Reef

Reef Platform: Comprehensive Problem Breakdown & Engineering Roadmap

Executive Summary

This document provides an exhaustive breakdown of the technical, architectural, and product challenges that need to be solved to build Reef - a personal data reef platform that transforms scattered digital experiences into coherent, interactive stories through intelligent agents.

The problems are organized into 6 major categories with 47 core problems and 200+ sub-problems, each with specific technical requirements, success criteria, and dependencies.

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🏗️ CATEGORY 1: DATA FOUNDATION & ARCHITECTURE

1.1 Multi-Service Data Integration Pipeline

Core Problem: Creating a unified, real-time data ingestion system that can handle 50+ different service APIs with varying schemas, rate limits, and authentication methods.

Sub-Problems:

• 1.1.1 Service Connector Architecture

  • Design pluggable connector framework for new services
  • Handle OAuth 2.0/2.1 flows for each service type
  • Implement service-specific rate limiting and quotas
  • Create connector health monitoring and failover
  • Build connector version management system
  • Handle service API deprecation and migrations

• 1.1.2 Data Schema Normalization

  • Design universal data model for all service types
  • Build schema mapping engine for service-specific formats
  • Handle nested/hierarchical data structures
  • Implement field-level data type conversion
  • Create schema evolution and backward compatibility
  • Build validation pipeline for incoming data

• 1.1.3 Real-time Stream Processing

  • Implement webhook receiver for real-time updates
  • Build event queue system for high-volume data
  • Create backpressure handling for burst traffic
  • Design exactly-once delivery semantics
  • Implement stream processing checkpointing
  • Build dead letter queue for failed processing

• 1.1.4 Service-Specific Challenges

  • Google Calendar: recurring events, timezone handling, attendee management
  • Gmail: thread reconstruction, attachment processing, spam filtering
  • Google Drive: file version tracking, sharing permissions, folder hierarchies
  • GitHub: repository relationships, issue linking, code change analysis
  • Social Media: privacy settings, content moderation, engagement metrics
  • IoT Devices: sensor data normalization, device lifecycle management

1.2 Intelligent Data Storage & Retrieval

Core Problem: Designing a hybrid storage system that combines relational data, vector embeddings, and time-series data for fast semantic search and relationship discovery.

Sub-Problems:

• 1.2.1 Vector Database Implementation

  • Deploy and configure pgvector extension for PostgreSQL
  • Design embedding generation pipeline using local/cloud models
  • Implement incremental vector index updates
  • Build vector similarity search optimization
  • Create embedding versioning for model updates
  • Design vector space partitioning for scale

• 1.2.2 Semantic Data Relationships

  • Build automatic relationship detection between data points
  • Implement temporal relationship mapping
  • Create cross-service entity resolution (same person across platforms)
  • Design relationship confidence scoring
  • Build relationship graph storage and traversal
  • Implement relationship decay over time

• 1.2.3 Time-Series Data Management

  • Design efficient storage for high-frequency sensor data
  • Implement data retention policies by data type
  • Build data aggregation and rollup strategies
  • Create time-window query optimization
  • Design data compression for historical data
  • Implement data archival and cold storage

• 1.2.4 Data Lineage & Provenance

  • Track source attribution for every data point
  • Build transformation history tracking
  • Implement data quality scoring
  • Create audit trail for data modifications
  • Design data freshness indicators
  • Build data dependency graph visualization

1.3 Data Privacy & Security Architecture

Core Problem: Implementing a zero-trust, privacy-first architecture that gives users granular control over their data while enabling AI processing.

Sub-Problems:

• 1.3.1 Encryption & Key Management

  • Implement field-level encryption for sensitive data
  • Design user-controlled encryption keys
  • Build key rotation and recovery systems
  • Create encryption at rest and in transit
  • Implement secure key escrow for account recovery
  • Design multi-tenant key isolation

• 1.3.2 Granular Privacy Controls

  • Build per-agent data access permissions
  • Create per-service data sharing controls
  • Implement time-based data access expiration
  • Design context-aware privacy settings
  • Build privacy impact assessment tools
  • Create privacy dashboard for users

