How to vibe codeIdeaFast

Prerequisites

• Node.jsfree

  Runtime environment for Next.js and TypeScript tooling.

• GitHubfree

  Version control and deployment pipeline source.

AI coding tools

• Claude Code

  Autonomous terminal agent capable of scaffolding the entire Next.js app, setting up API routes, and writing embedding scripts.

• Cursor

  Ideal for refining dashboard UI components and adjusting Tailwind layouts.

Build guide

1. 01Scaffold Next.js Project and Database Schema

   Initialize the Next.js application with Tailwind CSS, shadcn/ui, and configure the Neon Postgres database with pgvector for storing Reddit posts and embedding vectors.

   Create a new Next.js project using the App Router, TypeScript, and Tailwind CSS. Install shadcn/ui primitives (card, button, input, dialog, tabs). Configure a connection to Neon Postgres using Drizzle ORM or Prisma, ensuring the pgvector extension is enabled. Define database schemas for 'subreddits', 'posts' (with vector embedding column), 'pain_points' (storing score, summary, and category), and 'evidence_links' (mapping pain points to source posts). Ensure clean modular directory structures.

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2. 02Build Reddit Data Ingestion Script

   Implement an ingestion utility that fetches recent posts and comments from targeted subreddits via the Reddit API or public JSON endpoints, cleaning and formatting the text.

   Create a server-side ingestion utility in Next.js that takes a subreddit name and time window (7, 30, or 90 days). Fetch top posts and comments from the Reddit JSON API or official endpoints. Sanitize the text by removing URLs, markdown artifacts, and boilerplate text. Store the raw posts and comments in the Neon database with appropriate metadata (author, score, permalink, created_utc) so every insight retains its source receipt.

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3. 03Implement Embedding and Clustering Pipeline

   Generate vector embeddings for ingested comments and cluster them to isolate recurring customer pain points.

   Build an AI processing pipeline using the Vercel AI SDK and Anthropic API. Write a function that generates embeddings for stored Reddit comments, saves them to pgvector, and executes a clustering query or LLM summarization prompt to group related complaints into distinct 'pain points'. Calculate a Pain Signal Score (0-100) based on frequency, recency, and acuteness of the clustered comments. Save the resulting pain points and link them back to their source post IDs as receipts.

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4. 04Develop Startup Idea Generation and Validation Engine

   Create an LLM-powered module that reads clustered pain points and generates structured startup ideas and validation reports.

   Implement an idea generation feature that takes a clustered pain point and calls the Anthropic API to output 3-5 structured startup ideas, monetization angles, and validation reports. Display these ideas in an interactive modal or dedicated section linked directly to the underlying pain group and its 10+ evidence quotes.

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5. 05Build Dashboard UI and Research Explorer

   Design the main dashboard where users can input a subreddit, trigger a scan, view scored pain points, read quotes, and export reports.

   Build the primary dashboard interface in Next.js. Include an input form for selecting a subreddit and time window with a 'Scan Subreddit' CTA. Create a results view that renders pain points sorted by signal score, complete with expandable quote drawers ('The Receipts Rule') linking to original Reddit threads. Add export buttons for CSV, JSON, and PDF formats using client-side libraries. Ensure responsive, clean typography matching modern indie SaaS aesthetics.

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Cost vs paying for IdeaFast