222 lines
9.4 KiB
Markdown
222 lines
9.4 KiB
Markdown
# Trax v2 Research Brief: Next-Generation Transcription Platform
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## Current State Analysis
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### Trax v1.0.0 Achievements ✅
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- **95%+ accuracy** with Whisper distil-large-v3
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- **99%+ accuracy** with DeepSeek AI enhancement
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- **<30 seconds** processing for 5-minute audio
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- **Batch processing** with 8 parallel workers
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- **Protocol-based architecture** with clean interfaces
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- **Production-ready** with comprehensive testing
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### Current Limitations 🔍
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- **Single-pass processing** (no multi-pass refinement)
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- **Basic speaker handling** (no diarization)
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- **Limited context awareness** (no domain-specific processing)
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- **CLI-only interface** (no web UI)
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- **Local processing only** (no distributed scaling)
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- **Fixed enhancement pipeline** (no dynamic optimization)
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## v2 Research Priorities
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### 1. 🎯 **Multi-Pass Processing & Confidence Scoring**
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**Research Focus:**
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- **Ensemble Methods**: Combine multiple AI models for superior accuracy
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- **Confidence Scoring**: Advanced methods for accuracy assessment
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- **Iterative Refinement**: Multi-pass processing with quality gates
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- **Segment Merging**: Intelligent combination of transcription segments
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**Key Questions:**
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- What ensemble approaches provide the best accuracy improvements?
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- How can we implement reliable confidence scoring?
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- What multi-pass strategies are most effective for different content types?
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- How can we optimize the trade-off between accuracy and processing time?
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**Target Metrics:**
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- **99.5%+ accuracy** (up from 99%)
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- **<20 seconds** processing (down from 30 seconds)
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- **Reliable confidence scores** with 95%+ correlation to actual accuracy
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### 2. 🎤 **Speaker Diarization & Voice Profiling**
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**Research Focus:**
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- **Speaker Identification**: Advanced diarization techniques
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- **Voice Biometrics**: Speaker profiling and voice fingerprinting
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- **Multi-Speaker Enhancement**: Optimizing for conversations
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- **Privacy-Preserving Methods**: Techniques that protect speaker privacy
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**Key Questions:**
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- What are the most accurate speaker diarization models available?
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- How can we implement voice profiling while maintaining privacy?
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- What are the best practices for handling overlapping speech?
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- How can we optimize for different conversation types?
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**Target Metrics:**
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- **90%+ speaker accuracy** for clear audio
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- **<5 seconds** diarization time per minute
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- **Privacy compliance** with GDPR/CCPA requirements
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### 3. 🧠 **Context-Aware Processing**
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**Research Focus:**
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- **Domain-Specific Models**: Specialized processing for different content types
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- **Semantic Understanding**: Content classification and analysis
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- **Metadata Integration**: Leveraging context for better results
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- **Adaptive Enhancement**: Dynamic optimization based on content type
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**Key Questions:**
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- How can we implement domain-specific enhancement (technical, medical, legal)?
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- What semantic analysis methods provide the most value?
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- How can we leverage metadata and context for better accuracy?
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- What adaptive processing strategies are most effective?
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**Target Metrics:**
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- **Domain-specific accuracy** improvements of 10-20%
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- **Content classification** with 95%+ accuracy
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- **Adaptive processing** that reduces errors by 50%+
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### 4. ⚡ **Scalability & Performance**
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**Research Focus:**
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- **Distributed Processing**: Scaling across multiple machines
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- **Cloud-Native Architecture**: Containerization and orchestration
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- **Resource Optimization**: Advanced memory and CPU management
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- **Caching Strategies**: Intelligent caching for repeated content
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**Key Questions:**
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- What distributed processing architectures are most suitable for transcription?
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- How can we implement efficient cloud-native scaling?
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- What caching strategies provide the best performance improvements?
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- How can we optimize resource usage for different hardware configurations?
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**Target Metrics:**
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- **1000+ concurrent transcriptions** (up from 8)
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- **<1GB memory** per worker (down from 2GB)
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- **<$0.005 per transcript** (down from $0.01)
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- **99.9% uptime** with automatic failover
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### 5. 🌐 **Web Interface & User Experience**
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**Research Focus:**
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- **Modern Web UI**: React/Vue-based interface with real-time updates
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- **Real-time Collaboration**: Multi-user editing and review capabilities
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- **Advanced Export Options**: Rich formatting and integration options
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- **Workflow Automation**: Streamlined processing workflows
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**Key Questions:**
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- What are the most effective UX patterns for transcription platforms?
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- How can we implement real-time collaboration features?
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- What export formats and integrations are most valuable to users?
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- How can we optimize the interface for different user types?
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**Target Metrics:**
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- **<2 second** page load times
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- **Real-time updates** with <500ms latency
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- **Mobile-responsive** design with 95%+ usability score
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- **Intuitive workflow** with <5 minutes to first transcription
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### 6. 🔌 **API & Integration Ecosystem**
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**Research Focus:**
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- **RESTful/GraphQL APIs**: Modern API design patterns
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- **Third-party Integrations**: Popular platform integrations
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- **Plugin System**: Extensible architecture for custom features
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- **Workflow Automation**: Integration with automation platforms
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**Key Questions:**
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- What API design patterns are most effective for transcription services?
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- Which third-party integrations provide the most value?
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- How can we design an extensible plugin architecture?
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- What workflow automation opportunities exist?
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**Target Metrics:**
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- **<100ms API response** times
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- **99.9% API uptime** with comprehensive monitoring
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- **10+ popular integrations** (Notion, Obsidian, etc.)
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- **Plugin ecosystem** with 20+ community plugins
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## Research Methodology
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### Phase 1: Technology Landscape Analysis (Week 1)
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- **Academic Research**: Latest papers in AI transcription and enhancement
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- **Industry Analysis**: Study of leading transcription platforms
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- **Technology Evaluation**: Assessment of emerging AI/ML technologies
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- **Performance Benchmarking**: Testing of different approaches
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### Phase 2: Architecture & Design Research (Week 2)
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- **System Architecture**: Analysis of current limitations and opportunities
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- **Scalability Patterns**: Research of distributed processing approaches
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- **User Experience**: Analysis of successful transcription platforms
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- **Integration Opportunities**: Study of API and ecosystem patterns
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### Phase 3: Implementation Strategy (Week 3)
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- **Feature Prioritization**: Ranking of features by impact and effort
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- **Implementation Roadmap**: Detailed development timeline
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- **Risk Assessment**: Analysis of technical and business risks
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- **Cost-Benefit Analysis**: ROI analysis for each major feature
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## Success Criteria
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### Technical Success
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- **Clear implementation path** for all high-priority features
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- **Performance improvements** of 50%+ in accuracy or speed
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- **Scalability improvements** of 10x+ in concurrent processing
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- **Cost optimization** of 50%+ reduction in processing costs
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### Business Success
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- **Competitive differentiation** from existing platforms
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- **User value proposition** that addresses key pain points
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- **Market positioning** that captures target segments
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- **Revenue potential** through new features and integrations
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### Implementation Success
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- **Feasible timeline** with realistic milestones
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- **Manageable risk** with clear mitigation strategies
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- **Resource requirements** that align with available capacity
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- **Maintenance overhead** that's sustainable long-term
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## Expected Outcomes
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### Primary Deliverables
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1. **Technical Research Report** (40-60 pages)
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2. **Feature Specification Document** (detailed specs for each feature)
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3. **Architecture Blueprint** (system design and implementation approach)
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4. **Implementation Roadmap** (timeline and milestones)
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5. **Competitive Analysis** (market positioning and differentiation)
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### Secondary Deliverables
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6. **Performance Benchmarks** (comparison with current state)
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7. **Cost Analysis** (implementation and operational costs)
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8. **Risk Assessment** (technical and business risks)
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9. **Recommendations** (prioritized feature list)
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10. **Next Steps** (immediate actions for v2 development)
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## Research Questions for Investigators
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### Technical Questions
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1. **What are the most effective ensemble approaches for transcription accuracy?**
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2. **How can we implement domain-specific enhancement while maintaining generality?**
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3. **What distributed processing architectures are most suitable for transcription workloads?**
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4. **How can we implement real-time collaboration without sacrificing performance?**
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5. **What caching strategies provide the best performance improvements for transcription?**
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### Business Questions
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1. **Which features provide the most competitive differentiation?**
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2. **What pricing models are most effective for transcription platforms?**
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3. **Which integrations provide the most user value?**
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4. **How can we position Trax v2 in the market?**
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5. **What are the key success factors for transcription platform adoption?**
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### Implementation Questions
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1. **What is the optimal development timeline for v2 features?**
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2. **How can we minimize risk while maximizing innovation?**
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3. **What resources are required for successful v2 implementation?**
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4. **How can we maintain backward compatibility during v2 development?**
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5. **What testing strategies are most effective for v2 features?**
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---
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**Note**: This research brief focuses on the most impactful areas for Trax v2 development. The goal is to identify features and approaches that will position Trax as a leading transcription platform while maintaining the clean, iterative architecture that made v1 successful.
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