Merge pull request #9 from musicjunkieg/feat/scoring-algorithm

+835 -1

package-lock.json

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+6 -2

package.json

··· 8 8 "start": "next start", 9 9 "lint": "eslint", 10 10 "transcribe": "tsx scripts/transcribe.ts", 11 - "build-talk-index": "tsx scripts/build-talk-index.ts" 11 + "build-talk-index": "tsx scripts/build-talk-index.ts", 12 + "test": "vitest run", 13 + "test:watch": "vitest" 12 14 }, 13 15 "dependencies": { 14 16 "@atproto/api": "^0.19.6", ··· 29 31 "eslint-config-next": "16.2.2", 30 32 "tailwindcss": "^4", 31 33 "tsx": "^4.21.0", 32 - "typescript": "^5" 34 + "typescript": "^5", 35 + "vite-tsconfig-paths": "^6.1.1", 36 + "vitest": "^4.1.4" 33 37 } 34 38 }

+228

src/lib/scoring/combine.test.ts

··· 1 + import { describe, it, expect } from "vitest"; 2 + import { 3 + combineLayers, 4 + DEFAULT_ACTIVE_LAYERS, 5 + type ActiveLayers, 6 + } from "./combine"; 7 + import type { Layer1Result, ScoringWeights } from "./types"; 8 + 9 + const DEFAULT_WEIGHTS: ScoringWeights = { 10 + surpriseSlider: 0.5, 11 + friendsSlider: 0.5, 12 + }; 13 + 14 + function l1(attentionInverse: number): Layer1Result { 15 + return { 16 + uniqueFollows: 0, 17 + totalFollows: 0, 18 + reachRatio: 1 - attentionInverse, 19 + attentionInverse, 20 + }; 21 + } 22 + 23 + describe("combineLayers — DEFAULT_ACTIVE_LAYERS sentinel", () => { 24 + it("has both layers off by default", () => { 25 + expect(DEFAULT_ACTIVE_LAYERS).toEqual({ layer2: false, layer3: false }); 26 + }); 27 + }); 28 + 29 + describe("combineLayers — Layer 1 only (today's deployment)", () => { 30 + const active: ActiveLayers = { layer2: false, layer3: false }; 31 + 32 + it("returns layer1.attentionInverse for fully missed talk", () => { 33 + const result = combineLayers( 34 + l1(0.95), 35 + { interestScore: 0 }, 36 + { friendBoost: 0, recommenders: [] }, 37 + DEFAULT_WEIGHTS, 38 + active, 39 + ); 40 + expect(result).toBeCloseTo(0.95, 6); 41 + }); 42 + 43 + it("returns layer1.attentionInverse for partially engaged talk", () => { 44 + const result = combineLayers( 45 + l1(0.4), 46 + { interestScore: 0 }, 47 + { friendBoost: 0, recommenders: [] }, 48 + DEFAULT_WEIGHTS, 49 + active, 50 + ); 51 + expect(result).toBeCloseTo(0.4, 6); 52 + }); 53 + 54 + it("uses DEFAULT_ACTIVE_LAYERS when active arg omitted", () => { 55 + const result = combineLayers( 56 + l1(0.7), 57 + { interestScore: 1.0 }, // ignored — layer 2 is inactive 58 + { friendBoost: 1.0, recommenders: ["did:plc:x"] }, // ignored 59 + DEFAULT_WEIGHTS, 60 + ); 61 + expect(result).toBeCloseTo(0.7, 6); 62 + }); 63 + }); 64 + 65 + describe("combineLayers — Layer 1 + Layer 2 (future stage)", () => { 66 + const active: ActiveLayers = { layer2: true, layer3: false }; 67 + 68 + it("rescales weights to [0.5/0.8, 0.3/0.8] and reaches 1.0 at maximum", () => { 69 + // (1.0 * 0.5 + 1.0 * (1 - 0) * 0.3) / 0.8 = 0.8 / 0.8 = 1.0 70 + const result = combineLayers( 71 + l1(1.0), 72 + { interestScore: 1.0 }, 73 + { friendBoost: 0, recommenders: [] }, 74 + { surpriseSlider: 0, friendsSlider: 0.5 }, 75 + active, 76 + ); 77 + expect(result).toBeCloseTo(1.0, 6); 78 + }); 79 + 80 + it("returns 0.625 for fully missed talk with no interest score", () => { 81 + // (1.0 * 0.5 + 0 * 0.5 * 0.3) / 0.8 = 0.5 / 0.8 = 0.625 82 + const result = combineLayers( 83 + l1(1.0), 84 + { interestScore: 0 }, 85 + { friendBoost: 0, recommenders: [] }, 86 + DEFAULT_WEIGHTS, 87 + active, 88 + ); 89 + expect(result).toBeCloseTo(0.625, 6); 90 + }); 91 + }); 92 + 93 + describe("combineLayers — Layer 1 + Layer 3 (future stage)", () => { 94 + const active: ActiveLayers = { layer2: false, layer3: true }; 95 + 96 + it("rescales weights to [0.5/0.7, 0.2/0.7]", () => { 97 + // (0 * 0.5 + 1.0 * 1 * 0.2) / 0.7 = 0.2 / 0.7 ≈ 0.2857 98 + const result = combineLayers( 99 + l1(0.0), 100 + { interestScore: 0 }, 101 + { friendBoost: 1.0, recommenders: ["did:plc:a"] }, 102 + { surpriseSlider: 0.5, friendsSlider: 1 }, 103 + active, 104 + ); 105 + expect(result).toBeCloseTo(0.2 / 0.7, 6); 106 + }); 107 + }); 108 + 109 + describe("combineLayers — all three layers active (final stage)", () => { 110 + const active: ActiveLayers = { layer2: true, layer3: true }; 111 + 112 + it("matches the design-doc formula exactly at maximum", () => { 113 + // 1.0 * 0.5 + 1.0 * 1 * 0.3 + 1.0 * 1 * 0.2 = 1.0 114 + const result = combineLayers( 115 + l1(1.0), 116 + { interestScore: 1.0 }, 117 + { friendBoost: 1.0, recommenders: ["did:plc:a"] }, 118 + { surpriseSlider: 0, friendsSlider: 1 }, 119 + active, 120 + ); 121 + expect(result).toBeCloseTo(1.0, 6); 122 + }); 123 + }); 124 + 125 + describe("combineLayers — clamping and NaN guards", () => { 126 + it("clamps result to [0, 1] when slider drives raw above 1", () => { 127 + // surprise = -2 → (1 - (-2)) = 3 multiplier on l2 128 + // (1.0 * 0.5 + 1.0 * 3 * 0.3) / 0.8 = 1.4 / 0.8 = 1.75 → clamped to 1.0 129 + const result = combineLayers( 130 + l1(1.0), 131 + { interestScore: 1.0 }, 132 + { friendBoost: 0, recommenders: [] }, 133 + { surpriseSlider: -2, friendsSlider: 0.5 }, 134 + { layer2: true, layer3: false }, 135 + ); 136 + expect(result).toBeCloseTo(1.0, 6); 137 + }); 138 + 139 + it("coerces NaN slider to 0 (equivalent to surprise=0)", () => { 140 + // surprise=NaN → safe(NaN)=0 → (1.0 * 0.5 + 1.0 * 1 * 0.3) / 0.8 = 1.0 141 + const result = combineLayers( 142 + l1(1.0), 143 + { interestScore: 1.0 }, 144 + { friendBoost: 0, recommenders: [] }, 145 + { surpriseSlider: Number.NaN, friendsSlider: 0.5 }, 146 + { layer2: true, layer3: false }, 147 + ); 148 + expect(result).toBeCloseTo(1.0, 6); 149 + }); 150 + 151 + it("coerces ±Infinity to 0", () => { 152 + const result = combineLayers( 153 + l1(1.0), 154 + { interestScore: Number.POSITIVE_INFINITY }, 155 + { friendBoost: Number.NEGATIVE_INFINITY, recommenders: [] }, 156 + DEFAULT_WEIGHTS, 157 + { layer2: true, layer3: true }, 158 + ); 159 + // All non-finite stub values become 0; only L1 contributes. 160 + // (1.0 * 0.5 + 0 + 0) / 1.0 = 0.5 161 + expect(result).toBeCloseTo(0.5, 6); 162 + }); 163 + 164 + it("zeroes out layer 2 contribution when surpriseSlider is exactly 1", () => { 165 + // surprise = 1 → (1 - 1) = 0, so layer 2 contributes nothing even with 166 + // layer 2 active and a positive interestScore. Equivalent to 167 + // intensity = (1.0 * 0.5 + 0) / 0.8 = 0.625 168 + const result = combineLayers( 169 + l1(1.0), 170 + { interestScore: 1.0 }, 171 + { friendBoost: 0, recommenders: [] }, 172 + { surpriseSlider: 1, friendsSlider: 0.5 }, 173 + { layer2: true, layer3: false }, 174 + ); 175 + expect(result).toBeCloseTo(0.625, 6); 176 + }); 177 + 178 + it("zeroes out layer 3 contribution when friendsSlider is exactly 0", () => { 179 + // friends = 0 → friendBoost * 0 * w3 = 0, so layer 3 contributes nothing 180 + // even with layer 3 active. (0 * 0.5 + 1.0 * 0 * 0.2) / 0.7 = 0 181 + const result = combineLayers( 182 + l1(0.0), 183 + { interestScore: 0 }, 184 + { friendBoost: 1.0, recommenders: ["did:plc:a"] }, 185 + { surpriseSlider: 0.5, friendsSlider: 0 }, 186 + { layer2: false, layer3: true }, 187 + ); 188 + expect(result).toBeCloseTo(0, 6); 189 + }); 190 + }); 191 + 192 + describe("combineLayers — REGRESSION: per-talk discontinuity bug", () => { 193 + // This test specifically locks in the correct behavior the renormalization 194 + // fix introduced. If a future refactor reintroduces a per-talk `> 0` branch 195 + // on stub outputs, this assertion will fail loudly. 196 + // 197 + // The bug: with a per-talk `> 0` check, two talks with identical Layer 1 198 + // (0.95) but different Layer 2 (0 vs 0.4) would rank as: 199 + // Talk A (interest=0): takes "stub-only" branch → 0.95 200 + // Talk B (interest=0.4): takes "weighted" branch → 0.535 201 + // Talk B (the one user cares about per L2) ranks BELOW Talk A. 202 + it("ranks talk with positive interest score above identical-L1 talk with zero interest", () => { 203 + const active: ActiveLayers = { layer2: true, layer3: false }; 204 + 205 + const intensityA = combineLayers( 206 + l1(0.95), 207 + { interestScore: 0.0 }, 208 + { friendBoost: 0, recommenders: [] }, 209 + DEFAULT_WEIGHTS, 210 + active, 211 + ); 212 + 213 + const intensityB = combineLayers( 214 + l1(0.95), 215 + { interestScore: 0.4 }, 216 + { friendBoost: 0, recommenders: [] }, 217 + DEFAULT_WEIGHTS, 218 + active, 219 + ); 220 + 221 + // Pre-computed expected values from spec §11.2: 222 + // intensityA = (0.95*0.5 + 0.0*0.5*0.3) / 0.8 = 0.59375 223 + // intensityB = (0.95*0.5 + 0.4*0.5*0.3) / 0.8 = 0.66875 224 + expect(intensityA).toBeCloseTo(0.59375, 6); 225 + expect(intensityB).toBeCloseTo(0.66875, 6); 226 + expect(intensityB).toBeGreaterThan(intensityA); 227 + }); 228 + });

