Case Study

19 days

Solo Built

Subsight Tracker

The average person has 7 to 12 subscriptions costing $273/month. You probably don’t know your number. Tracking apps ask for bank access. Spreadsheets break with annual and quarterly billing cycles. So most people just guess, and overspend.

1. The Founder's Problem

2. Why Traditional Solutions Failed

✗Mint and YNAB require bank account linking. Many people won’t give that permission.
✗Spreadsheets work until you have mixed billing cycles. Then formulas break silently.
✗No tool offers privacy-first tracking with accurate calculations. You have to pick one.

3. What We Built

✓Manual subscription entry with optional AI auto-fill (bring your own Gemini key)
✓Calculation engine handling monthly, yearly, quarterly, and custom billing cycles
✓Real-time budget simulation: toggle subscriptions on/off without mutating your actual data
✓Multi-format export: JSON, CSV, and PDF with proper Unicode support
✓Vitest unit tests covering financial edge cases: leap years, month-end dates, timezone offsets

4. The Process

WEEK 1

Database schema (2 days), Calculation engine (4 days), Unit tests (1 day)

WEEK 2

Dashboard UI + Recharts (4 days), Export functionality (3 days)

WEEK 3

AI integration (2 days), PDF generation (2 days), Deploy (1 day)

5. Results & Metrics

Users

34 signups

Revenue

$0 (free tool)

GitHub

10 stars

Performance

100% calculation accuracy across 3 export formats

Code

98.2% TypeScript, 23 commits

6. Tech Stack & Why

Next.js 16Static generation for landing pages, SSR for the dashboard. Fast everywhere.

SupabaseFree tier handles 500MB database and 2GB bandwidth. Enough for MVP scale.

RechartsLighter than Chart.js, better TypeScript integration. Clean SVG output.

jsPDFClient-side PDF generation. No server costs, no latency.

Financial edge cases are infinite. Leap years, month-end dates, and time zones each need dedicated test suites.
Manual entry isn’t a weakness, it’s a trust signal. Users preferred entering data themselves.
Export functionality tripled perceived value. Always ship exports. PDF, CSV, JSON.

7. Lessons Learned

Financial edge cases are infinite. Leap years, month-end dates, and time zones each need dedicated test suites.
Manual entry isn’t a weakness, it’s a trust signal. Users preferred entering data themselves.
Export functionality tripled perceived value. Always ship exports. PDF, CSV, JSON.

What This Actually Proves

✓I ship fast: 7 MVPs in 14 to 21 days each. Not prototypes, actual working products with auth, payments, databases.
✓I'm honest about metrics: 10 to 50 users, $0 revenue (test mode), 7 to 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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Case Study

19 days

Solo Built

Subsight Tracker

1. The Founder's Problem

2. Why Traditional Solutions Failed

✗Mint and YNAB require bank account linking. Many people won’t give that permission.
✗Spreadsheets work until you have mixed billing cycles. Then formulas break silently.
✗No tool offers privacy-first tracking with accurate calculations. You have to pick one.

3. What We Built

✓Manual subscription entry with optional AI auto-fill (bring your own Gemini key)
✓Calculation engine handling monthly, yearly, quarterly, and custom billing cycles
✓Real-time budget simulation: toggle subscriptions on/off without mutating your actual data
✓Multi-format export: JSON, CSV, and PDF with proper Unicode support
✓Vitest unit tests covering financial edge cases: leap years, month-end dates, timezone offsets

4. The Process

WEEK 1

Database schema (2 days), Calculation engine (4 days), Unit tests (1 day)

WEEK 2

Dashboard UI + Recharts (4 days), Export functionality (3 days)

WEEK 3

AI integration (2 days), PDF generation (2 days), Deploy (1 day)

5. Results & Metrics

Users

34 signups

Revenue

$0 (free tool)

GitHub

10 stars

Performance

100% calculation accuracy across 3 export formats

Code

98.2% TypeScript, 23 commits

6. Tech Stack & Why

Next.js 16Static generation for landing pages, SSR for the dashboard. Fast everywhere.

SupabaseFree tier handles 500MB database and 2GB bandwidth. Enough for MVP scale.

RechartsLighter than Chart.js, better TypeScript integration. Clean SVG output.

jsPDFClient-side PDF generation. No server costs, no latency.

Financial edge cases are infinite. Leap years, month-end dates, and time zones each need dedicated test suites.
Manual entry isn’t a weakness, it’s a trust signal. Users preferred entering data themselves.
Export functionality tripled perceived value. Always ship exports. PDF, CSV, JSON.

7. Lessons Learned

Financial edge cases are infinite. Leap years, month-end dates, and time zones each need dedicated test suites.
Manual entry isn’t a weakness, it’s a trust signal. Users preferred entering data themselves.
Export functionality tripled perceived value. Always ship exports. PDF, CSV, JSON.

What This Actually Proves

✓I ship fast: 7 MVPs in 14 to 21 days each. Not prototypes, actual working products with auth, payments, databases.
✓I'm honest about metrics: 10 to 50 users, $0 revenue (test mode), 7 to 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).

More case studies

Built in 18 days

Built in 20 days

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