VibeCheck: Privacy-First Mood-Based Media Recommendations for iOS

[michaeloboyle]

Summary

VibeCheck is a privacy-first iOS app that solves the "45-minute decision problem" by using Apple Health biometrics to recommend media content based on the user's current mood state. All processing happens on-device - health data never leaves the phone.

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🎯 Hackathon Track Alignment

Primary Track: Entertainment Discovery

"Solve the 45-minute decision problem - help users find what to watch"

VibeCheck directly addresses this by:

• Biometric-Based Recommendations: Instead of asking users what they want, we detect their mood from HealthKit data
• Instant Personalization: No browsing needed - the app shows content that matches your current state
• Context-Aware: Recommendations adapt to time of day, energy levels, and stress patterns

Secondary Tracks

Track	How VibeCheck Applies
Multi-Agent Systems	RuvectorBridge orchestrates WASM-based ML agents for mood detection, preference learning, and content matching
Agentic Workflows	LearningMemory service creates autonomous feedback loops that improve recommendations without user intervention
Open Innovation	Novel approach: biometrics → mood → content (no existing app does this)

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📱 Screenshots (Build r16)

For You (Mellow Mood)	Performance Benchmark	Vibe Check Tab
Mood detected from HRV/sleep	11/11 benchmarks passing	HealthKit readings + mood history

UI Components

• VibeRing: Dual-ring visualization
  • Outer ring (thick): Energy level (purple→green→orange)
  • Inner ring (thin): Stress level (mint→blue→red)
• Mood Detection: "You seem [Mellow/Energetic/Tired/etc.]"
• Quick Override: Tired, Stressed, Energetic, Chill buttons
• Content Cards: Genre-aware recommendations with platform availability

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🦀 Ruvector WASM Integration

VibeCheck integrates ruvector.wasm - a Rust-compiled WebAssembly module created by @ruvnet as part of the hackathon toolkit. This provides on-device machine learning capabilities for privacy-preserving personalization.

Credit: The ruvector WASM module and its ML capabilities are authored by @ruvnet. VibeCheck demonstrates a novel mobile integration using WasmKit for pure-Swift WASM execution on iOS.

Why WASM for Mobile ML?

Approach	Pros	Cons
Cloud API	Latest models, easy updates	Privacy concerns, latency, requires network
CoreML	Apple-optimized, fast	Requires model conversion, limited flexibility
WASM (ruvector)	Portable, privacy-first, App Store safe	Interpreter overhead

We chose WASM because:

1. Privacy: Health data never leaves the device
2. Portability: Same WASM runs on iOS, Android, web
3. Hackathon Integration: Uses @ruvnet's ruvector from the hackathon toolkit
4. App Store Safe: WasmKit is a pure Swift interpreter (no JIT)

Current WASM Functions (v1.0.1-r16)

Function	Purpose	Status
ios_learner_init()	Initialize on-device ML learner	✅ Working
ios_get_energy(hrv, sleep, steps, stress, hour, min)	Predict energy from biometrics	✅ Working
ios_learner_iterations()	Get training iteration count	✅ Working
compute_similarity(hash1, hash2)	Compute vector similarity	✅ Working
hnsw_size()	Get vector index count	✅ Working
hnsw_insert(ptr, dim, id)	Insert vector into HNSW index	✅ Implemented
hnsw_search(ptr, dim, k)	k-NN search in HNSW	✅ Implemented
has_simd()	Check SIMD capability	✅ Working
app_usage_init()	Initialize usage tracker	✅ Working
calendar_init()	Initialize calendar learner	✅ Working

How It Works

// RuvectorBridge.swift - Predict energy from health data using @ruvnet's WASM
let result = try getEnergyFunc.invoke([
    .f32(hrv.bitPattern),       // Heart rate variability (ms)
    .f32(sleep.bitPattern),     // Sleep hours last night
    .f32(steps.bitPattern),     // Steps today
    .f32(stress.bitPattern),    // Derived stress level
    .i32(hour),                 // Current hour (time context)
    .i32(minute)                // Current minute
])
// Returns: Float 0.0 (exhausted) to 1.0 (wired)

