A timeline of recent open source CVE intensity and volume

Hacker News

I was curious what it would look like if I plotted the intensity and volume of software supply chain CVEs over time, given what seemed like a flood of compromises lately. It looked exactly as I expected, and I expect it to get worse before it gets better. Yes, an LLM was used but because I wanted the simplest possible architecture, I steered away from using any back end at all. Instead it's just GitHub pages with a static json document as the source of data, updated daily by a GitHub action which stores and parses the OSV repository. I wanted to include the Linux kernel but the complexities around how CVEs are assigned there made it difficult -- if I find a simple solution in future I'll add it.

Visit product View on Hacker NewsMay 21, 2026

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

80%80% 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.

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, io · Missing: https docs, excited, just released

48%48% 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 · Missing: supports, reddit linkedin, podcasting

42%42% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

36%36% 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

16%16% 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.

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