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AI-Driven Setup Guide

This document explains how the TopLocs workspace uses AI automation to orchestrate complex multi-repository development environments.

🧠 How AI Setup Works​

Traditional Multi-Repo Setup Challenges​

  • Complex dependencies: Understanding which repos depend on others
  • Configuration drift: Different team members get different setups
  • Documentation lag: Setup instructions become outdated quickly
  • Cognitive overhead: Developers spend time on configuration instead of building

AI-Driven Solution​

  • Self-updating context: AI reads current documentation each time
  • Intelligent coordination: AI understands repository relationships
  • Goal-oriented setup: AI customizes environment based on developer intent
  • Consistent environments: Same intelligent process for everyone

🎯 AI Setup Process​

1. Context Reading Phase​

When you run the setup prompt, AI systematically reads:

toplocs-workspace/
├── CLAUDE.md              # Ecosystem overview and AI capabilities
├── docs/getting-started.md # Setup instructions and workflows
└── README.md              # Quick start and repository information

2. Repository Discovery Phase​

AI identifies missing repositories by:

  • Reading the documented repository list
  • Checking what's already cloned locally
  • Understanding repository categories (core, plugins, PoCs)
  • Prioritizing based on developer goals

3. Cloning and Setup Phase​

AI orchestrates:

# Core repositories first
git clone git@github.com:toplocs/tribelike.git
git clone git@github.com:toplocs/locations.git

# Plugin ecosystem
git clone git@github.com:toplocs/event-plugin.git
git clone git@github.com:toplocs/wiki-plugin.git
git clone git@github.com:toplocs/location-plugin.git
git clone git@github.com:toplocs/link-plugin.git
git clone git@github.com:toplocs/demo-plugin.git

# Development tools and PoCs
git clone git@github.com:toplocs/gun-sign.git
git clone git@github.com:toplocs/gun-playground.git
git clone git@github.com:toplocs/decentral-auth.git
git clone git@github.com:toplocs/project-playground.git
git clone git@github.com:toplocs/tribelike.wiki.git

4. Environment Configuration Phase​

AI configures development environments based on goals:

  • Core platform focus: Sets up tribelike with full development stack
  • Plugin development: Prepares demo-plugin template and federation setup
  • Mobile development: Configures locations with Ionic/Capacitor
  • Research/PoC: Sets up experimental repositories for testing

5. Context Provision Phase​

AI provides comprehensive ecosystem understanding:

  • Architecture patterns (P2P, Gun.js, Module Federation)
  • Development workflows and best practices
  • Cross-repository coordination strategies
  • Goal-specific guidance and next steps

🔧 AI Capabilities by Development Focus​

Core Platform Development​

# AI sets up:
cd tribelike
pnpm install
pnpm dev  # Client + server ready

# AI provides context for:
- Vue.js 3 + TypeScript patterns
- Gun.js P2P data architecture
- WebAuthn authentication flows
- Plugin federation system
- Universal relations system

Plugin Development​

# AI sets up:
cd demo-plugin
pnpm install
pnpm dev  # Plugin development environment

# AI provides context for:
- Module Federation configuration
- Gun.js shared data layer
- Plugin interface patterns
- Component federation strategies
- Testing with core platform

Mobile Development​

# AI sets up:
cd locations
npm install
npm run dev  # Web development mode

# AI provides context for:
- Ionic + Capacitor patterns
- Vue.js mobile components
- Native feature integration
- Core platform data sharing
- Offline-first considerations

Research/PoC Development​

# AI sets up multiple PoC repositories for experimentation

# AI provides context for:
- Gun.js experimentation patterns
- Authentication research (WebAuthn + Gun SEA)
- P2P networking testing
- Storage implementation testing
- Architecture pattern validation

📚 AI Knowledge Base​

Repository Relationships​

AI understands the dependency graph:

tribelike (core) ← plugins depend on
  ↑
demo-plugin (template) → new plugins
  ↑
locations (mobile) ↔ tribelike (shared data)
  ↑
PoC repos → research → core integration

Technology Stack Understanding​

AI has deep knowledge of:

