PortfolioIQ
AI stock intelligence, built end-to-end in a weekend
Problem
Individual investors face the same financial markets as institutions but none of their analytical infrastructure. Earnings calls, SEC filings, and macro data pile up faster than any one person can parse. Could a single PM, without raising a single engineering ticket, build an AI layer that synthesises this into actionable signals — in a weekend?
What I Learned
Writing a thorough PRD before touching any code is what makes AI-assisted development fast. The skill isn't coding. It's specification, architecture decisions, and understanding cost tradeoffs between models like Sonnet and Haiku. That clarity is the same skill that makes cross-functional programmes work at scale.
Stack
Deep Dive
Full architecture: 12 tools, 7 layers
Design system: Stitch to Figma + MCP
Cost breakdown: $5 to ship a full product
Keeping costs near zero removes the friction of learning. It forces you to understand how cost works in AI products: how much each API call costs, what caching does to your bill, and how cost-per-response can be controlled through architecture.
Tool decisions: PM thinking behind the stack
Why kill OpenAI?
Claude Sonnet does vision just as well. One vendor = one API key, one bill, one set of docs. Simplicity compounds.
Why Supabase?
Postgres + auth + storage in one free tier. Row Level Security means the database enforces permissions, not my code.
Why cut Polygon.io?
Finnhub's free tier already covers quotes, fundamentals, and news. Product lesson: audit before you add.
Why Claude Code?
10 sequential prompts. Each builds on the last. The PRD I wrote is what made this fast.
Why Stitch then Figma?
Stitch for "give me a starting point." Figma for "make this production-ready." MCP connected the two worlds.
Why Vercel?
Push to GitHub, get a URL. Every deployment step you eliminate is time for making the product better.