• 1.3.3 On-Device Processing

  • Implement local AI model deployment
  • Build differential privacy mechanisms
  • Create federated learning for model updates
  • Design secure computation protocols
  • Implement homomorphic encryption for cloud processing
  • Build trusted execution environments

• 1.3.4 Regulatory Compliance

  • GDPR compliance: right to be forgotten, data portability
  • CCPA compliance: opt-out mechanisms, data disclosure
  • HIPAA compliance for health data
  • SOX compliance for financial data
  • Build automated compliance reporting
  • Create consent management system

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🤖 CATEGORY 2: AGENT INTELLIGENCE SYSTEM

2.1 Agent Architecture & Runtime

Core Problem: Building a scalable, multi-agent system where diverse AI agents can collaborate, maintain memory, and evolve over time while processing personal data.

Sub-Problems:

• 2.1.1 Agent Lifecycle Management

  • Design agent creation and deployment pipeline
  • Implement agent versioning and updates
  • Build agent health monitoring and recovery
  • Create agent resource allocation and scaling
  • Design agent retirement and data migration
  • Implement agent marketplace and distribution

• 2.1.2 Agent Memory Systems

  • Build episodic memory for agent experiences
  • Implement semantic memory for knowledge
  • Create working memory for active processing
  • Design memory consolidation and forgetting
  • Build memory sharing between agents
  • Implement memory compression techniques

• 2.1.3 Agent Communication Protocols

  • Design inter-agent messaging system
  • Build collaborative task execution
  • Implement conflict resolution mechanisms
  • Create agent reputation and trust scoring
  • Design agent negotiation protocols
  • Build agent coordination for complex tasks

• 2.1.4 Agent Personality & Behavior

  • Implement consistent personality traits
  • Build emotional state modeling
  • Create behavioral adaptation over time
  • Design personality compatibility scoring
  • Implement mood and context awareness
  • Build personality trait inheritance

2.2 Specialized Agent Types

Core Problem: Developing distinct agent categories with specialized capabilities for different aspects of data processing and story generation.

Sub-Problems:

• 2.2.1 Data Processing Agents

  • Calendar Agent: meeting extraction, scheduling patterns, time analysis
  • Email Agent: communication analysis, relationship mapping, sentiment tracking
  • Code Agent: development patterns, project tracking, skill assessment
  • Health Agent: wellness trends, habit analysis, goal tracking
  • Finance Agent: spending patterns, investment tracking, budget analysis
  • Travel Agent: location correlation, trip planning, exploration patterns

• 2.2.2 Analysis Agents

  • Pattern Recognition Agent: identifying recurring behaviors and trends
  • Sentiment Analysis Agent: emotional state tracking across platforms
  • Anomaly Detection Agent: identifying unusual patterns or events
  • Predictive Analysis Agent: forecasting based on historical patterns
  • Correlation Agent: finding relationships between disparate data
  • Insight Generation Agent: creating actionable insights from data

• 2.2.3 Narrative Agents

  • Story Structure Agent: organizing events into narrative arcs
  • Character Development Agent: tracking personal growth and changes
  • Scene Setting Agent: contextualizing events with rich descriptions
  • Dialogue Agent: reconstructing conversations and interactions
  • Theme Agent: identifying recurring themes and motifs in life
  • Plot Agent: connecting events into meaningful storylines

• 2.2.4 Creative Agents

  • Writing Style Agent: adapting narrative voice and tone
  • Visual Agent: generating images and visualizations for stories
  • Audio Agent: creating soundscapes and music for narratives
  • Interactive Agent: building choose-your-own-adventure elements
  • Multimedia Agent: combining text, images, audio, and video
  • Presentation Agent: formatting stories for different mediums

2.3 Agent Learning & Adaptation

Core Problem: Creating agents that improve over time through user feedback, experience, and collaborative learning while maintaining privacy.

