CodeBoarding – Visualize Code Structure

Hacker News

I noticed many projects I was getting acquainted with often had an overview that could not be easily explained by the file structure. I tried to prompt using Claude Code and Cursor, but I was still not happy with the results for medium-large codebases. So four months later, I have now built an open-source repository (CodeBoarding) that analyzes your code-flows, and generates an abstract overview of your codebase that you can interact with. To do this, I extract code-flows using an LSP server to reduce the context and serve it to an LLM in multiple steps (read metadata, explore packages, validate diagrams, etc.). The final results are now hosted on the website url, and you can find the repo from there. Would be super happy to hear feedback, thanks HN!

Visit product View on Hacker NewsAug 26, 2025

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Product HuntOn track for Day 1 leaderboard · Strong signals: cursor, claude, context · Missing: mac, agents, macos

87%87% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting

65%65% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

Hacker NewsStrong engagement from HN community · Missing: https docs, excited, just released

62%62% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

TrustMRRLess likely to generate early MRR · Strong signals: month, visualize · Missing: mobile apps, ios, personal

44%44% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Strong signals: host · Missing: plus, platform, intuitive

20%20% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

19%19% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto

5%5% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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