15:I[981,["658","static/chunks/658-a698693f56d0116a.js","183","static/chunks/183-6beeeb801c274aaa.js","474","static/chunks/app/%5Btype%5D/layout-631c4730b83c8223.js"],"Posts"] 16:T604,Hi HN,

I built ast-visualizer.com because I wanted a way to visualize the architecture/structure of a Python repo before dived into the code.

Most tools tell you what the code does; I wanted to see how it's built.

The Problem: Onboarding onto a large codebase is a nightmare. LLMs help with single functions, but they struggle to show you the "God Objects," circular dependencies, or high-complexity hotspots across 50+ files.

What it does:

Dependency Graph: Visualizes imports and file complexity to find architectural bottlenecks.

Radial AST Heatmaps: Maps individual files and color-codes them by Cyclomatic Complexity (McCabe). If a node is red, the section of code is a refactoring candidate.

Complexity Dashboard: High-level metrics like nesting depth and maintainability scores as well as files recommended to be refactored.

Privacy & Security (The Big One): I know how sensitive source code is. We do not store your code. Files are processed in (RAM) on our servers and discarded immediately after the visualization is generated. No permanent storage, no databases of your source, and zero training on your data.

Early Adopter Offer: I’m looking for feedback from the community to improve the UI and parsing. I’ve opened an Early Adopter tier that enables all features (including repo-wide analysis) completely free in exchange for your honest feedback.

Let me know: What’s the one thing you wish you could "see" visually when you open a new repository for the first time?17:T4de,I am building a data engineering framework for my PhD research proposal to solve a specific pain point in high-velocity telemetry: schema drift.

In live environments (like F1 racing or clinical monitoring), data producers often change types mid-stream (e.g., sending an int when the schema expects a float). Usuall

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