Sub-Problems:

• 2.3.1 Reinforcement Learning from Human Feedback

  • Build user feedback collection system
  • Implement RLHF training pipeline
  • Create reward model from user preferences
  • Design continuous learning without catastrophic forgetting
  • Build A/B testing framework for agent improvements
  • Implement personalized agent adaptation

• 2.3.2 Federated Learning Across Users

  • Design privacy-preserving model updates
  • Implement differential privacy in model sharing
  • Build consensus mechanisms for model improvements
  • Create user contribution incentives
  • Design model poisoning attack prevention
  • Implement selective knowledge sharing

• 2.3.3 Experience Replay & Knowledge Distillation

  • Build agent experience replay systems
  • Implement knowledge distillation between agents
  • Create synthetic experience generation
  • Design experience prioritization algorithms
  • Build cross-agent knowledge transfer
  • Implement experience compression techniques

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📖 CATEGORY 3: STORY GENERATION ENGINE

3.1 Narrative Structure & Templates

Core Problem: Creating a flexible system for generating compelling narratives from raw personal data while maintaining coherence and engagement.

Sub-Problems:

• 3.1.1 Story Template Framework

  • Design modular story template system
  • Build template inheritance and composition
  • Create template parameter systems
  • Implement template version control
  • Design community template sharing
  • Build template performance analytics

• 3.1.2 Narrative Arc Construction

  • Implement classic story structures (Hero’s Journey, Three-Act, etc.)
  • Build dynamic pacing algorithms
  • Create tension and resolution identification
  • Design character arc integration
  • Implement subplot weaving
  • Build narrative coherence scoring

• 3.1.3 Multi-Perspective Narratives

  • Build multiple viewpoint generation
  • Create perspective switching algorithms
  • Implement unreliable narrator techniques
  • Design perspective consistency checking
  • Build viewpoint character development
  • Create perspective-based fact checking

• 3.1.4 Genre & Style Adaptation

  • Implement multiple writing genres (memoir, fiction, technical, etc.)
  • Build tone and voice consistency
  • Create genre-appropriate language models
  • Design style transfer between genres
  • Implement cultural and temporal style adaptation
  • Build style preference learning

3.2 Content Generation & Enhancement

Core Problem: Transforming structured data into engaging prose while maintaining factual accuracy and personal voice.

Sub-Problems:

• 3.2.1 Natural Language Generation

  • Build context-aware text generation
  • Implement fact-grounded language models
  • Create personalized writing style adaptation
  • Design factual consistency checking
  • Build controlled text generation
  • Implement multi-language support

• 3.2.2 Scene Reconstruction

  • Build detailed scene descriptions from data
  • Create environmental context inference
  • Implement sensory detail generation
  • Design atmosphere and mood setting
  • Build character interaction reconstruction
  • Create realistic dialogue generation

• 3.2.3 Temporal Narrative Weaving

  • Build chronological story ordering
  • Implement flashback and foreshadowing
  • Create temporal transition smoothing
  • Design time compression techniques
  • Build parallel timeline management
  • Implement causal relationship tracking

• 3.2.4 Multimedia Story Integration

  • Build image-text story integration
  • Create video summarization for narratives
  • Implement audio transcription and integration
  • Design interactive story elements
  • Build document and file integration
  • Create multimedia timeline synchronization

3.3 Story Quality & Personalization

Core Problem: Ensuring generated stories are engaging, accurate, and personally meaningful while avoiding hallucination and maintaining user voice.

Sub-Problems:

• 3.3.1 Quality Assessment & Control

  • Build automated story quality scoring
  • Implement factual accuracy verification
  • Create engagement prediction models
  • Design readability optimization
  • Build coherence and consistency checking
  • Implement plagiarism and originality verification

• 3.3.2 Personalization & Voice

  • Learn individual writing preferences
  • Build personal vocabulary and phrase usage
  • Create emotional tone preferences
  • Design story length and detail preferences
  • Implement topic interest weighting
  • Build narrative perspective preferences

• 3.3.3 User Collaboration & Editing

  • Build real-time collaborative editing
  • Implement suggestion and approval workflows
  • Create version history and rollback
  • Design comment and annotation systems
  • Build change tracking and attribution
  • Implement collaborative decision making

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🔧 CATEGORY 4: TECHNICAL INFRASTRUCTURE

4.1 Scalable Backend Architecture

Core Problem: Building a high-performance, scalable backend that can handle millions of users with real-time data processing and AI workloads.

