Kanbi Board
18 days
1. The Founder's Problem
Professionals waste 15-30 min daily converting scattered notes into actionable tasks. Manual task extraction is repetitive, error prone, and kills momentum.
2. Why Traditional Solutions Failed
- ✗Existing tools force you to switch between note taking and task management
- ✗Manual copy paste loses context and formatting
- ✗No AI tools handle unstructured text reliably
3. What We Built
- ✓AI task extraction with dual providers (Gemini + Groq) for 95%+ accuracy
- ✓Drag and drop Kanban with optimistic UI updates
- ✓Supabase RLS for secure multi tenant data isolation
- ✓Stripe subscriptions (test mode to prove payment integration skills)
- ✓JSON export with proper data serialization
4. The Process
WEEK 1
Database schema (3 days), Supabase RLS policies (2 days), Auth flow (2 days)
WEEK 2
AI integration + prompt engineering (4 days), Kanban UI (3 days)
WEEK 3
Stripe webhooks (2 days), Testing edge cases (2 days), Deploy (1 day)
5. Results & Metrics
users
23 signups
revenue
$0 (test mode only)
github
8 stars
performance
<3s task extraction, <500ms AI failover
code
95.4% TypeScript, 19 commits
6. Tech Stack & Why
Next.js 15
App Router for server components, faster than Pages Router
Supabase
PostgreSQL + RLS cheaper than Firebase, better for relational data
Groq + Gemini
Groq for speed (800 tokens/s), Gemini for accuracy, dual failover
Stripe
Industry standard, better docs than Paddle/LemonSqueezy
7. What We Cut to Ship Fast
- —Real time collaboration (would add 5+ days)
- —Comments on tasks (not core to MVP)
- —Task templates (nice to have, not must have)
8. Lessons Learned
- →Spent 40% of time on AI prompt engineering. Consistency > speed.
- →RLS policies are hard. Test with multiple users from day 1.
- →Stripe webhooks need idempotency keys. Learned this the hard way.
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