SBoM dashboard that pulls from GitHub release assets

Hacker News

i've been setting up supply chain security for my project kftray. mainly the release workflow ( https://github.com/hcavarsan/kftray/blob/main/.github/workfl... ) is generating CycloneDX SBOMs with Syft, scanning for vulns with Grype, signing everything with Cosign, and using OpenVEX to suppress false positives i wanted a simple way to expose a overview about all this without spinning up Dependency-Track or similar. so i built this (public) page that reads directly from GitHub release assets and shows components, vulnerabilities by severity, and also aggregates OpenSSF Scorecard and best practices into a summary card. ( https://sbom.kftray.app ) basically a simple react/ bun code source code isn't public yet… if there's interest i'd be happy to open source it… would love feedback on the approach.

Visit product View on Hacker NewsJan 31, 2026

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

61%61% 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

61%61% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

48%48% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: open source · Missing: https docs, excited, just released

45%45% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

43%43% 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

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

0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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