Dojo Core Manual

This is a starter document outlining the MOOD MNKY Dojo platform. Future iterations will include more detailed implementation guides and examples.

Introduction

The Dojo platform is MOOD MNKY’s integrated learning and development environment, designed to create personalized “life OS” experiences for members. This manual outlines the core principles, architecture, and implementation guidelines for the Dojo platform, serving as the primary reference for developers, content creators, and platform administrators.

Vision and Purpose

The Dojo platform exists to transform personal development from isolated learning experiences into an integrated growth journey. It combines AI-guided learning, community engagement, personalized content, and token-based incentives into a cohesive ecosystem.

Core Principles

Personalized Learning Pathways: Adapting to individual goals, preferences, and learning styles
Community-Enhanced Growth: Leveraging collective wisdom and support
Multi-sensory Integration: Connecting digital content with physical experiences
Applied Knowledge Focus: Emphasizing practical application over passive consumption
Continuous Evolution: Learning from user engagement to improve the platform

Platform Architecture

System Overview

The Dojo platform consists of several integrated components:

Key Components

Learning Environment Engine

Creates and manages learning spaces

Environment templates
Content integration
Progress tracking
Assessment capabilities

Community Platform

Enables member connections and collaboration

Discussion forums
Collaborative projects
Mentorship matching
Event management

Content Management System

Organizes and delivers learning content

Multi-format content library
Content creation tools
Recommendation engine
Content effectiveness analytics

Token Economy System

Manages engagement incentives

Token earning mechanisms
Redemption marketplace
Achievement system
Economic balance tooling

Technical Implementation

Frontend Architecture

The Dojo platform frontend is built with:

Next.js: Application framework
React: UI component library
Tailwind CSS: Styling framework
ShadCN/UI: Component library

Key implementation patterns:

// Example Learning Environment Component
import { useState, useEffect } from 'react';
import { useRouter } from 'next/router';
import { useEnvironment } from '@/hooks/useEnvironment';
import { EnvironmentHeader } from '@/components/environment/EnvironmentHeader';
import { ContentViewer } from '@/components/content/ContentViewer';
import { ProgressTracker } from '@/components/progress/ProgressTracker';
import { AgentAssistant } from '@/components/agent/AgentAssistant';

export default function LearningEnvironment() {
  const router = useRouter();
  const { environmentId } = router.query;
  const { environment, loading, error } = useEnvironment(environmentId);
  
  if (loading) return <EnvironmentSkeleton />;
  if (error) return <ErrorDisplay message={error.message} />;
  
  return (
    <div className="flex flex-col min-h-screen">
      <EnvironmentHeader 
        title={environment.title}
        description={environment.description}
        progress={environment.progress}
      />
      
      <div className="flex flex-col lg:flex-row flex-1">
        <div className="lg:w-3/4">
          <ContentViewer 
            content={environment.currentContent}
            onComplete={environment.markContentComplete}
          />
        </div>
        
        <div className="lg:w-1/4 border-l border-gray-200">
          <ProgressTracker 
            modules={environment.modules}
            currentModuleId={environment.currentModuleId}
            onModuleSelect={environment.selectModule}
          />
          
          <AgentAssistant 
            environmentId={environmentId}
            contextType="learning"
          />
        </div>
      </div>
    </div>
  );
}

Backend Services

Core backend services include:

Environment Service
- Environment template management
- Instance creation and configuration
- Progress tracking and state management
- Environment recommendations
Content Service
- Content storage and retrieval
- Metadata management
- Content recommendation engine
- Content effectiveness analytics
Community Service
- Member profiles and connections
- Discussion management
- Activity feeds
- Moderation tools
Token Economy Service
- Token ledger management
- Earning rule definitions
- Redemption marketplace
- Economic health monitoring

Database Schema

The Dojo platform uses Supabase (PostgreSQL) with this core schema:

-- Example schema (simplified)

-- Users and Profiles
CREATE TABLE profiles (
  id UUID REFERENCES auth.users NOT NULL PRIMARY KEY,
  display_name TEXT,
  bio TEXT,
  avatar_url TEXT,
  preferences JSONB,
  created_at TIMESTAMP WITH TIME ZONE DEFAULT NOW(),
  updated_at TIMESTAMP WITH TIME ZONE DEFAULT NOW()
);

