Vetpkg.dev – open-source Package Security Dashboard

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Hello HN! I want to share a new open source project I built. https://vetpkg.dev/ The goal is to provide a single view for developers and security engineers with all the information they need to make a decision about safety of an open source package. As the developer of https://github.com/safedep/vet , I often get feedback from users that installing and setting up vet in CI/CD with custom policies takes time. A web page using which anyone can quickly see package security metadata is a good start for engineers to quickly get new OSS packages approved internally. vetpkg.dev is open source, developed at https://github.com/safedep/vetpkg.dev It is a simple frontend over APIs that we built by aggregating public and private data sources on OSS package security and code analysis. vetpkg.dev also aggregates data from our OSS package code analysis tool that we run on all new PyPI and npm packages to proactively detect malicious code. Love to hear if it helps you make a decision about adopting OSS packages quickly within your SDLC.

Visit product View on Hacker NewsFeb 19, 2025

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

91%91% 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 · Missing: supports, reddit linkedin, podcasting

73%73% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

Hacker NewsStrong engagement from HN community · Strong signals: open source, ide, io · Missing: https docs, excited, just released

57%57% 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: users · Missing: mobile apps, ios, personal

50%50% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Strong signals: users · Missing: plus, platform, intuitive

34%34% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Strong signals: active · Missing: arr, mrr, revenue

17%17% 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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