TokenCap 소개 — 최신 코드베이스를 위한 컨텍스트 엔지니어링

One of the biggest challenges in AI-assisted development isn’t choosing the right model.

It’s providing the right context.

As codebases grow, important information gets scattered across files, commits, architecture decisions, documentation, and debugging history. Developers spend valuable time manually gathering context before they can effectively use AI tools.

That’s why I built TokenCap.

TokenCap helps developers generate structured, AI-ready project context from their codebase, making it easier for AI assistants to understand projects without consuming unnecessary tokens.

Current Features

1. – Project Knowledge Graph Visualizes relationships between files, components, services, routes, and dependencies.
2. – Context Memory Captures project decisions, architecture notes, constraints, and development history.
3. – Change Intelligence (tokencap diff) Transforms raw git diffs into impact analysis, risk assessment testing recommendations, and AI review prompts.
4. – Context Packing Prioritizes and compresses relevant project information into token-budgeted context packs optimized for AI workflows.

Why TokenCap?
Reduce context-switching overhead
Improve AI response quality
Save tokens and costs
Understand large codebases faster
Generate structured project knowledge automatically

I’m actively working on the next generation of TokenCap, including an Obsidian-style interactive graph and advanced context management capabilities.

Would love feedback from developers building with AI.

Website: https://tokencap.vansharora.app/