-- Learning Environments
CREATE TABLE environments (
  id UUID PRIMARY KEY DEFAULT uuid_generate_v4(),
  title TEXT NOT NULL,
  description TEXT,
  template_id UUID REFERENCES environment_templates,
  owner_id UUID REFERENCES profiles NOT NULL,
  is_public BOOLEAN DEFAULT FALSE,
  settings JSONB,
  created_at TIMESTAMP WITH TIME ZONE DEFAULT NOW(),
  updated_at TIMESTAMP WITH TIME ZONE DEFAULT NOW()
);

-- Environment Templates
CREATE TABLE environment_templates (
  id UUID PRIMARY KEY DEFAULT uuid_generate_v4(),
  title TEXT NOT NULL,
  description TEXT,
  icon TEXT,
  modules JSONB,
  category TEXT,
  is_featured BOOLEAN DEFAULT FALSE,
  created_at TIMESTAMP WITH TIME ZONE DEFAULT NOW(),
  updated_at TIMESTAMP WITH TIME ZONE DEFAULT NOW()
);

-- Content Library
CREATE TABLE content_items (
  id UUID PRIMARY KEY DEFAULT uuid_generate_v4(),
  title TEXT NOT NULL,
  description TEXT,
  content_type TEXT NOT NULL,
  content_data JSONB NOT NULL,
  metadata JSONB,
  tags TEXT[],
  created_by UUID REFERENCES profiles,
  is_published BOOLEAN DEFAULT FALSE,
  created_at TIMESTAMP WITH TIME ZONE DEFAULT NOW(),
  updated_at TIMESTAMP WITH TIME ZONE DEFAULT NOW()
);

-- Learning Progress
CREATE TABLE progress_records (
  id UUID PRIMARY KEY DEFAULT uuid_generate_v4(),
  user_id UUID REFERENCES profiles NOT NULL,
  environment_id UUID REFERENCES environments NOT NULL,
  module_id TEXT NOT NULL,
  content_id UUID REFERENCES content_items,
  status TEXT NOT NULL,
  completion_date TIMESTAMP WITH TIME ZONE,
  assessment_results JSONB,
  created_at TIMESTAMP WITH TIME ZONE DEFAULT NOW(),
  updated_at TIMESTAMP WITH TIME ZONE DEFAULT NOW(),
  UNIQUE(user_id, environment_id, module_id, content_id)
);

-- Token Transactions
CREATE TABLE token_transactions (
  id UUID PRIMARY KEY DEFAULT uuid_generate_v4(),
  user_id UUID REFERENCES profiles NOT NULL,
  amount INTEGER NOT NULL,
  transaction_type TEXT NOT NULL,
  reference_id UUID,
  reference_type TEXT,
  metadata JSONB,
  created_at TIMESTAMP WITH TIME ZONE DEFAULT NOW()
);

API Design

The Dojo platform exposes a comprehensive API:

Environment API

Manage learning environments

Create environment
Update configuration
Track progress
Access environment content

Content API

Work with learning content

Retrieve content items
Submit content
Rate and review
Track content engagement

Community API

Engage with community features

Member profiles
Discussion forums
Connection management
Activity feeds

Token API

Interact with token economy

Token balances
Transaction history
Redemption marketplace
Achievement tracking

Example API endpoint definition:

/**
 * Create a new learning environment
 * 
 * POST /api/environments
 * 
 * @body {
 *   templateId: string;
 *   title: string;
 *   description?: string;
 *   isPublic?: boolean;
 *   settings?: Record<string, any>;
 * }
 * 
 * @returns {
 *   id: string;
 *   title: string;
 *   description: string;
 *   isPublic: boolean;
 *   ownerId: string;
 *   createdAt: string;
 *   modules: Array<{
 *     id: string;
 *     title: string;
 *     contentItems: string[];
 *   }>;
 * }
 */
export async function createEnvironment(
  req: NextApiRequest,
  res: NextApiResponse
) {
  const { templateId, title, description, isPublic, settings } = req.body;
  const session = await getSession({ req });
  
  if (!session || !session.user) {
    return res.status(401).json({ error: 'Unauthorized' });
  }
  
  try {
    // Get the template
    const { data: template, error: templateError } = await supabase
      .from('environment_templates')
      .select('*')
      .eq('id', templateId)
      .single();
    
    if (templateError || !template) {
      return res.status(404).json({ error: 'Template not found' });
    }
    
