brain (deterministic — same every reload). */
+export const BRAIN = buildBrainScaffold();