Shieldcode – Automated PR Code Reviews and Vulnerability Scanner

Hacker News

I got fed up with endless PR reviews and the constant fear of missing a security bug, so I built Shieldcode. What it does: - Scans new pull requests for bugs and vulnerabilities automatically - Comments directly on GitHub with clear, actionable feedback - Works out of the box with no complex setup required Next up: NPM VScan, which will check your npm dependencies on every push for vulnerabilities or malware. I’d love to hear your thoughts and feedback! https://shieldcode.space

Visit product View on Hacker NewsSep 21, 2025

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Product HuntOn track for Day 1 leaderboard · Strong signals: new, code · Missing: mac, agents, macos

86%86% 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.

AppSumoStrong fit for a featured deal · Strong signals: reviews · Missing: plus, platform, intuitive

55%55% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Indie HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting

48%48% 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: code review, io · 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 · Strong signals: scanner · Missing: mobile apps, ios, personal

26%26% 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

20%20% 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

3%3% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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