Simulated User Flow — Rabbit-Hole Inference Algorithm (RHIA)
Let’s take a user named Ayo, who just joined Buddy.me.
1. User Onboarding → Enters Roadmap
Ayo chooses: Roadmap: “Rebuild My Morning Routine” Quests:
- Q1: Wake up by 6am for 3 days
- Q2: Journal 5 mins daily
- Q3: No phone for 30 mins after waking
- Checkpoint 1 follows after Q3
Buddy.me does not ask Ayo about preferences, colors, personality, or even if he’s a “morning person.” It observes.
2. Data Signals Begin
Ayo:
- Completes Q1 after 5 days (slow start)
- Finishes Q2 instantly (indicates resonance)
- Struggles with Q3, retries 3x, pauses for a day
This data generates a trajectory vector:
typescript
[Q1: delayed], [Q2: fast], [Q3: retried]RHIA flags:
- Commitment is mid-high
- Weakness: digital discipline
- Strength: introspective tasks
- Pattern: pushes through friction
3. Checkpoint Matchmaking (Core of RHIA)
Buddy.me finds Nina, another user who:
- Did same roadmap
- Also struggled with Q3
- Re-attempted with same lag pattern
- But did better with time-boxing techniques
The match happens not via tags, but by inferred micro-patterns:
“These two climb the same mountain, but with different gear. Let’s pair them.”
They meet. Their roadmaps merge:
- Now shared activities
- XP rewards tied to both succeeding
- Support nudges encouraged at pain points (e.g., Q3-like steps)
4. Emergence of Connection
Ayo sees Nina use a journaling method he hadn’t thought of Nina sees Ayo’s retry messages and feels less alone
Both don’t know each other’s favorite color. They’re connected anyway.
This is my goal with RHIA:
- Infer through struggle rhythm
- Match through overlap in effort
- Sustain through mutual dependency