Sub-Problems:

• 4.1.1 Microservices Architecture

  • Design service boundaries and responsibilities
  • Implement service discovery and registry
  • Build inter-service communication (gRPC, REST, events)
  • Create service mesh for traffic management
  • Design circuit breakers and fault tolerance
  • Implement distributed tracing and monitoring

• 4.1.2 Container Orchestration

  • Deploy Kubernetes cluster management
  • Build container image optimization
  • Implement auto-scaling policies
  • Create resource quotas and limits
  • Design rolling deployment strategies
  • Build health check and readiness probes

• 4.1.3 Database Scaling & Sharding

  • Design database sharding strategies
  • Implement read replicas and load balancing
  • Build connection pooling optimization
  • Create database migration systems
  • Design backup and disaster recovery
  • Implement database performance monitoring

• 4.1.4 Caching & Performance

  • Build multi-level caching strategy
  • Implement Redis for session and temporary data
  • Create CDN integration for static content
  • Design cache invalidation strategies
  • Build performance monitoring and alerting
  • Implement query optimization

4.2 AI/ML Infrastructure

Core Problem: Creating a scalable AI/ML infrastructure that can support multiple models, training pipelines, and inference workloads efficiently.

Sub-Problems:

• 4.2.1 Model Serving & Management

  • Build model registry and versioning
  • Implement A/B testing for models
  • Create model performance monitoring
  • Design model rollback mechanisms
  • Build canary deployments for models
  • Implement model explainability tools

• 4.2.2 Training Infrastructure

  • Build distributed training systems
  • Implement GPU cluster management
  • Create experiment tracking and management
  • Design hyperparameter optimization
  • Build data pipeline for training
  • Implement model validation pipelines

• 4.2.3 Edge Computing & Local Models

  • Build edge device deployment
  • Implement model quantization and compression
  • Create offline inference capabilities
  • Design federated learning infrastructure
  • Build edge-to-cloud synchronization
  • Implement local privacy preservation

4.3 DevOps & Reliability

Core Problem: Ensuring high availability, reliability, and maintainability of a complex distributed system with AI components.

Sub-Problems:

• 4.3.1 CI/CD Pipelines

  • Build automated testing frameworks
  • Implement code quality gates
  • Create deployment automation
  • Design integration testing
  • Build security scanning
  • Implement documentation generation

• 4.3.2 Monitoring & Observability

  • Build comprehensive metrics collection
  • Implement distributed logging
  • Create alerting and incident response
  • Design performance dashboards
  • Build error tracking and debugging
  • Implement user behavior analytics

• 4.3.3 Security & Compliance

  • Build security scanning and testing
  • Implement vulnerability management
  • Create access control and authentication
  • Design audit logging and compliance
  • Build threat detection and response
  • Implement security incident management

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🎨 CATEGORY 5: USER EXPERIENCE & INTERFACES

5.1 Interactive Story Editor

Core Problem: Creating an intuitive interface that allows users to collaborate with AI agents in creating and editing their personal narratives.

Sub-Problems:

• 5.1.1 Rich Text Editing Interface

  • Build collaborative rich text editor
  • Implement real-time multi-user editing
  • Create version history and conflict resolution
  • Design inline commenting and suggestions
  • Build formatting and styling tools
  • Implement spell check and grammar assistance

• 5.1.2 AI-Human Collaboration UI

  • Design AI suggestion integration
  • Build approval/rejection workflows
  • Create alternative generation options
  • Implement AI reasoning explanations
  • Design confidence indicators for AI content
  • Build human feedback collection

• 5.1.3 Story Structure Visualization

  • Build narrative arc visualization
  • Create character development timelines
  • Implement plot point mapping
  • Design story pacing visualization
  • Build theme and motif tracking
  • Create story health dashboards

5.2 Data Canvas & Visualization

Core Problem: Creating an interactive platform where users can visually explore their data, assign agents, and watch stories emerge in real-time.

Sub-Problems:

• 5.2.1 Timeline Visualization

  • Build interactive timeline interface
  • Implement multi-scale time views (day/week/month/year)
  • Create data point clustering and filtering
  • Design drag-and-drop data manipulation
  • Build timeline zoom and navigation
  • Implement temporal relationship visualization

• 5.2.2 Data Relationship Mapping

  • Build force-directed graph visualization
  • Create relationship strength indicators
  • Implement node filtering and grouping
  • Design interactive exploration tools
  • Build relationship discovery interfaces
  • Create data correlation heatmaps

• 5.2.3 Agent Activity Visualization

  • Build agent workflow visualization
  • Create agent performance dashboards
  • Implement agent interaction mapping
  • Design agent resource utilization views
  • Build agent decision explanation interfaces
  • Create agent collaboration visualizations

5.3 Mobile & Multi-Platform Experience

Core Problem: Providing seamless access to Reef functionality across devices while maintaining performance and privacy.