+90

src/lib/scoring/combine.ts

··· 1 + import type { Layer1Result, ScoringWeights } from "./types"; 2 + import type { InterestStubResult } from "./interestStub"; 3 + import type { FriendStubResult } from "./friendStub"; 4 + 5 + function clamp(n: number, min: number, max: number): number { 6 + return Math.max(min, Math.min(max, n)); 7 + } 8 + 9 + /** 10 + * Coerce non-finite numeric inputs (NaN, ±Infinity) to 0. Defense in depth 11 + * against uninitialized React state or JSON-parsed nulls slipping past 12 + * TypeScript types and propagating into the sort key. 13 + */ 14 + function safe(n: number): number { 15 + return Number.isFinite(n) ? n : 0; 16 + } 17 + 18 + /** 19 + * Which scoring layers have a live data source. Layer 1 is always live; 20 + * Layers 2 and 3 flip to true when their respective implementations land 21 + * (#21–24 for Layer 2, #18 for Layer 3). Today both are false. 22 + */ 23 + export interface ActiveLayers { 24 + readonly layer2: boolean; 25 + readonly layer3: boolean; 26 + } 27 + 28 + // Frozen so the exported sentinel can't be mutated by accident — it's used 29 + // as a default parameter value in combineLayers/scoreTalk/rankTalks, so any 30 + // mutation would corrupt every subsequent call that takes the default. 31 + export const DEFAULT_ACTIVE_LAYERS: Readonly<ActiveLayers> = Object.freeze({ 32 + layer2: false, 33 + layer3: false, 34 + }); 35 + 36 + /** 37 + * Design-doc weights from `docs/understory-design.md` §"The Scoring Algorithm". 38 + * These values are the canonical contribution shares when all three layers 39 + * are live; they are rescaled in `combineLayers` for partial deployments. 40 + */ 41 + const DESIGN_WEIGHTS = { 42 + layer1: 0.5, 43 + layer2: 0.3, 44 + layer3: 0.2, 45 + } as const; 46 + 47 + /** 48 + * Combine the three layers into a 0–1 intensity score. 49 + * 50 + * Per the design doc: 51 + * final = (attention_inverse * 0.5) 52 + * + (interest_score * (1 - surprise_slider) * 0.3) 53 + * + (friend_boost * friends_slider * 0.2) 54 + * 55 + * Weights are rescaled over the active layer set so the maximum achievable 56 + * intensity is always 1.0: 57 + * - Today (layer 1 only): w1 = 0.5/0.5 = 1.0 → intensity == attentionInverse 58 + * - Layer 1 + 2: w1 = 0.5/0.8, w2 = 0.3/0.8 (sum = 1.0) 59 + * - Layer 1 + 3: w1 = 0.5/0.7, w3 = 0.2/0.7 (sum = 1.0) 60 + * - All three: w1 = 0.5, w2 = 0.3, w3 = 0.2 (already sum to 1.0) 61 + * 62 + * Stubs are still consulted when their layer is inactive, but their values 63 + * are multiplied by a zero weight — so swapping a stub for a real 64 + * implementation is purely a data change once the active flag flips. 65 + */ 66 + export function combineLayers( 67 + layer1: Layer1Result, 68 + layer2: InterestStubResult, 69 + layer3: FriendStubResult, 70 + weights: ScoringWeights, 71 + active: ActiveLayers = DEFAULT_ACTIVE_LAYERS, 72 + ): number { 73 + const w1 = DESIGN_WEIGHTS.layer1; 74 + const w2 = active.layer2 ? DESIGN_WEIGHTS.layer2 : 0; 75 + const w3 = active.layer3 ? DESIGN_WEIGHTS.layer3 : 0; 76 + const total = w1 + w2 + w3; // always > 0 because layer 1 is always live 77 + 78 + const l1 = safe(layer1.attentionInverse); 79 + const l2 = active.layer2 ? safe(layer2.interestScore) : 0; 80 + const l3 = active.layer3 ? safe(layer3.friendBoost) : 0; 81 + const surprise = safe(weights.surpriseSlider); 82 + const friends = safe(weights.friendsSlider); 83 + 84 + const raw = 85 + l1 * w1 + 86 + l2 * (1 - surprise) * w2 + 87 + l3 * friends * w3; 88 + 89 + return clamp(raw / total, 0, 1); 90 + }

