Building TokenCap: Making AI Understand Large Codebases Better

Over the last few months, I've been working on a problem I kept running into while using AI for software development.

The issue wasn't the AI model.

The issue was context.

Modern codebases contain thousands of files, dependencies, architectural decisions, business logic, configuration files, debugging history, and undocumented assumptions. Most AI tools only see the small portion of code you manually provide, which often leads to incomplete or inaccurate responses.

To solve this, I started building TokenCap.

TokenCap is a developer tool that helps transform a codebase into structured, AI-ready context.

Instead of manually collecting files and explaining project architecture every time you interact with an AI assistant, TokenCap generates project intelligence automatically.

What TokenCap Does

Project Knowledge Graph

Maps relationships between files, modules, routes, services, components, and dependencies to provide a high-level understanding of the project structure.

Context Memory

Stores important project decisions, architecture notes, constraints, and development history so valuable context isn't lost between development sessions.

Change Intelligence

Converts raw Git diffs into meaningful reports that explain:

• What changed

• Why it matters

• Potential risks

• Impacted areas

• Testing recommendations

Context Packing

Large codebases often exceed practical AI context limits.

TokenCap prioritizes and compresses relevant project information into token-budgeted context packs optimized for debugging, code reviews, and development tasks.

Why I Built It

As AI becomes a bigger part of software development, context is becoming just as important as the model itself.

Developers don't need another chatbot.

They need tools that help AI understand their projects.

That's the direction I'm exploring with TokenCap.

I'm currently working on the next version, which includes an interactive knowledge graph inspired by tools like Obsidian, along with deeper context management capabilities.

If you're building with AI and have faced challenges with context windows, codebase understanding, or token limits, I'd love to hear how you're solving it.

Website: TOKENCAP