Weekly Podcast on CVEs

Hacker News

Hello HN, Keeping up with security vulnerabilities each week can be overwhelming. To make it easier, we’ve created The Exploit Podcast—an automated weekly podcast covering CVEs and security news. You can listen on: Apple Podcasts: https://podcasts.apple.com/us/podcast/the-exploit-podcast-cv... Spotify: https://open.spotify.com/show/6d4yfU1geTLIKtaY7lQJvm?si=YCrh... We have tried to make it automated so that the creation of podcast is not a burden to us. To help in this, we built CVEingest[1], a tool that: 1. Crawls GitHub Advisory and CVE.org, along with referenced sources. 2. Fetches code diffs (if patch is present) 3. Generates a SSML script where host and guest are talking which can be used to create audio. 4. Creates an audio podcast via Microsoft Speech (or lets you download CVE data in JSON). This podcast will help you stay ahead of emerging threats. 1. CVEingest: https://github.com/BandarLabs/cveingest

Visit product View on Hacker NewsJan 31, 2025

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

78%78% 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 · Strong signals: created · Missing: supports, reddit linkedin, podcasting

77%77% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

TrustMRRFits verified-revenue profile · Missing: mobile apps, ios, personal

56%56% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

50%50% 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 · Strong signals: host · 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

13%13% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Strong signals: audio · 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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