+20

src/lib/scoring/friendStub.ts

··· 1 + import type { TalkEntry } from "@/lib/types"; 2 + 3 + export interface FriendStubResult { 4 + friendBoost: number; 5 + recommenders: string[]; 6 + } 7 + 8 + /** 9 + * Layer 3 stub. Returns 0 until the following issues land: 10 + * - #18: friend recommendation reader 11 + * - #5: publish lexicons 12 + * 13 + * When implemented, this should return the normalized sum of friend 14 + * recommendation intensities (1–3 each, capped at a sensible max) and the 15 + * DIDs of the recommending follows. 16 + */ 17 + export function computeFriendStub(talk: TalkEntry): FriendStubResult { 18 + void talk; 19 + return { friendBoost: 0, recommenders: [] }; 20 + }

+21

src/lib/scoring/index.ts

··· 1 + // Public surface for the scoring module. Consumers should import from 2 + // `@/lib/scoring`, not from individual files, so refactors inside the module 3 + // don't break call sites. 4 + 5 + export type { 6 + TalkScore, 7 + TalkScoreState, 8 + Layer1Result, 9 + ScoringWeights, 10 + ScoringInputs, 11 + TalkMention, 12 + TalkMentions, 13 + } from "./types"; 14 + 15 + export { DEFAULT_WEIGHTS } from "./types"; 16 + 17 + export type { ActiveLayers } from "./combine"; 18 + export { DEFAULT_ACTIVE_LAYERS, combineLayers } from "./combine"; 19 + 20 + export { computeLayer1 } from "./networkAttention"; 21 + export { scoreTalk, rankTalks } from "./rank";

+24

src/lib/scoring/interestStub.ts

··· 1 + import type { TalkEntry } from "@/lib/types"; 2 + 3 + export interface InterestStubResult { 4 + interestScore: number; 5 + } 6 + 7 + /** 8 + * Layer 2 stub. Returns 0 until the following issues land: 9 + * - #21: generate transcript embeddings 10 + * - #22: publish topicIndex records 11 + * - #23: user interest profiling 12 + * - #24: cosine similarity matching 13 + * 14 + * When implemented, this should return cosine similarity in [0, 1] between 15 + * the user's recent-post embedding and the talk's topicIndex embedding. 16 + * 17 + * The `void talk;` statement marks the parameter as intentionally unused so 18 + * `@typescript-eslint/no-unused-vars` doesn't fire while the stub awaits its 19 + * real implementation. 20 + */ 21 + export function computeInterestStub(talk: TalkEntry): InterestStubResult { 22 + void talk; 23 + return { interestScore: 0 }; 24 + }

+56

src/lib/scoring/networkAttention.test.ts

··· 1 + import { describe, it, expect } from "vitest"; 2 + import { computeLayer1 } from "./networkAttention"; 3 + import type { TalkMention } from "@/lib/crawl/types"; 4 + 5 + // Build a TalkMention with `n` distinct follow DIDs. 6 + // Note: this is "follows engaged with this talk", NOT the user's total 7 + // follow count — that's passed separately as the second arg to computeLayer1. 8 + function makeMention(n: number): TalkMention { 9 + const follows = Array.from({ length: n }, (_, i) => `did:plc:f${i}`); 10 + return { 11 + count: n, 12 + follows, 13 + posts: [], 14 + rsvps: [], 15 + }; 16 + } 17 + 18 + describe("computeLayer1", () => { 19 + it("returns attentionInverse 1.0 when zero follows engaged", () => { 20 + const result = computeLayer1(makeMention(0), 100); 21 + expect(result.uniqueFollows).toBe(0); 22 + expect(result.totalFollows).toBe(100); 23 + expect(result.reachRatio).toBeCloseTo(0, 6); 24 + expect(result.attentionInverse).toBeCloseTo(1.0, 6); 25 + }); 26 + 27 + it("returns attentionInverse 0.5 when half engaged", () => { 28 + const result = computeLayer1(makeMention(50), 100); 29 + expect(result.reachRatio).toBeCloseTo(0.5, 6); 30 + expect(result.attentionInverse).toBeCloseTo(0.5, 6); 31 + }); 32 + 33 + it("returns attentionInverse 0.0 when fully engaged", () => { 34 + const result = computeLayer1(makeMention(100), 100); 35 + expect(result.reachRatio).toBeCloseTo(1.0, 6); 36 + expect(result.attentionInverse).toBeCloseTo(0.0, 6); 37 + }); 38 + 39 + it("returns attentionInverse 1.0 when followCount is 0 (divide-by-zero guard)", () => { 40 + const result = computeLayer1(makeMention(3), 0); 41 + expect(result.reachRatio).toBeCloseTo(0, 6); 42 + expect(result.attentionInverse).toBeCloseTo(1.0, 6); 43 + }); 44 + 45 + it("clamps reachRatio to 1 when stale data has more follows than followCount", () => { 46 + const result = computeLayer1(makeMention(110), 100); 47 + expect(result.reachRatio).toBeCloseTo(1.0, 6); 48 + expect(result.attentionInverse).toBeCloseTo(0.0, 6); 49 + }); 50 + 51 + it("treats undefined mention as zero engagement", () => { 52 + const result = computeLayer1(undefined, 100); 53 + expect(result.uniqueFollows).toBe(0); 54 + expect(result.attentionInverse).toBeCloseTo(1.0, 6); 55 + }); 56 + });

