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I built a tool for policy driven vetting of open source packages

Hacker News

I built a tool for policy driven vetting of open source packages

Linux Foundation survey says 70-90% of modern software constitute OSS code. Yet we are stuck with tools that scan only for vulnerabilities in 3rd party libraries and that too with high degree of false positives. I built `vet` for policy and data driven analysis of 3rd party packages that goes beyond only vulnerability and allows codifying organisational policies related to OSS consumption. https://github.com/safedep/vet Looking forward to feedback and suggestions from HN :)

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Launch Intel predictions

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Hacker NewsStrong engagement from HN community · Strong signals: open source, io · Missing: https docs, excited, just released
67%67% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
56%56% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Product HuntUnlikely to reach the leaderboard · Strong signals: code, open · Missing: mac, agents, macos
34%34% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
31%31% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · 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
14%14% 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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