Let's be real for a second. Building anything related to personal finance is an absolute nightmare. 💸
The expectations are instantly sky-high: it needs to look like a polished $10M SaaS, sync data perfectly in real-time, and oh, it needs to be as secure as a bank. No pressure for a solo developer, right?
I recently shipped Nestly (you can play with the live app here: nestly1.netlify.app), a platform built to help families manage their budgets.
Instead of spending three weeks configuring an Express server, writing boilerplate auth, and messing up a database schema, I wanted to ship something robust in days. Here is the exact stack setup that made it happen, and the honest truth about how AI fit into the workflow.
The Backend: Just use Supabase 🔒
If you're still writing custom auth and basic CRUD APIs from scratch for your MVPs, you're losing time.
For Nestly, I went all-in on Supabase. When you're dealing with people's financial data, security isn't a "fix it in production" feature. I relied heavily on PostgreSQL's Row Level Security (RLS).
Pro tip: Writing RLS policies directly in the DB means your data isolation happens at the lowest level possible. Even if your frontend has a bug, users physically cannot query someone else's data.
Combined with their real-time listeners, budget updates just reflect instantly across the UI without complex polling logic.
The Frontend: Keeping it snappy 📈
For the client side, the goal was keeping things incredibly fast and responsive.
- State Management: Used lightweight, modern state management to handle complex financial data streams without hammering the server with unnecessary re-renders.
- Data Viz: Integrated clean charting libraries. Let's face it—if users can't see their spending patterns visually in two seconds, your finance app is just a glorified Excel sheet.
How AI Actually Saved My Codebase 🤖
We've all seen those generic "AI wrote my entire SaaS in 5 minutes" posts on Twitter. Let's be honest: that code is usually garbage and unmaintainable.
But where AI actually shines is acting as a hyper-efficient sounding board. I didn't let it blindly generate my app, but I used it to destroy dev friction:
- Postgres RLS Policies: Instead of fighting with complex SQL syntax for edge-case security policies, I used AI to scaffold the query logic instantly.
- Rubber Duck Debugging: When a state update wasn't triggering correctly, pasting the component into the AI caught the silent bug in 10 seconds instead of wasting my entire evening.
- Architecture Sanity Checks: Asking it to review component boundaries to make sure the code stays modular and readable.
It didn't replace me, but it gave me the execution speed of a 3-person team while keeping the quality high.
Over to you 👇
I'm curious how the rest of the community is shipping nowadays.
- What's your absolute go-to stack for spinning up secure, production-ready MVPs at lightning speed?
- Are you actually letting AI write raw features for you, or are you using it strictly for debugging, architecture, and boilerplates?
Let's talk in the comments!
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