I'm Shivansh, and I built CodeContext to solve a problem every growing engineering team faces: new developers taking 2-3 months before their first meaningful contribution.

The core insight: Most onboarding time isn't spent learning to code—it's spent figuring out which 15 files (out of 10,000) actually matter, and in what order to read them.

What CodeContext does:

Scans your repository (any language)

Builds a dependency graph using AST parsing

Runs PageRank to identify "knowledge hotspots" (the UserService.java that's called by 47 other files)

Generates a topologically-sorted "reading list" (simple → complex)

Creates interactive D3.js visualizations

Example output:

text Your Codebase Map ━━━━━━━━━━━━━━━━━━━━━━━ Knowledge Hotspots: 1. AuthService.kt (89 connections) - CRITICAL 2. DatabaseConfig.kt (67 connections) 3. UserRepository.kt (45 connections)

Week 1 Onboarding Path: → README.md, ARCHITECTURE.md → Models: User.kt, Product.kt (low complexity) → Config: application.yml, DatabaseConfig.kt → First task: Add validation to UserDTO Technical stack:

Kotlin (multiplatform-ready)

Tree-sitter for parsing 40+ languages

JGraphT for graph algorithms

D3.js for interactive visualizations

Git history analysis for change patterns

Why this approach works:

Traditional docs go stale immediately

Code search is great if you know what to search for

AI assistants generate code but don't teach architecture

Juniors need a "start here" button, not a search box

What's different from Sourcegraph/CodeSee:

Lightweight (single CLI command, no infrastructure)

Onboarding-first (not code search)

Free & open source (MIT license)

Runs locally (no code leaves your machine)

I've been testing this with 3 teams (50-200 developers). Average time to first PR went from 47 days → 19 days.

Try it:

bash npm install -g codecontext codecontext analyze /path/to/repo Open to feedback! What features would make this immediately useful for your team?

GitHub: https://github.com/sonii-shivansh/CodeContext