+34

src/lib/scoring/networkAttention.ts

··· 1 + import type { TalkMention } from "@/lib/crawl/types"; 2 + import type { Layer1Result } from "./types"; 3 + 4 + /** 5 + * Compute the Layer 1 (network attention, inverted) score for a single talk. 6 + * 7 + * Returns the fraction of the user's follows who engaged with the talk 8 + * (`reachRatio`) and its inverse (`attentionInverse`), where 1.0 means 9 + * "nobody in your network engaged" and 0.0 means "every single one of your 10 + * follows engaged." 11 + * 12 + * We use `mention.follows.length` rather than `mention.count` so the algorithm 13 + * is robust to a future crawler change that decouples the two. Today the 14 + * crawler enforces `count === follows.length`. 15 + */ 16 + export function computeLayer1( 17 + mention: TalkMention | undefined, 18 + followCount: number, 19 + ): Layer1Result { 20 + const uniqueFollows = mention?.follows.length ?? 0; 21 + // Clamp to [0, 1]: uniqueFollows can theoretically exceed followCount if a 22 + // CrawlResult is reused after the user's follow list changes (someone 23 + // unfollowed but still appears in cached mentions). The clamp prevents 24 + // attentionInverse from going negative in that edge case. 25 + const reachRatio = 26 + followCount > 0 ? Math.min(1, uniqueFollows / followCount) : 0; 27 + const attentionInverse = 1 - reachRatio; 28 + return { 29 + uniqueFollows, 30 + totalFollows: followCount, 31 + reachRatio, 32 + attentionInverse, 33 + }; 34 + }