Planned WASM Features

Feature	Functions	Purpose
Learning Memory	hnsw_insert, hnsw_search	Store (mood, media, feedback) tuples
Preference Learning	ios_learn_health	Train from user feedback
Temporal Patterns	calendar_*	Learn time-of-day preferences
Smart Notifications	ios_is_good_comm_time	When to notify user

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🏗 Architecture

┌─────────────────────────────────────────────────────────────────┐
│                      VibeCheck iOS App                          │
├─────────────────────────────────────────────────────────────────┤
│  SwiftUI Views              │  Services                         │
│  ├─ ForYouView             │  ├─ HealthKitManager              │
│  │   └─ VibeRing           │  │   └─ HRV, Sleep, Steps, HR     │
│  ├─ VibeCheckView          │  ├─ RecommendationEngine          │
│  ├─ WatchlistView          │  │   └─ Mood → Content mapping    │
│  ├─ SettingsView           │  ├─ VectorEmbeddingService        │
│  └─ BenchmarkView          │  │   └─ NLEmbedding (512-dim)     │
│                             │  └─ LearningMemoryService         │
│                             │      └─ Feedback → HNSW           │
├─────────────────────────────────────────────────────────────────┤
│                     RuvectorBridge.swift                        │
│  ┌────────────────────────────────────────────────────────────┐│
│  │  WasmKit Engine  →  WASI Bridge  →  ruvector.wasm         ││
│  │                        (by @ruvnet)                        ││
│  │  • ios_get_energy() - ML energy prediction from biometrics ││
│  │  • hnsw_insert/search() - Vector similarity for learning   ││
│  │  • compute_similarity() - Content matching                 ││
│  │  • ios_learn_health() - Preference training                ││
│  └────────────────────────────────────────────────────────────┘│
├─────────────────────────────────────────────────────────────────┤
│  Apple Frameworks                                               │
│  ├─ HealthKit: HRV, sleep, steps, resting HR                   │
│  ├─ NaturalLanguage: 512-dim sentence embeddings               │
│  ├─ SwiftData: Local persistence                                │
│  └─ SwiftUI: Modern declarative UI                              │
└─────────────────────────────────────────────────────────────────┘

---

📊 How Mood Detection Works

Input: HealthKit Biometrics

Metric	What It Tells Us	Example Values
HRV (Heart Rate Variability)	Autonomic nervous system state	20-100ms (higher = more relaxed)
Sleep Hours	Recovery and energy reserves	4-10 hours
Step Count	Activity level today	0-20,000 steps
Resting HR	Baseline cardiovascular state	50-80 bpm

Processing: MoodState Classification

HRV + Sleep + Steps → EnergyLevel (exhausted/low/moderate/high/wired)
HRV + Time of Day   → StressLevel (relaxed/calm/neutral/tense/stressed)
Energy × Stress     → MoodState (tired/chill/balanced/focused/energetic)

Output: Content Recommendations

Mood State	Content Type	Examples
Tired	Comfort, familiar	Comfort comedies, rewatches
Chill	Light, easy	Light dramas, documentaries
Balanced	Engaging	Popular series, new releases
Focused	Stimulating	Complex dramas, thrillers
Energetic	Exciting	Action, adventure, sports

---

🔒 Privacy Architecture

VibeCheck is designed with privacy as a core feature, not an afterthought:

Data Type	Where Stored	Leaves Device?
Health metrics	HealthKit (Apple encrypted)	❌ Never
Mood history	SwiftData (local)	❌ Never
Preferences	UserDefaults	❌ Never
Learning data	WASM memory	❌ Never
Content catalog	Bundled JSON	❌ Never

No accounts. No cloud sync. No analytics. No tracking.

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⚡ Performance (Build r16)

Benchmark	Target	Actual	Status
WASM Load	<100ms	~45ms	✅ Pass
Dot Product (100 iter)	<1ms	~0.02ms	✅ Pass
HNSW Query	<5ms	~0.3ms	✅ Pass
ML Inference	<10ms	~0.04ms	✅ Pass
NL Embedding	<50ms	~12ms	✅ Pass
Vector Search	<20ms	~8ms	✅ Pass

All 11 benchmarks passing. App launches in <2 seconds on iPhone 12 Pro Max.