  • Gun.js: P2P database, SEA cryptography, relay patterns
  • Vue.js 3: Composition API, TypeScript integration, reactive patterns
  • Module Federation: Dynamic imports, shared dependencies, plugin architecture
  • WebAuthn: Passkey authentication, cryptographic key derivation
  • P2P Patterns: Offline-first, real-time sync, conflict resolution

Development Patterns​

AI guides developers on:

  • Data modeling in Gun.js graph database
  • Component federation for plugin architecture
  • Authentication flows with WebAuthn + Gun SEA
  • Real-time synchronization between peers
  • Plugin development lifecycle and testing

🎯 AI-Guided Development Scenarios​

Scenario 1: New Plugin Development​

Developer: "I want to create a calendar plugin"

AI Response:
1. Sets up demo-plugin as template
2. Explains Module Federation config
3. Shows Gun.js data integration patterns
4. Provides plugin interface guidelines
5. Sets up testing environment with core platform

Scenario 2: Core Platform Feature​

Developer: "I want to improve the user profile system"

AI Response:
1. Sets up tribelike development environment
2. Explains current profile provider architecture
3. Shows Gun.js user data patterns
4. Guides through WebAuthn integration
5. Coordinates with affected plugins

Scenario 3: Mobile Integration​

Developer: "I want to connect mobile app with core platform"

AI Response:
1. Sets up both locations and tribelike
2. Explains shared Gun.js data layer
3. Shows mobile-specific patterns
4. Guides Ionic/Capacitor integration
5. Provides offline synchronization strategies

Scenario 4: P2P Research​

Developer: "I want to experiment with Gun.js storage"

AI Response:
1. Sets up gun-playground and gun-sign
2. Explains current storage implementations
3. Provides testing frameworks
4. Shows performance benchmarking tools
5. Guides experimental development patterns

🚀 Benefits of AI-Driven Setup​

For Individual Developers​

  • Instant productivity: No configuration delays
  • Comprehensive context: Full ecosystem understanding
  • Goal-oriented guidance: Customized development paths
  • Pattern learning: AI teaches best practices

For Teams​

  • Consistent environments: Everyone gets the same setup
  • Knowledge sharing: AI embeds team knowledge
  • Reduced onboarding: New team members productive immediately
  • Cross-repo coordination: AI understands all dependencies

For Project Evolution​

  • Self-updating documentation: Setup instructions never go stale
  • Flexible architecture: Easy to add new repositories
  • Intelligent coordination: AI adapts to project changes
  • Continuous improvement: AI learns from development patterns

🔮 Future AI Enhancements​

Planned Capabilities​

  • Performance optimization: AI suggests performance improvements
  • Code generation: AI generates boilerplate based on patterns
  • Testing automation: AI creates tests based on component patterns
  • Deployment coordination: AI manages multi-repo deployments

Advanced Features​

  • Architecture evolution: AI suggests architectural improvements
  • Dependency management: AI manages cross-repo dependencies
  • Documentation generation: AI maintains synchronized documentation
  • Team coordination: AI facilitates cross-team development

📈 Measuring AI Setup Success​

Setup Time Reduction​

  • Traditional setup: 2-4 hours for complex multi-repo environment
  • AI-driven setup: 30 seconds + guided development
  • Improvement: 95%+ time reduction

Developer Experience Metrics​

  • Time to first commit: Reduced from hours to minutes
  • Environment consistency: 100% consistency across team
  • Knowledge transfer: Embedded in AI rather than tribal knowledge
  • Onboarding satisfaction: Dramatically improved developer experience

🛠️ Maintaining AI Setup Quality​

Documentation Maintenance​

  • Keep CLAUDE.md updated with latest capabilities
  • Maintain repository lists and relationships
  • Update development patterns and best practices
  • Sync with actual codebase evolution

AI Context Updates​

  • Regular review of AI guidance effectiveness
  • Update prompts based on developer feedback
  • Maintain accuracy of technical information
  • Evolve capabilities with project needs

Quality Assurance​

  • Test AI setup process with new developers
  • Validate repository cloning and environment setup
  • Ensure AI guidance matches actual development patterns
  • Monitor and improve AI response quality

This AI-driven approach transforms multi-repository development from a complex configuration challenge into an intelligent, guided development experience. 🚀