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AI / PRODUCT ENGINEERING / Current

Goal Digger.

An intelligent productivity companion

01 / OVERVIEW

The project

A team-built mobile application connecting goal planning, structured milestones, adaptive scheduling, focus sessions and a virtual companion. The application combines user-facing product design with an AI-assisted planning pipeline and deterministic safeguards.

02 / THE PROBLEM

What needed solving?

How can a goal-planning app turn open-ended goals into realistic, editable actions while remaining usable when AI services fail?

03 / APPROACH

Engineering approach

  • Structure the planning flow around validation, milestones and deterministic scheduling.
  • Integrate Flutter screens with Firebase authentication, persistence and backend functions.
  • Support review and adjustment of generated tasks before commitment.
  • Provide fallback paths for supported AI failures without silently bypassing goal validation.
04 / HIGHLIGHTS

Highlights & results

  • Goal validation and AI-generated milestone planning
  • Adaptive scheduling and calendar workflows
  • Android-native focus support and virtual companion
  • Fallback behavior for AI-planning failures
05 / REFLECTION

Lessons learned

Reliable AI features need explicit boundaries, good failure handling and understandable user controls—not just impressive model outputs.

INTERACTIVE LAB / 動手探索

Explore the engineering.

Not a working copy of the original product—an educational interaction demonstrating a relevant concept.

AI PIPELINE / INTERACTIVE EXPLAINER illustrative · no real API calls
01Goal input"Learn Python"
→
02Goal guardValidated
→
03MilestonesStructured tasks
→
04ScheduleDeterministic spacing

Validated goals proceed through AI-generated milestones and deterministic scheduling.

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