+237

src/lib/scoring/rank.test.ts

··· 1 + import { describe, it, expect } from "vitest"; 2 + import { scoreTalk, rankTalks } from "./rank"; 3 + import { DEFAULT_WEIGHTS } from "./types"; 4 + import { DEFAULT_ACTIVE_LAYERS, type ActiveLayers } from "./combine"; 5 + import type { TalkEntry } from "@/lib/types"; 6 + import type { TalkMention, TalkMentions } from "@/lib/crawl/types"; 7 + 8 + function makeTalk(rkey: string, overrides: Partial<TalkEntry> = {}): TalkEntry { 9 + return { 10 + rkey, 11 + title: `Talk ${rkey}`, 12 + vodUri: `at://example/${rkey}`, 13 + vodCid: "bafy", 14 + hlsUrl: "", 15 + durationMs: 0, 16 + createdAt: "", 17 + eventUri: `at://event/${rkey}`, 18 + description: null, 19 + speakers: [], 20 + room: null, 21 + talkType: null, 22 + category: null, 23 + startsAt: null, 24 + endsAt: null, 25 + transcriptFile: null, 26 + ...overrides, 27 + }; 28 + } 29 + 30 + // Build a TalkMention with `n` distinct follow DIDs (engaged follows for 31 + // this talk, NOT the user's total follow count — that's a separate arg). 32 + function makeMention(n: number): TalkMention { 33 + const follows = Array.from({ length: n }, (_, i) => `did:plc:f${i}`); 34 + return { 35 + count: n, 36 + follows, 37 + posts: [], 38 + rsvps: [], 39 + }; 40 + } 41 + 42 + describe("scoreTalk — state derivation", () => { 43 + const talk = makeTalk("a"); 44 + 45 + it("returns unknown when mentions is null", () => { 46 + const score = scoreTalk(talk, null, 100); 47 + expect(score.state).toBe("unknown"); 48 + expect(score.intensity).toBe(0); 49 + }); 50 + 51 + it("returns unknown when followCount is 0", () => { 52 + const score = scoreTalk(talk, { a: makeMention(5) }, 0); 53 + expect(score.state).toBe("unknown"); 54 + }); 55 + 56 + it("returns unknown when the talk has no mention entry (out of crawl scope)", () => { 57 + const score = scoreTalk(talk, {}, 100); 58 + expect(score.state).toBe("unknown"); 59 + }); 60 + 61 + it("returns missed when uniqueFollows is 0 but talk is in scope", () => { 62 + const score = scoreTalk(talk, { a: makeMention(0) }, 100); 63 + expect(score.state).toBe("missed"); 64 + expect(score.intensity).toBeCloseTo(1.0, 6); 65 + }); 66 + 67 + it("returns engaged when at least one follow engaged", () => { 68 + const score = scoreTalk(talk, { a: makeMention(3) }, 100); 69 + expect(score.state).toBe("engaged"); 70 + expect(score.intensity).toBeCloseTo(0.97, 6); 71 + }); 72 + 73 + it("returns unknown when followCount is negative", () => { 74 + const score = scoreTalk(talk, { a: makeMention(5) }, -3); 75 + expect(score.state).toBe("unknown"); 76 + // totalFollows is sanitized to 0, not the bogus -3, so JSON serialization 77 + // and downstream consumers see a stable shape. 78 + expect(score.layer1.totalFollows).toBe(0); 79 + }); 80 + 81 + it("returns unknown when followCount is NaN", () => { 82 + const score = scoreTalk(talk, { a: makeMention(5) }, Number.NaN); 83 + expect(score.state).toBe("unknown"); 84 + expect(score.layer1.totalFollows).toBe(0); 85 + }); 86 + 87 + it("returns unknown when followCount is +Infinity", () => { 88 + const score = scoreTalk( 89 + talk, 90 + { a: makeMention(5) }, 91 + Number.POSITIVE_INFINITY, 92 + ); 93 + expect(score.state).toBe("unknown"); 94 + expect(score.layer1.totalFollows).toBe(0); 95 + }); 96 + 97 + it("returns unknown when followCount is -Infinity", () => { 98 + const score = scoreTalk( 99 + talk, 100 + { a: makeMention(5) }, 101 + Number.NEGATIVE_INFINITY, 102 + ); 103 + expect(score.state).toBe("unknown"); 104 + expect(score.layer1.totalFollows).toBe(0); 105 + }); 106 + }); 107 + 108 + describe("DEFAULT_WEIGHTS / DEFAULT_ACTIVE_LAYERS — frozen sentinels", () => { 109 + it("DEFAULT_WEIGHTS is frozen so accidental mutation throws or no-ops", () => { 110 + // Object.freeze makes assignment a silent no-op in sloppy mode and throws 111 + // in strict mode. Either way, the value cannot change. 112 + expect(Object.isFrozen(DEFAULT_WEIGHTS)).toBe(true); 113 + }); 114 + 115 + it("DEFAULT_ACTIVE_LAYERS is frozen so accidental mutation throws or no-ops", () => { 116 + expect(Object.isFrozen(DEFAULT_ACTIVE_LAYERS)).toBe(true); 117 + }); 118 + }); 119 + 120 + describe("scoreTalk — defaults", () => { 121 + const talk = makeTalk("a"); 122 + const mentions: TalkMentions = { a: makeMention(0) }; 123 + 124 + it("uses both DEFAULT_WEIGHTS and DEFAULT_ACTIVE_LAYERS when both omitted", () => { 125 + const score = scoreTalk(talk, mentions, 100); 126 + expect(score.intensity).toBeCloseTo(1.0, 6); 127 + }); 128 + 129 + it("uses DEFAULT_ACTIVE_LAYERS when active omitted but explicit weights supplied", () => { 130 + const score = scoreTalk(talk, mentions, 100, { 131 + surpriseSlider: 0.25, 132 + friendsSlider: 0.75, 133 + }); 134 + // active defaults to both-off → L1-only branch → weights don't enter 135 + // the math at all → intensity == layer1.attentionInverse == 1.0 136 + expect(score.intensity).toBeCloseTo(1.0, 6); 137 + }); 138 + 139 + it("uses DEFAULT_WEIGHTS when weights omitted but explicit active supplied", () => { 140 + const score = scoreTalk(talk, mentions, 100, undefined, { 141 + layer2: true, 142 + layer3: false, 143 + }); 144 + // L1+L2 active, L2 stub returns 0, default surprise=0.5 145 + // (1.0*0.5 + 0*0.5*0.3) / 0.8 = 0.625 146 + expect(score.intensity).toBeCloseTo(0.625, 6); 147 + }); 148 + }); 149 + 150 + describe("rankTalks — sort order", () => { 151 + const A = makeTalk("aaa"); 152 + const B = makeTalk("bbb"); 153 + const C = makeTalk("ccc"); 154 + const D = makeTalk("ddd"); 155 + const E = makeTalk("eee"); 156 + 157 + it("sorts missed first, then engaged (intensity desc), then unknown", () => { 158 + const mentions: TalkMentions = { 159 + aaa: makeMention(1), // engaged, intensity 0.99 160 + bbb: makeMention(0), // missed, intensity 1.0 161 + ccc: makeMention(50), // engaged, intensity 0.5 162 + // D, E: no mentions → unknown 163 + }; 164 + const result = rankTalks({ 165 + talks: [A, B, C, D, E], 166 + mentions, 167 + followCount: 100, 168 + }); 169 + 170 + expect(result.map((s) => s.rkey)).toEqual(["bbb", "aaa", "ccc", "ddd", "eee"]); 171 + }); 172 + 173 + it("uses rkey ascending as a deterministic tiebreak", () => { 174 + const Z = makeTalk("zzz"); 175 + const A = makeTalk("aaa"); 176 + const mentions: TalkMentions = { 177 + zzz: makeMention(0), 178 + aaa: makeMention(0), 179 + }; 180 + const result = rankTalks({ 181 + talks: [Z, A], // intentionally not in rkey order 182 + mentions, 183 + followCount: 100, 184 + }); 185 + 186 + // Both missed with intensity 1.0; tiebreak puts "aaa" before "zzz" 187 + expect(result[0].rkey).toBe("aaa"); 188 + expect(result[1].rkey).toBe("zzz"); 189 + }); 190 + 191 + it("threads weights and active flags through to combineLayers", () => { 192 + const active: ActiveLayers = { layer2: true, layer3: false }; 193 + const mentions: TalkMentions = { aaa: makeMention(0) }; 194 + const result = rankTalks({ 195 + talks: [A], 196 + mentions, 197 + followCount: 100, 198 + active, 199 + }); 200 + // L1 only contributes; L2 stub returns 0; rescale: 0.5/0.8 = 0.625 201 + expect(result[0].intensity).toBeCloseTo(0.625, 6); 202 + }); 203 + }); 204 + 205 + describe("rankTalks — empty / degenerate inputs", () => { 206 + it("returns [] for empty talks array", () => { 207 + const result = rankTalks({ 208 + talks: [], 209 + mentions: {}, 210 + followCount: 100, 211 + }); 212 + expect(result).toEqual([]); 213 + }); 214 + 215 + it("returns all unknown sorted by rkey when mentions is null", () => { 216 + const result = rankTalks({ 217 + talks: [makeTalk("ccc"), makeTalk("aaa"), makeTalk("bbb")], 218 + mentions: null, 219 + followCount: 100, 220 + }); 221 + expect(result.map((s) => s.state)).toEqual(["unknown", "unknown", "unknown"]); 222 + expect(result.map((s) => s.rkey)).toEqual(["aaa", "bbb", "ccc"]); 223 + }); 224 + 225 + it("returns all unknown when followCount is 0", () => { 226 + const result = rankTalks({ 227 + talks: [makeTalk("aaa"), makeTalk("bbb"), makeTalk("ccc")], 228 + mentions: { 229 + aaa: makeMention(5), 230 + bbb: makeMention(10), 231 + ccc: makeMention(0), 232 + }, 233 + followCount: 0, 234 + }); 235 + expect(result.map((s) => s.state)).toEqual(["unknown", "unknown", "unknown"]); 236 + }); 237 + });

