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I Vibecoded a Python Class Hierarchy Checker (Needs Your Eyes)

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I Vibecoded a Python Class Hierarchy Checker (Needs Your Eyes)

I vibecoded a static checker for Python class hierarchies. Basically a tool that scans inheritance trees, spots structural issues (like unimplemented abstract methods or messy multiple inheritance), and helps enforce clean design patterns without running the code. Additionally, if you provide an OpenAI key, it suggests you how to refactor with composition. It’s still rough and definitely needs polish and tests, but I wanted to put it out there early to see if it might be interesting or useful to others. I’d love your thoughts. Is this something you’d find useful? Or is it solving a non-problem? Curious to hear what you think. Contributions are very welcome. Feel free to open issues or PRs if you have ideas or just want to help shape it. Repo: https://github.com/agaz1985/umberto

Visit productView on Hacker NewsMay 27, 2025
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Launch Intel predictions

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
60%60% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Product HuntOn track for Day 1 leaderboard · Strong signals: openai, code, open · Missing: mac, agents, macos
52%52% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal
42%42% 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
37%37% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: ide, io · Missing: https docs, excited, just released
21%21% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
15%15% 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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