Introduction

The development of velocity.new represents an extensive exploration journey through the landscape of AI-powered code generation platforms. This document chronicles our comprehensive research, experimentation, and iterative decision-making process that led to the final architecture. Each exploration provided valuable insights that shaped our understanding of what works, what doesn't, and why certain approaches excel in specific contexts.


Phase 1: Competitive Intelligence & Architecture Analysis

Exploring GPT Engineer (Lovable.dev)

Approach Studied: Single Agent with Tools Access

Our exploration began with GPT Engineer, the precursor to lovable.dev, which represented one of the earliest successful implementations of AI-powered code generation. We conducted deep analysis of their methodology:

Key Insights Discovered:

Analyzing Refine AI Enterprise Platform

Approach Studied: Distributed Multi-Agent System with Token Dilution Method

Refine AI's enterprise-focused platform introduced us to distributed multi-agent architectures and the innovative token dilution methodology:

Technical Deep Dive: