Problem → Solution → Technical Metrics → Outcome → Learning. Each project shipped in 7 to 21 days with public GitHub repos. See the actual code, commits, and architecture decisions.
AI Task Extraction SaaS
Users struggle to convert scattered notes into organized tasks manually. The process is repetitive and error prone, wasting valuable time daily.
Built Next.js Stack base SaaS with dual AI providers for reliable task extraction. Implemented Supabase PostgreSQL with Row Level Security for secure data isolation.
Shipped production SaaS with payment processing and dual AI failover. Proved ability to integrate multiple third party services reliably.
Frontend
Next.js 15 + TypeScript
Backend
Supabase PostgreSQL + RLS
AI Integration
Google Gemini + Groq Llama
Payments
Stripe with webhooks
Auth
Email + Google OAuth
Monitoring
Sentry error tracking
Processing Speed
<3s
API Failover
<500ms
TypeScript
95.4%
Uptime
99.9%
AI integration needs robust error handling for timeouts and rate limits. TypeScript catches bugs before production. User experience matters more than feature count.
Multi Feature AI Platform
Multi feature SaaS platforms often suffer from feature bloat and poor code organization. Scaling multiple AI workflows requires careful architecture planning.
Built platform with 5 distinct AI workflows and 25 writing combinations. Implemented Groq AI with model specific routing for optimal performance and cost efficiency.
Shipped multi feature platform with disciplined code organization. Demonstrated ability to orchestrate multiple AI models with fallback logic.
Frontend
Next.js 15 + TypeScript
AI Models
Groq Llama 3.1 & 3.3
Database
PostgreSQL with feature schema
Analytics
PostHog + Sentry
Deployment
Vercel with auto scaling
Features
5 workflows, 25 combinations
API Response
<2s
Chat Speed
<500ms
TypeScript
94.8%
Commits
25
Feature based folder structure is essential for scalability. API cost monitoring must happen before launch. Error boundaries prevent cascading failures.
Privacy First Subscription Management
Users can't accurately track subscription costs without manual spreadsheets. Privacy concerns prevent connecting bank accounts directly to apps.
Built Next.js app with Supabase backend and privacy first design. Implemented calculation engine handling monthly/yearly/custom billing cycles with 100% accuracy.
Shipped privacy first SaaS with precise financial calculations. Proved strong product thinking with clear limitations documentation.
Frontend
Next.js 15 + TypeScript
Backend
Supabase PostgreSQL + RLS
Calculations
100% accuracy, all cycles
Exports
JSON, CSV, PDF formats
Testing
Vitest unit tests
Deployment
Vercel with PWA support
Accuracy
100%
Export Formats
3
TypeScript
98.2%
Commits
23
Financial calculations need extensive edge case testing. Export functionality increases perceived value. Unit tests prevent regression bugs.
Startup Runway & Financial Projections
Founders manually calculate runway using spreadsheets, prone to errors. Need instant financial projections without complex formulas.
Built financial engine with core formulas: Burn Rate = Monthly Expenses - Revenue, Runway = Cash / Burn Rate. Integrated Groq + Gemini for AI recommendations.
Shipped subscription SaaS with instant feedback (<1s). Proved clear free vs paid differentiation drives conversions.
Frontend
Next.js 15 + TypeScript
Backend
Supabase with usage tracking
Calculations
100% accuracy vs Excel
Payments
Stripe subscriptions
AI Integration
Groq + Gemini dual providers
Deployment
Vercel with auto scaling
Speed
<1s
AI Response
<3s
TypeScript
97.9%
Commits
11
Financial apps demand precision rounding errors compound. Instant feedback is critical for UX. AI recommendations need domain specificity.
AI Pitch Deck Generator
Founders struggle to write compelling pitch decks. AI output is inconsistent without proper prompt engineering and structured formatting.
Built productivity app with Groq Llama 3.3 70B integration. Engineered prompt system generating 8 structured components with JSON output across 24+ industries.
Shipped AI generator with consistent structured output. Proved industry specific context injection improves output quality.
Frontend
Next.js 15 + TypeScript
AI Model
Groq Llama 3.3 70B
Industries
24+ categories with context
Auth
Google + GitHub OAuth
Payments
Stripe subscription model
Deployment
Vercel with middleware
Generation
45 to 60s
Industries
24+
TypeScript
95.6%
Commits
15
AI prompt engineering requires 15+ iterations for consistency. Structured output needs explicit format specifications. Generation speed matters for ideation tools.
AI Email Personalization Engine
Email personalization at scale is time consuming and inconsistent. Users need AI assistance with quality scoring to trust the output.
Built Craft Email app with dual AI (Gemini 2.5 Flash + Groq fallback). Added 0 to 100 effectiveness scoring based on personalization depth and CTA strength.
Shipped dual AI provider system with automatic failover. Proved effectiveness scoring builds user trust in AI output.
Frontend
Next.js 15 + TypeScript
AI Providers
Gemini 2.5 + Groq fallback
Scoring
0 to 100 effectiveness algorithm
Storage
Browser localStorage auto save
Exports
TXT, HTML, JSON formats
Deployment
Vercel with usage limits
Generation
2 to 3m
Scoring
0 to 100
TypeScript
93.4%
Commits
10
AI quality depends on input data quality. Effectiveness scoring needs transparent methodology. Browser storage has limitations for auto save. Rich text editing requires XSS sanitization.
Projects Shipped
6
Avg Timeline
18 days
TypeScript
95%+
Open Source
100%