AI Backend (Learning Module)

Purpose

This module analyzes structured productivity data and produces basic metrics and insights.
It is built for learning system design, not for production use.

Current scope (v1):

• Clean and normalize productivity data
• Compute basic metrics
• Combine all the data into a single report
• Use NLP models to generate insights

Non-goals (for now):

• No automatic scheduling
• No habit tracking logic
• No direct database access
• No agents or orchestration frameworks

Data Inputs

• Task records (created, completed)
• Focus session hours (per day)
• Daily logs

All inputs are provided as structured JSON.

Outputs

• Task completion rate
• Average focus hours per day
• Simple textual summary (printed to console)

v1 matrice

• plan_adherence_rate → Did I do what I said I would today?
• tasks_completed / tasks_planned → Execution clarity
• focus_hours → Reality check (energy + discipline)
• carryover_tasks → Friction detector
• one_win / one_miss (manual log -> can use category for this(win/lose)) → Human reflection (tiny text)

Tools

• Python
• pandas (for data processing)

Learning Goals

• Understand data pipelines
• Practice separation of concerns
• Build AI-ready system foundations