u/Klutzy_Newspaper3196

It has been 4 years, I started my own company , I built entire prototype using chatgpt and got paid users. After few years, I got headache with our vibecoded project repo because we skipped many fundamental steps, PRDs, code review. Eventually, production bugs started coming and I was not able to trace huge complex repo. I eventually forgot what different edge cases, hidden business rules are written inside the code. I hired new developers they also faced a same issue and changing anything in production was too cumbersome.

I tried many graph context tools, but none of them gave us business workflows or anything on business context

So we developed unvibecode python package which traces all business workflows inside the code and give proper connected codemap context to LLM

https://github.com/FinanceFlash/unvibecode

If you guys face similar experience in maintaining or understanding vibecoded repo do share your feedback or write issues on git

It has been 4 years, I started my own company , I built entire prototype using chatgpt and got paid users. After few years, I got headache with our vibecoded project repo because we skipped many fundamental steps, PRDs, code review. Eventually, production bugs started coming and I was not able to trace huge complex repo. I eventually forgot what different edge cases, hidden business rules are written inside the code. I hired new developers they also faced a same issue and changing anything in production was too cumbersome.

I tried many graph context tools, but none of them gave us business workflows or anything on business context

So we developed unvibecode which traces all business workflows in the code and give proper connected codemap context to LLM

https://github.com/FinanceFlash/unvibecode

If you guys face similar experience in maintaining or understanding vibecoded repo do share your feedback or write issues on git

Hey everyone,

Three years ago, I heavily leveraged Claude and Cursor to "vibe code" an early prototype. It got us from 0 to 1 rapidly, but it left behind a massive architectural bottleneck: a ballooning codebase of 500+ scattered files filled with disjointed logic.

When production edge cases broke, dumping 500-line complex functions back into LLMs failed.

Out of necessity, I spent the last 3 months building UnvibeCode—a completely free, Apache-2.0 licensed local Python tool designed to systematically untangle complex repos.

Quick Start

pip install unvibecode
python -m unvibecode review --repository "/path/to/repository"

4 Practical Outputs from unvibecode:

1. Interactive Connected Code Map: A local, interactive graph showing cross-file dependencies so you can visually understand the architecture. [1]
2. LLM-Ready Context JSON: Automatically packages only the connected, relevant code chunks for debugging so you can feed LLMs the exact context.
3. Business Workflow Reconstruction: Reconstructs distributed code paths into logical workflows (e.g., tracking a payment across routes, queues, and APIs).
4. Business Risk Findings: Scans the completed workflows to flag structural business risks like duplicate payment loops, authorization edge cases, or broken failure paths.

Seeking Blunt Technical Feedback

I am looking for harsh, honest stress-testing from the community:

• If you run this on an unfamiliar or complex codebase, does the interactive graph layout generate a readable system architecture, or does it turn into visual spaghetti?
• Is attempting to reverse-engineer business logic out of chaotic AI-generated code a winning strategy, or am I fighting a losing battle?

GitHub Repo: https://github.com/FinanceFlash/unvibecode

Please tear the logic, graph rendering, and schema design apart. I want the real technical flaws so I can fix them directly in the rep

Disclaimer: I am the author of this tool. It runs locally, is completely free, and is licensed under Apache 2.0.

Hey everyone,

3 years ago, I heavily leveraged Claude and Cursor to "vibe code" an early prototype. It got us from 0 to 1 rapidly, got paid users, but it left behind a massive architectural bottleneck: a ballooning codebase of 500 scattered files filled with disjointed logic.

When edge cases broke production, dumping 500 line functions back into Cursor failed. Existing tools only lint or summarize single files. they don't map business workflows

Out of my own necessity, I spent the last 3 months building open source local python package UnvibeCode

how to use unvibecode:

pip install unvibecode
python -m unvibecode review --repository "/path/to/repository"

4 Practical Outputs it Gives You:

1. Interactive Connected Code Map: A local, interactive graph showing connected codes across different files so that you can understand the code
2. LLM-Ready Context JSON: Automatically packages the connected, relevant code chunks for debugging so you can give LLM the right context
3. Business Workflow Reconstruction: Reconstructs workflows hidden in complex (e.g., tracking a payment across routes).
4. Business Risk Findings: Scans the workflows to flag structural business risks like duplicate payment loops, authorization edge cases, or broken failure paths.

I am looking for blunt technical feedback from the community:

1. If you run pip install unvibecode on an unfamiliar or complex codebase, does the interactive graph layout generate a readable system architecture?
2. Am I approaching this the right way, or is trying to map 1,000s of business workflow scenarios from AI slop a losing battle?

Repo: https://github.com/FinanceFlash/unvibecode

PS: (GIF of the tool in action posted in the comments below!) Please tear the logic and schema design apart, share your thoughts to help me fix things in the repo