Video Walkthrough

Before vs After

Before → Initial Unfinished Prototype

The original version of CodePulse AI started as an experimental AI-powered repository analyzer. While the foundation existed, the platform lacked visual polish, modern UX, stable AI workflows, and advanced engineering intelligence features.

The initial prototype:

• used IBM Watsonx.ai for inference


• had incomplete repository analysis flows


• lacked polished architecture visualization


• had minimal dependency mapping


• had static and unfinished UI components


• lacked blast radius prediction


• had limited developer experience optimization

Before Screenshots

1. Original Landing Page

3. Initial Legacy Code Analysis Page

5. Basic Repository Visualization

After → Revived & Fully Polished Platform

During the GitHub Finish-Up-A-Thon, I completely revived and transformed CodePulse AI into a production-style AI-powered engineering intelligence platform.

The platform now features:

• Gemini 2.5 Flash integration


• Blast Radius dependency analysis


• Interactive repository intelligence


• Modern SaaS-inspired UI


• Animated dependency graph previews


• Security & code quality analysis


• Improved loading and analysis flows


• Advanced architecture visualization


• Responsive developer-focused UX

Major Improvements

AI Stack Migration

One of the biggest upgrades was migrating the entire AI inference layer from IBM Watsonx.ai to Gemini 2.5 Flash.

This migration included:

• rebuilding the backend proxy layer


• refactoring request/response handling


• converting payloads to OpenAI-compatible chat completion format


• fixing malformed JSON parsing issues


• redesigning Gemini fallback handling


• updating environment configuration and model management

UI/UX Redesign

The frontend was completely redesigned into a modern AI SaaS-style experience inspired by:

• GitHub


• Linear


• Vercel


• Cursor

New additions included:

• animated dependency graph previews


• futuristic grid backgrounds


• improved typography


• polished loading states


• responsive layouts


• glassmorphism-inspired UI


• dark mode refinement

Blast Radius Analysis

One of the biggest new features was Blast Radius Analysis.

This system:

• maps repository dependency relationships


• visualizes affected nodes


• predicts propagation impact across services


• helps developers understand change risk before deployment

Repository Intelligence

The platform now provides:

• architecture diagrams


• dependency insights


• security analysis


• tech stack detection


• repository exploration


• AI-generated documentation


• legacy code archaeology

After Screenshots

1. Redesigned Hero Section

3. Blast Radius Analysis Visualization

5. Improved Loading & Analysis Flow

7. After About Page

Transformation Summary

The Comeback Story

CodePulse AI originally began as an unfinished side project focused on AI-assisted repository understanding. While the core idea was strong, the platform was incomplete and lacked a polished user experience.

The original system:

• used IBM Watsonx.ai for inference


• had unstable response parsing


• lacked proper architecture visualization


• had static UI components


• had incomplete analysis workflows


• did not clearly communicate repository impact analysis

During the Finish-Up-A-Thon, I decided to fully revive the project and transform it into a polished developer intelligence platform.

The project evolved from a rough experimental prototype into a fully redesigned engineering intelligence platform capable of:

• dependency analysis


• blast radius prediction


• AI-powered architecture understanding


• repository exploration


• security insights


• modern developer-focused UX

My Experience with GitHub Copilot

GitHub Copilot became my pair programmer throughout the revival process.

I used Copilot extensively for:

• refactoring React + TypeScript components


• redesigning Tailwind layouts


• generating animation logic


• debugging Gemini integration issues


• restructuring API payload handling


• improving loading workflows


• accelerating UI polishing


• rebuilding analysis components

Copilot was especially helpful while:

• migrating from IBM Watsonx.ai to Gemini 2.5 Flash


• implementing animated dependency graph previews


• refactoring the backend inference layer


• improving frontend responsiveness and styling

Instead of generating the entire project automatically, Copilot acted as a collaborative engineering assistant that helped speed up iteration, experimentation, debugging, and polishing.

Tech Stack

Frontend

• React


• TypeScript


• Tailwind CSS


• Framer Motion


• Mermaid.js

Backend

• Node.js


• Express.js

AI

• Gemini 2.5 Flash


• AIML API

Features

• Repository Analysis


• Blast Radius Visualization


• Security Insights


• Dependency Mapping


• AI Documentation Generation


• Legacy Code Archaeology

Transformation Summary

What I Learned

This project taught me:

• how to refactor and revive unfinished software


• how to migrate AI inference providers


• how to build production-style developer tooling


• how to design modern SaaS interfaces


• how to improve architecture visualization


• how to work alongside AI-assisted development tools effectively

Most importantly, this challenge helped me finally finish and polish a project that had previously been left incomplete.

Built for the GitHub Finish-Up-A-Thon 🚀