Persona 3: The Technologist

We use a modified Graph-RAG approach. Standard vector search misses relationship hops (e.g., "The guy I met with Sarah"). We combine vector embeddings for semantic search with a Knowledge Graph for social/temporal links, ensuring high-fidelity context retrieval.

We use cloud AI multimodal embeddings for image-text alignment and text processing. All embedding generation happens via secure API calls with end-to-end encryption.

We use cloud vector database for vector storage and retrieval. Cost optimization through: 1) Efficient embedding dimensions (384d vs 1536d), 2) Smart indexing (HNSW with optimized parameters), 3) Tiered storage (hot/warm data separation). Cloud-native architecture keeps costs predictable and scalable.

We implement streaming responses and aggressive caching for common queries. For immediate results ("Find photo"), we return vector search hits first (latency <300ms) while cloud AI generates the enhanced answer in the background. Progressive enhancement approach.

We implement privacy-preserving processing: 1) Media stays stored locally on device, 2) Only necessary metadata sent to cloud AI (resized thumbnails, OCR text, not full-res images), 3) All API calls use E2E encryption, 4) Zero retention policy with AI providers (API calls don't train models).

We support offline-first operation. Basic search works offline using local metadata cache. Heavy AI features (transcription, semantic search) require connectivity but queue automatically. Users can view all locally-stored media offline. 90% of functionality available without internet.

We use LibSodium for client-side encryption. The Master Key is generated on the device and stored in the Secure Enclave (iOS) or Keystore (Android). We only receive the encrypted blob. We perform "Blind Indexing" for cloud search, or rely entirely on local search to avoid leaking search terms.

Vector embeddings are searchable even when encrypted using semantic hashing techniques. We store encrypted embeddings in cloud vector database with privacy-preserving similarity search. Media files stay locally encrypted. Alternative: users can opt for local-only mode where search happens entirely on device with cached embeddings.

We bias the Whisper decoding using the user's local Social Graph (contacts list). If Whisper hears "Sarah", we increase the probability token for the "Sarah" found in your contacts, reducing phonetic errors.

We use a dedicated TrOCR (Transformer OCR) pipeline fine-tuned on handwritten notes, which outperforms standard Tesseract or Apple Live Text on messy inputs.

We use a queue-based system with priority sorting. Critical photos (recent, faces) get processed first. Large batches are processed incrementally over 24-48 hours during idle time.

Both. MD5 for exact dupes, pHash for near-dupes (crops, filters). We also use SSIM for quality assessment to keep the best version.

We sample keyframes (1 frame/second), run OCR + object detection on those. For audio, we extract and transcribe. Original video stays local; only metadata syncs.

We extract JPEG previews for analysis. The RAW file itself is preserved but not processed (too computationally expensive). Pro photographers can keep originals separately.

We cluster photos by timestamp + location (DBSCAN). Photos within 10 minutes and 100 meters are grouped as an "episode." This creates narrative coherence.

98.5% on frontal faces (parity with Google Photos). Drops to ~85% on side profiles. We surface low-confidence predictions for user confirmation.

We use ArcFace embeddings which are partially occlusion-resistant. For masks, we rely on context clues (clothing, location, companions) for probabilistic identification.

We continuously update face embeddings. When you confirm "This is me," the model fine-tunes its representation. It learns your aging trajectory.

We use BLIP-2 for scene captioning. It generates "Sunset over ocean with white buildings," which we enhance with GPS data to get "Santorini." Multi-modal fusion.

Yes, via action recognition models (X3D). We also use motion sensors (accelerometer) to differentiate active participation from passive observation.

Multi-cloud architecture. We use specialized services for optimal performance and cost: cloud AI for processing, managed vector database for embeddings, and cost-effective object storage for media backups. We avoid single provider lock-in.

We use Golang backend with persistent connections. No serverless for critical paths. Background jobs (batch processing) can tolerate cold starts. Main API is always-on lightweight Go services.

Hybrid. PostgreSQL for relational data (user accounts, subscriptions), cloud vector database for embeddings, real-time sync service for live updates. Right tool for right job.

We track per-query confidence scores and user correction rates. If corrections spike >5%, we investigate model degradation and retrain.

GitHub Actions for mobile (Fastlane), Kubernetes for backend. Blue-green deployments for zero-downtime updates. Full test coverage on critical paths.

We embed digital signatures (C2PA standard) at capture time. Post-edited images are flagged. We also use forensic detectors (noise analysis) as a secondary check.

We preprocess with denoising (DnCNN). Modern vision models are surprisingly noise-tolerant, but we also store the original for user viewing.

Our OCR supports 100+ languages via Tesseract + PaddleOCR. We auto-detect language and route to the appropriate model.

We detect panoramas (via EXIF), split them into logical segments, and analyze each. This prevents the LLM from getting confused by ultra-wide scenes.

