I vibe-coded a tool to decode a legacy system nobody understood

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My team came to me: "We can't estimate this project. We don't understand how the system works." We'd inherited a decade-old Yii2 codebase. Two previous teams had tried and failed to modernize it. The code followed proper patterns—ORM, fat models, MVC—but one feature would touch 40+ files across 8 layers. Original developers gone. Docs from 2019. Cursor and Claude Code couldn't help. They're brilliant at individual files, but they can't see how features connect across hundreds of files. Limited context windows. No memory between sessions. So I vibe-coded something to solve it. Not a professional dev—founder desperation. 18 months later, it's CodeCompass: - Maps capabilities to scattered code - Traces logic across layers - Answers "how does X work?" in seconds - Gives AI assistants persistent context Open sourced it. Every enterprise I talk to has this same problem. https://pearlthoughts.com/codecompass

Visit product View on Hacker NewsNov 25, 2025

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

95%95% 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

79%79% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

Hacker NewsStrong engagement from HN community · Strong signals: open source, io · Missing: https docs, excited, just released

64%64% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

TrustMRRFits verified-revenue profile · Strong signals: month, answers · Missing: mobile apps, ios, personal

53%53% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

38%38% 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

23%23% 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

0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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