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Any Tracker V2 Structure

· 3 min read
Max Kaido
Max Kaido
Architect

Gotcha, Max—let's slice this baby into clean, tight modules, interfaces, and methods, hyper-focusing on the juicy parts specific to Any Tracker. No fluff, pure clarity, ready for your NestJS monorepo dance.

Here's your lean, mean, destructured machine:

🔥 Core Modules & Responsibilities​

1. Tracking Module (NestJS TypeScript Service)​

Handles substance tracking, minimal-input NLP parsing, and initial logging.

  • Interfaces:

    interface SubstanceLog {
      userId: string;
      timestamp: Date;
      substance: string;
      dose: number;
      rawInput: string;
    }

    interface TrackingService {
      logIntake(rawInput: string, userId: string): Promise<SubstanceLog>;
    }

  • Methods:

    • parseNaturalInput(rawInput: string): { substance: string; dose: number; }
    • logIntake(rawInput, userId): SubstanceLog

2. Anonymization Module (NestJS TypeScript Middleware)​

Preprocesses and anonymizes data before sending to the engine.

  • Interfaces:

    interface AnonymizedData {
      substance: string;
      dose: number;
      context: UserContext;
      anonymizedUserId: string;
      timestamp: Date;
    }

    interface Anonymizer {
      anonymize(log: SubstanceLog, context: UserContext): AnonymizedData;
    }

  • Methods:

    • generateAnonymizedUserId(userId: string): string
    • anonymize(log: SubstanceLog, context: UserContext): AnonymizedData

3. Contextual Module (NestJS TypeScript Service)​

Collects and structures contextual data (time of day, environment, social context).

  • Interfaces:

    interface UserContext {
      timeOfDay: string;
      previousActivities: string[];
      socialContext: 'alone' | 'group' | 'public';
    }

    interface ContextService {
      collectContext(userId: string): Promise<UserContext>;
    }

  • Methods:

    • inferContextFromHistory(userId): UserContext

Manages data analysis, optimal schedules, interaction analysis using Neo4j.

  • Interfaces:

    interface GraphService {
      addLog(anonymizedData: AnonymizedData): Promise<void>;
      queryOptimalSchedule(
        anonymizedUserId: string,
      ): Promise<SubstanceSchedule[]>;
      checkInteractions(
        substance: string,
        context: UserContext,
      ): Promise<InteractionWarning[]>;
    }

    interface SubstanceSchedule {
      substance: string;
      recommendedDose: number;
      recommendedTime: string;
      confidence: number;
    }

    interface InteractionWarning {
      substancesInvolved: string[];
      severity: 'low' | 'medium' | 'high';
      message: string;
    }

  • Methods:

    • addLog(data: AnonymizedData)
    • queryOptimalSchedule(anonymizedUserId)
    • checkInteractions(substance, context)

5. Local AI Assistant Module (NestJS TypeScript wrapper for Ollama API)​

Provides intelligent local inference using Ollama models.

  • Interfaces:

    interface LocalAIService {
      suggestNextAction(
        context: UserContext,
        userId: string,
      ): Promise<ActionSuggestion>;
    }

    interface ActionSuggestion {
      suggestedAction: string;
      reason: string;
      confidence: number;
    }

  • Methods:

    • generateActionSuggestion(context, userId)

6. Behavioral Optimization Module (NestJS TypeScript Service using Reinforcement Learning via REST API)​

Uses RL or Contextual Bandits to optimize timing and dosage based on sparse rewards.

  • Interfaces:

    interface OptimizationService {
      recommendNextIntake(
        anonymizedUserId: string,
        context: UserContext,
      ): Promise<SubstanceSchedule>;
      submitUserFeedback(
        anonymizedUserId: string,
        feedback: UserFeedback,
      ): Promise<void>;
    }

    interface UserFeedback {
      scheduleId: string;
      positiveExperience: boolean;
      notes?: string;
    }

  • Methods:

    • recommendNextIntake(anonymizedUserId, context)
    • submitUserFeedback(anonymizedUserId, feedback)

7. Marketplace Protection Module (NestJS TypeScript Service)​

Integration layer for verifying legitimate marketplace interactions and protecting from scams.

  • Interfaces:

    interface MarketplaceVerificationService {
      verifyMarketplaceLink(url: string): Promise<MarketplaceVerificationResult>;
    }

    interface MarketplaceVerificationResult {
      verified: boolean;
      reason?: string;
    }

  • Methods:

    • verifyMarketplaceLink(url)

🚀 Additional Supporting Interfaces​

interface CostFunction {
  substanceRisks: Record<string, number>;
  interactionRisks: Record<string, number>;
  preferenceWeights: Record<string, number>;
}

interface UserPreferencesService {
  getUserCostFunction(userId: string): Promise<CostFunction>;
  updateUserPreferences(userId: string, feedback: UserFeedback): Promise<void>;
}

📁 Folder Structure for NestJS Monorepo​

Clean and sharp:

packages/
│
├── anytracker-client (Telegram Mini App Frontend)
│
├── anytracker-backend (NestJS, core logic)
│   ├── tracking
│   ├── anonymization
│   ├── context
│   ├── graph-analytics
│   ├── local-ai
│   ├── behavioral-optimization
│   ├── marketplace-protection
│   └── user-preferences
│
├── shared-modules (common NestJS modules)
│
└── scripts (utility scripts)
  • Backend: NestJS (Typescript), Neo4j Graph DB, Ollama (local inference), Python (optional analytics via REST)
  • Client: Telegram Mini App (TypeScript/Node.js)
  • Data Layer: Neo4j (graph DB for analysis), Redis (fast context caching)
  • AI: Ollama (local, self-hosted inference)
  • RL Backend (optional): Python service using Contextual Bandits (simple, effective, fast)

💡 Integration Flow Example​

User Input -> TrackingService (logs and parses)
  -> ContextService (gathers context)
    -> Anonymizer (prepares safe payload)
      -> GraphService (logs & analyzes)
        -> BehavioralOptimization (RL-based recommendation)
          -> LocalAI (interprets and explains suggestions)
            -> Telegram Client (display intuitive suggestion to user)

🎯 Key Implementation Priorities:​

  1. Start with basic tracking and context modules.
  2. Rapidly prototype the graph analytics & RL optimization modules separately.
  3. Connect Local AI Assistant to offer immediate user feedback and validation.
  4. Implement Marketplace Protection in collaboration with marketplace partners.

This breakdown should streamline your Any Tracker implementation, keep modules decoupled and scalable, and let you hack away at the parts that make Any Tracker special without fussing about boilerplate.

Let's build this beast! 🔥