    // Create the environment
    const { data: environment, error } = await supabase
      .from('environments')
      .insert({
        title,
        description,
        template_id: templateId,
        owner_id: session.user.id,
        is_public: isPublic || false,
        settings
      })
      .select()
      .single();
    
    if (error) {
      return res.status(500).json({ error: error.message });
    }
    
    // Initialize modules from template
    const modules = template.modules;
    
    return res.status(201).json({
      id: environment.id,
      title: environment.title,
      description: environment.description,
      isPublic: environment.is_public,
      ownerId: environment.owner_id,
      createdAt: environment.created_at,
      modules
    });
  } catch (error) {
    return res.status(500).json({ error: (error as Error).message });
  }
}

Learning Environment Structure

Environment Templates

The Dojo platform provides standardized environment templates:

Self-Care Studio

Wellness practice development

Routine building
Self-care education
Practice tracking
Fragrance integration

Fragrance Academy

Scent education and creation

Scent theory
Blending techniques
Olfactory training
Custom creation

Creative Workshop

Creative skill development

Project-based learning
Technique tutorials
Inspiration resources
Showcase features

Module Structure

Each environment contains modules with standardized structure:

interface Module {
  id: string;
  title: string;
  description: string;
  contentItems: ContentItem[];
  assessments: Assessment[];
  activities: Activity[];
  completionRequirements: CompletionRequirement[];
}

interface ContentItem {
  id: string;
  type: 'article' | 'video' | 'audio' | 'interactive' | 'reference';
  title: string;
  description: string;
  content: any; // Type depends on content type
  estimatedTime: number; // In minutes
  tags: string[];
}

interface Assessment {
  id: string;
  type: 'quiz' | 'reflection' | 'project' | 'practice';
  title: string;
  description: string;
  questions: Question[];
  passingScore: number;
  allowRetries: boolean;
}

interface Activity {
  id: string;
  type: 'practice' | 'reflection' | 'creation' | 'social';
  title: string;
  description: string;
  instructions: string;
  resources: Resource[];
  submissionType: 'text' | 'media' | 'external' | 'none';
}

Content Types

The platform supports multiple content formats:

Articles: Text-based educational content
Videos: Instructional and demonstration videos
Audio: Podcasts, guided meditations, ambient sounds
Interactive: Quizzes, exercises, simulations
References: Glossaries, resource lists, tools

Personalization System

Personalization Framework

The Dojo platform adapts to individual preferences through:

Implementation Approach

// Example personalization service
export class PersonalizationService {
  // Get personalized recommendations
  async getRecommendations(userId: string, context: PersonalizationContext): Promise<Recommendation[]> {
    // 1. Retrieve user profile and preferences
    const userProfile = await this.profileService.getProfile(userId);
    
    // 2. Get user activity history
    const activityHistory = await this.activityService.getUserActivity(userId);
    
    // 3. Analyze learning patterns
    const learningPatterns = this.analyzeLearningPatterns(activityHistory);
    
    // 4. Generate personalization vectors
    const personalizationVectors = this.generatePersonalizationVectors(
      userProfile, 
      learningPatterns,
      context
    );
    
    // 5. Query content database with vectors
    const contentItems = await this.contentService.findSimilarContent(
      personalizationVectors.contentAffinities,
      context.contentType,
      context.limit
    );
    
    // 6. Rank and diversify recommendations
    return this.rankAndDiversify(contentItems, personalizationVectors);
  }
  
  // Generate personalization vectors from user data
  private generatePersonalizationVectors(
    profile: UserProfile,
    learningPatterns: LearningPatterns,
    context: PersonalizationContext
  ): PersonalizationVectors {
    return {
      contentAffinities: this.calculateContentAffinities(profile, learningPatterns),
      formatPreferences: this.determineFormatPreferences(profile, learningPatterns),
      difficultyPreference: this.calculateOptimalDifficulty(learningPatterns),
      pacePreference: this.determineLearningPace(learningPatterns),
      socialPreferences: this.analyzeSocialEngagement(profile, learningPatterns)
    };
  }
  
  // Other methods for personalization logic...
}

Token Economy

Token System Architecture

The token economy incentivizes engagement:

Earning Mechanisms

Ways to earn tokens

Learning milestone completion
Content contribution
Community participation
Challenge completion
Daily engagement

Redemption Options

Ways to use tokens

Product discounts
Premium content access
Virtual goods and customizations
Real-world experiences
Exclusive community access

Token Implementation

// Example token transaction service
export class TokenService {
  // Award tokens to a user
  async awardTokens(
    userId: string, 
    amount: number, 
    reason: TokenReason, 
    reference?: TokenReference
  ): Promise<TokenTransaction> {
    // Validate the token award
    this.validateTokenAward(userId, amount, reason);
    
