Chapter 2 -- The 50-Day Plan For Building A Personal Assistant Agentic System (PAAS)

PHASE 3: ADVANCED AGENT CAPABILITIES (Days 26-40)

Day 38-40: User Preference Learning

These final days of Phase 3 focus on creating systems that learn and adapt to your preferences over time, making your PAAS increasingly personalized and valuable. You'll explore techniques for capturing explicit and implicit feedback about what information is most useful to you. You'll develop user modeling approaches that can predict your interests and information needs. You'll build recommendation systems that prioritize the most relevant content based on your past behavior and stated preferences. Throughout, you'll implement these capabilities using Rust for efficient processing and strong privacy guarantees, ensuring your preference data remains secure.

• Morning (3h): Study preference learning techniques with Rust implementation

  • Explicit vs. implicit feedback: Learn different approaches for gathering user preferences, from direct ratings and feedback to implicit signals like reading time and click patterns. Implement efficient event tracking in Rust that can capture user interactions with minimal overhead, using type-safe event definitions to ensure consistent data collection.
  • User modeling approaches with Rust safety: Explore methods for building user interest profiles, including content-based, collaborative filtering, and hybrid approaches that combine multiple signals. Develop user modeling components in Rust that provide strong privacy guarantees through encryption and local processing, using Rust's memory safety to prevent data leaks.
  • Recommendation systems with Rust performance: Study recommendation algorithms that can identify relevant content based on user profiles, including matrix factorization, neural approaches, and contextual bandits for exploration. Implement core recommendation algorithms in Rust for performance, creating hybrid systems that combine offline processing with real-time adaptation to user behavior.

• Afternoon (3h): Implement preference system with Tauri

  • Build user feedback collection: Create interfaces for gathering explicit feedback on summaries, articles, and recommendations, with Svelte components for rating, commenting, and saving items of interest. Implement a feedback processing pipeline in Rust that securely stores user preferences locally within the Tauri application, maintaining privacy while enabling personalization.
  • Create content relevance scoring: Develop algorithms that rank incoming information based on predicted relevance to your interests, considering both explicit preferences and implicit behavioral patterns. Implement efficient scoring functions in Rust that can rapidly evaluate thousands of items, using parallel processing to maintain responsiveness even with large information volumes.
  • Implement adaptive filtering with Rust: Build systems that automatically adjust filtering criteria based on your feedback and changing interests, balancing exploration of new topics with exploitation of known preferences. Create a Rust-based reinforcement learning system that continuously optimizes information filtering parameters, using Bayesian methods to handle uncertainty about preferences while maintaining explainability.