Sub-Problems:

• 5.3.1 Progressive Web App

  • Build offline-capable PWA
  • Implement service workers for caching
  • Create responsive design for all screen sizes
  • Design touch-optimized interactions
  • Build push notification system
  • Implement app-like installation experience

• 5.3.2 Cross-Device Synchronization

  • Build real-time data synchronization
  • Implement conflict resolution for offline edits
  • Create device-specific optimization
  • Design bandwidth-aware content delivery
  • Build cross-device session management
  • Implement device trust and security

• 5.3.3 Voice & Conversational Interfaces

  • Build voice command integration
  • Implement natural language queries
  • Create conversational story editing
  • Design voice-activated agent interactions
  • Build speech-to-text for story input
  • Implement accessibility features

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🚀 CATEGORY 6: BUSINESS & ECOSYSTEM

6.1 Platform Ecosystem

Core Problem: Building a thriving ecosystem around Reef with community contributions, third-party integrations, and sustainable business model.

Sub-Problems:

• 6.1.1 Agent Marketplace

  • Build agent publishing platform
  • Create agent rating and review system
  • Implement revenue sharing for creators
  • Design agent certification process
  • Build agent compatibility testing
  • Create agent update distribution

• 6.1.2 API Platform & Integrations

  • Build comprehensive developer APIs
  • Create SDK for multiple languages
  • Implement API rate limiting and quotas
  • Design webhook system for real-time integration
  • Build integration marketplace
  • Create developer documentation and tools

• 6.1.3 Community & User-Generated Content

  • Build story sharing and discovery
  • Create community discussion forums
  • Implement user-contributed templates
  • Design story recommendation system
  • Build social features and following
  • Create content moderation tools

6.2 Business Intelligence & Analytics

Core Problem: Building analytics and insights that drive product decisions, user engagement, and business growth.

Sub-Problems:

• 6.2.1 User Behavior Analytics

  • Build user journey tracking
  • Implement feature usage analytics
  • Create cohort analysis tools
  • Design A/B testing framework
  • Build churn prediction models
  • Implement user satisfaction scoring

• 6.2.2 Platform Performance Metrics

  • Build story generation success rates
  • Implement agent performance tracking
  • Create data processing efficiency metrics
  • Design system reliability dashboards
  • Build cost optimization analytics
  • Implement security incident tracking

• 6.2.3 Business Model Optimization

  • Build pricing model analytics
  • Implement conversion funnel tracking
  • Create revenue attribution models
  • Design customer lifetime value prediction
  • Build competitive analysis tools
  • Implement market research integration

6.3 Legal & Compliance Framework

Core Problem: Ensuring legal compliance, intellectual property protection, and ethical AI usage across multiple jurisdictions.

Sub-Problems:

• 6.3.1 Data Protection & Privacy

  • Implement GDPR compliance framework
  • Build CCPA privacy controls
  • Create data processing agreements
  • Design consent management system
  • Build privacy impact assessments
  • Implement data breach response procedures

• 6.3.2 Intellectual Property & Content

  • Build copyright protection for generated content
  • Implement content attribution systems
  • Create fair use guidelines for data processing
  • Design IP licensing for community content
  • Build DMCA compliance tools
  • Implement content originality verification

• 6.3.3 AI Ethics & Bias Prevention

  • Build bias detection and mitigation tools
  • Implement fairness metrics for AI systems
  • Create ethical AI guidelines
  • Design algorithmic transparency tools
  • Build AI decision audit trails
  • Implement bias reporting mechanisms

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📊 IMPLEMENTATION PRIORITY MATRIX

Phase 1: Foundation (Months 1-6)

Critical Dependencies - Must be completed first

1. Database & Storage Architecture (1.2)

   • Priority: P0 - Blocks everything else
   • Complexity: High
   • Timeline: 3 months