    // Create the transaction record
    const transaction = await this.createTransaction({
      userId,
      amount,
      transactionType: 'CREDIT',
      reason,
      reference,
      metadata: {
        awardedAt: new Date().toISOString(),
        awardReason: reason
      }
    });
    
    // Update user balance
    await this.updateUserBalance(userId, amount);
    
    // Trigger any achievement checks
    await this.checkAchievements(userId);
    
    // Notify the user
    await this.notificationService.notifyTokenAward(userId, amount, reason);
    
    return transaction;
  }
  
  // Spend tokens from a user's balance
  async spendTokens(
    userId: string,
    amount: number,
    reason: TokenReason,
    reference?: TokenReference
  ): Promise<TokenTransaction> {
    // Validate the token spend
    const currentBalance = await this.getUserBalance(userId);
    
    if (currentBalance < amount) {
      throw new Error('Insufficient token balance');
    }
    
    // Create the transaction record
    const transaction = await this.createTransaction({
      userId,
      amount: -amount,
      transactionType: 'DEBIT',
      reason,
      reference,
      metadata: {
        spentAt: new Date().toISOString(),
        spendReason: reason
      }
    });
    
    // Update user balance
    await this.updateUserBalance(userId, -amount);
    
    // Trigger redemption process if applicable
    if (reference && reference.type === 'REDEMPTION') {
      await this.processRedemption(userId, reference.id);
    }
    
    return transaction;
  }
  
  // Other token management methods...
}

Community Features

Community Architecture

The community system enables connection and collaboration:

Implementation Examples

// Example community service
export class CommunityService {
  // Create a discussion thread
  async createDiscussionThread(
    authorId: string,
    title: string,
    content: string,
    categoryId: string,
    tags: string[] = []
  ): Promise<DiscussionThread> {
    // Validate inputs
    if (!title || !content) {
      throw new Error('Title and content are required');
    }
    
    // Check user permissions
    const canCreate = await this.permissionService.canCreateThread(authorId, categoryId);
    if (!canCreate) {
      throw new Error('User does not have permission to create threads in this category');
    }
    
    // Create the thread
    const thread = await this.threadRepository.create({
      authorId,
      title,
      content,
      categoryId,
      tags,
      status: 'ACTIVE',
      createdAt: new Date()
    });
    
    // Process mentions and notifications
    const mentions = this.extractMentions(content);
    if (mentions.length > 0) {
      await this.processMentions(thread.id, mentions);
    }
    
    // Index for search
    await this.searchService.indexThread(thread);
    
    // Award tokens for contribution
    await this.tokenService.awardTokens(
      authorId,
      10, // Base amount for thread creation
      'CONTENT_CREATION',
      { type: 'DISCUSSION_THREAD', id: thread.id }
    );
    
    return thread;
  }
  
  // Connect with another member
  async createConnection(
    userId: string,
    targetUserId: string,
    connectionType: 'FRIEND' | 'FOLLOW' | 'MENTOR' | 'MENTEE'
  ): Promise<UserConnection> {
    // Prevent self-connections
    if (userId === targetUserId) {
      throw new Error('Cannot connect with yourself');
    }
    
    // Check if connection already exists
    const existingConnection = await this.connectionRepository.findByUsers(
      userId,
      targetUserId
    );
    
    if (existingConnection) {
      throw new Error('Connection already exists');
    }
    
    // Create connection record
    const connection = await this.connectionRepository.create({
      userId,
      targetUserId,
      connectionType,
      status: connectionType === 'FOLLOW' ? 'ACTIVE' : 'PENDING',
      createdAt: new Date()
    });
    
    // Notify target user if approval needed
    if (connectionType !== 'FOLLOW') {
      await this.notificationService.notifyConnectionRequest(
        targetUserId,
        userId,
        connectionType
      );
    }
    
    return connection;
  }
  
  // Other community methods...
}

Agent Integration

SAGE MNKY Integration

The Dojo platform deeply integrates with the SAGE MNKY agent:

Learning Assistance

Agent support for learning

Concept explanations
Learning path guidance
Practice facilitation
Progress assessments

Content Creation

Agent-assisted content development

Content suggestion
Draft assistance
Resource curation
Media generation

Community Support

Agent engagement in community

Discussion facilitation
Question answering
Connection recommendations
Group coordination

Personalization

Tailored agent interactions

Learning style adaptation
Interest-based recommendations
Pace adjustment
Challenge calibration

