Sec.Today – A searchable archive for all things infosec

Hacker News

We are a small group of security researchers. We are building a site to make everything searchable in the area of infosec. https://Sec.Today Every year there are more than a few dozen security conferences, hundreds if not thousands of infosec talks are given around the world. But when it comes to the daily work of security research, valuable information buried in past talks are often hard to find. Each conference has its own address, style, format for its talk archives and speaker information. By aggregating and indexing all the major conferences and metadata, this site made all the talks and speakers searchable. It's like Everything or Spotlight in the area of infosec. More than that. We also cross-referenced MITRE, NVD, and multiple other data sources, to collect information about high risk vulnerabilities that are being discovered each month. By "cross-reference" I mean we know the exact finder of each vulnerability, attributing it to a team or person. We even made a monthly ranking based on this information. This site also links everything together. From a security conference you have a list of all speakers. You can click a speaker's name to find out his/her past talks on other conferences, vulnerabilities he/she discovered, etc. A few examples: James Forshaw https://sec.today/humans/5cb6f83a-3823-4058-8e17-45ea55db3a0... Patrick Wardle https://sec.today/humans/4e61f3cc-433e-4269-a1d7-1809a3668d6... Palo Alto Networks https://sec.today/humans/641d97c3-f725-4527-ae19-e3d1460056a... Black Hat USA 2018 https://sec.today/events/3c9c8539-596d-4958-87e1-fe0e41b924e... macOS (Search) https://sec.today/search/?q=macOS Let me know what you think. If you like it, please help spread the word. Follow us at https://twitter.com/TodaySec

Visit product View on Hacker NewsSep 28, 2018

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

66%66% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

58%58% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting

57%57% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

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

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

39%39% 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.

Incorrect prediction on native model

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