+112

src/lib/scoring/rank.ts

··· 1 + import type { TalkEntry } from "@/lib/types"; 2 + import type { TalkMentions } from "@/lib/crawl/types"; 3 + import { 4 + type TalkScore, 5 + type TalkScoreState, 6 + type ScoringInputs, 7 + type ScoringWeights, 8 + DEFAULT_WEIGHTS, 9 + } from "./types"; 10 + import { computeLayer1 } from "./networkAttention"; 11 + import { computeInterestStub } from "./interestStub"; 12 + import { computeFriendStub } from "./friendStub"; 13 + import { 14 + type ActiveLayers, 15 + DEFAULT_ACTIVE_LAYERS, 16 + combineLayers, 17 + } from "./combine"; 18 + 19 + function unknownScore(rkey: string, followCount: number): TalkScore { 20 + // Sanitize: if followCount is non-finite or negative (e.g., from a corrupted 21 + // cache or a slider-derived value that wasn't validated upstream), don't 22 + // propagate the bad number into the result. Stash 0 so JSON serialization, 23 + // React rendering, and downstream consumers see a stable shape. 24 + const safeTotalFollows = 25 + Number.isFinite(followCount) && followCount > 0 ? followCount : 0; 26 + return { 27 + rkey, 28 + intensity: 0, 29 + state: "unknown", 30 + layer1: { 31 + uniqueFollows: 0, 32 + totalFollows: safeTotalFollows, 33 + reachRatio: 0, 34 + attentionInverse: 0, 35 + }, 36 + }; 37 + } 38 + 39 + /** 40 + * Score a single talk. Pass the full `mentions` map (or null if no crawl 41 + * has run yet) — the function looks up the talk's mention internally so 42 + * callers don't have to encode "do we have crawl data" as a separate flag. 43 + * 44 + * Returns `unknown` state when: 45 + * - mentions is null (crawl hasn't run) 46 + * - followCount is non-finite or ≤ 0 (user has no follows OR a corrupted 47 + * value snuck through — reach is undefined either way) 48 + * - mention is absent (talk is out of crawl scope, e.g. no eventUri) 49 + * 50 + * Otherwise runs Layer 1 + the two stubs through `combineLayers` with the 51 + * given weights and active layer flags. 52 + */ 53 + export function scoreTalk( 54 + talk: TalkEntry, 55 + mentions: TalkMentions | null, 56 + followCount: number, 57 + weights: ScoringWeights = DEFAULT_WEIGHTS, 58 + active: ActiveLayers = DEFAULT_ACTIVE_LAYERS, 59 + ): TalkScore { 60 + // Robust guard: catches null mentions, zero/negative followCount, NaN, and 61 + // ±Infinity in a single check. Anything that isn't a finite positive integer 62 + // routes to `unknown` rather than silently producing a wrong "missed". 63 + if (mentions === null || !Number.isFinite(followCount) || followCount <= 0) { 64 + return unknownScore(talk.rkey, followCount); 65 + } 66 + const mention = mentions[talk.rkey]; 67 + if (!mention) { 68 + // Talk is not in crawl scope (e.g. no eventUri so the crawler skipped it). 69 + return unknownScore(talk.rkey, followCount); 70 + } 71 + 72 + const layer1 = computeLayer1(mention, followCount); 73 + const layer2 = computeInterestStub(talk); 74 + const layer3 = computeFriendStub(talk); 75 + const intensity = combineLayers(layer1, layer2, layer3, weights, active); 76 + 77 + const state: TalkScoreState = 78 + layer1.uniqueFollows === 0 ? "missed" : "engaged"; 79 + 80 + return { rkey: talk.rkey, intensity, state, layer1 }; 81 + } 82 + 83 + const STATE_ORDER: Record<TalkScoreState, number> = { 84 + missed: 0, 85 + engaged: 1, 86 + unknown: 2, 87 + }; 88 + 89 + function compareTalkScores(a: TalkScore, b: TalkScore): number { 90 + // Primary: state group (missed first, then engaged, then unknown) 91 + const stateDelta = STATE_ORDER[a.state] - STATE_ORDER[b.state]; 92 + if (stateDelta !== 0) return stateDelta; 93 + // Secondary: intensity descending (highest glow first within each state) 94 + const intensityDelta = b.intensity - a.intensity; 95 + if (intensityDelta !== 0) return intensityDelta; 96 + // Tertiary: rkey ascending — deterministic tiebreak so the order is stable 97 + // across renders (matters for React reconciliation). 98 + return a.rkey.localeCompare(b.rkey); 99 + } 100 + 101 + export function rankTalks(inputs: ScoringInputs): TalkScore[] { 102 + const { 103 + talks, 104 + mentions, 105 + followCount, 106 + weights = DEFAULT_WEIGHTS, 107 + active = DEFAULT_ACTIVE_LAYERS, 108 + } = inputs; 109 + return talks 110 + .map((talk) => scoreTalk(talk, mentions, followCount, weights, active)) 111 + .sort(compareTalkScores); 112 + }