Implementation Patterns

// Example agent integration component
import { useState, useEffect } from 'react';
import { useAgent } from '@/hooks/useAgent';
import { AgentMessage } from '@/components/agent/AgentMessage';
import { UserMessage } from '@/components/agent/UserMessage';
import { AgentInput } from '@/components/agent/AgentInput';

export function AgentAssistant({ 
  environmentId, 
  contextType 
}: AgentAssistantProps) {
  const [messages, setMessages] = useState<Message[]>([]);
  const [inputValue, setInputValue] = useState('');
  const { 
    sendMessage, 
    isLoading, 
    agentContext, 
    suggestedPrompts 
  } = useAgent('SAGE_MNKY', {
    environmentId,
    contextType,
    historyLength: 10
  });
  
  // Load initial context-based greeting
  useEffect(() => {
    if (agentContext) {
      const greeting = generateContextualGreeting(agentContext);
      setMessages([{
        id: 'greeting',
        role: 'agent',
        content: greeting,
        timestamp: new Date()
      }]);
    }
  }, [agentContext]);
  
  // Handle message submission
  const handleSubmit = async (event: React.FormEvent) => {
    event.preventDefault();
    if (!inputValue.trim()) return;
    
    // Add user message to the list
    const userMessage: Message = {
      id: `user-${Date.now()}`,
      role: 'user',
      content: inputValue,
      timestamp: new Date()
    };
    
    setMessages(prev => [...prev, userMessage]);
    setInputValue('');
    
    // Get agent response
    const response = await sendMessage(inputValue);
    
    // Add agent response to the list
    const agentMessage: Message = {
      id: `agent-${Date.now()}`,
      role: 'agent',
      content: response.content,
      timestamp: new Date(),
      actions: response.actions
    };
    
    setMessages(prev => [...prev, agentMessage]);
  };
  
  return (
    <div className="flex flex-col h-full border rounded-md">
      <div className="p-4 border-b bg-gray-50">
        <h3 className="font-medium">SAGE MNKY Assistant</h3>
        <p className="text-sm text-gray-500">
          Learning support and guidance
        </p>
      </div>
      
      <div className="flex-1 overflow-y-auto p-4 space-y-4">
        {messages.map(message => (
          message.role === 'agent' ? (
            <AgentMessage 
              key={message.id} 
              content={message.content} 
              actions={message.actions}
              timestamp={message.timestamp}
            />
          ) : (
            <UserMessage 
              key={message.id} 
              content={message.content}
              timestamp={message.timestamp}
            />
          )
        ))}
        
        {isLoading && <AgentTypingIndicator />}
      </div>
      
      {suggestedPrompts?.length > 0 && (
        <div className="p-2 border-t flex gap-2 overflow-x-auto">
          {suggestedPrompts.map(prompt => (
            <button
              key={prompt.id}
              className="text-sm bg-gray-100 hover:bg-gray-200 rounded-full px-3 py-1 whitespace-nowrap"
              onClick={() => setInputValue(prompt.text)}
            >
              {prompt.text}
            </button>
          ))}
        </div>
      )}
      
      <form onSubmit={handleSubmit} className="p-2 border-t">
        <AgentInput
          value={inputValue}
          onChange={e => setInputValue(e.target.value)}
          onSubmit={handleSubmit}
          placeholder="Ask SAGE MNKY..."
          isLoading={isLoading}
        />
      </form>
    </div>
  );
}

Analytics and Measurement

Learning Analytics

The platform collects and analyzes learning data:

Implementation Approach

// Example analytics service
export class AnalyticsService {
  // Track a learning event
  async trackLearningEvent(event: LearningEvent): Promise<void> {
    // Validate event data
    this.validateEvent(event);
    
    // Enrich event with context
    const enrichedEvent = await this.enrichEvent(event);
    
    // Store the event
    await this.eventRepository.store(enrichedEvent);
    
    // Process real-time analytics
    await this.processRealTimeAnalytics(enrichedEvent);
    
    // Queue for batch processing
    await this.analyticsQueue.add(enrichedEvent);
  }
  
  // Get learning progress for a user and environment
  async getLearningProgress(
    userId: string,
    environmentId: string
  ): Promise<LearningProgress> {
    // Get environment structure
    const environment = await this.environmentService.getEnvironment(environmentId);
    
    // Get all progress records
    const progressRecords = await this.progressRepository.findByUserAndEnvironment(
      userId,
      environmentId
    );
    
