How to vibe codeConsensus

Prerequisites

• Node.jsfree

  Runtime environment for Next.js full-stack framework

• GitHubfree

  Source control and repository hosting

• 

  OpenAI or Anthropic API Keypay-as-you-go

  LLM calls for paper synthesis and structured extraction

AI coding tools

• Claude Code

  Best-in-class multi-step agent for building out full-stack RAG pipelines and database models

• Cursor

  Ideal for fine-tuning UI components, evidence tables, and citation hover states

Build guide

1. 01Project Scaffolding & Database Schema

   Initialize a Next.js project with Tailwind CSS, shadcn/ui, and configure Supabase with the pgvector extension for storing paper metadata and vector embeddings.

   Create a new Next.js 16 project with Tailwind CSS and TypeScript. Set up Supabase client connections using environment variables. Write a database migration script for Supabase using pgvector to create a 'papers' table with columns: id, title, abstract, authors, year, journal, citation_count, study_type, sample_size, and embedding (vector dimension 1536). Also create a 'saved_papers' table for user bookmarks and user libraries. Ensure proper indexing for vector similarity search.

   ⧉copy promptopen in Claude CodeCopen in Codexopen in Cursor

2. 02Open-Access Paper Ingestion Pipeline

   Build a background script or API route that queries the arXiv API or Crossref API, fetches open-access papers, generates embeddings via OpenAI, and stores them in Supabase.

   Build an API route and ingestion service in Next.js that accepts search queries, fetches relevant open-access research papers from the arXiv API or Semantic Scholar API, downloads their metadata and abstracts, generates vector embeddings using OpenAI's text-embedding-3-small model, and inserts them into the Supabase 'papers' table. Implement robust error handling for rate limits and malformed XML/JSON responses.

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3. 03Natural Language Search & RAG Synthesis Engine

   Implement the core search endpoint that performs vector similarity search over stored papers and prompts an LLM to synthesize an evidence-backed answer with inline citations.

   Create the core search and synthesis engine. When a user enters a research question, perform a pgvector similarity search against the 'papers' table to retrieve the top 10 most relevant papers. Construct a structured prompt for Claude/GPT that passes these paper abstracts and instructs the model to synthesize a natural-language answer, explicitly citing papers using bracketed numbers [1], [2]. Return both the synthesized markdown answer and the raw structured paper objects to the frontend.

   ⧉copy promptopen in Claude CodeCopen in Codexopen in Cursor

4. 04Frontend Search Interface & Study Snapshots

   Build the main search dashboard, results view with expandable study snapshots (methods, sample size, population), and citation preview cards.

   Build a responsive search dashboard in Next.js resembling an academic search engine. Include a prominent search input, filter pills for study type and publication year, and a results view that renders the AI-synthesized answer with interactive citation badges. Clicking or hovering a citation badge should open a popover card showing the paper's title, authors, journal, year, and extracted study snapshot (sample size, methodology, population).

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5. 05Reference Manager & PDF Upload

   Add functionality for users to upload custom PDF papers, parse their text content, generate embeddings, and save items to personal libraries.

   Implement a reference manager feature allowing users to upload custom PDF files. Build a server-side PDF parsing utility (using pdf-parse or unpdf) to extract text, chunk the document, generate embeddings, and store it in a user-specific collection table in Supabase. Create a library view where users can browse their saved papers, view bookmarks, and chat directly with their uploaded document collection.

   ⧉copy promptopen in Claude CodeCopen in Codexopen in Cursor

Cost vs paying for Consensus