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SCANOSS Now Scans Container Images

Hacker News

SCANOSS Now Scans Container Images

We’ve just added container image scanning to SCANOSS. It now supports generating full SBOMs from Docker/OCI containers, including undeclared and inherited packages from base images. Why it matters: SBOMs usually stop at your app. Containers are like mini OSs, often containing hundreds of packages you didn’t explicitly include — but are still shipping. See it in action: https://www.scanoss.com/post/introducing-container-scanning GitHub: https://github.com/scanoss/scanoss.py

Visit product View on Hacker NewsMay 28, 2025

Actual performance

2points

Did not reach leaderboard

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

52%52% 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 HackersIH features products with proven revenue · Strong signals: supports, including · Missing: reddit linkedin, podcasting, created

47%47% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal

43%43% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Product HuntUnlikely to reach the leaderboard · Strong signals: dock · Missing: mac, agents, macos

41%41% 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

35%35% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

13%13% 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

1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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