AI Life Logger - Product Vision

Core Concept

A frictionless life logging and time tracking system that uses natural language AI as the interface. Makes tracking so easy you actually do it, then provides real behavioral insights from your actual time usage patterns.

Current State

• ✅ Natural language event scheduling
• ✅ Basic calendar operations (create, move, delete)
• ✅ JSON schema-based "AI as API" pattern

Feature Roadmap

Input & Logging

Natural Language Interface

• Bulk scheduling: "lunch at 12, work 2-5, dinner 6-7, call 7-8"
• Retroactive logging: "yesterday: work 8-4, gym 5-6, dinner with Sarah"
• Edits via NL: "that meeting actually went until 4:30"
• Voice input (speech-to-text → same AI pipeline)
• Quick templates: "normal work day" expands to your standard schedule

Smart Input

• Pattern recognition: "You usually do X on Mondays"
• Auto-duration inference: "gym" = 1hr, "dentist" = 1.5hrs with travel
• Conflict detection before scheduling
• Batch operations: "move everything after 2pm to tomorrow"

Intelligence Layer

Learning Your Patterns

• Time estimation learning: track planned vs actual duration
• Category auto-detection (work/personal/health/social)
• Context awareness: infer details from minimal input
• Personal defaults: your "meeting" might be 30min, someone else's is 1hr
• Scheduling preferences: when you typically do focused work vs meetings

Smart Suggestions

• Better time estimates based on your history
• Optimal scheduling based on energy patterns
• "You need 8hrs for this project, here's when you usually have focus time"
• Warning when underestimating (again): "Your client meetings usually run long"

Analysis & Insights

Time Analysis

• Actual vs planned comparison
• Time estimation accuracy over time
• Weekly/monthly breakdown: where did the time actually go?
• Category totals: how much time on work/health/social/etc
• Goal tracking: "did I hit 7hrs sleep?" "worked more than 40hrs?"
• Year-over-year trends

Behavioral Insights

• Pattern detection: "You're most productive 9-11am based on logged deep work"
• Estimation bias: "You underestimate morning tasks by 30%"
• Schedule drift: "Meetings scheduled for 1hr actually take 1.5hrs"
• Life balance metrics (real ones from actual data, not aspirational)
• Energy mapping: when you actually do what types of work

Reconciliation

• End-of-day review: "Here's what I think happened - confirm real vs planning noise"
• Auto-cleanup of scheduling artifacts vs actual events
• Merge similar events: "3 'work' blocks = 1 continuous work session"
• Confirmation UI for ambiguous cases

Data & Privacy

Your Data, Your Control

• Full export/backup capabilities
• Local-first or encrypted storage
• Long-term history retention
• API access to your own data
• Shareable insights (optional, privacy-controlled)

Integrations (Future)

• Calendar sync (Google/Apple/Outlook)
• Deadline/project management tools
• Habit trackers
• Sleep tracking data
• Productivity tool integrations

Key Differentiators

Why This Works Where Others Failed

Frictionless Input

• Traditional time tracking: start/stop timers (tedious)
• Calendar apps: too much UI friction
• Bullet journals: not queryable
• This: "worked 8-4, gym, dinner" → done

AI as API Pattern

• Natural language → structured JSON
• Bounded, predictable AI costs
• Clear success/failure criteria
• Easy to optimize and make reliable

Real Behavioral Data

• Not aspirational planning (most calendar apps)
• Not passive tracking (often inaccurate)
• Active but easy logging = accurate data
• Accurate data = actionable insights

Personal Time Estimation

• Generic productivity advice doesn't work
• YOUR patterns are what matter
• Build a database of your actual behavior
• Get better at planning YOUR life

Technical Architecture

AI Usage Strategy (Minimal & Efficient)

Where AI is Used:

• Input parsing: NL → JSON (every interaction)
• Pattern summarization: weekly insights (batch, async)
• Suggestions: based on historical patterns (cached when possible)

Where AI is NOT Used:

• Data storage: plain calendar events
• Analysis: SQL/traditional queries
• Visualization: standard charting
• Conflict detection: deterministic logic

Cost Optimization:

• Small model for simple parsing
• Large model only for ambiguous commands
• Cache common patterns
• Client-side validation before AI calls
• Batch operations where possible

Data Model

Core Entity: Time Block

.txt

{
  "id": "...",
  "title": "...",
  "start": "ISO-8601",
  "end": "ISO-8601",
  "category": "work|personal|health|social|...",
  "status": "planned|actual|confirmed",
  "metadata": {
    "originalEstimate": "...",
    "editHistory": [...],
    "aiConfidence": 0.95
  }
}

Monetization (Future)

Free Tier

• Basic NL scheduling
• 30 days of history
• Simple weekly summaries
• Core calendar operations

Pro ($8-12/month)

• Unlimited history
• Advanced analytics & insights
• Time estimation learning
• Pattern recognition
• Export capabilities
• Voice input

Premium ($20-30/month)

• Team/shared calendars
• Coaching-level insights
• API access
• Advanced integrations
• Custom categories & rules

Development Priorities

Phase 1: Reliability (Current)

• Make NL parsing bulletproof
• Handle edge cases gracefully
• Confirmation flows for destructive ops
• Error handling & validation

Phase 2: Data Quality

• End-of-day reconciliation
• Planned vs actual tracking
• Clean separation of intent vs reality
• Smart categorization

Phase 3: Insights

• Basic analysis: weekly summaries
• Time estimation learning
• Pattern detection
• Simple visualizations

Phase 4: Intelligence

• Suggestions based on patterns
• Predictive scheduling
• Energy mapping
• Behavioral coaching

Phase 5: Scale

• Multi-platform (web, mobile, desktop)
• Real-time sync
• Integrations
• Team features

Success Metrics

Usage:

• Daily active logging (did they log today?)
• Accuracy of data (confirmed vs edited events)
• Retention (are they using it weeks/months later?)

Value:

• Time estimation improvement over time
• Goal achievement (hit sleep/work targets?)
• User reported behavior change

Product:

• NL parsing success rate (valid JSON from user input)
• AI cost per user per month
• Feature adoption (which insights do people actually use?)

Why This Could Be Big

1. Real problem: Everyone wants to understand where their time goes, few people actually track it
2. Novel solution: AI interface makes logging effortless
3. Actual insights: Personal behavioral data > generic productivity advice
4. Network effects: The longer you use it, the better it gets
5. Switching cost: Your life data = high retention
6. Good economics: Minimal AI usage, high perceived value

The quantified self movement failed because tracking was too tedious. This makes it actually easy.