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GemGuard – a security auditing tool for Linux and Windows

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GemGuard – a security auditing tool for Linux and Windows

I’ve been working on a small security auditing tool called GemGuard and wanted to share it with the community. GemGuard collects system information — running processes, network connections, and recently installed packages — and then uses Google’s Gemini models to generate a human-readable assessment of anything that might look suspicious or worth checking. The tool is cross-platform and works on: Linux (Fedora, Ubuntu/Debian, Kali, Alpine) and Windows 10/11 It offers both a CLI and a Textual-based TUI, supports multiple Gemini models, and includes a quiet mode for automation or integration with other tools. Features include: - Process auditing - Package review (auto-detects package manager) - Network/port inspection - Optional raw AI output (quiet mode) - Works in Bash/Zsh/CMD/PowerShell I’m not a security expert, so the project is experimental, and contributions or feedback are very welcome. Repo: https://github.com/AlvaroHoux/gem-guard

Visit productView on Hacker NewsNov 27, 2025
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Indie HackersFits the IH revenue-focused audience · Strong signals: supports, gemini · Missing: reddit linkedin, podcasting, created
84%84% 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: model, google, models · Missing: mac, agents, macos
67%67% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: platform · Missing: plus, intuitive, reviews
47%47% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: google · Missing: mobile apps, ios, personal
34%34% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: io · Missing: https docs, excited, just released
31%31% 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
11%11% 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.

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