Create mock backend to simulate AI responses for UI development:
Backend Implementation:
- tools/fake-backend/server.js: Express server with CORS
- POST /api/analyze: Accepts log, returns mock AI analysis with 1.5s delay
- GET /health: Health check endpoint
- Pattern matching for different log types (PostgreSQL, Nginx, Node.js, Disk)
- Error handling: 400 for empty payload, 500 for server errors
- Mock responses for common errors (OOM, 502, connection refused, disk full)
Container Setup:
- Dockerfile: Node.js 20 Alpine container
- docker-compose.yml: Added fake-backend service on port 3000
- Health checks for both frontend and backend services
- Environment variable VITE_API_URL for frontend
Frontend Integration:
- InteractiveDemo.tsx: Replaced static data with real fetch() calls
- API_URL configurable via env var (default: http://localhost:3000)
- Error handling with user-friendly messages
- Shows backend URL in demo section
- Maintains loading states and UI feedback
Documentation:
- docs/tools_fake_backend.md: Complete usage guide
- README.md: Updated with tools/fake-backend structure and usage
Development Workflow:
1. docker compose up -d (starts both frontend and backend)
2. Frontend calls http://fake-backend:3000/api/analyze
3. Backend returns realistic mock responses
4. No OpenRouter API costs during development
Safety First:
- No real API calls during development
- Isolated mock logic in dedicated tool
- Easy switch to real backend by changing URL
- CORS enabled only for development
Refs: Sprint 4 preparation, API development workflow
Update documentation to reflect demo simulation status:
README.md: Add note explaining demo uses static mock data
CHANGELOG.md: Add Interactive Demo entry marked as Mock
roadmap_ideas.md: Update status to in-evaluation with priority note
Prevents user confusion about AI capabilities in demo section.
Refs: Sprint 3, demo clarification
Create comprehensive living document to track improvement suggestions
and potential new features for LogWhisperer AI roadmap.
Structure includes:
- Status legend (💡🤔📅🚧✅❌)
- Categorized by priority and area:
* Core Features (Backend, AI)
* UX/UI & Frontend
* Security & Compliance
* Integrations (Slack, Discord, etc.)
* Monitoring & Analytics
* Developer Experience
* Internationalization
* Monetization
- Completed sprints tracking
- Rejected ideas section with rationale
- Notes on performance, costs, scalability
- Contribution guidelines
Serves as central hub for team to propose, evaluate,
and track feature ideas before they enter formal roadmap.
Refs: Sprint 3 planning, team brainstorming
- Add MCP servers documentation (n8n, context7, sequential-thinking)
- Update README.md with complete project structure and requirements.txt
- Transform agents.md into comprehensive agent staff catalog (9 agents)
- Update CHANGELOG.md with [Unreleased] MCP entries
- Fix ingestion_script.md acceptance criteria checkboxes
- Add .opencode/opencode.json to .gitignore for security
- Include new agent configs: n8n_specialist_agent, context_auditor_agent
- Include new skill playbooks: n8n_automation, context7_documentation
Security: API credentials in .opencode/opencode.json are now gitignored
- Add latest commit 88cfe9a to recent commits section
- Update Version 0.1.1 entry with Project Review details
- Update cronologia completa table with new commit
- Update Sprint 1 status to Completed and Approved
- Update statistics: 21 total commits
- Add Project Review reference and Go/No-Go decision
- Define 7 AI agent roles and responsibilities
- Document tools and focus areas for each agent
- Include operational workflow guidelines
- Configure for Spec-Driven and Safety First workflow
- Create comprehensive git history documentation
- Track all commits with dates, authors, and types
- Include sprint history section
- Add statistics and update instructions
- Maintainable format for future updates
- Add logwhisperer.sh script for tailing and monitoring system logs
- Implement pattern matching for critical errors (FATAL, ERROR, OOM, segfault)
- Add JSON payload generation with severity levels
- Implement rate limiting and offset tracking per log source
- Add install.sh with interactive configuration and systemd support
- Create comprehensive test suite with pytest
- Add technical specification documentation
- Update CHANGELOG.md following Common Changelog standard
All 12 tests passing. Follows Metodo Sacchi (Safety first, little often, double check).
