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Data Hogo – scan your repo for security issues

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Data Hogo – scan your repo for security issues

AI is quickly becoming the default way people build software. Even if you try to enforce some rules, security often falls through the cracks. There are just too many surfaces to cover, and the context gets messy fast. I kept running into the same pattern: projects that work, but have security gaps that are easy to miss. So I built Data Hogo — a tool that scans your repo and points out security issues in plain English. No long reports, just what’s wrong, why it matters, and how to fix it. If you want to try it, you can scan your repo and see what comes up. Takes a few minutes, and I’d be curious what kind of results people get — even if it finds nothing. Take your free scan at https://www.datahogo.com

Visit productView on Hacker NewsMar 30, 2026
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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
77%77% 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: context, plain · Missing: mac, agents, macos
67%67% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: io · Missing: https docs, excited, just released
53%53% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
45%45% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: way · Missing: mobile apps, ios, personal
44%44% predicted probability of success on TrustMRR, 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
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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