+53

src/lib/scoring/types.ts

··· 1 + import type { TalkEntry } from "@/lib/types"; 2 + import type { TalkMention, TalkMentions } from "@/lib/crawl/types"; 3 + // Local-import-then-re-export so `ScoringInputs` gets a usable local binding 4 + // for `ActiveLayers` AND callers can `import { ActiveLayers } from "@/lib/scoring/types"` 5 + // without needing to know `combine.ts` owns it. 6 + import type { ActiveLayers } from "./combine"; 7 + export type { ActiveLayers }; 8 + // Runtime re-export of the default sentinel so callers importing from 9 + // scoring/types get both the type and its default value in one place. 10 + export { DEFAULT_ACTIVE_LAYERS } from "./combine"; 11 + 12 + export type TalkScoreState = "engaged" | "missed" | "unknown"; 13 + 14 + export interface Layer1Result { 15 + uniqueFollows: number; 16 + totalFollows: number; 17 + reachRatio: number; // uniqueFollows / totalFollows, clamped to [0, 1] 18 + attentionInverse: number; // 1 - reachRatio, clamped to [0, 1] 19 + } 20 + 21 + export interface TalkScore { 22 + rkey: string; 23 + intensity: number; // 0–1; UI uses for glow + ordering 24 + state: TalkScoreState; 25 + layer1: Layer1Result; 26 + layer2?: { interestScore: number }; 27 + layer3?: { friendBoost: number; recommenders: string[] }; 28 + } 29 + 30 + export interface ScoringWeights { 31 + readonly surpriseSlider: number; // 0–1; controls Layer 2 contribution (high = serendipity) 32 + readonly friendsSlider: number; // 0–1; controls Layer 3 contribution (high = friends override) 33 + } 34 + 35 + // Frozen so the exported sentinel can't be mutated by accident — it's used 36 + // as a default parameter value in scoreTalk/rankTalks, so any mutation would 37 + // corrupt every subsequent call that takes the default. 38 + export const DEFAULT_WEIGHTS: Readonly<ScoringWeights> = Object.freeze({ 39 + surpriseSlider: 0.5, 40 + friendsSlider: 0.5, 41 + }); 42 + 43 + export interface ScoringInputs { 44 + talks: TalkEntry[]; 45 + mentions: TalkMentions | null; // null = crawl not yet completed 46 + followCount: number; // from CrawlResult.followCount 47 + weights?: ScoringWeights; 48 + active?: ActiveLayers; // omitted = layer 1 only (today's deployment) 49 + } 50 + 51 + // Re-export TalkMention for downstream consumers that import only from 52 + // scoring/types — saves them having to know about the crawl module. 53 + export type { TalkMention, TalkMentions };

+9

vitest.config.ts

··· 1 + import { defineConfig } from "vitest/config"; 2 + import tsconfigPaths from "vite-tsconfig-paths"; 3 + 4 + export default defineConfig({ 5 + plugins: [tsconfigPaths()], 6 + test: { 7 + environment: "node", 8 + }, 9 + });

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