We detect screenshots (pixel patterns, UI elements) and treat them as "Document Memory" rather than "Visual Memory." They're indexed differently.

Not applicable for our architecture. We use cloud-based AI which handles quantization internally. For client-side processing, we use efficient Flutter widgets and native mobile APIs, not on-device ML models.

Not directly - we rely on efficient cloud AI models optimized for mobile applications. Our focus is on efficient API usage patterns and smart caching rather than model optimization.

LRU cache with 30-day TTL. Common queries ("Where's my passport?") hit cache 90% of the time, dropping cost to near zero.

Yes. We show cached/simple results in <200ms, then stream LLM-enhanced results. User sees something immediately.

Delta syncing (only changed vectors), compression (zstd), and smart scheduling (defer large syncs to Wi-Fi). Mobile data usage is <10MB/day.

Yes. Cosine similarity on CLIP embeddings. Click any photo -> "Find similar" -> instant visual search.

Yes. HDBSCAN on (embedding + time + location) space. We auto-generate "Trip to Tokyo" or "Emma's Birthday" without manual tagging.

We use triplet loss training. The model learns that "Baby Emma" and "Adult Emma" share identity despite appearance changes.

Yes. We extract color histograms and dominant colors. Queries like "red dress" are decomposed into color + object filters.

We integrate ACRCloud for music recognition. Background songs are tagged to memories automatically.

We log (with user consent) the query, retrieved vectors, LLM prompt, and final output. This creates a debugging trace for model improvement.

P95 latency, cache hit rate, user correction rate, query abandonment rate, and MAU per feature. We're obsessive about performance.

We have a fallback chain: GPT-4 -> Claude -> On-Device LLM. If all fail, we return raw vector results with "AI unavailable" notice.

Yes. We use Statsig for feature flags. Testing model variants, prompt strategies, and UX flows continuously.

Minimal. Anonymous usage patterns ("User queried 5 times today") but never query content. Privacy-first telemetry.

Not planned. Our architecture is optimized for cloud AI which provides best performance-cost ratio for mobile applications. Supporting local LLMs would require complete re-architecture and wouldn't provide enough value for the complexity.

Exploring it. Users could opt-in to share gradient updates (not raw data) to improve global models while preserving privacy.

ARKit/ARCore APIs. Overlay metadata on real-world objects ("This is where you left your keys"). Spatial anchoring + memory linkage.

No immediate plans. The user experience is bad. If demand grows, we'd use a lightweight ledger, not a full blockchain.

We're monitoring post-quantum cryptography standards (NIST). When ready, we'll migrate to quantum-resistant algorithms (Kyber, Dilithium).

iOS launches first (80% done). Android follows 2 months later. Core features identical; some OS-specific optimizations differ.

Hybrid. Core ML for Vision tasks (fast, native), custom TFLite for NLP (more flexible). Best of both worlds.

We pass with flying colors. Minimal data collection. Everything is user-controlled. No third-party tracking.

Yes. React + WebAssembly for lightweight client-side processing. WASM allows us to run efficient caching and embedding storage in-browser without heavy server calls.

Electron wrapper around the web app. Native performance isn't critical for desktop; cross-platform consistency is.

Efficient on-device vector search at scale. Indexing 100k vectors on a phone without killing battery or memory. HNSW + quantization made it possible.

LLM inference cost. As usage scales, cloud inference gets expensive. We're aggressively optimizing (caching, model compression, edge inference).

Data corruption. If a user loses their memory index due to a bug, that's catastrophic. We have redundant backups and integrity checks everywhere.

Versioned data formats. The app auto-migrates old schemas to new ones on update. We test migrations extensively in QA.

85% for backend, 70% for mobile. Critical paths (encryption, data sync) have 100% coverage. We prioritize high-risk code.

We're considering it. The moat is the data, not the model. Open-sourcing could build trust and attract contributors.

Whisper, CLIP, LanceDB, Fastlane, libsodium. We contribute back (bug fixes, documentation). Open source is our foundation.

Not yet, but planned for post-launch. We'll offer rewards for security vulnerabilities, especially around encryption.

Phase 2. We'll open APIs for "Memory Apps"—third-party tools that read from your Dzikra index (with permission). Plugin ecosystem incoming.

We'll undergo third-party pen testing and cryptographic audits before GA launch. Security isn't negotiable.

Multimodal fusion. Combining video, audio, location, and biometric data into a single unified memory model. True "4D" recall.

Not initially. We fine-tune existing models. If we reach 10M users, we'll consider training a "Memory-Specialized" foundation model.

That's the "AR Glasses" endgame. Continuous capture + real-time indexing. The tech exists; the UX and social acceptance don't. Yet.

Horizontal scaling (Kubernetes), aggressive caching (Redis/Cloudflare), edge computing (bring inference closer to users), and ruthless optimization.

Time to successful recall. How fast can a user go from "I need X" to "I found X." Currently ~3 seconds. Goal: <1 second.