    // Calculate module completion
    const moduleProgress = environment.modules.map(module => {
      const moduleRecords = progressRecords.filter(
        record => record.moduleId === module.id
      );
      
      const completedItems = moduleRecords.filter(
        record => record.status === 'COMPLETED'
      ).length;
      
      const totalItems = module.contentItems.length;
      
      return {
        moduleId: module.id,
        title: module.title,
        completedItems,
        totalItems,
        percentComplete: totalItems > 0 ? (completedItems / totalItems) * 100 : 0,
        isComplete: completedItems === totalItems && totalItems > 0
      };
    });
    
    // Calculate overall progress
    const totalComplete = moduleProgress.reduce(
      (sum, module) => sum + module.completedItems, 
      0
    );
    
    const totalItems = moduleProgress.reduce(
      (sum, module) => sum + module.totalItems, 
      0
    );
    
    return {
      userId,
      environmentId,
      environmentTitle: environment.title,
      moduleProgress,
      overallPercentComplete: totalItems > 0 ? (totalComplete / totalItems) * 100 : 0,
      startedAt: progressRecords.length > 0 
        ? new Date(Math.min(...progressRecords.map(r => r.createdAt.getTime())))
        : null,
      lastActivityAt: progressRecords.length > 0
        ? new Date(Math.max(...progressRecords.map(r => r.updatedAt.getTime())))
        : null,
      isComplete: totalItems > 0 && totalComplete === totalItems
    };
  }
  
  // Other analytics methods...
}

Administration and Management

Admin Dashboard

The platform includes comprehensive management tools:

Content Management

Content creation and curation

Content library management
Media asset management
Template administration
Quality assurance tools

User Management

Member account administration

Account management
Role assignments
Permission controls
Activity monitoring

Community Management

Community monitoring and support

Moderation tools
Content review
Report handling
Community insights

Analytics Dashboard

Platform performance monitoring

Usage statistics
Engagement metrics
Content effectiveness
Economic health indicators

Implementation Resources

Component Library

UI components for Dojo implementation

API Documentation

Detailed API references and examples

Environment Templates

Starter templates for learning environments

Integration Guides

Integration with other platform services

Roadmap and Evolution

The Dojo platform will evolve in these key areas:

Enhanced Personalization
- AI-driven learning path optimization
- Dynamic difficulty adjustment
- Emotional state-aware interactions
- Multimodal learning style adaptation
Expanded Content Capabilities
- Interactive simulation environments
- AR/VR learning experiences
- AI-generated adaptive content
- Real-time collaborative creation
Community Evolution
- Mentor marketplace and matching
- Expertise verification and badging
- Micro-community frameworks
- Cross-platform community integration
Advanced Token Economy
- Contribution-based token mining
- Governance participation rewards
- Multi-tier token structure
- Value-backed redemption options

Dojo Platform

User Experience

Community Features

Strategic Planning

​Dojo Core Manual

​Introduction

​Vision and Purpose

​Core Principles

​Platform Architecture

​System Overview

​Key Components

Learning Environment Engine

Community Platform

Content Management System

Token Economy System

​Technical Implementation

​Frontend Architecture

​Backend Services

​Database Schema

​API Design

Environment API

Content API

Community API

Token API

​Learning Environment Structure

​Environment Templates

Self-Care Studio

Fragrance Academy

Creative Workshop

​Module Structure

​Content Types

​Personalization System

​Personalization Framework

​Implementation Approach

​Token Economy

​Token System Architecture

Earning Mechanisms

Redemption Options

​Token Implementation

​Community Features

​Community Architecture

​Implementation Examples

​Agent Integration

​SAGE MNKY Integration

Learning Assistance

Content Creation

Community Support

Personalization

​Implementation Patterns

​Analytics and Measurement

​Learning Analytics

​Implementation Approach

​Administration and Management

​Admin Dashboard

Content Management

User Management

Community Management

Analytics Dashboard

​Implementation Resources

Component Library

API Documentation

Environment Templates

Integration Guides

​Roadmap and Evolution

Dojo Core Manual

Introduction

Vision and Purpose

Core Principles

Platform Architecture

System Overview

Key Components

Technical Implementation

Frontend Architecture

Backend Services

Database Schema

API Design

Learning Environment Structure

Environment Templates

Module Structure

Content Types

Personalization System

Personalization Framework

Implementation Approach

Token Economy

Token System Architecture

Token Implementation

Community Features

Community Architecture

Implementation Examples

Agent Integration

SAGE MNKY Integration

Implementation Patterns

Analytics and Measurement

Learning Analytics

Implementation Approach

Administration and Management

Admin Dashboard

Implementation Resources

Roadmap and Evolution