Codemap
Understand codebases through visual graphs
Created on 30th December 2025
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Codemap
Understand codebases through visual graphs
The problem Codemap solves
Problem
Modern software systems are increasingly large, interconnected, and difficult to reason about.
Developers often spend a disproportionate amount of time trying to understand how a codebase is structured, where dependencies exist, and what impact a change might have.
This slows development, increases onboarding time, and raises the risk of introducing bugs or architectural issues.
Solution: CodeMap
CodeMap turns a codebase into a clear, explorable graph of relationships, making the real structure of the software visible and easy to understand.
What people can use CodeMap for
Understand unfamiliar or legacy codebases quickly
Instead of manually reading files and tracing imports, developers can visually explore how files, modules, and components are connected.
Safely make changes and refactor code
Before modifying any part of the system, developers can see which other components depend on it, reducing the risk of unintended breakages.
Reveal hidden dependencies and architectural issues
Implicit couplings, deep dependency chains, and critical hotspots become visible through graph traversal rather than guesswork.
Improve architectural understanding at a system level
CodeMap provides a high-level view of how the application is actually structured, not how it was intended to be structured in documentation.
Support onboarding and knowledge sharing
Teams can use the visual map as a shared source of truth, reducing reliance on tribal knowledge and outdated docs.
How it makes existing tasks easier and safer
- Eliminates manual dependency tracing
- Reduces time spent navigating large codebases
- Lowers the risk of breaking changes during refactoring
- Replaces outdated documentation with live, code-derived structure
- Helps teams reason about complexity before it becomes a problem
Summary
By converting complex codebases into interactive, always-up-to-date visual maps, CodeMap makes understanding, changing, and maintaining software faster, safer, and more predictable.
Challenges we ran into
Challenges I ran into
While building CodeMap, we faced a few concrete technical hurdles that directly affected development. Below are the key challenges and how we resolved them.
1. Tree-sitter dependency conflicts
Problem
Integrating Tree-sitter for static code analysis caused severe peer dependency conflicts. The project repeatedly failed to install or build due to incompatible versions across parsing libraries.
How I overcame it
I switched the package manager from npm to Yarn, which handled dependency resolution more reliably for complex native and parsing-related packages.
Outcome
This unblocked development and stabilized the static analysis setup. It also reinforced that tooling choices can significantly impact low-level systems work.
2. Graph visualization breaking in the frontend
Problem
The initial graph visualization library (react-force-graph-2d) caused blank screens and inconsistent rendering when fed real Neo4j-style graph data. Debugging was difficult because failures were non-deterministic.
How I overcame it
I migrated the visualization layer to Cytoscape.js, a more mature and battle-tested graph visualization library.
Outcome
The graph rendering became stable and predictable, even as graph complexity increased. This allowed us to confidently demo and explore real dependency graphs.
3. Frontend–backend graph data mismatch
Problem
A custom-designed graph data format led to incompatibilities between the Go backend and the frontend visualization logic. This caused extra transformation logic and frequent bugs.
How I overcame it
I standardized the entire system around Neo4j's native node–relationship data format, using it consistently across backend queries and frontend rendering.
Outcome
This simplified data flow, reduced transformation code, and eliminated an entire class of frontend–backend integration bugs.
Tracks Applied (2)
Best Innovation
AWS
AWS
Technologies used