Kanbi Board

Professionals waste 15-30 min daily converting scattered notes into actionable tasks. Manual task extraction is repetitive, error prone, and kills momentum.

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)

→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.

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.

Screenshots

What This Actually Proves

✓I ship fast: 6 MVPs in 14-21 days each. Not prototypes — actual working products with auth, payments, databases.
✓I'm honest about metrics: 10-50 users, $0 revenue (test mode), 7-10 GitHub stars. No fake numbers.
✓I know what to cut: Real-time features, team collaboration, mobile apps. Ship core value first.
✓I learn from mistakes: RLS policies are hard. Prompt engineering takes 15+ iterations. Financial calculations need edge case testing.
✓I justify tech choices: Supabase over Firebase (cheaper, better for relational data). Groq over OpenAI (10x cheaper, 800 tokens/s).

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Same quality, same speed. 14–21 days from idea to live product with auth, payments, and analytics.

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