From Instagram Clone to Infinite Apps: A Practical Methodology for Building with AI, Bolt, and Supabase

Modern AI coding tools have changed how we build applications—but not what good application architecture looks like. In the video “Using bolt.new and Supabase to build an Instagram clone”, we see a repeatable, pragmatic workflow emerge: start with a working UI, iterate locally with dummy data, and only then connect a real backend. This article breaks down that methodology and shows how it can be reused to build many different types of apps, not just social networks.

The Core Idea: Build the App Before the Backend

A key insight from the video is that backend integration should be the last step, not the first. Rather than immediately wiring databases, authentication, and storage, the process deliberately starts with a fully functional frontend powered by local state and mock data.

This mirrors how apps were often built before AI coding tools—except now the UI and logic can be generated and refactored at lightning speed using an AI agent like Bolt.

The high-level flow looks like this:

  1. Generate a realistic UI clone

  2. Make every interaction work locally

  3. Add authentication gates (still local)

  4. Replace local state with Supabase

  5. Harden security with Row Level Security

  6. Scale, polish, and monetize

This exact sequence is demonstrated step by step in the Instagram clone walkthrough .

Step 1: Clone First, Ask Questions Later

The process begins with a deliberately vague prompt:

“Create an Instagram clone.”

Instead of over-specifying requirements, the AI is allowed to generate:

  • A realistic file structure

  • Modular components (feed, post, header, sidebar)

  • A visually accurate UI

This works because clones provide strong prior context. The AI already understands what Instagram is supposed to do, so even a loose prompt produces a usable baseline.

The key takeaway:
Cloning is not about copying—it’s about scaffolding.

Step 2: Make Everything Work with Dummy Data

Once the UI exists, the focus shifts to functionality:

  • Likes increment

  • Comments can be added

  • Posts can be created

  • State updates correctly

Crucially, everything is stored locally at this stage—usually in React state. No database. No authentication provider. No network requests.

This has two advantages:

  1. You can verify the UX before committing to a data model.

  2. You can refactor fearlessly because nothing persistent can break.

The video repeatedly reinforces this mindset: a working app with fake data is more valuable than a broken app with a real backend .

Step 3: Add Auth as a Gate, Not a Dependency

Authentication is introduced conceptually before it is implemented technically.

Actions like:

  • Liking a post

  • Commenting

  • Creating content

…are blocked unless the user is “logged in”—but that login state is still just local state.

This lets you:

  • Design the login/signup UX

  • Decide when auth is required

  • Avoid backend complexity while iterating

Only once this flow feels right does the app move on to real authentication.

Step 4: Swap Local State for Supabase

At this point, Supabase is introduced—not as a redesign, but as a drop-in replacement for local data.

The process looks like:

  • Create Supabase project

  • Add auth

  • Generate SQL tables for posts, users, likes, comments

  • Enable Row Level Security

  • Replace local state with database queries

Because the app already works, the risk is low: if something breaks, you know it’s a backend issue, not a UI one.

This is where Supabase shines:

  • Auth maps cleanly to the existing login flow

  • Storage replaces placeholder images

  • RLS enforces per-user access rules

The Bigger Insight: This Isn’t About Instagram

The Instagram clone is just a demo. The real lesson is that this methodology works for almost any app that has users and content.

Once you understand the pattern, you can apply it broadly.

What Else Can Be Built This Way?

Here are categories of apps that fit this methodology perfectly:

Social & Community Apps

  • Twitter/X-style microblog

  • Reddit-style forums

  • Private communities

  • Slack-style chat apps

These map directly to posts, comments, likes, and users.

Creator & Media Platforms

  • Photo portfolios

  • Video sharing apps

  • Podcast hosting platforms

  • Writing/blog platforms

Supabase Storage + auth + RLS makes these especially straightforward.

Productivity & SaaS Tools

  • Notion-style notes

  • Kanban boards

  • Habit trackers

  • Internal dashboards

Local-first state works exceptionally well here before persistence is added.

Marketplaces & Business Apps

  • Job boards

  • Booking systems

  • Simple marketplaces

  • CRM tools

Role-based access and RLS become the main backend concern.

AI-Enhanced Apps

  • Journaling apps

  • Idea vaults

  • Resume builders

  • Content planning tools

AI features layer cleanly on top once the core CRUD flows exist.

Why This Methodology Works So Well with AI

AI coding agents are strongest when:

  • The goal is clear

  • The scope is limited

  • Feedback is immediate

By starting with UI and local state:

  • You give the AI fast feedback

  • You reduce cascading failures

  • You keep control of architecture decisions

The video repeatedly shows that being slightly ambiguous often works better than being overly specific, especially when prompting AI to generate or refactor code .

Final Thoughts

This isn’t a “no-code” shortcut—it’s a modernized version of good software engineering:

  • Build incrementally

  • Validate early

  • Integrate backend last

  • Secure everything properly

The Instagram clone is just the beginning. Once you internalize this workflow, you’re no longer limited by frameworks or boilerplate—you’re limited only by ideas.

If you’d like, I can turn this article into a blog post, expand it with diagrams, or map this methodology to a specific app idea you have in mind.

Vibe Coding, SupabaseFrancesca Tabor