2. Basic OAuth & Service Integration (1.1.1-1.1.2)

   • Priority: P0 - Core functionality
   • Complexity: Medium
   • Timeline: 2 months

3. Agent Runtime Framework (2.1.1-2.1.2)

   • Priority: P0 - Core platform
   • Complexity: High
   • Timeline: 4 months

4. Basic Story Templates (3.1.1)

   • Priority: P1 - User value
   • Complexity: Medium
   • Timeline: 2 months

Phase 2: Intelligence (Months 7-12)

Core AI and processing capabilities

1. Vector Database & Semantic Search (1.2.1)

   • Priority: P1 - AI foundation
   • Complexity: High
   • Timeline: 2 months

2. Basic Agent Types (2.2.1)

   • Priority: P1 - User value
   • Complexity: Medium
   • Timeline: 3 months

3. Story Generation Engine (3.2.1-3.2.2)

   • Priority: P1 - Core feature
   • Complexity: High
   • Timeline: 4 months

4. Web Interface & Editor (5.1.1-5.1.2)

   • Priority: P1 - User experience
   • Complexity: Medium
   • Timeline: 3 months

Phase 3: Advanced Features (Months 13-18)

Enhanced intelligence and user experience

1. Agent Learning & Adaptation (2.3)

   • Priority: P2 - Differentiation
   • Complexity: Very High
   • Timeline: 6 months

2. Advanced Story Features (3.1.2-3.1.3)

   • Priority: P2 - User engagement
   • Complexity: High
   • Timeline: 4 months

3. Data Canvas & Visualization (5.2)

   • Priority: P2 - User experience
   • Complexity: Medium
   • Timeline: 3 months

4. Privacy & Security Framework (1.3)

   • Priority: P1 - Trust & compliance
   • Complexity: High
   • Timeline: 4 months

Phase 4: Scale & Ecosystem (Months 19-24)

Platform scaling and community features

1. Scalable Infrastructure (4.1)

   • Priority: P2 - Growth enablement
   • Complexity: High
   • Timeline: 4 months

2. Mobile & Multi-Platform (5.3)

   • Priority: P2 - User reach
   • Complexity: Medium
   • Timeline: 3 months

3. Agent Marketplace (6.1.1)

   • Priority: P3 - Revenue & community
   • Complexity: Medium
   • Timeline: 3 months

4. Advanced Analytics (6.2)

   • Priority: P2 - Business intelligence
   • Complexity: Medium
   • Timeline: 2 months

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🎯 SUCCESS METRICS & VALIDATION

Technical Metrics

• Performance: <100ms response time for story generation
• Reliability: 99.9% uptime for core services
• Scalability: Support 1M+ users with linear cost growth
• Data Processing: Real-time ingestion from 50+ services
• AI Quality: >90% user satisfaction with generated content

User Engagement Metrics

• Adoption: 80% of new users create their first story within 24 hours
• Retention: 60% monthly active user retention
• Collaboration: 70% of users actively edit AI-generated content
• Sharing: 30% of stories are shared or exported
• Growth: Users connect average 5+ data sources

Business Metrics

• Conversion: 15% free-to-paid conversion rate
• Revenue: $50+ monthly revenue per paid user
• Churn: <5% monthly churn rate for paid users
• NPS: >50 Net Promoter Score
• Community: 1000+ community-contributed agents

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📋 PROBLEM TRACKING SYSTEM

Each problem in this document should be tracked with:

• Problem ID: Unique identifier (e.g., 1.1.1)
• Status: Not Started / In Progress / In Review / Completed / Blocked
• Owner: Person/team responsible
• Dependencies: Other problems that must be completed first
• Timeline: Estimated completion date
• Complexity: Low / Medium / High / Very High
• Business Impact: Low / Medium / High / Critical
• Technical Risk: Low / Medium / High / Very High
• Success Criteria: Specific, measurable outcomes
• Testing Strategy: How the solution will be validated

This creates a comprehensive roadmap with over 200 specific problems to solve, each contributing to the ultimate vision of Reef as a personal data reef platform that transforms scattered digital experiences into coherent, interactive stories through intelligent agents.

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This document should be updated regularly as problems are solved, new challenges emerge, and the platform evolves. Each problem should have its own detailed technical specification and implementation plan.