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TechDebt-Tracker to Help keep your codebase maintainable

Hacker News

TechDebt-Tracker to Help keep your codebase maintainable

Hi HN! I wanted to share this tool that I created in order to try to track technical debt, and get some sort of overview of a code-base's health, quality, and maintainability. I know from work how difficult it can be to get maintenance, improvements, and refactoring efforts to be prioritized, especially without any concrete numbers/KPIs on how it will impact a product. So that's why I wanted to share this tool, which should aid in such situations, at least by bringing some numbers to the table. I wanted to write the tool in rust so that it could handle large code-bases in a fast manner, since those are also the code-bases that are most likely to have a higher amount of technical debt. In the future, I hope to extend the functionality so that the tools can analyze code-base that use a wide-range of different languages (maybe by taking advantage of existing syntax highlighting grammar files?). Please feel free to leave any comments below, or open any issues/feature-requests in the repo. I'm very open to feedback, and look forward to hearing from y'all.

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Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Strong signals: code, open · Missing: mac, agents, macos
77%77% 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 · Strong signals: created · Missing: supports, reddit linkedin, podcasting
54%54% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: exist, existing, ide · Missing: https docs, excited, just released
43%43% 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 · Missing: mobile apps, ios, personal
40%40% 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
12%12% 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.

Correct prediction on native model

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