---

🛠 Technical Stack

• UI: SwiftUI with custom VibeRing component
• Health: HealthKit integration (HRV, sleep, activity)
• ML: ruvector.wasm by @ruvnet + NaturalLanguage.framework
• Runtime: WasmKit (pure Swift WASM interpreter)
• Storage: SwiftData for local persistence
• Compatibility: iOS 17+, tested on iOS 26 beta

---

🚧 Known Issues

1. Memory Usage: ~244MB vs 15MB target (investigating SwiftData caching)
2. CloudKit Sync: Disabled for iOS 26 beta (crashes on auth)
3. HNSW Empty: Vector index needs media catalog population on first launch

---

✅ Completed

• HealthKit integration with real biometric data
• Mood detection from HRV, sleep, steps, HR
• VibeRing visualization (energy + stress rings)
• Recommendation engine with mood→content mapping
• WASM integration via WasmKit (using @ruvnet's ruvector.wasm)
• ML inference with ios_get_energy()
• HNSW vector operations (insert/search)
• Performance benchmarks (11/11 passing)
• Privacy-first architecture (no cloud)

📋 Remaining Work

• Index media catalog into HNSW on first launch
• Wire up LearningMemory to store user feedback
• Connect to live media API (FlixPatrol/JustWatch) via ARW
• Optimize memory usage
• Add widget support (WidgetKit)
• Add watchOS companion app

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🧪 Test Plan

• App launches on iOS 26 beta device
• HealthKit permission flow works
• Mood detection from real biometrics
• Recommendation engine generates content
• Settings preferences persist
• Performance benchmarks run (11/11 pass)
• WASM module loads and executes
• ML inference returns valid predictions
• CloudKit sync (disabled for iOS 26 beta)
• HNSW search with populated index

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👏 Credits

• ruvector.wasm: @ruvnet - WASM ML module and hackathon toolkit
• VibeCheck iOS: @michaeloboyle - iOS app development

🤖 Generated with Claude Code

[Copilot]

Pull request overview

This PR introduces VibeCheck, a privacy-first iOS app that uses Apple Health biometrics (HRV, sleep, activity) to recommend media content based on the user's current mood state. The implementation integrates WebAssembly for on-device machine learning and includes comprehensive test coverage.

Key changes:

• WebAssembly integration using WasmKit for privacy-preserving on-device ML inference
• Health data processing via HealthKit to detect energy and stress levels
• Recommendation engine combining rule-based, semantic vector, and ARW backend search
• Complete SwiftUI interface with mood visualization (VibeRing) and media cards

Reviewed changes

Copilot reviewed 49 out of 61 changed files in this pull request and generated 4 comments.

Show a summary per file

File	Description
integrate_ruvector_xcode.sh	Shell script automating Xcode integration for Ruvector WASM module
WASM-Integration-Plan.md	Comprehensive documentation for WebAssembly integration strategy
RuvectorBridge.swift	Swift wrapper for WASM module with memory management and ML functions
VibePredictor.swift	Mood prediction engine using biometrics with ML/rule-based fallback
LearningMemory.swift	HNSW vector-based learning memory for personalized recommendations
RecommendationEngine.swift	Multi-strategy recommendation system (local + semantic + ARW)
HealthKitManager.swift	HealthKit integration for HRV, sleep, steps, and heart rate data
ForYouView.swift	Main recommendation UI with mood detection and media cards
BenchmarkView.swift	Performance benchmarking suite with 11 real-time tests
Various test files	Comprehensive test coverage for WASM, ARW, CloudKit, and agents
Info.plist & entitlements	HealthKit permissions and app configuration
Package.swift	SPM configuration for WasmKit dependency
natural-language-search.ts	Enhanced error handling for AI parsing in media discovery service

Files not reviewed (2)

• apps/media-discovery/package-lock.json: Language not supported
• apps/vibecheck-ios/VibeCheck.xcodeproj/project.xcworkspace/contents.xcworkspacedata: Language not supported

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[Copilot]

The file path has a typo: .was should be .wasm

[Copilot]

Typo in error message: "RATIONAL" should be "RATIONALE"

[Copilot]

Typo in variable name: "existingContext" should be "excitingContext"

[Copilot]

Typo in comment: "No logic fallback" should be "No artwork fallback"