MarketMiner

Inspiration

The inspiration for MarketMiner came from observing the struggles of aspiring entrepreneurs and small business owners who wanted to enter the e-commerce space but lacked the tools to make data-driven product decisions. I witnessed countless individuals making costly mistakes by choosing products based on gut feeling rather than market intelligence.

What it does

It is an intelligent product discovery and market intelligence platform that helps entrepreneurs and businesses identify profitable product opportunities through comprehensive market analysis. The application combines real-time product scraping, trend analysis, competitor research, and AI-powered insights to provide data-driven product recommendations. The problem was clear: How do you identify profitable product opportunities in a sea of market data? Traditional approaches required manual research across multiple platforms, expensive market research tools, time-consuming trend analysis, guesswork about competitor strategies. We envisioned a solution that could democratize market intelligence, making sophisticated product discovery accessible to everyone. The goal was to create a platform that could answer the fundamental question "What should I sell next?" so we developed the hybrid scraping system that attempts real scraping first, then gracefully falls back to realistic mock data. This ensures demos always work while still providing real data when possible.

How we built it

Rather than diving straight into code, I followed Kiro's systematic three-phase approach: Phase 1: Requirements Engineering Started with a simple statement which was "I want to build a product discovery tool." Kiro guided me through creating 17 detailed requirements using EARS format (Easy Approach to Requirements Syntax). Each requirement included a user story, acceptance criteria with measurable outcomes, error scenarios, and security related issues. Phase 2: System Design Kiro suggested various data models for different parts of the project. Such as product entities with relationships. Phase 3: Implementation Tasks Created 20+ incremental tasks, each building on previous work:

Database Foundation - Models, relationships, indexes
Core Services - Scraping, trends, AI analysis
API Layer - Endpoints, validation, error handling
Frontend Components - UI, state management, optimization
Integration & Testing - E2E tests, performance tuning

Challenges we ran into

There are quiet a few challenges we ran into.

Initial challenge was figuring out the logic. I think Kiro helped us with a lot of it.
Amazon and eBay frequently block scraping attempts, causing inconsistent user experiences.
Managing complex state across multiple API calls (products, trends, competitors, AI analysis) led to race conditions and inconsistent UI states.
Initial implementation had slow response times due to inefficient database queries and lack of caching.
During development, products weren't displaying in the frontend despite successful API responses.

Accomplishments that we're proud of

Sub-second response times with optimization
Zero critical security vulnerabilities
Comprehensive error handling for all failure scenarios
Requirements-first thinking prevents scope creep
Incremental implementation ensures no broken states
Systematic testing catches issues early

What we learned

This project became a masterclass in modern software development, teaching me invaluable lessons across multiple domains: The most transformative learning was Kiro's spec-driven approach. This methodology taught me the power of systematic planning before coding. How proper requirements prevent scope creep Production Readiness

Database optimization with indexing and query performance
Caching strategies with automatic cleanup
Circuit breaker patterns for external API resilience
Comprehensive logging and monitoring Technical Lessons: Full-Stack Architecture Design
Learned to design scalable microservices-style backends
Learned React's modern patterns including Suspense and lazy loading
Implemented production-ready error handling and validation
Developed expertise in API design and RESTful principles Data Engineering & Analysis
Web scraping with intelligent fallback mechanisms
Real-time data processing and caching strategies
Sentiment analysis and natural language processing
Statistical modeling for opportunity scoring Performance Optimization
The opportunity scoring algorithm uses a weighted formula