Overview

⚙️ Code Forge

Transform ideas into production-ready web applications using a single prompt.

Code Forge is an AI-powered system that generates full-stack applications, deploys them automatically, and pushes code directly to GitHub — delivering an end-to-end workflow with minimal human intervention.

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🧠 Motivation

This project was built out of curiosity to deeply understand how platforms like Lovable, v0, and Bolt generate complete web applications purely from prompts.

Rather than treating these tools as black boxes, the goal was to explore how such systems work under the hood — from prompt interpretation to execution, memory management, and agent orchestration.

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🔍 Key Learnings & Technical Insights

While building Code Forge, I gained hands-on experience with:

🧩 Prompt & Agent Design

• Prompt engineering fundamentals and how LLMs interpret and execute user intent
• How LLMs perform actions on behalf of users using tools and agents
• Backend orchestration and agent control flows

🧠 Memory System Architecture

Designed and implemented a multi-layered memory system, including:

• Short-term memory
• Long-term memory
• Factual memory
• Episodic memory
• Semantic memory

Context & Relationship Modeling

• Building relationships between prompts using a graph-based approach
• Using Neo4j (Graph Database) to model conversations, context, and relationships
• Storing all chat interactions in a database

Context Window Optimization

• Summarized the last 50 messages to avoid hitting LLM context limits while preserving relevance

Tooling & Orchestration

• Creating and orchestrating custom tools using LangGraph
• Managing execution flow across multiple tools and agents

Human-in-the-Loop Systems

• Understanding when and how to pause agent execution
• Safely handing control back to users during critical decision points

Language Expansion

• Learned Python from scratch, transitioning from a JavaScript-first background

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🚀 Impact

This project significantly deepened my understanding of:

• LLM system design
• Agent-based architectures
• Memory management strategies
• AI-driven developer tooling

It goes far beyond basic prompt experimentation and explores how real-world AI developer platforms are architected and scaled.