mirror of
https://github.com/SamyRai/turash.git
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000eab4740
Repository Structure:
- Move files from cluttered root directory into organized structure
- Create archive/ for archived data and scraper results
- Create bugulma/ for the complete application (frontend + backend)
- Create data/ for sample datasets and reference materials
- Create docs/ for comprehensive documentation structure
- Create scripts/ for utility scripts and API tools
Backend Implementation:
- Implement 3 missing backend endpoints identified in gap analysis:
* GET /api/v1/organizations/{id}/matching/direct - Direct symbiosis matches
* GET /api/v1/users/me/organizations - User organizations
* POST /api/v1/proposals/{id}/status - Update proposal status
- Add complete proposal domain model, repository, and service layers
- Create database migration for proposals table
- Fix CLI server command registration issue
API Documentation:
- Add comprehensive proposals.md API documentation
- Update README.md with Users and Proposals API sections
- Document all request/response formats, error codes, and business rules
Code Quality:
- Follow existing Go backend architecture patterns
- Add proper error handling and validation
- Match frontend expected response schemas
- Maintain clean separation of concerns (handler -> service -> repository)
87 lines
4.4 KiB
Markdown
87 lines
4.4 KiB
Markdown
## 24. Summary & Implementation Priorities
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*For detailed tactical roadmap with monthly milestones, see [28_project_roadmap.md](28_project_roadmap.md)*
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*For high-level prototype roadmap with phase breakdowns, see [24_prototype_roadmap.md](24_prototype_roadmap.md)*
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### Lessons Learned Integration
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**Critical Findings from 30+ Platform Analysis** (See [Section 30](../concept/30_lessons_learned_application_analysis.md) for full analysis):
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1. **Critical Mass Requirements**: 50+ participants required (not 20-50) for meaningful network effects
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2. **Local Clustering**: ≥70% participants within 5km radius (40% higher match rates)
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3. **Data Quality Strategy**: Progressive refinement with scoring and incentives (prevents "data quality death spiral")
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4. **Hybrid Automation**: 80% automated matches, 20% facilitated (prevents "facilitation dependency")
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5. **Geographic Expansion**: Must achieve 50+ participants AND ≥20% match rate before expanding (prevents "geographic overreach")
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### Implementation Priority
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1. **Critical for MVP** (Revised based on lessons learned):
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- ADR framework
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- Go 1.25 stack setup (Gin/Fiber, Neo4j driver, NATS/Redis Streams)
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- Basic caching (Redis)
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- Security foundation (JWT, OAuth2)
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- **Data Quality Scoring System** (prevents data quality death spiral)
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- **Economic Calculations Integration** (must-have feature for match acceptance)
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- **Hybrid Automation Model Definition** (automated vs. facilitated routing)
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- **50+ Participants Target** (critical mass threshold)
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2. **Important for Scale**:
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- Event-driven architecture with Go workers
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- Graph database optimization
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- Go 1.25 experimental features (JSON v2, GreenTea GC)
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- Monitoring (Prometheus, OpenTelemetry)
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- **IoT Integration Priority** (30%+ integration rate target by Phase 2)
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- **Facilitator Marketplace** (enables hybrid automation model)
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- **Data Quality Incentives** (progressive refinement requirements)
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3. **Future Enhancements**:
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- GraphQL (gqlgen)
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- Mobile app (PWA)
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- Advanced analytics
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- gRPC microservices
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- AI/ML integration (Phase 3+, appropriately timed)
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### Key Go 1.25 Advantages
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- **Performance**: 10-40% GC overhead reduction with GreenTea
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- **JSON**: 3-10x faster with JSON v2
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- **Container**: Automatic CPU resource management
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- **Concurrency**: Goroutines for efficient parallel processing
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- **Deployment**: Single binary, smaller containers
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### Technical Spine
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That's your **technical spine**. It's tractable, uses boring reliable tech (Neo4j + Go 1.25 + React), and scales linearly with data.
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You can build a credible demo in a quarter and have something real to sell to municipalities or industrial parks before you ever touch pipes or pumps.
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### Tactical Execution Priorities (From User Feedback Integration)
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**MVP Focus Areas** (address cold start and adoption):
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1. **Multi-Channel Data Acquisition**:
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- **Seed Data**: Import public registries, building footprints, utility maps, industry association data
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- **Forced Adoption**: Bundle with mandatory processes (CSRD, energy audits, municipal permits)
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- **Partner Ingestion**: Utilities/industrial parks provide anonymized facility data
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- **Incentive Rewards**: Priority matching for complete profiles, fee discounts for data contributors
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2. **Privacy-First Design**: Public/network-only/private tiers with anonymized discovery
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3. **Low-Capex Matches**: Focus on waste pickup, maintenance, shared services (no pipes required)
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4. **Trust Building**: White-label municipal operation, industry association backing
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5. **Facilitator Ecosystem**: Curate and train local engineering/ESG consultancies with platform templates
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**Revenue Model Evolution**:
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1. **Free Tier**: See + Match (drive network effects)
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2. **Basic**: Shared OPEX deals (€50/facility/month)
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3. **Business**: Full symbiosis + facilitators (€150/facility/month)
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4. **Enterprise**: Integration + white-label (€500/facility/month)
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**Channel Strategy**:
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- **Utility Partnerships**: They provide data, you provide UI
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- **Municipal Dashboards**: Charge cities, give companies free access
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- **Facilitator Marketplace**: 10-20% commission on intermediated deals
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**Success Metrics**:
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- 20 companies, ≥5 actionable matches, ≥3 implemented connections
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- ≥60% data completion rate, clear revenue path within 6 months
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Each recommendation should be evaluated against project constraints (time, budget, team expertise) and documented as an ADR when implemented.
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